Tag Archives: cognition

Models in Social Psychology and Agent-Based Social simulation – an interdisciplinary conversation on similarities and differences

By Nanda Wijermans, Geeske Scholz, Rocco Paolillo, Tobias Schröder, Emile Chappin, Tony Craig, and Anne Templeton


Understanding how individual or group behaviour are influenced by the presence of others is something both social psychology and agent-based social simulation are concerned with. However, there is only limited overlap between these two research communities, which becomes clear when terms such as “variable”, “prediction”, or “model” come into play, and we build on their different meanings. This situation challenges us when working together, since it complicates the uptake of relevant work from each community and thus hampers the potential impact that we could have when joining forces.

We[1] – a group of social psychologists and social simulation modellers – sought to clarify the meaning of models and modelling from an interdisciplinary perspective involving these two communities. This occurred while starting our collaboration to formalise ‘social identity approaches’ (SIA). It was part of our journey to learn how to communicate and understand each other’s work, insights, and arguments during our discussions.

We present a summary of our reflections on what we learned from and with each other in this paper, which we intend to be part of a conversation, complementary to existing readings on ABM and social psychology (e.g., Lorenz, Neumann, & Schröder, 2021; Smaldino, 2020; Smith & Conrey, 2007). Complementary, because one comes to understand things differently when engaging directly in conversation with people from other communities, and we hope to extend this from our network to the wider social simulation community.

What are variable- and agent-based models?

We started the discussion by describing to each other what we mean when we talk about “a model” and distinguishing between models in the two communities as variable-based models in social psychology and agent-based modelling in social simulation.

Models in social psychology generally come in two interrelated variants. Theoretical models, usually stated verbally and typically visualised with box-and-arrow diagrams as in Figure 1 (left), reflect assumptions of causal (but also correlational) relations between a limited number of variables. Statistical models are often based in theory and fitted to empirical data to test how well the explanatory variables predict the dependent variables, following the causal assumptions of the corresponding theoretical model. We therefore refer to social-psychological models as variable-based models (VBM). Core concepts are prediction and effect size. A prediction formulates whether one variable or combination of more variables causes an effect on an outcome variable. The effect size is the result of testing a prediction by indicating the strength of that effect, usually in statistical terms, the magnitude of variance explained by a statistical model.

It is good to realise that many social psychologists strive for a methodological gold standard using controlled behavioural experiments. Ideally, one predicts data patterns based on a theoretical model, which is then tested with data. However, observations of the real world are often messier. Inductive post hoc explanations emerge when empirical findings are unexpected or inconclusive. The discovery that much experimental work is not replicable has led to substantial efforts to increase the rigour of the methods, e.g., through the preregistration of experiments (Eberlen, Scholz & Gagliolo, 2017).

Models in Social Simulation come in different forms – agent-based models, mathematical models, microsimulations, system dynamic models etc – however here we focus on agent-based modelling as it is the dominant modelling approach within our SIAM network. Agent-based models reflect heterogeneous and autonomous entities (agents) that interact with each other and their environments over time (Conte & Paolucci, 2014; Gilbert & Troitzsch, 2005). Relationships between variables in ABMs need to be stated formally (equations or logical statements) in order to implement theoretical/empirical assumptions in a way that is understandable by a computer. An agent-based model can reflect assumptions about causal relations between as many variables as the modeller (team) intends to represent. Agent-based models are often used to help understand[2] why and how observed (macro) patterns arise by investigating the (micro/meso) processes underlying them (see Fig 1, right).

The extent to which social simulation models relate to data ranges from ‘no data used whatsoever’ to ‘fitting every variable value’ to empirical data. Put differently, the way one uses data does not define the approach. Note that assumptions based on theory and/or empirical observations do not suffice but require additional assumptions to make the model run.

Fig. 1: Visualisation of what a variable-based model in social psychology is (left) and what an agent-based model in social simulation is (right).

