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Towards contextualized social simulation: Complementary use of Multi-Fuzzy Cognitive Maps and MABS

By Oswaldo Terán1 and Jose Aguilar2

1Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo, Chile, and CESIMO, Universidad de Los Andes, Mérid.

2CEMISID, Universidad de Los Andes, Merida, Venezuela; GIDITIC, Universidad EAFIT, Medellin, Colombia; and Universidad de Alcala, Dpto. Automatica, Alcala de Henares, Spain.

Abstract.

This work suggests to complementarily use Multi-Fuzzy Cognitive Maps (MFCM) and Multi-agent Based Simulation (MABS) for social simulation studies, to overcome deficiencies of MABS for contextually understanding social systems, including difficulties for considering the historical and political domains of the systems, variation of social constructs such as goals and interest, as well as modeler’s perspective and assumptions. MFCM are a construction much closer than MABS to natural language and narratives, used to model systems appropriately conceptualized, with support of data and/or experts in the modeled domains. Diverse domains of interest can be included in a MFCM, permitting to incorporate the history and context of the system, explicitly represent and vary agents’ social constructs, as well as take into account modeling assumptions and  perspectives.  We briefly describe possible forms of complementarily use these modeling paradigms, and exemplifies the importance of the approach by considering its relevance to investigate othering and polarization.

1. Introduction

In order to understand better issues such as othering and polarization, there is a claim in social simulation for research that includes the important domains of history, politics and game of power, as well as for greater use of social science data, make more explicit and conscious about the models’ assumptions, and be more cautious in relation to the interpretation of the simulations’ results (Edmonds et al., 2020). We describe a possible form of dealing with these difficulties: combining Multi-Agent based Simulation (MABS) and Multi Fuzzy Cognitive Maps (MFCM) (or other forms of cognitive maps), suggesting new forms of dealing with complexity of social behavior. By using MFCM an alternative modeling perspective to MABS is introduced, which facilitates expressing the context of the model, and the modelers’ assumptions, as suggested in Terán (2004).  We will consider as a case studying othering and polarization, given the difficulties for modeling it via MABS (Edmonds et al., 2020). Our proposal permits to explicitly represent social constructs such as goals, interest and influence of powerful actors on, e.g., people’s othering and polarization, and so in better contextualizing the simulated model. Variations of social constructs (e.g., goals, othering, polarization, interests) can be characterized and modeled by using MFCM.

Combined use of MABS and Fuzzy Cognitive Maps (FCM) (MFCM, multi FCM, are an extension of FCM, see the Annex) has already been suggested, see for example Giabbanelli (2017). MABS develop models at the micro level, while FCM and MFCM permits us to create models at the macro or contextual level; the idea is to use one to complement the other, i.e., to generate rich feedback between them and enhance the modeling process. Additionally, Giabbanelli propose FCM as a representation closer than MABS to natural language, allowing more participatory models, and better representation of the decision making process. Giabbanelli recommend forms of combining these two modeling approaches, highlighting key questions modelers must be careful about. In this line, we also propose a combined usage of a MFCM and MABS to overcome deficiencies of MABS modelling in Social Simulation.

Initially (in section 2) we offer a description of human societies from a broad view point,  which recognizes their deep complexities and clarifies the need for better contextualizing simulation models, allowing modeling of diverse agents’ constructs, and making explicit modelers’ assumptions and perspectives. Afterwards (in section 3), we briefly review the drawbacks of MABS for modeling some of these deep complexities. Then (in section 4), MFCM are briefly described, supported on a brief technical account in the Annex. Following (in section 5), we suggest to complementarily use MABS and MFCM for having a more comprehensive representation of human societies and their context, e.g., to better model problems such as othering and polarization. MFCM will model context and give a conceptual mark for MABS (allowing to model variation of context, e.g., changes of agents’ interests or goals, making explicit modelers’ perspective and assumptions, among other advantages), which, in turn, can be used to explore in detail specific configurations or scenarios of interest suggested by the MFCM.  Finally (in section 6), some conclusions are given.

2. (A wide view of) Human societies and influence of communication media on actual culture

As humans and primates, we recognise the social groups within which we develop as people (e.g., family, the community where we grow up, partners at the school or at work) as part of our “large home”, in which its members develop a common identity, with strong rational and emotional links. Other groups beyond these close ones are “naturally” estrangers for us and its members “instinctively” seen as others. In large civilizations such as western society, we extend somewhat these limits to include nations, in certain respects. In groups we develop perspectives, follow certain myths and rites, and have common interests, viewpoints about problems, solutions for these, and give meaning to our life. Traditionally, human societies evolve from within groups by direct face to face interaction of its members, with diverse perspectives, goals, interest, and any other social construct with respect to other groups. Nowadays this evolution mainly from natural interaction has been importantly altered in some societies, especially western and western influenced societies, where social media has introduced a new form of communication and grouping: virtual grouping. Virtual grouping consists in the creation of groups, either formally or informally, by using the internet, and social networks such as Facebook, Instagram, etc. In this process, we access certain media sites, while discarding others, in accordance with our preferences, which in turn depends on our way of thinking and preferences created in social, both virtual and direct (face to face), interaction. Currently, social media, and traditional media (TV, newspapers, etc.) have a strong influence on our culture, impacting on ours myths, rites, perspectives, forms of life, goals, interests, opinion, reasoning, emotions, and othering.

Characteristics of virtual communication, e.g., anonymity, significantly impacts on all these constructs, which, differently from natural interaction, have a strong potential to promote polarization, because of several reasons, e.g., given that virtual environments usually create less reflexive groups, and emotional communication is poorer or lack deepness. Virtual interaction is poorer than direct social interaction: the lack of physical contact strongly reduces our emotional and reflexive connection. Virtual social interaction is “colder” than direct social interaction; e.g., lack of visual contact stops communication of many emotions that are transmitted via gestures, and prevents the call for attention from the other that visual contact and gestures demands.

Even more, many times sources and veracity of information, comments, ideas, and whatever is in social media, are not clear. Even more, fake news are common in social media, what generate false beliefs, and behavior of people influenced and somewhat controlled by those who promote fake news. Fake news can in this sense generate polarisation, as some groups in the society prefer certain media, and other groups choose a different one. As these media may promote different perspectives following interest of powerful actors (e.g., political parties), conflicting perspectives are induced in the different groups, what in turn generates polarization. Social media are highly sensitive to manipulation by powerful actors worldwide, including governments (because of, e.g., their geopolitical interests and strategies), corporations (in accordance with their economic goals), religious groups, political parties, among many others. Different groups of interests influence in direct and indirect, visible and hidden, forms the media, following a wide diversity of strategies, e.g., those of business marketing, which are supported by knowledge of people (e.g., psychology, sociology, games theory, etc.). Thus, the media can create and contribute to create visions of the word, or perspectives, in accordance with the interest of powerful international or national actors. For more about all this, see, e.g, Terán and Aguilar (2018).

As a consequence, people following media that promotes a world view, related with some powerful actor(s) (e.g., a political party or a group of governments) virtually group around media that support this world view, while other people do the same in relation to other media and powerful actor(s), who promote(s) a different perspective, which many times is in conflict with the first one. Thus, grouping following the media sometimes promotes groups with conflicting perspectives, goals, interests, etc.,  which generates polarization. We can find examples of this in diverse regions and countries of the world. The media has important responsibility for polarization in a diversity of issues such as regional integration in Europe, war in Ukraine, migrations from Middle East or Africa to Europe, etc. Consequently, media manipulation sometimes allow powerful actors to influence and somewhat control perspectives and social behavior. Even more, the influence of social media on people is sometimes stronger than the influence of direct social interaction. All these introduce deep complex issues in social human interaction and behavior. This is why we have chosen polarization as the case study for his essay.

Consequently, to comprehend actual human behavior, and in particular polarization, it is necessary to appropriately take into account the social context, what permits to understand better the actual complexity of social interaction, e.g., how powerful international, national, and local actors’ influence on media affects people perspectives, goals, interest, and polarization, as well as their strategies and actions in doing so. Contextualized modeling will help in determining social constructs (goals, interests, etc.) in certain situations, and their variation from situation to situation. For this, we suggest complementing MABS with MFCM. For more about the consequences of virtual interaction, see for example Prensky (2001a, 2001b). Prensky (2009) has also suggested forms to overcome such consequences: to promote digital wisdom. MABS and MFCM models will help in defining forms of dealing with the problems of high exposure to social networks, in line with Prensky’s concerns.

3. Weakness of the MABS approach for modeling context

Edmonds et al. (2020) recognize that MABS models assume a “state of the world” or “state of nature” that does not include the historical context of the agent, e.g., in such a way that they explicitly present goals, interests, etc., and pursue them via political actions, sometimes exerting power over others. For instance, the agents can not change their goals, interests or desires during the simulation, to show certain evolution, as a consequence of reflection and experience at the level of desires, allowing cognitive variations. The models are strongly limited in relation to representing the context of the social interaction, which in part determines variation of important factors of agents’ behavior, e.g., goals. This, to a good extent, is due to lack of representation of the agents’ context. For the same reason, it is difficult to represent modelers assumptions and perspectives, which might also be influenced by social media and powerful actors, as explained above.

The Special Issue of the Social Science Computer Review. (Volume 38, Issue 4, August 2020, see for instance Edmonds et al. (2020) and Rocco and Wander (2020)), presents several models aiming at dealing with some of these drawbacks of MABS, specifically, to relate models to social science data, be more aware about the models’ assumptions, and be more cautious in relation to the interpretation of the simulations’ results. However, in these works diverse difficulties are not addressed, e.g.,  having appropriate representation of the context in order to explicitly consider diverse constructs, e.g., goals and interests, as well as having a wide representation of modelers perspectives and assumptions so that diverse perspectives can be addressed and compared, among other important matters.

MABS represent social interaction, i.e., the interaction in a group, where the agent’s goal, and other social constructs are assumed given, not variable, and to understand the context where they appear is not of interest or is out of reach (too difficult). However, as explained above, agents are in diverse social groups, not only in the simulated one, and so goals, interests, and beliefs in the modeled group are shaped in accordance to their interactions in diverse groups, and the influence of multiple, virtual and natural groups in which they participate. In order to represent variations of such elements, the context must be taken into account, as well as to elaborate models from narratives. MFCM is naturally close to narratives, as it is elaborated from conceptual frameworks. In this sense, MFCM might represent an intermediate step towards MABS models. In a MFCM and in the steps towards elaborating the MABS, modelers’ perspectives and assumptions can be made explicit. In addition, MABS presents limitations to determine the conditions for which a certain behavior or tendency occurs (Terán, 2001; Terán et al. 2001), i.e., for making strong inferences and theorem proving of tendencies for subsets of the theory of the simulation, which could more easily be performed in the MFCM. Hopefully, exploring configurations of the MFCM the proof could be carried out indirectly, in a higher level than in MABS, as has already been suggested in previous papers (Aguilar et al., 2020; Perozo et al., 2013).

4. Multi-Fuzzy Cognitive Maps (MFCM)

We suggest conceptual or cognitive maps as a more flexible form than MABS to represent context of a social situation, and in particular MFCM, as implemented by Aguilar and others (see e.g., Kosko, 1986; Aguilar 2005, 2013, 2016; Aguilar et al., 2016, 2020; Contreras and Aguilar, 2010; and Sánchez et al., 2019; Puerto et al., 2019). A brief description of Fuzzy and Multi-fuzzy cognitive maps, following Sánchez et al. (2019), is given in the Annex.

Fuzzy cognitive maps help us in describing the context via qualitative (e.g., very low, low, medium, high, too high) and quantitative variables, as indicated in the annex. The system is represented by the network of concepts (variables) interrelated via weights (also given by variables). The high level of the MFCM paradigm, differently from a MABS, permits us to explicit different elements of the models such as the agents constructs (goals, interests, etc.), as well as the modelers assumptions and perspectives, as suggested in Terán (2004). MFCM will facilitate to explicit the accumulated set of assumptions (“abstraction-assumptions, design-assumptions, inference-assumptions, analysis-assumptions, interpretation-assumptions and application-assumptions”, as these are summarized in Terán, 2004).

In a MFCM, a particular situation of the system is given by a specific configuration of the weights (see, e.g., Sánchez et al., 2019). Suppose we are dealing with a model similar to that elaborated in Sánchez et al. (2019) to study the quality of opinion in a community. Sánchez et al. examine the capabilities of the MFCM for knowledge description and extraction about opinions presented in a certain topic, allowing the assessment of the quality of public opinion. Special attention is offered to the influence of the media on public opinion. The evolution of the concepts and relationships is presented. Concepts define the relevant aspects from which public opinion emerges, covering diverse domains, for instance, the social, technological and psycho-biological ones. The MFCM permits to identify the media preferred by the public in order to better understand several issues, including the high esteem that the new communication media hold.

In line with this, let us assume that we want to understand the quality of public opinion in a community of Europe about diverse issues during 2022. This network, with a certain configuration of the weights, but unspecified concepts, represents a social system with a certain structure (as the weights are given) that is in some sense general, as the values of the concepts can still vary. Variation of the concepts represents different scenarios of the social system (with a given structure, defined by the weights); e.g., the model of a European community considered in relation to three scenarios regarding the state of public opinion in relation to specific issues: 1: climate change, 2: situation of tourism in the community, 3: secondary effects of the COVID-19 vaccines. The weights of the network are determined by using a variety of scenarios; i.e., the network is trained with several scenarios, for which all possible values of the concepts are known. Once the network is trained, it can be used to infer unknown specific values of the concepts for other scenarios (following Aguilar et al. 2020; Sánchez et al. 2019; Terán y Aguilar, 2018); e.g., the state of public opinion in relation to the involvement of EU in the war in Ukraine. Even more, by exploring an appropriate set of scenarios, proofs about the state of certain concepts can be developed; e.g., that a majority of people in the community is against direct EU involvement in the war in Ukraine. The proof could be carried out for a subset of the possible configurations of a domain, several domains, or part of a domain, e.g., for the psycho-biological domain. Additionally, having an appropriate elaboration of the model would allow evaluating how polarized is the opinion of the community in relation to the involvement of EU in that war.

Diverse configurations of the MFCM can represent different modelers’ perspectives and assumptions, as well as various agents’ constructs, such as goals, interests, etc., allowing to deal with the above described drawbacks of MABS to cope with complexity of social systems.

5. Combined use of MABS and cognitive maps

The combined usage will give at least two levels of modeling: the inner, defined by the agents’ interaction concreted in a MABS, and the outer or contextual one, given by the MFCM. These will be the two last levels in the description given in 5.1. Interaction between these models occurs as the modeler interprets each model outputs and feedbacks the other. Ideally, we would have direct automatic feedback between these models.

5.1 Levels of description of the System

In order to contextually model a social system and investigate problems such as polarization, we suggest below five levels of description of the system. The first three levels are not directly associated to computational models, while levels four and five are descriptions that assist development of the computational models: MFCM and MABS, respectively. Each level gives context to the following one (the first gives context to the second, etc.). A lower level (e.g., 1. in relation to 2.) of description corresponds to a more general language, as suggested in Terán (2004). Each level must take into account the previous levels, especially the immediately superior level, which gives the most immediate context to it. This description is in line with the suggestions given in Terán (Idem). Each description makes certain assumptions and is shaped by the modeler’s perspective, which in part is coming from those actors given information to build the model. Assumptions and perspectives introduced in level of modeling i, i = 1, …, 6, can be called Assumptions-given in (i) and Perspectives-given in (i). At levels of description j, Assumption i = 1, 2, …, i are accumulated, and can be called Assumptions(j), as well as holistic Perspective(j) based on Perspectives-given in (i), i = 1, 2, .., i.  These assumptions and perspectives correspond to those defined in Terán (2004) as “abstraction-assumptions, design-assumptions, inference-assumptions, analysis-assumptions, interpretation-assumptions and application-assumptions”.

  1. Describe in natural language the system, including its relevant history, with emphasis in culture (practices, costumes, etc.) and behavior of individuals and groups relevant to the object of the study, e.g., from a historical-ontological perspective. Here, how the system has reached its actual situation is explained. This will give a global view of the society and the general form of behavior, problematics, conflicts, etc.
  2. Describe the diverse relevant domains given context to the system of interest in accordance with the study, e.g., political, economical, dominant actors, etc., and the relationships among them. Concrete specifications of these domains sets scenarios for the real system, i.e., possible configurations of it.
  3. Describe the particular social group of interest as part of the society explained above, in 1., and the domains given in 2., showing its particularities, e.g., culturally, in terms of interests, situation and social interaction of this group in relation with other groups in the whole society, in accordance to the problematic addressed in the study.
  4. Elaborate a cognitive map of the situation of the social group of interest, following the description given in 2 and 3. This is a description to be represented in a computational language, such as the MFCM tool developed by Contreras and Aguilar (2010).
  5. Describe the MABS model. The MABS model is then represented in a simulation language.
  6. The computational MFCM (or other cognitive map) and the MABS developed following 4. and 5. are then used to generate the virtual outputs and simulation study.