Comparing models

The discussion then moved from describing the meaning of “a model” to comparing similarities and differences between the concepts and approaches, but also what seems similar but is not…

Similar. The core commonalities of models in social psychology (VBM) and agent-based social simulation (ABM) are 1) the use of models to specify, test and/or explore (causal) relations between variables and 2) the ability to perform systematic experiments, surveys, or observations for testing the model against the real world. This means that words like ‘experimental design’, ‘dependent, independent and control variables’ have the same meaning. At the same time some aspects that are similar are labelled differently. For instance, the effect size in VBMs reflects the magnitude of the effect one can observe. In ABMs the analogy would be the sensitivity analysis, where one tests for the importance or role of certain variables on the emerging patterns in the simulation outcomes.

False Friends. There are several concepts that are given similar labels, but have different meanings. These are particularly important to be aware of in interdisciplinary settings as they can present “false friends”. The false friends we unpacked in our conversations are the following:

  • Model: whether the model is variable-based in social psychology (VBM) or agent-based in social simulation (ABM). The VBM focuses on the relation between two or a few variables typically in one snapshot of time, whereas the ABM focuses on the causal relations (mechanisms/processes) between (entities (agents) containing a number of) variables and simulates the resulting interactions over time.
  • Prediction: in VBMs a prediction is a variable-level claim, stating the expected magnitude of a  relation between two or few variables. In ABMs prediction would instead be a claim about the future real-world system-level developments on the basis of observed phenomena in the simulation outcomes. In case such prediction is not the model purpose (which is likely), each future simulated system state is sometimes labelled nevertheless as a prediction, though it doesn’t mean to be necessarily accurate as a prediction to the real-world future. Instead, it can for example be a full explanation of the mechanisms required to replicate the particular phenomenon or a possible trajectory of which reality is just one. 
  • Variable: here both types of models have variables (a label of some ‘thing’ that can have a certain ‘value’). In ABMs there can be many variables, some that have the same function as the variables in VBM (i.e., denoting a core concept and its value). Additionally, ABMs also have (many) variables to make things work.
  • Effect size: in VBM the magnitude of how much the independent variable can explain a dependent variable. In ABM the analogy would be sensitivity analysis, to determine the extent to which simulation outcomes are sensitive to changes in input settings. Note that, while effect size is critical in VBMs, in ABMs small effect sizes in micro interactions can lead toward large effects on the macro level.
  • Testing: VBMs usually test models using some form of hypothesis testing, whereas ABMs can be tested in very different ways (see David et al (2019)), depending on the purpose they have (e.g., explanation, theoretical exposition, prediction, see Edmonds et al. (2019)), and on different levels. For instance, testing can relate to the verification of the implementation of the model (software development specific), to make sure the model behaves as designed. However, testing can also relate to validation – checking whether the model lives up to its purpose – for instance testing the results produced by the ABM against real data if the aim is prediction of the real world-state.
  • Internal validity: in VBM this is to assure the causal relation between variables and their effect size. In ABMs it refers to the plausibility in assumptions and causal relations used in the model (design), e.g., by basing these on expert knowledge, empirical insights, or theory rather than on the modeller’s intuition only.