5.2 Possible combined uses of MFCM and MABS.

The MFCM (in general a cognitive or conceptual map) gives context to the MABS model (modeler’s assumptions and perspectives are added in the process, as indicated above), while the MABS model represents in detail the interaction of the agents’ considered in the MFCM for a specific scenario of this, as indicated in the levels of modeling given above. With this idea in mind, among the specific forms a combined usage of ABM and MFCM, we have:

i) Offering feedback from the MFCM to the MABS. A MABS in a certain configuration can be used to generate either directly or indirectly (e.g., with additional verification or manipulations) input for a simulation model. For example, in parallel to the model presented in Sánchez et al. (2019), where the domains social, biological-psychological, technological and state of the opinion are displayed, a MABS model can be developed to represent the interaction between social entities, such as people who receive information from the media, the media itself, and powerful actors who design the agenda setting of the media. This MABS model might use diverse methodologies, e.g., endorsements or BDI, to represent social interaction, or a higher level of interaction where actors share resources, on which they have interest, as in a System of Organized Action (see, e.g., SocLab models Terán and Sibertin-Blanc, 2020; Sibertin-Blanc et al., 2013). Constructs required as inputs for the model, e.g., goals, interests, values to define the endorsements schema, etc. could be deducted from the MFCM, as direct values of some concepts or functions (mathematical, logical, etc.) of the concepts. These operations could be defined by experts in the modeled domains (e.g., media owners, academics working in the area, etc.). Ideally, we would have an isomorphic relation between some variables of the MABS and variables of the MFCM – however, this is not the usual case –. In this process, the MABS is contextualized by the MFCM, whose modeling level also permits to identify modeler’s assumptions and perspectives. Also, in a narrative, and then in the MFCM, goal, interest, and other constructs can be explicitly represented, then varied and their consequence understood in order to feedback the MABS model.

ii) Giving feedback to the MFCM from the MABS. Inputs and outputs values of the MABS simulation can be used as an input to the MFCM, e.g., as a set of scenarios to train the network and determine a certain structure of the MFCM, or to determine a specific scenario/ configuration (where both, the weights and the concepts are known).

iii) Determining conditions of correspondence among the models. By simulating the MABS associated to certain scenarios of the MFCM, or, vice-verse, by determining the scenarios of MFCM related to certain MABS, the consistency among the two models and possible errors, omissions, etc. in one of the models can be detected, and then the corrections applied. Even more, this exercise can hopefully determine certain rules or conditions of correspondence among the MABS and the MFCM.

iv) Using a model to verify properties in the other model. Once certain correspondence among the models has been determined, we can use one of the models to help in determining properties of the other. For instance, a proof of a tendency in an MABS (this has been an important area of research, Terán, 2001; Terán et al. 2001; Edmonds et al., 2006) could be developed in a much easier form in the corresponding MFCM. For this, we need to characterize the set of configurations of the MFCM corresponding to the set of configurations of the MABS for which we want to perform the proof.

These possible combined uses of MFCM and MABS do not exhaust all potentials, and diverse other alternatives could appear in accordance with the needs in a particular study. Even more, automatic feedback between MFCM (or other cognitive or conceptual map) and MABS could be implemented in the future, to facilitate the mutual contributions between the two modeling approaches. This would cover modeling requirements the MABS in itself does not support at present.

5.3 A case: Modeling othering and polarization, the case of “children with virtually mediated culture”.

We outline a possible model that considers othering and polarization. In section 2 we described a society. In a society, as virtual groups become homogeneous in beliefs, motivations, intentions and behavior, certain sort of endogamy of ideas and opinions appear, constraining the variety and richness of perspectives from which people observe and judge others, making them generally less tolerant to others, more restrictive in accepting opinions and behavior of others, so less inclusive. This has diverse additional effects, for instance, increase of polarization between virtual groups regarding a diversity of themes. Problems such as polarization occur also in children with strong usage of virtual social networks (see, Prensky, 2001a, 2001b, 2009).

To investigate this problem and support the MABS, as a case, we suggest a MFCM with four levels (see Figure 1). The goal of the models (MFCM and MABS) would be to better understand the differences between the communities of children whose interaction is basically virtually mediated and the community of children whose interaction is face to face, or direct, people to people. In general, it is of interest to determine the state of othering and polarization for diverse configurations of the MFCM. As we explained above, there are clear differences between virtual and face to face interaction, consequently the upper layers in Figure 1 are the two possible niches of cultural acquisition (costumes, points of view, etc.), during life of people (ontogenesis), namely, the virtual mediated culture and the direct, face to face, cultural acquisition. These two layers involve interaction among diverse actors (e.g., people, media and powerful actors are present in layer 2). Layer 2 represents technological actors, while layer 1 represents social interaction, but both of them might involve other elements, if required. The third level represent those biological aspects related with behavior, which are created via culture: the psycho-biological level. Both levels, 1 and 2, affect the third layer, as emotions, reasoning, etc., are founded on people interaction and have a cultural base. Constructs of behavior such as goals, interests, desires, polarization, etc., appear and can be explicitly represented at this level. This third level, in turn, impacts on the overall state of the community, e.g., on the auto-generative capacity of the society, finally affecting global society/community’s othering and polarization, as our emotions, reasoning, etc., impact on our view point, on othering, etc. These last are variables defined in terms of the previous levels. In this model, the definition of concepts such as “othering” and “polarization” is crucial, and indicates basic modeling assumptions and perspective. Finally, the overall state of society/community impacts back on the cultural niches (layers 1 and 2).

In a specific situation, the whole interaction (1) of the society or community is divided between the two niches given by layers 1 and 2, a proportion of interaction frequency occurs as virtual communication, and the compliment (one minus the proportion of virtual interaction), occurs as direct, face to face, contact. This is represented in Figure 3 by the variable “Proportion of virtual interaction type”. Changes of this variable allows us to explore diverse configurations or scenarios of interaction, ranging from total virtual interaction (null direct contact) (the variable takes the value 1), to null virtual interaction (total direct contact) (the variable takes the value 0).

The four levels of MFCM

Figure 1. The four levels of the MFCM. The two alternative niches structuring the psycho-biology of people are at the top of the process. The overall state represents general measures such as the auto-generative character of a social system, and attitudes including othering and polarization.

Example of possible variables in some levels (Figure 1) are:

i) Face to face interaction, and ii) virtual interaction or technological: The next variables are candidates to be at these levels. Degree of:

  •  Coherence of the interaction (possible state: good, etc.);
  •  Identification of the others in the interaction (good or clear, etc.);
  •  Richness of the interaction (high or good, etc.);
  •  Truthfulness of the messages (fairness) (e.g., good: messages and communication are fair);
  •  Openness of the community (e.g., high: usually people is open to interact with others);
  •  Speed of the interaction (e.g.: low, medium, …);
  •  Intentioned influence and control of the communication by powerful actors (e.g., high, medium, low, ..);

iii) Psycho-biological level

  •  Reflection (state: good means that people question their experiences, and observed phenomena);
  • Closeness of interpretations, attitudes, desires, intentions, and plans (a high value means that people’s interpretations, etc., are not very different);
  •  Emotion and mood;
  •  Empathy;
  •  Addiction to virtual interaction;
  • Goal;
  •  Interest;
  •  Immediatism (propensity to do things quickly and constantly change focus of reasoning).

iv) Overall state of people and society:

  •  Auto-generative capacity of the society;
  •  Capacity of people to reflect about social situations (and autonomously look for solutions);
  •  Othering;
  •  Polarization; 

Concepts at each layer impacts concepts at the other layers. E.g., concepts of level three have a strong impact on concepts of the fourth layer, such as “polarization”, and “auto-generative character of the society”.

As indicated above, to understand the dynamics of the MFCM we can develop a wide range of scenarios, for instance, varying the switch “Proportion of virtual interaction” in the interval [0, 1], to explore a set of scenarios for which the degree of virtual interaction increases from 1 to 0, as the proportion of direct interaction decreases from 1 to 0 (the real case corresponds to an intermediate value between 1 and 0). These experiments will help us in understanding better the consequences of virtual mediated culture. Even when the outline of the model presented here might need some adjustments and improvements, the present proposal keeps its potential to reach this goal.

The MFCM will be useful to deal with many issues and questions of interest, for instance:

  • How social networks affect basic social attitudes such as: i) critical rationality (people’s habit for questioning and explaining their experience (issues/phenomena in their life)), ii) tolerance, iii) compromise with public well-being, and iv) othering and polarization
  •  How social networks affect social feelings, such as empathy.

The MABS model will be elaborated in accordance with the description of the MFCM indicated above. In particular, different values of the social constructs at levels 1 and 2 (e.g., goals and interests of the actors), and the corresponding state of layer 3 (e.g., Polarization), imply diverse MABS models.

The whole network of concepts, the particular network of concepts at each level, and the definition of each concept, offers a perspective of the modelers. Different modelers can develop these elements of the model differently. Assumptions can be identified also at each level. Both, perspectives and assumptions come from the modelers as well as from the theories, consult to experts to create the model, etc.. An specific model is not part of this essay, but rather a subject of future work.

6. Conclusion

Social simulation has been widely recognized as an alternative to study social systems, using diverse modeling tools, including MABS, which, however, present some limitations, like any other research tool. One of the deficiencies of MABS is their limitations to contextually modeling social systems; e.g., to suitably include the historical and political contexts or domains; difficulties to represent variation of agents’ constructs, e.g., goals and interest; and drawbacks to made explicit modeler’s assumptions and perspectives. In this paper, we have suggested to mutually complement MABS and MFCM, to overcome MABS drawbacks, to potentiate the usefulness of MABS to represent social systems.

We argue that the high level of the MFCM paradigm permits us to express different elements of the models such as the agents’ constructs (goals, interests, etc.), as well as the modelers assumptions and perspectives, as suggested in Terán (2004). Thus, MFCM facilitates the identification of the accumulated set of assumptions during the modeling process. Even more, diverse configurations of a MFCM can represent diverse modelers’ perspectives and assumptions, as well as diverse agents constructs, such as goals, interests, etc., allowing to deal with the above described complexity. This permits us to more realistically elaborate models of a wide diversity of social problems, e.g., polarization, and consequences of the influence of social networks in culture.

Among the forms MFCM and MABS complement each other we have identified the followings:  mutual feedbacking of variables and concepts between the MFCM and the MABS, determining conditions of correspondence among the models, what facilitate other modeling needs, e.g., using a model to verify properties in the other model (e.g., proofs required in a MABS could be carried out in a corresponding MFCM).

A case study was outlined to exemplify the problematic that can be addressed and the advantages of using MFCM to complement MABS: Modeling othering and polarization, the case of children with virtually mediated culture. Combined use of MFCM and MABS in this case will contribute to understand better the problems created by the high use of digital interaction, especially social networks, as described by Prensky (2001a, 2001b, 2009), given that virtual interaction has strong influence on our culture, impacting on ours myths, rites, perspectives, goals, interests, opinion, othering and polarization, etc. Characteristics of virtual communication, e.g., anonymity, significantly impacts on all these constructs, which, differently from natural interaction, have a strong potential to promote certain tendencies of such constructs, e.g., polarization or our opinions, because of several reasons, for instance, given that virtual environments usually create less reflexive groups, while emotional communication is poorer or lack deepness. It is difficult to represent all these dynamics in a MABS, but it can be alternatively expressed in a MFCM.

The purpose of the work was to give an outline of the proposal; future work will be conducted to wholly develop concrete study cases with complementary MABS and MFCM models.

References

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Aguilar, J. (2016) “Multilayer Cognitive Maps in the Resolution of Problems using the FCM Designer Tool”, Applied Artificial Intelligence, 30 (7), pp. 720-743.

Aguilar J., Hidalgo J., Osuna F., Perez N.(2016) Multilayer Cognitive Maps to Model Problems”, Proc. IEEE World Congress on Computational Intelligence,  pp. 1547-1553, 2016.

Aguilar Jose, Yolmer Romero y OswaldoTerán (2020). “Analysis of the effect on the marketing of the sport product from the “par Conditio” principle in Latin American football and baseball competitions”. Submitted to International Journal of Knowledge-Based and Intelligent Engineering Systems.

Contreras, J. Aguilar, J. (2010). “The FCM Designer Tool”. Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Application (Ed. M. Glykas), Springer, pp. 71-88, 2010.

Edmonds, B. and Hales, D. and Lessard-Phillips, L.(2020). Simulation Models of Ethnocentrism and Diversity: An Introduction to the Special Issue. Social Science Computer Review. Volume 38 Issue 4, August 2020, 359–364, https://journals.sagepub.com/toc/ssce/38/4

Edmonds Bruce, Oswaldo Terán y Grary Polhill (2006). “To the Outer Limits and Beyond –characterising the envelope of social simulation trajectories”, Proceedings of theThe First World Congress on Social Simulation WCSS”, Kyoto, 21-25 Agosto, 2006.

Giabbanelli Philippe, Steven Gray and Payam Aminpour (2017), Combining fuzzy cognitive maps with agent-based modeling: Frameworks and pitfalls of a powerful hybrid modeling approach to understand human-environment interactions, Environmental Modeling and Software, September 2017,  95:320-325 DOI:10.1016/j.envsoft.2017.06.040

Kosko, B. (1986). Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24, pp. 65-75.

Perozo Niriaska, Jose Aguilar, Oswaldo Terán y Heidi Molina (2013). A Verification Method for MASOES, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, Vol 43, num 1, February, pp. 64-76,  ISBN: 1083-4419. DOI: 10.1109/TSMCB.2012.2199106

Prensky, Marc (2001a). Digital Natives, Digital Immigrants Part 1, On the Horizon, Vol. 9, No. 5, September, pp 1-6. DOI: 10.1108/10748120110424816

Prensky, Marc (2001b). Do They Really Think Differently? Digital Natives, Digital Immigrants Part 2, On the Horizon, Vol. 9, No. 6, October 2001, pp 1-6. DOI: 10.1108/10748120110424843

Prensky, Marc (2009). “H. Sapiens Digital: From Digital Immigrants and Digital Natives to Digital Wisdom,” Innovate: Journal of Online Education: Vol. 5 : Issue 3, Article 1. Available at: https://nsuworks.nova.edu/innovate/vol5/iss3/1

Puerto E., Aguilar J., Chávez D., López C. (2019) Using Multilayer Fuzzy Cognitive Maps to Diagnose Autism Spectrum Disorder, Applied Soft Computing Journal, 75, pp. 58–71.

Rocco Paolillo, and Wander Jager (2020). Simulating Acculturation Dynamics Between Migrants and Locals in Relation to Network Formation, Social Science Computer Review. Volume 38 Issue 4, August 2020, pp. 365–386. https://journals.sagepub.com/toc/ssce/38/4

Sánchez Hebert, Jose Aguilar, Oswaldo Terán, José Gutiérrez de Mesa (2019). “Modeling the process of shaping the public opinion through Multilevel Fuzzy Cognitive Maps”, Applied Soft Computing, Volume 85. https://doi.org/10.1016/j.asoc.2019.105756.

Sibertin-Blanc, C., Roggero, P., Adreit, F., Baldet, B., Chapron, P., El-Gemayel, J., Mailliard, M., and Sandri, S. (2013). “SocLab: A Framework for the Modeling, Simulation and Analysis of Power in Social Organizations”, Journal of Artificial Societies and Social Simulation (JASSS), 16(4). http://jasss.soc.surrey.ac.uk/

Terán Oswaldo (2001). Emergent Tendencies in Multi-Agent Based Simulations Using Constraint-Based Methods to Effect Practical Proofs Over Finite Subsets of Simulation Outcomes, Doctoral Thesis, Centre for Policy Modelling, Manchester Metropolitan University, 2001.

Terán Oswaldo  (2004). Understanding MABS and Social Simulation: Switching Between Languages in a Hierarchy of Levels, Journal of Artificial Societies and Social Simulation vol. 7, no. 4. http://jasss.soc.surrey.ac.uk/7/4/5.html

Terán Oswaldo, Edmonds Bruce and Steve Wallis (2001) “Mapping the Envelope of Social Simulation Trajectories”, Lecture Notes in Artificial Intelligence (Subserie de Lecture Notes in Computer Science): MABS, Volume 1979, pp. 229-243, Springer, Alemania, 2001

Terán Oswaldo y Jose Aguilar (2018). “Modelo del proceso de influencia de los medios de comunicación social en la opinión pública”, EDUCERE, 21 (71), Universidad de Los Andes, Mérida, Venezuela. https://www.redalyc.org/toc.oa?id=356&numero=56002

Terán Oswaldo and Christophe Sibertin-Blanc (2020). Impact on Cooperation of altruism, tenacity, and the need of a resource,  accepted in IEEE Transactions on Computational Social Systems. DOI: 10.1109/TCSS.2021.3136821.

Annex. Fuzzy Cognitive Maps (FCM) and Muti-Fuzzy Cognitive Maps (MFCM).

Cognitive map theory is based on symbolic representation for the description of a system. It uses information, knowledge and experience, to describe particular domains using concepts (variables, states, inputs, outputs), and the relationships between them (Aguilar 2005, 2013, 2016). Cognitive maps can be understood as directed graphs, whose arcs represent causal connections between the nodes (concepts), used to denote knowledge. An arc with a positive sign (alternatively, negative sign), going from node X to node Y means that X (causally) increases (alternatively, decreases) Y. Cognitive maps are graphically represented: concepts are connected by arcs through a connection matrix. In the connection matrix, the i-nth line represents the weight of the arc connections directed outside of the  concept. The i-nth column lists the arcs directed toward , i.e., those affecting .

The conceptual development of FCMs rests on the definition and dynamic of concepts and relationships created by the theory of fuzzy sets (Kosko, 1986). FCM can describe any system using a causality-based model (that indicates positive or negative relationships), which takes fuzzy values and is dynamic (i.e., the effect of a change in one concept/node affects other nodes, which then affect further nodes). This structure establishes the forward and backward propagation of causality (Aguilar, 2005, 2013, 2016). Thus, the concepts and relations can be represented as fuzzy variables (expressed in linguistic terms), such as “Almost Always”, “Always”, “Normally”, “Some (see Figure 2).

The value of a concept depends on its previous iterations, following the equation (1):

Screenshot 2022-05-23 at 15.10.24

Cm(i+1) stands for the value of the concept in the next iteration after the iteration i, N indicates the number of concepts, wm,k represents the value of the causal relationship between the concept Ck and the concept Cm, and S(y) is a function used to normalize the value of the concept.

ot-fig2

Figure 2. Example of an FCM (taken from Sánchez et al., 2019).

MFCM is an extension of the FCM. It is a FCM with several layers where each layer represents a set of concepts that define a specific domain of a system. To construct a MFCM, the previous equation for calculating the current status of the concepts of a FCM is modified, to describe the relationships between different layers (Aguilar, 2016):

Where F(p) is the input function generated by the relationships among different layers, and p is the set of concepts of the other layers that impact this concept. Thus, the update function of the concepts has two parts. The first part, the classic, calculates the value of  concept in iteration  based on the values of concepts in the previous iteration . All these concepts belong to the same layer where the “m” concept belongs. The second part is the result of the causal relationship between the concepts in different levels of the MFCM.


Terán, O. & Aguilar, J. (2022) Towards contextualized social simulation: Complementary use of Multi-Fuzzy Cognitive Maps and MABS. Review of Artificial Societies and Social Simulation, 25th May 2022. https://rofasss.org/2022/05/25/MFCM-MABS


Discussions on Qualitative & Quantitative Data in the Context of Agent-Based Social Simulation

By Peer-Olaf Siebers, in collaboration with Kwabena Amponsah, James Hey, Edmund Chattoe-Brown and Melania Borit

Motivation

1.1: Some time ago, I had several discussions with my PhD students Kwabena Amponsah and James Hey (we are all computer scientists, with a research interest in multi-agent systems) on the topic of qualitative vs. quantitative data in the context of Agent-Based Social Simulation (ABSS). Our original goal was to better understand the role of qualitative vs. quantitative data in the life cycle of an ABSS study. But as you will see later, we conquered more ground during our discussions.

1.2: The trigger for these discussions came from numerous earlier discussions within the RAT task force (Sebastian Achter, Melania Borit, Edmund Chattoe-Brown, and Peer-Olaf Siebers) on the topic, while we were developing the Rigour and Transparency – Reporting Standard (RAT-RS). The RAT-RS is a tool to improve the documentation of data use in Agent-Based Modelling (Achter et al 2022). During our RAT-RS discussions we made the observation that when using the terms “qualitative data” and “quantitative data” in different phases of the ABM simulation study life cycle these could be interpreted in different ways, and we felt difficult to clearly state the definition/role of these different types of data in the different contexts that the individual phases within the life cycle represent. This was aggravated by the competing understandings of the terminology within different domains (from social and natural sciences) that form the field of social simulation.

1.3: As the ABSS community is a multi-disciplinary one, often doing interdisciplinary research, we thought that we should share the outcome of our discussions with the community. To demonstrate the different views that exist within the topic area, we ask some of our friends from the social simulation community to comment on our philosophical discussions. And we were lucky enough to get our RAT-RS colleagues Edmund Chattoe-Brown and Melania Borit on board who provided critical feedback and their own view of things. In the following we provide a summary of the overall discussion Each of the following paragraph contains summaries of the initial discussion outcomes, representing the computer scientists’ views, followed by some thoughts provided by our two friends from the social simulation community (Borit’s in {} brackets and italic and Chattoe-Brown’s in [] brackets and bold), both commenting on the initial discussion outcomes of the computer scientists. To see the diverse backgrounds of all the contributors and perhaps to better understand their way of thinking and their arguments, I have added some short biographies of all contributors at the end of this Research Note. To support further (public) discussions I have numbered the individual paragraphs to make it easier to refer back to them. 

Terminology

2.1: As a starting point for our discussions I searched the internet for some terminology related to the topic of “data”. Following is a list of initial definitions of relevant terms [1]. First, the terms qualitative data and quantitative data, as defined by the Australian Bureau of Statistics: “Qualitative data are measures of ‘types’ and may be represented by a name, symbol, or a number code. They are data about categorical variables (e.g. what type). Quantitative data are measures of values or counts and are expressed as numbers. They are data about numeric variables (e.g. how many; how much; or how often).” (Australian Bureau of Statistics 2022) [Maybe don’t let a statistics unit define qualitative research? This has a topic that is very alien to us but argues “properly” about the role of different methods (Helitzer-Allen and Kendall 1992). “Proper” qualitative researchers would fiercely dispute this. It is “quantitative imperialism”.].

2.2: What might also help for this discussion is to better understand the terms qualitative data analysis and quantitative data analysis. Qualitative data analysis refers to “the processes and procedures that are used to analyse the data and provide some level of explanation, understanding, or interpretation” (Skinner et al 2021). [This is a much less contentious claim for qualitative data – and makes the discussion of the Australian Bureau of Statistics look like a distraction but a really “low grade” source in peer review terms. A very good one is Strauss (1987).] These methods include content analysis, narrative analysis, discourse analysis, framework analysis, and grounded theory and the goal is to identify common patterns. {These data analysis methods connect to different types of qualitative research: phenomenology, ethnography, narrative inquiry, case study research, or grounded theory. The goal of such research is not always to identify patterns – see (Miles and Huberman 1994): e.g., making metaphors, seeing plausibility, making contrasts/comparisons.} [In my opinion some of these alleged methods are just empire building or hot air. Do you actually need them for your argument?] These types of analysis must therefore use qualitative inputs, broadening the definition to include raw text, discourse and conceptual frameworks.

2.3 When it comes to quantitative data analysis “you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables.” (Business Research Methodology 2022) {One does the same in qualitative data analysis – turns raw words (or pictures etc.) into meaningful data through the application of interpretation based on rational and critical thinking. In quantitative data analysis you usually apply mathematical/statistical models to analyse the data.}. While the output of quantitative data analysis can be used directly as input to a simulation model, the output of qualitative data analysis still needs to be translated into behavioural rules to be useful (either manually or through machine learning algorithms). {What is meant by “translated” in this specific context? Do we need this kind of translation only for qualitative data or also for quantitative data? Is there a difference between translation methods of qualitative and quantitative data?} [That seems pretty contentious too. It is what is often done, true, but I don’t think it is a logical requirement. I guess you could train a neural net using “cases” or design some other simple “cognitive architecture” from the data. Would this (Becker 1953), for example, best be modelled as “rules” or as some kind of adaptive process? But of course you have to be careful that “rule” is not defined so broadly that everything is one or it is true by definition. I wonder what the “rules” are in this: Chattoe-Brown (2009).]

2.4: Finally, let’s have a quick look at the difference between “data” and “evidence”. For this, we found the following distinction by Wilkinson (2022) helpful: “… whilst data can exist on its own, even though it is essentially meaningless without context, evidence, on the other hand, has to be evidence of or for something. Evidence only exists when there is an opinion, a viewpoint or an argument.”

Hypothesis

3.1: The RAT-RS divides the simulation life cycle into five phases, in terms of data use: model aim and context, conceptualisation, operationalisation, experimentation, and evaluation (Siebers et al 2019). We started our discussion by considering the following hypothesis: The outcome of qualitative data analysis is only useful for the purpose of conceptualisation and as a basis for producing quantitative data. It does not have any other roles within the ABM simulation study life cycle. {Maybe this hypothesis in itself has to be discussed. Is it so that you use only numbers in the operationalisation phase? One can write NetLogo code directly from qualitative data, without numbers.} [Is this inevitable given the way ABM works? Agents have agency and therefore can decide to do things (and we can only access this by talking to them, probably “open ended”). A statistical pattern – time series or correlation – has no agency and therefore cannot be accessed “qualitatively” – though we also sometimes mean by “qualitative” eyeballing two time series rather than using some formal measure of tracking. I guess that use would >>not<< be relevant here.]

Discussion

4.1: One could argue that qualitative data analysis provides causes for behaviour (and indications about their importance (ranking); perhaps also the likelihood of occurrence) as well as key themes that are important to be considered in a model. All sounds very useful for the conceptual modelling phase. The difficulty might be to model the impact (how do we know we model it correctly and at the right level), if that is not easily translatable into a quantitative value but requires some more (behavioural) mechanistic structures to represent the impact of behaviours. [And, of course, there is a debate in psychology (with some evidence on both sides) about the extent to which people are able to give subjective accounts we can trust (see Hastorf and Cantril (1954).] This might also provide issues when it comes to calibration – how does one calibrate qualitative data? {Triangulation.} One random idea we had was that perhaps fuzzy logic could help with this. More brainstorming and internet research is required to confirm that this idea is feasible and useful. [A more challenging example might be ethnographic observation of a “neighbourhood” in understanding crime. This is not about accessing the cognitive content of agents but may well still contribute to a well specified model. It is interesting how many famous models – Schelling, Zaller-Deffuant – actually have no “real” environment.]

4.2: One could also argue that what starts (or what we refer to initially) as qualitative data always ends up as quantitative data, as whatever comes out of the computer are numbers. {This is not necessarily true. Check  the work on qualitative outputs using Grounded Theory by Neumann (2015).} Of course this is a question related to the conceptual viewpoint. [Not convinced. It sounds like all sociology is actually physics because all people are really atoms. Formally, everything in computers is numbers because it has to be but that isn’t the same as saying that data structures or whatever don’t constitute a usable and coherent level of description: We “meet” and “his” opinion changes “mine” and vice versa. Somewhere, that is all binary but you can read the higher level code that you can understand as “social influence” (whatever you may think of the assumptions). Be clear whether this (like the “rules” claim) is a matter of definition – in which case it may not be useful (even if people are atoms we have no idea of how to solve the “atomic physics” behind the Prisoner’s Dilemma) or an empirical one (in which case some models may just prove it false). This (Beltratti et al 1996) contains no “rules” and no “numbers” (except in the trivial sense that all programming does).]

4.3: Also, an algorithm is expressed in code and can only be processed numerically, so it can only deliver quantitative data as output. These can perhaps be translated into qualitative concepts later. A way of doing this via the use of grounded theory is proposed in Neumann and Lotzmann (2016). {This refers to the same idea as my previous comment.} [Maybe it is “safest” to discuss this with rules because everyone knows those are used in ABM. Would it make sense to describe the outcome of a non trivial set of rules – accessed for example like this: Gladwin (1989) – as either “quantitative” or “numbers?”]

4.4: But is it true that data delivered as output is always quantitative? Let’s consider, for example, a consumer marketing scenario, where we define stereotypes (shopping enthusiast; solution demander; service seeker; disinterested shopper; internet shopper) that can change over time during a simulation run (Siebers et al 2010). These stereotypes are defined by likelihoods (likelihood to buy, wait, ask for help, and ask for refund). So, during a simulation run an agent could change its stereotype (e.g. from shopping enthusiast to disinterested shopper), influenced by the opinion of others and their own previous experience. So, at the beginning of the simulation run the agent can have a different stereotype compared to the end. Of course we could enumerate the five different stereotypes, and claim that the outcome is numeric, but the meaning of the outcome would be something qualitative – the stereotype related to that number. To me this would be a qualitative outcome, while the number of people that change from one stereotype to another would be a quantitative outcome. They would come in a tandem. {So, maybe the problem is that we don’t yet have the right ways of expressing or visualising qualitative output?} [This is an interesting and grounded example but could it be easily knocked down because everything is “hard coded” and therefore quantifiable? You may go from one shopper type to another – and what happens depends on other assumptions about social influence and so on – but you can’t “invent” your own type. Compare something like El Farol (Arthur 1994) where agents arguably really can “invent” unique strategies (though I grant these are limited to being expressed in a specified “grammar”).]

4.5: In order to define someone’s stereotype we would use numerical values (likelihood = proportion). However, stereotypes refer to nominal data (which refers to data that is used for naming or labelling variables, without any quantitative value). The stereotype itself would be nominal, while the way one would derive the stereotype would be numerical. Figure 1 illustrates a case in which the agent moves from the disinterested stereotype to the enthusiast stereotype. [Is there a potential confusion here between how you tell an agent is a type – parameters in the code just say so – and how you say a real person is a type? Everything you say about the code still sounds “quantitative” because all the “ingredients” are.]

Figure 1: Enthusiastic and Disinterested agent stereotypes

4.6: Let’s consider a second example, related to the same scenario: The dynamics over time to get from an enthusiastic shopper (perhaps via phases) to a disinterested shopper. This is represented as a graph where the x-axis represents time and the y-axis stereotypes (categorical data). If you want to take a quantitative perspective on the outcome you would look at a specific point in time (state of the system) but to take a qualitative perspective of the outcome, you would look at the pattern that the curve represents over the entire simulation runtime. [Although does this shade into the “eyeballing” sense of qualitative rather than the “built from subjective accounts” sense? Another way to think of this issue is to imagine “experts” as a source of data. We might build an ABM based on an expert perception of say, how a crime gang operates. That would be qualitative but not just individual rules: For example, if someone challenges the boss to a fight and loses they die or leave. This means the boss often has no competent potential successors.]

4.7: So, the inputs (parameters, attributes) to get the outcome are numeric, but the outcome itself in the latter case is not. The outcome only makes sense once it’s put into the qualitative context. And then we could say that the simulation produces some qualitative outputs. So, does the fact that data needs to be seen in a context make it evidence, i.e. do we only have quantitative and qualitative evidence on the output side? [Still worried that you may not be happy equating qualitative interview data with qualitative eyeballing of graphs. Mixes up data collection and analysis? And unlike qualitative interviews you don’t have to eyeball time series. But the argument of qualitative research is you can’t find out some things any other way because, to run a survey say, or an experiment, you already have to have a pretty good grasp of the phenomenon.]

4.8: If one runs a marketing campaign that will increase the number of enthusiastic shoppers. This can be seen as qualitative data as it is descriptive of how the system works rather than providing specific values describing the performance of a system. You could also equally express this algebraic terms which would make it quantitative data. So, it might be useful to categorise quantitative data to make the outcome easier to understand. [I don’t think this argument is definitely wrong – though I think it may be ambiguous about what “qualitative” means – but I think it really needs stripping down and tightening. I’m not completely convinced as a new reader that I’m getting at the nub of the argument. Maybe just one example in detail and not two in passing?]

Outcome

5.1: How we understand things and how the computer processes things are two different things. So, in fact qualitative data is useful for the conceptualisation and for describing experimentation and evaluation output, and needs to be translated into numerical data or algebraic constructs for the operationalisation. Therefore, we can reject our initial hypothesis, as we found more places where qualitative data can be useful. [Yes, and that might form the basis for a “general” definition of qualitative that was not tied to one part of the research process but you would have to be clear that’s what you were aiming at and not just accidentally blurring two different “senses” of qualitative.]

5.2: In the end of the discussion we picked up the idea of using Fuzzy Logic. Could perhaps fuzzy logic be used to describe qualitative output, as it describes a degree of membership to different categories? An interesting paper to look at in this context would be Sugeno and Yasukawa (1993). Also, a random idea that was mentioned is if there is potential in using “fuzzy logic in reverse”, i.e. taking something that is fuzzy, making it crisp for the simulation, and making it fuzzy again for presenting the result. However, we decided to save this topic for another discussion. [Devil will be in the detail. Depends on exactly what assumptions the method makes. Devil’s advocate: What if qualitative research is only needed for specification – not calibration or validation – but it doesn’t follow from this that that use is “really” quantitative? How intellectually unappealing is that situation and why?]