Differences. There are several differences when it comes to VBM and ABM. Firstly, there is a difference in what a model should replicate, i.e., the target of the model: in social psychology the focus tends to be on the relations between variables underlying behaviour, whereas in ABM it is usually on the macro-level patterns/structures that emerge. Also, the concept of causality differs in psychology, VBM models are predominantly built under the assumption of linear causality[3], with statistical models aiming to quantify the change in the dependent variable due to (associated) change in the independent variable. A causality or correlation often derived with “snapshot data”, i.e., one moment in time and one level of analysis. In ABMs, on the other hand, causality appears as a chain of causal relations that occur over time. Moreover, it can be non-linear (including multicausality, nonlinearity, feedback loops and/or amplifications of models’ outcomes). Lastly, the underlying philosophy can differ tremendously concerning the number of variables that are taken into consideration. By design, in social psychology one seeks to isolate the effects of variables, maintaining a high level of control to be confident about the effect of independent variables or the associations between variables. For example, by introducing control variables in regression models or assuring random allocation of participants in isolated experimental conditions. Whereas in ABMs, there are different approaches/preferences: KISS versus KIDS (Edmonds & Moss, 2004). KISS (Keep It Simple Stupid) advocates for keeping it simple as possible: only complexify if the simple model is not adequate. KIDS (Keep It Descriptive Stupid), on the other end of the spectrum, embraces complexity by relating to the target phenomenon as much as one can and only simplify when evidence justifies it. Either way, the idea of control in ABM is to avoid an explosion of complexity that impedes the understanding of the model, that can lead to e.g., causes misleading interpretations of emergent outcomes due to meaningless artefacts.

We summarise some core take-aways from our comparison discussions in Table 1.

Table 1. Comparing models in social psychology and agent-based social simulation

Social psychology (VBM)Social Simulation (ABM)
AimTheory development and prediction (variable level)Not predefined. Can vary widely purpose. (system level)
Model targetReplicate and test relations between variablesReproduce and/or explain a social phenomenon – the macro level pattern
Composed ofVariables and relations between themAgents, environment & interactions
Strive forHigh control, (low number of variables and relations ReplicationPurpose-dependent. Model complexity: represent what is needed, not more, not less.
TestingHypotheses testing using statistics, including possible measuring the effect size a relation to assess confidence in the variable’s importance’Purpose-dependent. Can refer to verification, validation, sensitivity analysis or all of them. See text and refs under false friends.
Causality(or correlation) between variables Linear representationBetween variables and/or model entities.
Non-linear representation
Theory developmentCritical reflection on theory through confirmation. Through hypothesis testing (a prediction) theory gets validated or (if not confirmed) input for reconsideration of the theory.IFF aim of model, ways of doing is not predefined. It can be reproducing the theory prediction with or without internal validity. ABMs can further help to identify gaps in existing theory.
DynamismLittle – often within snapshot causalityCore – within snapshot and over time causality
External validity(the ability to say something about the actual target/ empirical  phenomenon)VBM aims at generalisation and has predictive value for the phenomenon in focus. VBMs in lab experiments are often criticised for their weak external validity, considered high for field experiments.ABMs insights are about the model, not directly about the real world. Without making predictive claims, they often do aim to say something about the real world.

Beyond blind spots, towards complementary powers

We shared the result of our discussions, the (seemingly) communalities and differences between models in social psychology and agent-based social simulation. We allowed for a peek into the content of our interdisciplinary journey as we invested time, allowed for trust to grow, and engaged in open communication. All of this was needed in the attempt to uncover conflicting ways of seeing and studying the social identity approach (SIA). This investment was crucial to be able to make progress in formalising SIA in ways that enable for deeper insights – formalisations that are in line with SIA theories, but also to push the frontiers in SIA theory. Joining forces allows for deeper insights, as VBM and ABM complement and challenge each other, thereby advancing the frontiers in ways that cannot be achieved individually (Eberlen, Scholz & Gagliolo, 2017; Wijermans et al. 2022,). SIA social psychologists bring to the table the deep understanding of the many facets of SIA theories and can engage in the negotiation dance of the formalisation process adding crucial understanding of the theories, placed in their theoretical context. Social psychology in general can point to empirically supported causal relations between variables, and thereby increase the realism of the assumptions of agents (Jager, 2017; Templeton & Neville 2020). Agent-based social simulation, on the other hand, pushes for over-time causality representation, bringing to light (logical) gaps of a theory and providing explicitness and thereby adding to the development of testable (extended) forms of (parts of) a theory, including the execution of those experiments that are hard or impossible in controlled experiments. We thus started our journey, hoping to shed some light on blind spots and releasing our complementary powers in the formalisation of SIA.