Conclusion

6.1: The purpose of this Research Note is really to stimulate you to think about, talk about, and share your ideas and opinions on the topic! What we present here is a philosophical impromptu discussion of our individual understanding of the topic, rather than a scientific debate that is backed up by literature. We still thought it is worthwhile to share this with you, as you might stumble across similar questions. Also, we don’t think we have found the perfect answers to the questions yet. So we would like to invite you to join the discussion and leave some comments in the chat, stating your point of view on this topic. [Is the danger of discussing these data types “philosophically”? I don’t know if it is realistic to use examples directly from social simulation but for sure examples can be used from social science generally. So here is a “quantitative” argument from quantitative data: “The view that cultural capital is transmitted from parents to their children is strongly supported in the case of pupils’ cultural activities. This component of pupils’ cultural capital varies by social class, but this variation is entirely mediated by parental cultural capital.” (Sullivan 2001). As well as the obvious “numbers” (social class by a generally agreed scheme) there is also a constructed “measure” of cultural capital based on questions like “how many books do you read in a week?” Here is an example of qualitative data from which you might reason: “I might not get into Westbury cos it’s siblings and how far away you live and I haven’t got any siblings there and I live a little way out so I might have to go on a waiting list … I might go to Sutton Boys’ instead cos all my mates are going there.” (excerpt from Reay 2002). As long as this was not just a unique response (but was supported by several other interviews) one would add to one’s “theory” of school choice: 1) Awareness of the impact of the selection system (there is no point in applying here whatever I may want) and 2) The role of networks in choice: This might be the best school for me educationally but I won’t go because I will be lonely.]

Biographies of the authors

Peer-Olaf Siebers is an Assistant Professor at the School of Computer Science, University of Nottingham, UK. His main research interest is the application of Computer Simulation and Artificial Intelligence to study human-centric and coupled human-natural systems. He is a strong advocate of Object-Oriented Agent-Based Social Simulation and is advancing the methodological foundations. It is a novel and highly interdisciplinary research field, involving disciplines like Social Science, Economics, Psychology, Geography, Operations Research, and Computer Science.

Kwabena Amponsah is a Research Software Engineer working for the Digital Research Service, University of Nottingham, UK. He completed his PhD in Computer Science at Nottingham in 2019 by developing a framework for evaluating the impact of communication on performance in large-scale distributed urban simulations.

James Hey is a PhD student at the School of Computer Science, University of Nottingham, UK. In his PhD he investigates the topic of surrogate optimisation for resource intensive agent based simulation of domestic energy retrofit uptake with environmentally conscious agents. James holds a Bachelor degree in Economics as well as a Master degree in Computer Science.

Edmund Chattoe-Brown is a lecturer in Sociology, School of Media, Communication and Sociology, University of Leicester, UK. His career has been interdisciplinary (including Politics, Philosophy, Economics, Artificial Intelligence, Medicine, Law and Anthropology), focusing on the value of research methods (particularly Agent-Based Modelling) in generating warranted social knowledge. His aim has been to make models both more usable generally and particularly more empirical (because the most rigorous social scientists tend to be empirical). The results of his interests have been published in 17 different peer reviewed journals across the sciences to date. He was funded by the project “Towards Realistic Computational Models Of Social Influence Dynamics” (ES/S015159/1) by the ESRC via ORA Round 5.

Melania Borit is an interdisciplinary researcher and the leader of the CRAFT Lab – Knowledge Integration and Blue Futures at UiT The Arctic University of Norway. She has a passion for knowledge integration and a wide range of interconnected research interests: social simulation, agent-based modelling; research methodology; Artificial Intelligence ethics; pedagogy and didactics in higher education, games and game-based learning; culture and fisheries management, seafood traceability; critical futures studies.

References

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Notes

[1] An updated set of the terminology, defined by the RAT task force in 2022, is available as part of the RAT-RS in Achter et al (2022) Appendix A1.


Peer-Olaf Siebers, Kwabena Amponsah, James Hey, Edmund Chattoe-Brown and Melania Borit (2022) Discussions on Qualitative & Quantitative Data in the Context of Agent-Based Social Simulation. Review of Artificial Societies and Social Simulation, 16th May 2022. https://rofasss.org/2022/05/16/Q&Q-data-in-ABM


Socio-Cognitive Systems – a position statement

By Frank Dignum1, Bruce Edmonds2 and Dino Carpentras3

1Department of Computing Science, Faculty of Science and Technology, Umeå University, frank.dignum@umu.se
2Centre for Policy Modelling, Manchester Metropolitan University, bruce@edmonds.name
3Department of Psychology, University of Limerick, dino.carpentras@gmail.com

In this position paper we argue for the creation of a new ‘field’: Socio-Cognitive Systems. The point of doing this is to highlight the importance of a multi-levelled approach to understanding those phenomena where the cognitive and the social are inextricably intertwined – understanding them together.

What goes on ‘in the head’ and what goes on ‘in society’ are complex questions. Each of these deserves serious study on their own – motivating whole fields to answer them. However, it is becoming increasingly clear that these two questions are deeply related. Humans are fundamentally social beings, and it is likely that many features of their cognition have evolved because they enable them to live within groups (Herrmann et al. 20007). Whilst some of these social features can be studied separately (e.g. in a laboratory), others only become fully manifest within society at large. On the other hand, it is also clear that how society ‘happens’ is complicated and subtle and that these processes are shaped by the nature of our cognition. In other words, what people ‘think’ matters for understanding how society ‘is’ and vice versa. For many reasons, both of these questions are difficult to answer. As a result of these difficulties, many compromises are necessary in order to make progress on them, but each compromise also implies some limitations. The main two types of compromise consist of limiting the analysis to only one of the two (i.e. either cognition or society)[1]. To take but a few examples of this.

  1. Neuro-scientists study what happens between systems of neurones to understand how the brain does things and this is so complex that even relatively small ensembles of neurones are at the limits of scientific understanding.
  2. Psychologists see what can be understood of cognition from the outside, usually in the laboratory so that some of the many dimensions can be controlled and isolated. However, what can be reproduced in a laboratory is a limited part of behaviour that might be displayed in a natural social context.
  3. Economists limit themselves to the study of the (largely monetary) exchange of services/things that could occur under assumptions of individual rationality, which is a model of thinking not based upon empirical data at the individual level. Indeed it is known to contradict a lot of the data and may only be a good approximation for average behaviour under very special circumstances.
  4. Ethnomethodologists will enter a social context and describe in detail the social and individual experience there, but not generalise beyond that and not delve into the cognition of those they observe.
  5. Other social scientists will take a broader view, look at a variety of social evidence, and theorise about aspects of that part of society. They (almost always) do not include individual cognition into account in these and do not seek to integrate the social and the cognitive levels.

Each of these in the different ways separate the internal mechanisms of thought from the wider mechanisms of society or limits its focus to a very specific topic. This is understandable; what each is studying is enough to keep them occupied for many lifetimes. However, this means that each of these has developed their own terms, issues, approaches and techniques which make relating results between fields difficult (as Kuhn, 1962, pointed out).

SCS Picture 1

Figure 1: Schematic representation of the relationship between the individual and society. Individuals’ cognition is shaped by society, at the same time, society is shaped by individuals’ beliefs and behaviour.

This separation of the cognitive and the social may get in the way of understanding many things that we observe. Some phenomena seem to involve a combination of these aspects in a fundamental way – the individual (and its cognition) being part of society as well as society being part of the individual. Some examples of this are as follows (but please note that this is far from an exhaustive list).

  • Norms. A social norm is a constraint or obligation upon action imposed by society (or perceived as such). One may well be mistaken about a norm (e.g. whether it is ok to casually talk to others at a bus stop), thus it is also a belief – often not told to one explicitly but something one needs to infer from observation. However, for a social norm to hold it also needs to be an observable convention. Decisions to violate social norms require that the norm is an explicit (referable) object in the cognitive model. But the violation also has social consequences. If people react negatively to violations the norm can be reinforced. But if violations are ignored it might lead to a norm disappearing. How new norms come about, or how old ones fade away, is a complex set of interlocking cognitive and social processes. Thus social norms are a phenomena that essentially involves both the social and the cognitive (Conte et al. 2013).
  • Joint construction of social reality. Many of the constraints on our behaviour come from our perception of social reality. However, we also create this social reality and constantly update it. For example, we can invent a new procedure to select a person as head of department or exit a treaty and thus have different ways of behaving after this change. However, these changes are not unconstrained in themselves. Sometimes the time is “ripe for change”, while at other times resistance is too big for any change to take place (even though a majority of the people involved would like to change). Thus what is socially real for us depends on what people individually believe is real, but this depends in complex ways on what other people believe and their status. And probably even more important: the “strength” of a social structure depends on the use people make of it. E.g. a head of department becomes important if all decisions in the department are deferred to the head. Even though this might not be required by university or law.
  • Identity. Our (social) identity determines the way other people perceive us (e.g. a sports person, a nerd, a family man) and therefore creates expectations about our behaviour. We can create our identities ourselves and cultivate them, but at the same time, when we have a social identity, we try to live up to it. Thus, it will partially determine our goals and reactions and even our feeling of self-esteem when we live up to our identity or fail to do so. As individuals we (at least sometimes) have a choice as to our desired identity, but in practice, this can only be realised with the consent of society. As a runner I might feel the need to run at least three times a week in order for other people to recognize me as runner. At the same time a person known as a runner might be excused from a meeting if training for an important event. Thus reinforcing the importance of the “runner” identity.
  • Social practices. The concept already indicates that social practices are about the way people habitually interact and through this interaction shape social structures. Practices like shaking hands when greeting do not always have to be efficient, but they are extremely socially important. For example, different groups, countries and cultures will have different practices when greeting and performing according to the practice shows whether you are part of the in-group or out-group. However, practices can also change based on circumstances and people, as it happened, for example, to the practice of shaking hands during the covid-19 pandemic. Thus, they are flexible and adapting to the context. They are used as flexible mechanisms to efficiently fit interactions in groups, connecting persons and group behaviour.

As a result, this division between cognitive and the social gets in the way not only of theoretical studies, but also in practical applications such as policy making. For example, interventions aimed at encouraging vaccination (such as compulsory vaccination) may reinforce the (social) identity of the vaccine hesitant. However, this risk and its possible consequences for society cannot be properly understood without a clear grasp of the dynamic evolution of social identity.

Computational models and systems provide a way of trying to understand the cognitive and the social together. For computational modellers, there is no particular reason to confine themselves to only the cognitive or only the social because agent-based systems can include both within a single framework. In addition, the computational system is a dynamic model that can represent the interactions of the individuals that connect the cognitive models and the social models. Thus the fact that computational models have a natural way to represent the actions as an integral and defining part of the socio-cognitive system is of prime importance. Given that the actions are an integral part of the model it is well suited to model the dynamics of socio-cognitive systems and track changes at both the social and the cognitive level. Therefore, within such systems we can study how cognitive processes may act to produce social phenomena whilst, at the same time, as how social realities are shaping the cognitive processes. Caarley and Newell (1994) discusses what is necessary at the agent level for sociality, Hofested et al. (2021) talk about how to understand sociality using computational models (including theories of individual action) – we want to understand both together. Thus, we can model the social embeddedness that Granovetter (1985) talked about – going beyond over- or under-socialised representations of human behaviour. It is not that computational models are innately suitable for modelling either the cognitive or the social, but that they can be appropriately structured (e.g. sets of interacting parts bridging micro-, meso- and macro-levels) and include arbitrary levels of complexity. Lots of models that represent the social have entities that stand for the cognitive, but do not explicitly represent much of that detail – similarly much cognitive modelling implies the social in terms of the stimuli and responses of an individual that would be to other social entities, but where these other entities are not explicitly represented or are simplified away.

Socio-Cognitive Systems (SCS) are: those models and systems where both cognitive and social complexity are represented with a meaningful level of processual detail.

A good example of an application where this appeared of the biggest importance was in simulations for the covid-19 crisis. The spread of the corona virus on macro level could be given by an epidemiological model, but the actual spreading depended crucially on the human behaviour that resulted from individuals’ cognitive model of the situation. In Dignum (2021) it was shown how the socio-cognitive system approach was fundamental to obtaining better insights in the effectiveness of a range of covid-19 restrictions.

Formality here is important. Computational systems are formal in the sense that they can be unambiguously passed around (i.e. unlike language, it is not differently re-interpreted by each individual) and operate according to their own precisely specified and explicit rules. This means that the same system can be examined and experimented on by a wider community of researchers. Sometimes, even when the researchers from different fields find it difficult to talk to one another, they can fruitfully cooperate via a computational model (e.g. Lafuerza et al. 2016). Other kinds of formal systems (e.g. logic, maths) are geared towards models that describe an entire system from a birds eye view. Although there are some exceptions like fibred logics Gabbay (1996), these are too abstract to be of good use to model practical situations. The lack of modularity and has been addressed in context logics Giunchiglia, F., & Ghidini, C. (1998). However, the contexts used in this setting are not suitable to generate a more general societal model. It results in most typical mathematical models using a number of agents which is either one, two or infinite (Miller and Page 2007), while important social phenomena happen with a “medium sized” population. What all these formalisms miss is a natural way of specifying the dynamics of the system that is modelled, while having ways to modularly describe individuals and the society resulting from their interactions. Thus, although much of what is represented in Socio-Cognitive Systems is not computational, the lingua franca for talking about them is.

The ‘double complexity’ of combining the cognitive and the social in the same system will bring its own methodological challenges. Such complexity will mean that many socio-cognitive systems will be, themselves, hard to understand or analyse. In the covid-19 simulations, described in (Dignum 2021), a large part of the work consisted of analysing, combining and representing the results in ways that were understandable. As an example, for one scenario 79 pages of graphs were produced showing different relations between potentially relevant variables. New tools and approaches will need to be developed to deal with this. We only have some hints of these, but it seems likely that secondary stages of analysis – understanding the models – will be necessary, resulting in a staged approach to abstraction (Lafuerza et al. 2016). In other words, we will need to model the socio-cognitive systems, maybe in terms of further (but simpler) socio-cognitive systems, but also maybe with a variety of other tools. We do not have a view on this further analysis, but this could include: machine learning, mathematics, logic, network analysis, statistics, and even qualitative approaches such as discourse analysis.

An interesting input for the methodology of designing and analysing socio-cognitive systems is anthropology and specifically ethnographical methods. Again, for the covid-19 simulations the first layer of the simulation was constructed based on “normal day life patterns”. Different types of persons were distinguished that each have their own pattern of living. These patterns interlock and form a fabric of social interactions that overall should satisfy most of the needs of the agents. Thus we calibrate the simulation based on the stories of types of people and their behaviours. Note that doing the same just based on available data of behaviour would not account for the underlying needs and motives of that behaviour and would not be a good basis for simulating changes. The stories that we used looked very similar to the type of reports ethnographers produce about certain communities. Thus further investigating this connection seems worthwhile.

For representing the output of the complex socio-cognitive systems we can also use the analogue of stories. Basically, different stories show the underlying (assumed) causal relations between phenomena that are observed. E.g. seeing an increase in people having lunch with friends can be explained by the fact that a curfew prevents people having dinner with their friends, while they still have a need to socialize. Thus the alternative of going for lunch is chosen more often. One can see that the explaining story uses both social as well as cognitive elements to describe the results. Although in the covid-19 simulations we have created a number of these stories, they were all created by hand after (sometimes weeks) of careful analysis of the results. Thus for this kind of approach to be viable, new tools are required.

Although human society is the archetypal socio-cognitive system, it is not the only one. Both social animals and some artificial systems also come under this category. These may be very different from the human, and in the case of artificial systems completely different. Thus, Socio-Cognitive Systems is not limited to the discussion of observable phenomena, but can include constructed or evolved computational systems, and artificial societies. Examination of these (either theoretically or experimentally) opens up the possibility of finding either contrasts or commonalities between such systems – beyond what happens to exist in the natural world. However, we expect that ideas and theories that were conceived with human socio-cognitive systems in mind might often be an accessible starting point for understanding these other possibilities.

In a way, Socio-Cognitive Systems bring together two different threads in the work of Herbert Simon. Firstly, as in Simon (1948) it seeks to take seriously the complexity of human social behaviour without reducing this to overly simplistic theories of individual behaviour. Secondly, it adopts the approach of explicitly modelling the cognitive in computational models (Newell & Simon 1972). Simon did not bring these together in his lifetime, perhaps due to the limitations and difficulty of deploying the computational tools to do so. Instead, he tried to develop alternative mathematical models of aspects of thought (Simon 1957). However, those models were limited by being mathematical rather than computational.

To conclude, a field of Socio-Cognitive Systems would consider the cognitive and the social in an integrated fashion – understanding them together. We suggest that computational representation or implementation might be necessary to provide concrete reference between the various disciplines that are needed to understand them. We want to encourage research that considers the cognitive and the social in a truly integrated fashion. If by labelling a new field does this it will have achieved its purpose. However, there is the possibility that completely new classes of theory and complexity may be out there to be discovered – phenomena that are denied if either the cognitive or the social are not taken together – a new world of a socio-cognitive systems.

Notes

[1] Some economic models claim to bridge between individual behaviour and macro outcomes, however this is traditionally notional. Many economists admit that their primary cognitive models (varieties of economic rationality) are not valid for individuals but are what people on average do – i.e. this is a macro-level model. In other economic models whole populations are formalised using a single representative agent. Recently, there are some agent-based economic models emerging, but often limited to agree with traditional models.

Acknowledgements

Bruce Edmonds is supported as part of the ESRC-funded, UK part of the “ToRealSim” project, grant number ES/S015159/1.

References

Carley, K., & Newell, A. (1994). The nature of the social agent. Journal of mathematical sociology, 19(4): 221-262. DOI: 10.1080/0022250X.1994.9990145

Conte R., Andrighetto G. and Campennì M. (eds) (2013) Minding Norms – Mechanisms and dynamics of social order in agent societies. Oxford University Press, Oxford.

Dignum, F. (ed.) (2021) Social Simulation for a Crisis; Results and Lessons from Simulating the COVID-19 Crisis. Springer.

Herrmann E., Call J, Hernández-Lloreda MV, Hare B, Tomasello M (2007) Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science 317(5843): 1360-1366. DOI: 10.1126/science.1146282

Hofstede, G.J, Frantz, C., Hoey, J., Scholz, G. and Schröder, T. (2021) Artificial Sociality Manifesto. Review of Artificial Societies and Social Simulation, 8th Apr 2021. https://rofasss.org/2021/04/08/artsocmanif/

Gabbay, D. M. (1996). Fibred Semantics and the Weaving of Logics Part 1: Modal and Intuitionistic Logics. The Journal of Symbolic Logic, 61(4), 1057–1120.

Ghidini, C., & Giunchiglia, F. (2001). Local models semantics, or contextual reasoning= locality+ compatibility. Artificial intelligence, 127(2), 221-259. DOI: 10.1016/S0004-3702(01)00064-9

Granovetter, M. (1985) Economic action and social structure: The problem of embeddedness. American Journal of Sociology 91(3): 481-510. DOI: 10.1086/228311

Kuhn, T,S, (1962) The structure of scientific revolutions. University of Chicago Press, Chicago

Lafuerza L.F., Dyson L., Edmonds B., McKane A.J. (2016) Staged Models for Interdisciplinary Research. PLoS ONE 11(6): e0157261, DOI: 10.1371/journal.pone.0157261

Miller, J. H., Page, S. E., & Page, S. (2009). Complex adaptive systems. Princeton university press.

Newell A, Simon H.A. (1972) Human problem solving. Prentice Hall, Englewood Cliffs, NJ

Simon, H.A. (1948) Administrative behaviour: A study of the decision making processes in administrative organisation. Macmillan, New York

Simon, H.A. (1957) Models of Man: Social and rational. John Wiley, New York


Dignum, F., Edmonds, B. and Carpentras, D. (2022) Socio-Cognitive Systems – A Position Statement. Review of Artificial Societies and Social Simulation, 2nd Apr 2022. https://rofasss.org/2022/04/02/scs


 

If you want to be cited, calibrate your agent-based model: A Reply to Chattoe-Brown

By Marijn A. Keijzer

This is a reply to a previous comment, (Chattoe-Brown 2022).

The social simulation literature has called on its proponents to enhance the quality and realism of their contributions through systematic validation and calibration (Flache et al., 2017). Model validation typically refers to assessments of how well the predictions of their agent-based models (ABMs) map onto empirically observed patterns or relationships. Calibration, on the other hand, is the process of enhancing the realism of the model by parametrizing it based on empirical data (Boero & Squazzoni, 2005). We would expect that presenting a validated or calibrated model serves as a signal of model quality, and would thus be a desirable characteristic of a paper describing an ABM.

In a recent contribution to RofASSS, Edmund Chattoe-Brown provocatively argued that model validation does not bear fruit for researchers interested in boosting their citations. In a sample of articles from JASSS published on opinion dynamics he observed that “the sample clearly divides into non-validated research with more citations and validated research with fewer” (Chattoe-Brown, 2022). Well-aware of the bias and limitations of the sample at hand, Chattoe-Brown calls on refutation of his hypothesis. An analysis of the corpus of articles in Web of Science, presented here, could serve that goal.

To test whether there exists an effect of model calibration and/or validation on the citation counts of papers, I compare citation counts of a larger number of original research articles on agent-based models published in the literature. I extracted 11,807 entries from Web of Science by searching for items that contained the phrases “agent-based model”, “agent-based simulation” or “agent-based computational model” in its abstract.[1] I then labeled all items that mention “validate” in its abstract as validated ABMs and those that mention “calibrate” as calibrated ABMs. This measure if rather crude, of course, as descriptions containing phrases like “we calibrated our model” or “others should calibrate our model” are both labeled as calibrated models. However, if mentioning that future research should calibrate or validate the model is not related to citations counts (which I would argue it indeed is not), then this inaccuracy does not introduce bias.

The shares of entries that mention calibration or validation are somewhat small. Overall, just 5.62% of entries mention validation, 3.21% report a calibrated model and 0.65% fall in both categories. The large sample size, however, will still enable the execution of proper statistical analysis and hypothesis testing.

How are mentions of calibration and validation in the abstract related to citation counts at face value? Bivariate analyses show only minor differences, as revealed in Figure 1. In fact, the distribution of citations for validated and non-validated ABMs (panel A) is remarkably similar. Wilcoxon tests with continuity correction—the nonparametric version of the simple t test—corroborate their similarity (W = 3,749,512, p = 0.555). The differences in citations between calibrated and non-calibrated models appear, albeit still small, more pronounced. Calibrated ABMs are cited slightly more often (panel B), as also supported by a bivariate test (W = 1,910,772, p < 0.001).

Picture 1

Figure 1. Distributions of number of citations of all the entries in the dataset for validated (panel A) and calibrated (panel B) ABMs and their averages with standard errors over years (panels C and D)

Age of the paper might be a more important determinant of citation counts, as panels C and D of Figure 1 suggest. Clearly, the age of a paper should be important here, because older papers have had much more opportunity to get cited. In particular, papers younger than 10 years seem to not have matured enough for its citation rates to catch up to older articles. When comparing the citation counts of purely theoretical models with calibrated and validated versions, this covariate should not be missed, because the latter two are typically much younger. In other words, the positive relationship between model calibration/validation and citation counts could be hidden in the bivariate analysis, as model calibration and validation are recent trends in ABM research.

I run a Poisson regression on the number of citations as explained by whether they are validated and calibrated (simultaneously) and whether they are both. The age of the paper is taken into account, as well as the number of references that the paper uses itself (controlling for reciprocity and literature embeddedness, one might say). Finally, the fields in which the papers have been published, as registered by Web of Science, have been added to account for potential differences between fields that explains both citation counts and conventions about model calibration and validation.

Table 1 presents the results from the four models with just the main effects of validation and calibration (model 1), the interaction of validation and calibration (model 2) and the full model with control variables (model 3).

Table 1. Poisson regression on the number of citations

# Citations
(1) (2) (3)
Validated -0.217*** -0.298*** -0.094***
(0.012) (0.014) (0.014)
Calibrated 0.171*** 0.064*** 0.076***
(0.014) (0.016) (0.016)
Validated x Calibrated 0.575*** 0.244***
(0.034) (0.034)
Age 0.154***
(0.0005)
Cited references 0.013***
(0.0001)
Field included No No Yes
Constant 2.553*** 2.556*** 0.337**
(0.003) (0.003) (0.164)
Observations 11,807 11,807 11,807
AIC 451,560 451,291 301,639
Note: *p<0.1; **p<0.05; ***p<0.01

The results from the analyses clearly suggest a negative effect of model validation and a positive effect of model calibration on the likelihood of being cited. The hypothesis that was so “badly in need of refutation” (Chattoe-Brown, 2022) will remain unrefuted for now. The effect does turn positive, however, when the abstract makes mention of calibration as well. In both the controlled (model 3) and uncontrolled (model 2) analyses, combining the effects of validation and calibration yields a positive coefficient overall.[2]

The controls in model 3 substantially affect the estimates from the three main factors of interest, while remaining in expected directions themselves. The age of a paper indeed helps its citation count, and so does the number of papers the item cites itself. The fields, furthermore, take away from the main effects somewhat, too, but not to a problematic degree. In an additional analysis, I have looked at the relationship between the fields and whether they are more likely to publish calibrated or validated models and found no substantial relationships. Citation counts will differ between fields, however. The papers in our sample are more often cited in, for example, hematology, emergency medicine and thermodynamics. The ABMs in the sample coming from toxicology, dermatology and religion are on the unlucky side of the equation, receiving less citations on average. Finally, I have also looked at papers published in JASSS specifically, due to the interest of Chattoe-Brown and the nature of this outlet. Surprisingly, the same analyses run on the subsample of these papers (N=376) showed a negative relationship between citation counts and model calibration/validation. Does the JASSS readership reveal its taste for artificial societies?

In sum, I find support for the hypothesis of Chattoe-Brown (2022) on the negative relationship between model validation and citations counts for papers presenting ABMs. If you want to be cited, you should not validate your ABM. Calibrated ABMs, on the other hand, are more likely to receive citations. What is more, ABMs that were both calibrated and validated are most the most successful papers in the sample. All conclusions were drawn considering (i.e. controlling for) the effects of age of the paper, the number of papers the paper cited itself, and (citation conventions in) the field in which it was published.

While the patterns explored in this and Chattoe-Brown’s recent contribution are interesting, or even puzzling, they should not distract from the goal of moving towards realistic agent-based simulations of social systems. In my opinion, models that combine rigorous theory with strong empirical foundations are instrumental to the creation of meaningful and purposeful agent-based models. Perhaps the results presented here should just be taken as another sign that citation counts are a weak signal of academic merit at best.

Data, code and supplementary analyses

All data and code used for this analysis, as well as the results from the supplementary analyses described in the text, are available here: https://osf.io/x9r7j/

Notes

[1] Note that the hyphen between “agent” and “based” does not affect the retrieved corpus. Both contributions that mention “agent based” and “agent-based” were retrieved.

[2] A small caveat to the analysis of the interaction effect is that the marginal improvement of model 2 upon model 1 is rather small (AIC difference of 269). This is likely (partially) due to the small number of papers that mention both calibration and validation (N=77).

Acknowledgements

Marijn Keijzer acknowledges IAST funding from the French National Research Agency (ANR) under the Investments for the Future (Investissements d’Avenir) program, grant ANR-17-EURE-0010.

References

Boero, R., & Squazzoni, F. (2005). Does empirical embeddedness matter? Methodological issues on agent-based models for analytical social science. Journal of Artificial Societies and Social Simulation, 8(4), 1–31. https://www.jasss.org/8/4/6.html

Chattoe-Brown, E. (2022) If You Want To Be Cited, Don’t Validate Your Agent-Based Model: A Tentative Hypothesis Badly In Need of Refutation. Review of Artificial Societies and Social Simulation, 1st Feb 2022. https://rofasss.org/2022/02/01/citing-od-models

Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of social influence: towards the next frontiers. Journal of Artificial Societies and Social Simulation, 20(4). https://doi.org/10.18564/jasss.3521


Keijzer, M. (2022) If you want to be cited, calibrate your agent-based model: Reply to Chattoe-Brown. Review of Artificial Societies and Social Simulation, 9th Mar 2022. https://rofasss.org/2022/03/09/Keijzer-reply-to-Chattoe-Brown


 

The Poverty of Suggestivism – the dangers of “suggests that” modelling

By Bruce Edmonds

Vagueness and refutation

A model[1] is basically composed of two parts (Zeigler 1976, Wartofsky 1979):

  1. A set of entities (such as mathematical equations, logical rules, computer code etc.) which can be used to make some inferences as to the consequences of that set (usually in conjunction with some data and parameter values)
  2. A mapping from this set to what it aims to represent – what the bits mean

Whilst a lot of attention has been paid to the internal rigour of the set of entities and the inferences that are made from them (1), the mapping to what that represents (2) has often been left as implicit or incompletely described – sometimes only indicated by the labels given to its parts. The result is a model that vaguely relates to its target, suggesting its properties analogically. There is not a well-defined way that the model is to be applied to anything observed, but a new map is invented each time it is used to think about a particular case. I call this way of modelling “Suggestivism”, because the model “suggests” things about what is being modelled.

This is partly a recapitulation of Popper’s critique of vague theories in his book “The Poverty of Historicism” (1957). He characterised such theories as “irrefutable”, because whatever the facts, these theories could be made to fit them. Irrefutability is an indicator of a lack of precise mapping to reality – such vagueness makes refutation very hard. However, it is only an indicator; there may be other reasons than vagueness for it not being possible to test a theory – it is their disconnection from well-defined empirical reference that is the issue here.

Some might go as far as suggesting that any model or theory that is not refutable is “unscientific”, but this goes too far, implying a very restricted definition of what ‘science’ is. We need analogies to think about what we are doing and to gain insight into what we are studying, e.g. (Hartman 1997) – for humans they are unavoidable, ‘baked’ into the way language works (Lakoff 1987). A model might make a set of ideas clear and help map out the consequences of a set of assumptions/structures/processes. Many of these suggestivist models relate to a set of ideas and it is the ideas that relate to what is observed (albeit informally) (Edmonds 2001). However, such models do not capture anything reliable about what they refer to, and in that sense are not part of the set of the established statements and theories that is at the core of science  (Arnold 2014).

The dangers of suggestivist modelling

As above, there are valid uses of abstract or theoretical modelling where this is explicitly acknowledged and where no conclusions about observed phenomena are made. So what are the dangers of suggestivist modelling – why am I making such a fuss about it?

Firstly, that people often seem to confuse a model as an analogy – a way of thinking about stuff – and a model that tells us reliably about what we are studying. Thus they give undue weight to the analyses of abstract models that are, in fact, just thought experiments. Making models is a very intimate way of theorising – one spends an extended period of time interacting with one’s model: developing, checking, analysing etc. The result is a particularly strong version of “Kuhnian Spectacles” (Kuhn 1962) causing us to see the world though our model for weeks after. Under this strong influence it is natural to confuse what we can reliably infer about the world and how we are currently perceiving/thinking about it. Good scientists should then pause and wait for this effect to wear off so that they can effectively critique what they have done, its limitations and what its implications are. However, often in the rush to get their work out, modellers often do not do this, resulting in a sloppy set of suggestive interpretations of their modelling.

Secondly, empirical modelling is hard. It is far easier (and, frankly, more fun) to play with non-empirical models. A scientific culture that treats suggestivist modelling as substantial progress and significantly rewards modellers that do it, will effectively divert a lot of modelling effort in this direction. Chattoe-Brown (2018) displayed evidence of this in his survey of opinion dynamics models – abstract, suggestivist modelling got far more reward (in terms of citations) than those that tried to relate their model to empirical data in a direct manner. Abstract modelling has a role in science, but if it is easier and more rewarding then the field will become unbalanced. It may give the impression of progress but not deliver on this impression. In a more mature science, researchers working on measurement methods (steps from observation to models) and collecting good data are as important as the theorists (Moss 1998).

Thirdly, it is hard to judge suggestivist models. Given their connection to the modelling target is vague there cannot be any decisive test of its success. Good modellers should declare the exact purpose of their model, e.g. that is analogical or merely exploring the consequences of theory (Edmonds et al. 2019), but then accept the consequences of this choice – namely, that it excludes  making conclusions about the observed world. If it is for a theoretical exploration then the comprehensiveness of the exploration, the scope of the exploration and the applicability of the model can be judged, but if the model is analogical or illustrative then this is harder. Whilst one model may suggest X, another may suggest the opposite. It is quite easy to fix a model to get the outcomes one wants. Clearly, if a model makes startling suggestions – illustrating totally new ideas or making a counter-example to widely held assumptions – then this helps science by widening the pool of theories or hypotheses that are considered. However most suggestivist modelling does not do this.

Fourthly, their sheer flexibility of as to application causes problems – if one works hard enough one can invent mappings to a wide range of cases, the limits are only those of our imagination. In effect, having a vague mapping from model to what it models adds in huge flexibility in a similar way to having a large number of free (non-empirical) parameters. This flexibility gives an impression of generality, and many desire simple and general models for complex phenomena. However, this is illusory because a different mapping is needed for each case, to make it apply. Given the above (1)+(2) definition of a model this means that, in fact, it is a different model for each case – what a model refers to, is part of the model. The same flexibility makes such models impossible to refute, since one can just adjust the mapping to save them. The apparent generality and lack of refutation means that such models hang around in the literature, due to their surface attractiveness.

Finally, these kinds of model are hugely influential beyond the community of modellers to the wider public including policy actors. Narratives that start in abstract models make their way out and can be very influential (Vranckx 1999). Despite the lack of rigorous mapping from model to reality, suggestivist models look impressive, look scientific. For example, very abstract models from the Neo-Classical ‘Chicago School’ of economists supported narratives about the optimal efficiency of markets, leading to a reluctance to regulate them (Krugman 2009). A lack of regulation seemed to be one of the factors behind the 2007/8 economic crash (Baily et al 2008). Modellers may understand that other modellers get over-enthusiastic and over-interpret their models, but others may not. It is the duty of modellers to give an accurate impression of the reliability of any modelling results and not to over-hype them.

How to recognise a suggestivist model

It can be hard to detangle how empirically vague a model is, because many descriptions about modelling work do not focus on making the mapping to what it represents precise. The reasons for this are various, for example: the modeller might be conflating reality and what is in the model in their minds, the researcher is new to modelling and has not really decided what the purpose of their model is, the modeller might be over-keen to establish the importance of their work and so is hyping the motivation and conclusions, they might simply not got around to thinking enough about the relationship between their model and what it might represent, or they might not have bothered to make the relationship explicit in their description. Whatever the reason the reader of any description of such work is often left with an archaeological problem: trying to unearth what the relationship might be, based on indirect clues only. The only way to know for certain is to take a case one knows about and try and apply the model to it, but this is a time consuming process and relies upon having a case with suitable data available. However, there are some indicators, albeit fallible ones, including the following.

  • A relatively simple model is interpreted as explaining a wide range of observed, complex phenomena
  • No data from an observed case study is compared to data from the model (often no data is brought in at all, merely abstract observations) – despite this, conclusions about some observed phenomena are made
  • The purpose of the model is not explicitly declared
  • The language of the paper seems to conflate talking about the model with what is being modelled
  • In the paper there are sudden abstraction ‘jumps’ between the motivation and the description of the model and back again to the interpretation of the results in terms of that motivation. The abstraction jumps involved are large and justified by some a priori theory or modelling precedents rather than evidence.

How to avoid suggestivist modelling

How to avoid the dangers of suggestivist modelling should be clear from the above discussion, but I will make them explicit here.

  • Be clear about the model purpose – that is does the model aim to achieve, which indicates how it should be judged by others (Edmonds et al 2019)
  • Do not make any conclusions about the real world if you have not related the model to any data
  • Do not make any policy conclusions – things that might affect other people’s lives – without at least some independent validation of the model outcomes
  • Document how a model relates (or should relate) to data, the nature of that data and maybe even the process whereby that data should be obtained (Achter et al 2019)
  • Be explicit as possible about what kinds of phenomena the model applies to – the limits of its scope
  • Keep the language about the model and what is being modelled distinct – for any statement it should be clear whether it is talking about the model or what it models (Edmonds 2020)
  • Highlight any bold assumptions in the specification of the model or describe what empirical foundation there is for them – be honest about these

Conclusion

Models can serve many different purposes (Epstein 2008). This is fine as long as the purpose of models are always made clear, and model results are not interpreted further than their established purpose allows. Research which gives the impression that analogical, illustrative or theoretical modelling can tell us anything reliable about observed complex phenomena is not only sloppy science, but can have a deleterious impact – giving an impression of progress whilst diverting attention from empirically reliable work. Like a bad investment: if it looks too good and too easy to be true, it probably isn’t.

Notes

[1] We often use the word “model” in a lazy way to indicate (1) rather than (1)+(2) in this definition, but a set of entities without any meaning or mapping to anything else is not a model, as it does not represent anything. For example, a random set of equations or program instructions does not make a model.

Acknowledgements

Bruce Edmonds is supported as part of the ESRC-funded, UK part of the “ToRealSim” project, grant number ES/S015159/1.

References

Achter, S., Borit, M., Chattoe-Brown, E., Palaretti, C. & Siebers, P.-O. (2019) Cherchez Le RAT: A Proposed Plan for Augmenting Rigour and Transparency of Data Use in ABM. Review of Artificial Societies and Social Simulation, 4th June 2019. https://rofasss.org/2019/06/04/rat/

Arnold, E. (2014). What’s wrong with social simulations?. The Monist, 97(3), 359-377. DOI:10.5840/monist201497323

Baily, M. N., Litan, R. E., & Johnson, M. S. (2008). The origins of the financial crisis. Fixing Finance Series – Paper 3, The Brookings Institution. https://www.brookings.edu/wp-content/uploads/2016/06/11_origins_crisis_baily_litan.pdf

Chattoe-Brown, E. (2018) What is the earliest example of a social science simulation (that is nonetheless arguably an ABM) and shows real and simulated data in the same figure or table? Review of Artificial Societies and Social Simulation, 11th June 2018. https://rofasss.org/2018/06/11/ecb/

Edmonds, B. (2001) The Use of Models – making MABS actually work. In. Moss, S. and Davidsson, P. (eds.), Multi Agent Based Simulation, Lecture Notes in Artificial Intelligence, 1979:15-32. http://cfpm.org/cpmrep74.html

Edmonds, B. (2020) Basic Modelling Hygiene – keep descriptions about models and what they model clearly distinct. Review of Artificial Societies and Social Simulation, 22nd May 2020. https://rofasss.org/2020/05/22/modelling-hygiene/

Edmonds, B., le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root H. & Squazzoni. F. (2019) Different Modelling Purposes. Journal of Artificial Societies and Social Simulation, 22(3):6. http://jasss.soc.surrey.ac.uk/22/3/6.html.

Epstein, J. M. (2008). Why model?. Journal of artificial societies and social simulation, 11(4), 12. https://jasss.soc.surrey.ac.uk/11/4/12.html

Hartmann, S. (1997): Modelling and the Aims of Science. In: Weingartner, P. et al (ed.) : The Role of Pragmatics in Contemporary Philosophy: Contributions of the Austrian Ludwig Wittgenstein Society. Vol. 5. Wien und Kirchberg: Digi-Buch. pp. 380-385. https://epub.ub.uni-muenchen.de/25393/

Krugman, P. (2009) How Did Economists Get It So Wrong? New York Times, Sept. 2nd 2009. https://www.nytimes.com/2009/09/06/magazine/06Economic-t.html

Kuhn, T.S. (1962) The Structure of Scientific Revolutions. Chicago: University of Chicago Press.

Lakoff, G. (1987) Women, fire, and dangerous things. University of Chicago Press, Chicago.

Morgan, M. S., & Morrison, M. (1999). Models as mediators. Cambridge: Cambridge University Press.

Moss, S. (1998) Social Simulation Models and Reality: Three Approaches. Centre for Policy Modelling  Discussion Paper: CPM-98-35, http://cfpm.org/cpmrep35.html

Popper, K. (1957). The poverty of historicism. Routledge.

Vranckx, An. (1999) Science, Fiction & the Appeal of Complexity. In Aerts, Diederik, Serge Gutwirth, Sonja Smets, and Luk Van Langehove, (eds.) Science, Technology, and Social Change: The Orange Book of “Einstein Meets Magritte.” Brussels: Vrije Universiteit Brussel; Dordrecht: Kluwer., pp. 283–301.

Wartofsky, M. W. (1979). The model muddle: Proposals for an immodest realism. In Models (pp. 1-11). Springer, Dordrecht.

Zeigler, B. P. (1976). Theory of Modeling and Simulation. Wiley Interscience, New York.


Edmonds, B. (2022) The Poverty of Suggestivism – the dangers of "suggests that" modelling. Review of Artificial Societies and Social Simulation, 28th Feb 2022. https://rofasss.org/2022/02/28/poverty-suggestivism


 

If You Want To Be Cited, Don’t Validate Your Agent-Based Model: A Tentative Hypothesis Badly In Need of Refutation

By Edmund Chattoe-Brown

As part of a previous research project, I collected a sample of the Opinion Dynamics (hereafter OD) models published in JASSS that were most highly cited in JASSS. The idea here was to understand what styles of OD research were most influential in the journal. In the top 50 on 19.10.21 there were eight such articles. Five were self-contained modelling exercises (Hegselmann and Krause 2002, 58 citations, Deffuant et al. 2002, 35 citations, Salzarulo 2006, 13 citations, Deffuant 2006, 13 citations and Urbig et al. 2008, 9 citations), two were overviews of OD modelling (Flache et al. 2017, 13 citations and Sobkowicz 2009, 10 citations) and one included an OD example in an article mainly discussing the merits of cellular automata modelling (Hegselmann and Flache 1998, 12 citations). In order to get in to the top 50 on that date you had to achieve at least 7 citations. In parallel, I have been trying to identify Agent-Based Models that are validated (undergo direct comparison of real and equivalent simulated data). Based on an earlier bibliography (Chattoe-Brown 2020) which I extended to the end of 2021 for JASSS and articles which were described as validated in the highly cited articles listed above, I managed to construct a small and unsystematic sample of validated OD models. (Part of the problem with a systematic sample is that validated models are not readily searchable as a distinct category and there are too many OD models overall to make reading them all feasible. Also, I suspect, validated models just remain rare in line with the larger scale findings of Dutton and Starbuck (1971, p. 130, table 1) and discouragingly, much more recently, Angus and Hassani-Mahmooei (2015, section 4.5, figure 9). Obviously, since part of the sample was selected by total number of citations, one cannot make a comparison on that basis, so instead I have used the best possible alternative (given the limitations of the sample) and compared articles on citations per year. The problem here is that attempting validated modelling is relatively new while older articles inevitably accumulate citations however slowly. But what I was trying to discover was whether new validated models could be cited at a much higher annual rate without reaching the top 50 (or whether, conversely, older articles could have a high enough total citations to get into the top 50 without having a particularly impressive annual citation rate.) One would hope that, ultimately, validated models would tend to receive more citations than those that were not validated (but see the rather disconcerting related findings of Serra-Garcia and Gneezy 2021). Table 1 shows the results sorted by citations per year.

Article Status Number of JASSS Citations[1] Number of Years[2] Citations Per Year
Bernardes et al. 2002 Validated 1 20 0.05
Bernardes et al. 2001 Validated 2 21 0.096
Fortunato and Castellano 2007 Validated 2 15 0.13
Caruso and Castorina 2005 Validated 4 17 0.24
Chattoe-Brown 2014 Validated 2 8 0.25
Brousmiche et al. 2016 Validated 2 6 0.33
Hegselmann and Flache 1998 Non-Validated 12 24 0.5
Urbig et al. 2008 Non-Validated 9 14 0.64
Sobkowicz 2009 Non-Validated 10 13 0.77
Deffuant 2006 Non-Validated 13 16 0.81
Salzarulo 2006 Non-Validated 13 16 0.81
Duggins 2017 Validated 5 5 1
Deffuant et al. 2002 Non-Validated 35 20 1.75
Flache et al. 2017 Non-Validated 13 5 2.6
Hegselmann and Krause 2002 Non-Validated 58 20 2.9

Table 1. Annual Citation Rates for OD Articles Highly Cited in JASSS (Systematic Sample) and Validated OD Articles in or Cited in JASSS (Unsystematic Sample)

With the notable (and potentially encouraging) exception of Duggins (2017), the most recent validated OD model I have been able to discover in JASSS, the sample clearly divides into non-validated research with more citations and validated research with fewer. The position of Duggins (2017) might suggest greater recent interest in validated OD models. Unfortunately, however, qualitative analysis of the citations suggests that these are not cited as validated models per se (and thus as a potential improvement over non-validated models) but merely as part of general classes of OD model (like those involving social networks or repulsion – moving away from highly discrepant opinions). This tendency to cite validated models without acknowledging that they are validated (and what the implications of that might be) is widespread in the articles I looked at.

Obviously, there is plenty wrong with this analysis. Even looking at citations per annum we are arguably still partially sampling on the dependent variable (articles selected for being widely cited prove to be widely cited!) and the sample of validated OD models is unsystematic (though in fairness the challenges of producing a systematic sample are significant.[3]) But the aim here is to make a distinctive use of RoFASSS as a rapid mode of permanent publication and to think differently about science. If I tried to publish this in a peer reviewed journal, the amount of labour required to satisfy reviewers about the research design would probably be prohibitive (even if it were possible). As a result, the case to answer about this apparent (and perhaps undesirable) pattern in data might never see the light of day.

But by publishing quickly in RoFASSS without the filter of peer review I actively want my hypothesis to be rejected or replaced by research based on a better design (and such research may be motivated precisely by my presenting this interesting pattern with all its imperfections). When it comes to scientific progress, the chance to be clearly wrong now could be more useful than the opportunity to be vaguely right at some unknown point in the future.

Acknowledgements

This analysis was funded by the project “Towards Realistic Computational Models Of Social Influence Dynamics” (ES/S015159/1) funded by ESRC via ORA Round 5 (PI: Professor Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University: https://gtr.ukri.org/projects?ref=ES%2FS015159%2F1).

Notes

[1] Note that the validated OD models had their citations counted manually while the high total citation articles had them counted automatically. This may introduce some comparison error but there is no reason to think that either count will be terribly inaccurate.

[2] Including the year of publication and the current year (2021).

[3] Note, however, that there are some checks and balances on sample quality. Highly successful validated OD models would have shown up independently in the top 50. There is thus an upper bound to the impact of the articles I might have missed in manually constructing my “version 1” bibliography. The unsystematic review of 47 articles by Sobkowicz (2009) also checks independently on the absence of validated OD models in JASSS to that date and confirms the rarity of such articles generally. Only four of the articles that he surveys are significantly empirical.

References

Angus, Simon D. and Hassani-Mahmooei, Behrooz (2015) ‘“Anarchy” Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012’, Journal of Artificial Societies and Social Simulation, 18(4), October, article 16, <http://jasss.soc.surrey.ac.uk/18/4/16.html>. doi:10.18564/jasss.2952

Bernardes, A. T., Costa, U. M. S., Araujo, A. D. and Stauffer, D. (2001) ‘Damage Spreading, Coarsening Dynamics and Distribution of Political Votes in Sznajd Model on Square Lattice’, International Journal of Modern Physics C: Computational Physics and Physical Computation, 12(2), February, pp. 159-168. doi:10.1140/e10051-002-0013-y

Bernardes, A. T., Stauffer, D. and Kertész, J. (2002) ‘Election Results and the Sznajd Model on Barabasi Network’, The European Physical Journal B: Condensed Matter and Complex Systems, 25(1), January, pp. 123-127. doi:10.1142/S0129183101001584

Brousmiche, Kei-Leo, Kant, Jean-Daniel, Sabouret, Nicolas and Prenot-Guinard, François (2016) ‘From Beliefs to Attitudes: Polias, A Model of Attitude Dynamics Based on Cognitive Modelling and Field Data’, Journal of Artificial Societies and Social Simulation, 19(4), October, article 2, <https://www.jasss.org/19/4/2.html>. doi:10.18564/jasss.3161

Caruso, Filippo and Castorina, Paolo (2005) ‘Opinion Dynamics and Decision of Vote in Bipolar Political Systems’, arXiv > Physics > Physics and Society, 26 March, version 2. doi:10.1142/S0129183105008059

Chattoe-Brown, Edmund (2014) ‘Using Agent Based Modelling to Integrate Data on Attitude Change’, Sociological Research Online, 19(1), February, article 16, <https://www.socresonline.org.uk/19/1/16.html>. doi:0.5153/sro.3315

Chattoe-Brown Edmund (2020) ‘A Bibliography of ABM Research Explicitly Comparing Real and Simulated Data for Validation: Version 1’, CPM Report CPM-20-216, 12 June, <http://cfpm.org/discussionpapers/256>

Deffuant, Guillaume (2006) ‘Comparing Extremism Propagation Patterns in Continuous Opinion Models’, Journal of Artificial Societies and Social Simulation, 9(3), June, article 8, <https://www.jasss.org/9/3/8.html>.

Deffuant, Guillaume, Amblard, Frédéric, Weisbuch, Gérard and Faure, Thierry (2002) ‘How Can Extremism Prevail? A Study Based on the Relative Agreement Interaction Model’, Journal of Artificial Societies and Social Simulation, 5(4), October, article 1, <https://www.jasss.org/5/4/1.html>.

Duggins, Peter (2017) ‘A Psychologically-Motivated Model of Opinion Change with Applications to American Politics’, Journal of Artificial Societies and Social Simulation, 20(1), January, article 13, <http://jasss.soc.surrey.ac.uk/20/1/13.html>. doi:10.18564/jasss.3316

Dutton, John M. and Starbuck, William H. (1971) ‘Computer Simulation Models of Human Behavior: A History of an Intellectual Technology’, IEEE Transactions on Systems, Man, and Cybernetics, SMC-1(2), April, pp. 128-171. doi:10.1109/TSMC.1971.4308269

Flache, Andreas, Mäs, Michael, Feliciani, Thomas, Chattoe-Brown, Edmund, Deffuant, Guillaume, Huet, Sylvie and Lorenz, Jan (2017) ‘Models of Social Influence: Towards the Next Frontiers’, Journal of Artificial Societies and Social Simulation, 20(4), October, article 2, <http://jasss.soc.surrey.ac.uk/20/4/2.html>. doi:10.18564/jasss.3521

Fortunato, Santo and Castellano, Claudio (2007) ‘Scaling and Universality in Proportional Elections’, Physical Review Letters, 99(13), 28 September, article 138701. doi:10.1103/PhysRevLett.99.138701

Hegselmann, Rainer and Flache, Andreas (1998) ‘Understanding Complex Social Dynamics: A Plea For Cellular Automata Based Modelling’, Journal of Artificial Societies and Social Simulation, 1(3), June, article 1, <https://www.jasss.org/1/3/1.html>.

Hegselmann, Rainer and Krause, Ulrich (2002) ‘Opinion Dynamics and Bounded Confidence Models, Analysis, and Simulation’, Journal of Artificial Societies and Social Simulation, 5(3), June, article 2, <http://jasss.soc.surrey.ac.uk/5/3/2.html>.

Salzarulo, Laurent (2006) ‘A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast’, Journal of Artificial Societies and Social Simulation, 9(1), January, article 13, <http://jasss.soc.surrey.ac.uk/9/1/13.html>.

Serra-Garcia, Marta and Gneezy, Uri (2021) ‘Nonreplicable Publications are Cited More Than Replicable Ones’, Science Advances, 7, 21 May, article eabd1705. doi:10.1126/sciadv.abd1705

Sobkowicz, Pawel (2009) ‘Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality’, Journal of Artificial Societies and Social Simulation, 12(1), January, article 11, <http://jasss.soc.surrey.ac.uk/12/1/11.html>.

Urbig, Diemo, Lorenz, Jan and Herzberg, Heiko (2008) ‘Opinion Dynamics: The Effect of the Number of Peers Met at Once’, Journal of Artificial Societies and Social Simulation, 11(2), March, article 4, <http://jasss.soc.surrey.ac.uk/11/2/4.html>.


Chattoe-Brown, E. (2022) If You Want To Be Cited, Don’t Validate Your Agent-Based Model: A Tentative Hypothesis Badly In Need of Refutation. Review of Artificial Societies and Social Simulation, 1st Feb 2022. https://rofasss.org/2022/02/01/citing-od-models


 

Today We Have Naming Of Parts: A Possible Way Out Of Some Terminological Problems With ABM

By Edmund Chattoe-Brown


Today we have naming of parts. Yesterday,
We had daily cleaning. And tomorrow morning,
We shall have what to do after firing. But to-day,
Today we have naming of parts. Japonica
Glistens like coral in all of the neighbouring gardens,
And today we have naming of parts.
(Naming of Parts, Henry Reed, 1942)

It is not difficult to establish by casual reading that there are almost as many ways of using crucial terms like calibration and validation in ABM as there are actual instances of their use. This creates several damaging problems for scientific progress in the field. Firstly, when two different researchers both say they “validated” their ABMs they may mean different specific scientific activities. This makes it hard for readers to evaluate research generally, particularly if researchers assume that it is obvious what their terms mean (rather than explaining explicitly what they did in their analysis). Secondly, based on this, each researcher may feel that the other has not really validated their ABM but has instead done something to which a different name should more properly be given. This compounds the possible confusion in debate. Thirdly, there is a danger that researchers may rhetorically favour (perhaps unconsciously) uses that, for example, make their research sound more robustly empirical than it actually is. For example, validation is sometimes used to mean consistency with stylised facts (rather than, say, correspondence with a specific time series according to some formal measure). But we often have no way of telling what the status of the presented stylised facts is. Are they an effective summary of what is known in a field? Are they the facts on which most researchers agree or for which the available data presents the clearest picture? (Less reputably, can readers be confident that they were not selected for presentation because of their correspondence?) Fourthly, because these terms are used differently by different researchers it is possible that valuable scientific activities that “should” have agreed labels will “slip down the terminological cracks” (either for the individual or for the ABM community generally). Apart from clear labels avoiding confusion for others, they may help to avoid confusion for you too!

But apart from these problems (and there may be others but these are not the main thrust of my argument here) there is also a potential impasse. There simply doesn’t seem to be any value in arguing about what the “correct” meaning of validation (for example) should be. Because these are merely labels there is no objective way to resolve this issue. Further, even if we undertook to agree the terminology collectively, each individual would tend to argue for their own interpretation without solid grounds (because there are none to be had) and any collective decision would probably therefore be unenforceable. If we decide to invent arbitrary new terminology from scratch we not only run the risk of adding to the existing confusion of terms (rather than reducing it) but it is also quite likely that everyone will find the new terms unhelpful.

Unfortunately, however, we probably cannot do without labels for these scientific activities involved in quality controlling ABMs. If we had to describe everything we did without any technical shorthand, presenting research might well become impossibly unwieldy.

My proposed solution is therefore to invent terms from scratch (so we don’t end up arguing about our different customary usages to no purpose) but to do so on the basis of actual scientific practices reported in published research. For example, we might call the comparison of corresponding real and simulated data (which at least has the endorsement of the much used Gilbert and Troitzsch 2005 – see pp. 15-19 – to be referred to as validation) CORAS – Comparison Of Real And Simulated. Similarly, assigning values to parameters given the assumptions of model “structures” might be called PANV – Parameters Assigned Numerical Values.

It is very important to be clear what the intention is here. Naming cannot solve scientific problems or disagreements. (Indeed, failure to grasp this may well be why our terminology is currently so muddled as people try to get their different positions through “on the nod”.) For example, if we do not believe that correspondence with stylised facts and comparison measures on time series have equivalent scientific status then we will have to agree distinct labels for them and have the debate about their respective value separately. Perhaps the former could be called COSF – Comparison Of Stylised Facts. But it seems plainly easier to describe specific scientific activities accurately and then find labels for them than to have to wade through the existing marsh of ambiguous terminology and try to extract the associated science. An example of a practice which does not seem to have even one generally agreed label (and therefore seems to be neglected in ABM as a practice) is JAMS – Justifying A Model Structure. (Why are your agents adaptive rather than habitual or rational? Why do they mix randomly rather than in social networks?)

Obviously, there still needs to be community agreement for such a convention to be useful (and this may need to be backed institutionally for example by reviewing requirements). But the logic of the approach avoids several existing problems. Firstly, while the labels are useful shorthand, they are not arbitrary. Each can be traced back to a clearly definable scientific practice. Secondly, this approach steers a course between the Scylla of fruitless arguments from current muddled usage and the Charybdis of a novel set of terminology that is equally unhelpful to everybody. (Even if people cannot agree on labels, they knew how they built and evaluated their ABMs so they can choose – or create – new labels accordingly.) Thirdly, the proposed logic is extendable. As we clarify our thinking, we can use it to label (or improve the labels of) any current set of scientific practices. We will do not have to worry that we will run out of plausible words in everyday usage.

Below I suggest some more scientific practices and possible terms for them. (You will see that I have also tried to make the terms as pronounceable and distinct as possible.)

Practice Term
Checking the results of an ABM by building another.[1] CAMWA (Checking A Model With Another).
Checking ABM code behaves as intended (for example by debugging procedures, destructive testing using extreme values and so on). TAMAD (Testing A Model Against Description).
Justifying the structure of the environment in which agents act. JEM (Justifying the Environment of a Model): This is again a process that may pass unnoticed in ABM typically. For example, by assuming that agents only consider ethnic composition, the Schelling Model (Schelling 1969, 1971) does not “allow” locations to be desirable because, for example, they are near good schools. This contradicts what was known empirically well before (see, for example, Rossi 1955) and it isn’t clear whether simply saying that your interest is in an “abstract” model can justify this level of empirical neglect.
Finding out what effect parameter values have on ABM behaviour. EVOPE (Exploring Value Of Parameter Effects).
Exploring the sensitivity of an ABM to structural assumptions not justified empirically (see Chattoe-Brown 2021). ESOSA (Exploring the Sensitivity Of Structural Assumptions).

Clearly this list is incomplete but I think it would be more effective if characterising the scientific practices in existing ABM and naming them distinctively was a collective enterprise.

Acknowledgements

This research is funded by the project “Towards Realistic Computational Models Of Social Influence Dynamics” (ES/S015159/1) funded by ESRC via ORA Round 5 (PI: Professor Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University: https://gtr.ukri.org/projects?ref=ES%2FS015159%2F1).

Notes

[1] It is likely that we will have to invent terms for subcategories of practices which differ in their aims or warranted conclusions. For example, rerunning the code of the original author (CAMWOC – Checking A Model With Original Code), building a new ABM from a formal description like ODD (CAMUS – Checking A Model Using Specification) and building a new ABM from the published description (CAMAP – Checking A Model As Published, see Chattoe-Brown et al. 2021).

References

Chattoe-Brown, Edmund (2021) ‘Why Questions Like “Do Networks Matter?” Matter to Methodology: How Agent-Based Modelling Makes It Possible to Answer Them’, International Journal of Social Research Methodology, 24(4), pp. 429-442. doi:10.1080/13645579.2020.1801602

Chattoe-Brown, Edmund, Gilbert, Nigel, Robertson, Duncan A. and Watts Christopher (2021) ‘Reproduction as a Means of Evaluating Policy Models: A Case Study of a COVID-19 Simulation’, medRXiv, 23 February. doi:10.1101/2021.01.29.21250743

Gilbert, Nigel and Troitzsch, Klaus G. (2005) Simulation for the Social Scientist, second edition (Maidenhead: Open University Press).

Rossi, Peter H. (1955) Why Families Move: A Study in the Social Psychology of Urban Residential Mobility (Glencoe, IL, Free Press).

Schelling, Thomas C. (1969) ‘Models of Segregation’, American Economic Review, 59(2), May, pp. 488-493. (available at https://www.jstor.org/stable/1823701)


Chattoe-Brown, E. (2022) Today We Have Naming Of Parts: A Possible Way Out Of Some Terminological Problems With ABM. Review of Artificial Societies and Social Simulation, 11th January 2022. https://rofasss.org/2022/01/11/naming-of-parts/


 

Challenges and opportunities in expanding ABM to other fields: the example of psychology

By Dino Carpentras

Centre for Social Issues Research, Department of Psychology, University of Limerick

The loop of isolation

One of the problems discussed during the last public meeting of the European Social Simulation Association (ESSA) at the Social Simulation Conference 2021 was the problem of reaching different communities outside the ABM one. This is a serious problem as we are risking getting trapped in a vicious cycle of isolation.

The cycle can be explained as follows. (a) Many fields are not familiar with ABM methods and standards. This results in the fact that (b) both reviewers and editors will struggle in understanding and evaluating the quality of an ABM paper. In general, this translates in a higher rejection rate and way longer time before publication. As results (c) fewer researchers in ABM will be willing to send their work to other communities, and, in general, fewer ABM works will be published in journals of other communities. Fewer articles using ABM makes it such that (d) fewer people would be aware of ABM, understand their methods and standards and even consider it an established research method.

Another point to consider is that, as time passes, each field evolves and develops new standards and procedures. Unfortunately, if two fields are not enough aware of each other, the new procedures will appear even more alien to members of the other community reinforcing the previously discussed cycle. A schematic of this is offered in figure 1.

fig1_v2

Figure 1: Vicious cycle of isolation

The challenge

Of course, a “brute force” solution would be to keep sending articles to journals in different fields until they get published. However, this would be extremely expensive in terms of time, and probably most researchers will not be happy of following this path.

A more elaborated solution could be framed as “progressively getting to know each other.” This would consist in modellers getting more familiar with the target community and vice versa. In this way, people from ABM would be able to better understand the jargon, the assumptions and even what is interesting enough to be the main result of a paper in a specific discipline. This would make it easier for members of our community to communicate research results using the language and methods familiar to the other field.

At the same time, researchers in the other field could slowly integrate ABM into their work, showing the potential of ABM and making it appear less alien to their peers. All of this would revert the previously discussed vicious cycle, by producing a virtuous one which would bring the two fields closer and closer.

Unfortunately, such goal cannot be obtained overnight, as it probably will require several events, collaborations, publications and probably several years (or even decades!). However, as result, our field would be familiar to and recognized by multiple other fields, enormously increasing the scientific impact of our research as well as the number of people working in ABM.

In this short communication, I would like to, firstly, highlight the importance and the challenges of reaching out other fields and, secondly, show a practical example with the field of psychology. I have chosen this field for no particular reason, besides the fact that I am currently working in the department of psychology. This gave me the opportunity of interacting with several researchers in this field.

In the next sections, I will summarize the main points of several informal discussions with these researchers. Specifically, I will try to highlight what they reported to be promising or interesting in ABM and also what felt alien or problematic to them.

Let me also stress that this does not want to be a complete overview, nor it should be thought as a summary of “what every psychologist think about ABM.” Instead, this is simply a summary of the discussions I had so far. What I hope, is that this will be at least a little useful to our community for building better connections with other fields.

The elephant in the room

Before moving to the list of comments on ABM I have collected, I want to address one point which appeared almost every time I discussed ABM with psychologists. Actually, it appeared almost every time I discuss ABM with people outside our field. This is the problem of experiments and validation.

I know there was recently a massive discussion on the SimSoc mailing list on opinion dynamics and validation, and this discussion will probably continue. Therefore, I am not going to discuss if all models should be tested, if a validated model should be considered superior, etc. Indeed, I do not want to discuss at all if validation should be considered important within our community. Instead, I want to discuss how important this is while interacting with other communities.

Indeed, many other fields give empirical data and validation a key role, having even developed different methods to test the quality of a hypothesis or a model when comparing it to empirical data (e.g. calculation of p-value, Krishnaiah 1980). Also, I repeatedly experienced disappointment or even mockery when I explained to non-ABM people that the model I was explaining them about was not empirically validated (e.g. the Deffuant model of opinion dynamics). In one single case, I even had a person laughing at me for this.

Unfortunately, many people which are not familiar with ABM end up considering it almost like a “nice exercise,” and even “not a real science.” This could be extremely dangerous for our field. Indeed, if multiple researchers will start thinking of ABM as a lesser science, communication with other fields – as well as obtaining funding for research – would get exponentially harder for our community.

Also, please, let me stress again to not “confuse the message with the messenger.” Here, I am not claiming that an unvalidated model should be considered inferior, or anything like that. What I am saying is that many people outside our field think in a similar fashion and this may eventually turn into a way bigger problem for us.

I will further discuss this point in the conclusion section, however, I will not claim that we should get rid of “pure models,” or that every model should be validated. What I will claim is that we should promote more empirical works as they will allow us to interact more easily with other fields.

Further points

In this section, I have collected (in no particular order) different comments and suggestions I have received from psychologist on the topic ABM. All of them had at least some experience of working side to side with a researcher developing ABMs.

Also in this case, please, remember that this are not my claims, but feedbacks I received. Furthermore, they should not be analysed as “what ABM is,” but more as “how ABM may look like to people in another field.”

  1. Some psychologists showed interest in the possibility of having loops in ABMs, which allow for relationships which go beyond simple cause and effect. Indeed, several models in psychology are structured in the form of “parameter X influences parameter Y” (and Y cannot influence X, forming a loop). While this approach is very common in psychology, many researchers are not satisfied with it, making ABMs are a very good opportunity for the development of more realistic models.
  2. Some psychologists said that at first impact, ABM looks very interesting. However, the extensive use of equations can confuse or even scare people who are not very used to them.
  3. Some praised Schelling’s model (Schelling 1971). Especially the approach of developing a hypothesis and then using an ABM to falsify it.
  4. Some criticized that often is not clear what an ABM should be used for or what such a model “is telling us.”
  5. Similarly, the use of models with a big number of parameters was criticized as “[these models] can eventually produce any result.”
  6. Another confusion that appeared multiple times was that often it is not clear if the model should be analysed and interpreted at the individual level (e.g. agents which start from state A often end up in state B) or at the more global level (e.g. distribution A results in distribution B).
  7. Another major complaint was that psychological measures are nominal or ordinal, while many models suppose interval-like variables.
  8. Another criticism was based on the fact that often agents behave all in the same way without including personal differences.
  9. In psychology there is a lot of attention on the sample size and if this is big enough to produce significant results. Some stressed that in many ABM works it is often not clear if the sample size (i.e. the number of agents) is sufficient for supporting the analysis.

Conclusion

I would like to stress again that these comments are not supposed to represent the thoughts of every psychologist, nor that I am suggesting that all the ABM literature should adapt to them or that they are always correct. For example, to my personal opinion, point 5 and 8 are pushing towards opposite directions; one aiming at simpler models and the other pushing towards complexity. Similarly, I do not think we should decrease the number of equations in our works to meet point 2. However, I think we should consider these feedbacks when planning interactions with the psychology community.

As mentioned before, a crucial role when interacting with other communities is played by experiments and validations. Even points 6 and especially points 7 and 9 suggest how member of this community often try to look for 1-to-1 relationships between agents of simulations and people in the real world.

fig2

Figure 2: (left) Empirical ABM acting as a bridge between theoretical ABM and other research fields. (Right) as the relationship between ABM and the other field matures, people become familiar with ABM standards and a direct link to theoretical ABM can be established.

As suggested by someone during the already mentioned discussion in the SimSoc mailing list, this could be solved by introducing a new figure (or, equivalently, a new research field) dedicated to empirical work in ABM. Following this solution, theoretical modellers could keep developing models without having to worry about validation. This would be similar to the work carried out by theoretical researchers in physics. At the same time, we would have also a stream of research dedicated to “experimental ABM.” People working on this topic will further explore the connection between models and the empirical world through experiments and validation processes. Of course, the two should not be mutually exclusive, as a researcher (or a piece of research) may still fall in both categories. However, having this distinction may help in giving more space to empirical work.

I believe that the role of experimental ABM could be crucial for developing good interactions between ABM and other communities. Indeed, this type of research could be accepted much more easily by other communities, producing better interactions with ABM. Especially, mentioning experiments and validation, could strongly decrease the initial mistrust that many people show when discussing ABM. Furthermore, as ABM develops stronger connections with another field, and our methods and standards become more familiar, we would probably also observe more people from the other community which would start looking into more theoretical ABM approaches and what-if scenarios (see fig 2).

References

Krishnaiah, P. R. (Ed.). (1980). A Hand Book of Statistics (Vol. 1). Motilal Banarsidass Publishe.

Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143-186.

Edmonds, B. and Moss, S. (2005) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In P. Davidsson et al. (Eds.): Multi Agent Based Simulation 2004. Springer, Lecture Notes in Artificial Intelligence, 3415:130–144.


Carpentras, D. (2020) Challenges and opportunities in expanding ABM to other fields: the example of psychology. Review of Artificial Societies and Social Simulation, 20th December 2021. https://rofasss.org/2021/12/20/challenges/


 

Benefits of Open Research in Social Simulation: An Early-Career Researcher’s Perspective

By Hyesop Shin

Research Associate at the School of Geographical and Earth Sciences, University of Glasgow, UK

In March 2017, in my first year of PhD, I attended a talk at the Microsoft Research Lab in Cambridge UK. It was about the importance of reproducibility and replicability in science. Inspired by the talk, I redesigned my research beyond my word processer and hard disk to open repositories and social media. Through my experience, there have been some challenges to learn other people’s work and replicate them to my project, but I found it more beneficial to share my problem and solutions for other people who may have encountered the same problem.

Having spoken to many early career researchers (ECRs) regarding the need for open science, specifically whether sharing codes is essential, the consensus was that it was not an essential component for their degree. A few answered that they were too embarrassed to share their codes online because their codes were not well coded enough. I somewhat empathised with their opinions, but at the same time, would insist that open research can gain more benefits than shame.

I wrote this short piece to openly discuss the benefits of conducting open research and suggest some points that ECRs should keep in mind. During the writing, there are some screenshots taken from my PhD work (Shin, 2021). I conclude my writing by accommodating personal experiences or other thoughts that might give more insights to the audience.

Benefits of Aiming an Open Project

I argue here that being transparent and honest about your model development strengthens the credibility of the research. In doing so, my thesis shared the original data, the scripts with annotations that are downloadable and executable, and wiki pages to summarise the outcomes and interpretations (see Figure 1 for examples). This evidence enables scholars and technicians to visit the repository if they are interested in the source codes or outcomes. Also, people can comment if any errors or bugs are identified, or the model is not executing on their machine or may suggest alternative ways to tackle the same problem. Even during the development, many developers share their work via online repositories (e.g. Github, Gitlab), and social media to ask for advice. Agent-based models are mostly uploaded on CoMSeS.net (previously named OpenABM). All of this can improve the quality of research.

Picture 1

Figure 1 A screenshot of a Github page showing how open platforms can help other people to understand the outcomes step by step

More practically, one can learn new ideas by helping each other. If there was a technical issue that can’t be solved, the problem should not be kept hidden, but rather be opened and solved together with experts online and offline. Figure 2 is a pragmatic example of posing questions to a wide range of developers on Stackoverflow – an online community of programmers to share and build codes. Providing my NetLogo codes, I asked how to send an agent group from one location to the other. The anonymous person, whose ID was JenB, kindly responded to me with a new set of codes, which helped me structure the codes more effectively.

Picture 2

Figure 2 Raising a question about sending agents from one location to another in NetLogo

Another example was about the errors I had encountered whilst I was running NetLogo with an R package “nlrx” (Salecker et al., 2019). Here, R was used as a compiler to submit iterative NetLogo jobs on the HPC (High Performance Computing) cluster to improve the execution speed. However, much to my surprise, I received error messages due to early terminations of failed HPC jobs. Not knowing what to do, I posed a question to the developer of the package (see Figure 3) and luckily got a response that the R ecosystem stores all the assigned objects in the RAM, but even with gigabytes of RAM, it struggles to write 96,822 patches over 8764 ticks on a spreadsheet.

Stackoverflow has kindly informed that NetLogo has a memory ceiling of 1GB[i] and keeps each run in the memory before it shuts down. Thus, if the model is huge and requires several iterations, then it is more likely that the execution speed will decrease after a few iterations. Before this information was seen, it was not understood why the model took 1 hour 20 minutes to finish the first run but struggled to maintain that speed on the twentieth run. Hence, sharing technical obstacles that occur in the middle of research can save a lot of time even for those who are contemplating similar research.

Picture 3

Figure 3 Comments posted on an online repository regarding the memory issue that NetLogo and R encountered

The Future for Open Research

For future quantitative studies in social simulation, this paper suggests students and researchers in their early careers should acclimatise themselves to using open-source platforms to conduct sustainable research. As clarity, conciseness, and coherence are featured as the important C’s for writing skills, good programming should take into consideration the following points.

First is clarity and conciseness (C&C). Here, clarity means that the scripts should be neatly documented. The computer does not know whether the codes are dirty or neat, it only cares whether it is syntactically correct, but it matters when other people attempt to understand the task. If the outcome produces the same results, it is always better to write clearer and simpler codes for other people and future upgrades. Thus, researchers should refer to other people’s work and learn how to code effectively. Another way to maintain clarity in coding is to keep descriptive and distinctive names for new variables. This statement might seem contradictory to the conciseness issue, but this is important as one of the common mistakes users make is to assign variables with abstract names such as LP1, LP2…LP10, which seems clear and concise for the model builder, but is even harder for the others when reviewing the code. The famous quote from Einstein, “Everything should be made as simple as possible, but not simpler.” is the appropriate phrase that model builders should always keep in mind. Hence, instead of coding LP9, names such as LandPriceIncreaseRate2009 (camel cases) or landprice_incrate_2009 (snake cases) can be more effective for the reviewers to understand the model.

Second is reproducibility and replicability (R&R). To be reproducible and replicable, initially, no errors should occur when others execute the script, and possible errors or bugs should be reported. It will also be more useful to document the libraries and the dependencies required. This is quite important as different OSs (operating systems) have different behaviours to install packages. For instance, the sf package in R has slightly different ways to install the package between OSs where Windows and MacOSX can be installed from the binary package while Linux needs to separately install GDAL (to read and write vector and raster data), Proj (which deals with projection), and GEOS (which provides geospatial functions) prior to the package installation. Finally, it would be very helpful if unit testing is included in the model. While R and Python provide splendid examples in their vignettes, NetLogo remains to offer the library models but goes no further than that. Offering unit testing examples can give a better understanding when the whole model is too complicated for others to comprehend. It can also give the impression that the modeller has full control of the model because without the unit test the verification process becomes error-prone. The good news is that NetLogo has most recently released the Beginner’s Interactive Dictionary with friendly explanations with videos and code examples[ii].

Third is to maintain version control. In terms of sustainability, researchers should be aware of software maintenance. Much programming software relies on libraries and packages that are built on a particular version. If the software is upgraded and no longer accepts the previous versions, then the package developers need to keep updating to run it on a new version. For example, NetLogo 6.0 experienced a significant change compared to versions 5.X. The biggest change was the replacement of tasks[iii] by anonymous procedures (Wilensky, 1999). This means that tasks are no longer primitives but are converted to arrow syntax. For example, if there is a list of [a b c], the previous task is asked to add the first, second, and third element as foreach [a b c] [ ?a+?b+?c ], while the new version does the same job as foreach [a b c][ add_all → a + b + c]. If the models haven’t converted to a new version it can be viewable as a read-only model but can’t be executed. Other geospatial packages in R such as rgdal and sf, have also struggled whenever a major update was made on their own packages or on the R version itself due to a lot of dependencies. Even ArcGIS, a UI (User Interface) software, had issues when they upgraded it from version 9.3 to 10. The projects that were coded under the VBA script in 9.3 were broken because it was not recognised as a correct function in the new version based on Python. This is also another example that backward compatibility and deprecation mechanisms are important.

Lastly, for more advanced users, it is also recommended to use a collaborative platform that executes every result from the codes with the exact version. One of the platforms is Codeocean. The Nature research team has recently chosen the platform to peer-review the codes (Perkel, 2019). The Nature editors and peer-reviewers strongly believed that coding has become a norm across many disciplines, and hence have asserted that the model process including the quality of data, conciseness, reproducibility, and documentation of the model should be placed as a requirement. Although the training procedure can be difficult at first, it will lead researchers to conduct themselves with more responsibility.

Looking for Opinions

With the advent of the era of big data and data science where people collaborate online and the ‘sharing is caring’ atmosphere has become a norm (Arribas-Bel et al., 2021; Lovelace, 2021), I insist that open research should no longer be an option. However, one may argue that although open research is by far an excellent model that can benefit many of today’s projects, there are certain types of risks that might concern ECRs such as intellectual property issues, code quality and technical security. Thus, if you have had different opinions regarding this issue, or simply favour adding your experiences during your PhD in social simulation, please add your thoughts via a thread.

Notes

[i] http://ccl.northwestern.edu/netlogo/docs/faq.html#how-big-can-my-model-be-how-many-turtles-patches-procedures-buttons-and-so-on-can-my-model-contain

[ii] https://ccl.northwestern.edu/netlogo/bind/

[iii] Tasks can be equations, x + y, or a set of lists [1 2 3 4 5]

References

Arribas-Bel, D., Alvanides, S., Batty, M., Crooks, A., See, L., & Wolf, L. (2021). Urban data/code: A new EP-B section. Environment and Planning B: Urban Analytics and City Science, 23998083211059670. https://doi.org/10.1177/23998083211059670

Lovelace, R. (2021). Open source tools for geographic analysis in transport planning. Journal of Geographical Systems, 23(4), 547–578. https://doi.org/10.1007/s10109-020-00342-2

Perkel, J. M. (2019). Make code accessible with these cloud services. Nature, 575(7781), 247. https://doi.org/10.1038/d41586-019-03366-x

Salecker, J., Sciaini, M., Meyer, K. M., & Wiegand, K. (2019). The nlrx r package: A next-generation framework for reproducible NetLogo model analyses. Methods in Ecology and Evolution, 10(11), 1854–1863. https://doi.org/10.1111/2041-210X.13286

Shin, H. (2021). Assessing Health Vulnerability to Air Pollution in Seoul Using an Agent-Based Simulation. University of Cambridge. https://doi.org/https://doi.org/10.17863/CAM.65615

Wilensky, U. (1999). Netlogo. Northwestern University: Evanston, IL, USA. https://ccl.northwestern.edu/netlogo/


Shin, H. (2021) Benefits of Open Research in Social Simulation: An Early-Career Researcher’s Perspective. Review of Artificial Societies and Social Simulation, 24th Nov 2021. https://rofasss.org/2021/11/23/benefits-open-research/


 

Reply to Frank Dignum

By Edmund Chattoe-Brown

This is a reply to Frank Dignum’s reply (about Edmund Chattoe-Brown’s review of Frank’s book)

As my academic career continues, I have become more and more interested in the way that people justify their modelling choices, for example, almost every Agent-Based Modeller makes approving noises about validation (in the sense of comparing real and simulated data) but only a handful actually try to do it (Chattoe-Brown 2020). Thus I think two specific statements that Frank makes in his response should be considered carefully:

  1. … we do not claim that we have the best or only way of developing an Agent-Based Model (ABM) for crises.” Firstly, negative claims (“This is not a banana”) are not generally helpful in argument. Secondly, readers want to know (or should want to know) what is being claimed and, importantly, how they would decide if it is true “objectively”. Given how many models sprang up under COVID it is clear that what is described here cannot be the only way to do it but the question is how do we know you did it “better?” This was also my point about institutionalisation. For me, the big lesson from COVID was how much the automatic response of the ABM community seems to be to go in all directions and build yet more models in a tearing hurry rather than synthesise them, challenge them or test them empirically. I foresee a problem both with this response and our possible unwillingness to be self-aware about it. Governments will not want a million “interesting” models to choose from but one where they have externally checkable reasons to trust it and that involves us changing our mindset (to be more like climate modellers for example, Bithell & Edmonds 2020). For example, colleagues and I developed a comparison methodology that allowed for the practical difficulties of direct replication (Chattoe-Brown et al. 2021).
  2. The second quotation which amplifies this point is: “But we do think it is an extensive foundation from which others can start, either picking up some bits and pieces, deviating from it in specific ways or extending it in specific ways.” Again, here one has to ask the right question for progress in modelling. On what scientific grounds should people do this? On what grounds should someone reuse this model rather than start their own? Why isn’t the Dignum et al. model built on another “market leader” to set a good example? (My point about programming languages was purely practical not scientific. Frank is right that the model is no less valid because the programming language was changed but a version that is now unsupported seems less useful as a basis for the kind of further development advocated here.)

I am not totally sure I have understood Frank’s point about data so I don’t want to press it but my concern was that, generally, the book did not seem to “tap into” relevant empirical research (and this is a wider problem that models mostly talk about other models). It is true that parameter values can be adjusted arbitrarily in sensitivity analysis but that does not get us any closer to empirically justified parameter values (which would then allow us to attempt validation by the “generative methodology”). Surely it is better to build a model that says something about the data that exists (however imperfect or approximate) than to rely on future data collection or educated guesses. I don’t really have the space to enumerate the times the book said “we did this for simplicity”, “we assumed that” etc. but the cumulative effect is quite noticeable. Again, we need to be aware of the models which use real data in whatever aspects and “take forward” those inputs so they become modelling standards. This has to be a collective and not an individualistic enterprise.

References

Bithell, M. and Edmonds, B. (2020) The Systematic Comparison of Agent-Based Policy Models – It’s time we got our act together!. Review of Artificial Societies and Social Simulation, 11th May 2021. https://rofasss.org/2021/05/11/SystComp/

Chattoe-Brown, E. (2020) A Bibliography of ABM Research Explicitly Comparing Real and Simulated Data for Validation. Review of Artificial Societies and Social Simulation, 12th June 2020. https://rofasss.org/2020/06/12/abm-validation-bib/

Chattoe-Brown, E. (2021) A review of “Social Simulation for a Crisis: Results and Lessons from Simulating the COVID-19 Crisis”. Journal of Artificial Society and Social Simulation. 24(4). https://www.jasss.org/24/4/reviews/1.html

Chattoe-Brown, E., Gilbert, N., Robertson, D. A., & Watts, C. J. (2021). Reproduction as a Means of Evaluating Policy Models: A Case Study of a COVID-19 Simulation. medRxiv 2021.01.29.21250743; DOI: https://doi.org/10.1101/2021.01.29.21250743

Dignum, F. (2020) Response to the review of Edmund Chattoe-Brown of the book “Social Simulations for a Crisis”. Review of Artificial Societies and Social Simulation, 4th Nov 2021. https://rofasss.org/2021/11/04/dignum-review-response/

Dignum, F. (Ed.) (2021) Social Simulation for a Crisis: Results and Lessons from Simulating the COVID-19 Crisis. Springer. DOI:10.1007/978-3-030-76397-8


Chattoe-Brown, E. (2021) Reply to Frank Dignum. Review of Artificial Societies and Social Simulation, 10th November 2021. https://rofasss.org/2021/11/10/reply-to-dignum/