To conclude, we felt that having a conversation together led to a qualitatively different understanding than would have been the case had we all ‘just’ reading informative papers. These conversations reflect a collaborative research process (Schlüter et al. 2019). In this RofASSS paper, we strive for widening this conversation to the social simulation community, connecting with others about our thoughts as well as hearing your experiences, thoughts and learnings while being on an interdisciplinary journey with minds shaped by variable-based or agent-based models, or both.


The many conversations we had in this stimulating scientific network since 2020 were funded by the the  Deutsche Forschungsgemeinschaft (DFG- 432516175)


Conte, R., & Paolucci, M. (2014). On agent-based modeling and computational social science. Frontiers in psychology, 5, 668. DOI:10.3389/fpsyg.2014.00668

David, N., Fachada, N., & Rosa, A. C. (2017). Verifying and validating simulations. In Simulating social complexity (pp. 173-204). Springer, Cham. DOI:10.1007/978-3-319-66948-9_9

Eberlen, J., Scholz, G., & Gagliolo, M. (2017). Simulate this! An introduction to agent-based models and their power to improve your research practice. International Review of Social Psychology, 30(1). DOI:10.5334/irsp.115/

Edmonds, B., & Moss, S. (2004). From KISS to KIDS–an ‘anti-simplistic’modelling approach. In International workshop on multi-agent systems and agent-based simulation (pp. 130-144). Springer, Berlin, Heidelberg. DOI:10.1007/978-3-540-32243-6_11

Edmonds, B., Le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root, H. and 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>. doi: 10.18564/jasss.3993

Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. McGraw-Hill Education (UK).

Jager, W. (2017). Enhancing the realism of simulation (EROS): On implementing and developing psychological theory in social simulation. Journal of Artificial Societies and Social Simulation, 20(3). https://jasss.soc.surrey.ac.uk/20/3/14.html

Lorenz, J., Neumann, M., & Schröder, T. (2021). Individual attitude change and societal dynamics: Computational experiments with psychological theories. Psychological Review, 128(4), 623-642.  https://doi.org/10.1037/rev0000291

Smaldino, P. E. (2020). How to Translate a Verbal Theory Into a Formal Model. Social Psychology, 51(4), 207–218. http://doi.org/10.1027/1864-9335/a000425

Schlüter, M., Orach, K., Lindkvist, E., Martin, R., Wijermans, N., Bodin, Ö., & Boonstra, W. J. (2019). Toward a methodology for explaining and theorizing about social-ecological phenomena. Current Opinion in Environmental Sustainability, 39, 44-53. DOI:10.1016/j.cosust.2019.06.011

Smith, E.R. & Conrey, F.R. (2007): Agent-based modeling: a new approach for theory building in social psychology. Pers Soc Psychol Rev, 11:87-104. DOI:10.1177/1088868306294789

Templeton, A., & Neville, F. (2020). Modeling collective behaviour: insights and applications from crowd psychology. In Crowd Dynamics, Volume 2 (pp. 55-81). Birkhäuser, Cham. DOI:10.1007/978-3-030-50450-2_4

Wijermans, N., Schill, C., Lindahl, T., & Schlüter, M. (2022). Combining approaches: Looking behind the scenes of integrating multiple types of evidence from controlled behavioural experiments through agent-based modelling. International Journal of Social Research Methodology, 1-13. DOI:10.1080/13645579.2022.2050120


[1] Most VBMs are linear (or multilevel linear models), but not all.  In the case of non-normally distributed data changes the tests that are used.

[2] We are researchers keen to use, extend, and test the social identity approach (SIA) using agent-based modelling. We started from interdisciplinary DFG network project (SIAM: Social Identity in Agent-based Models, https://www.siam-network.online/) and now form a continuous special-interest group at the European Social Simulation Association (ESSA) http://www.essa.eu.org/.

[3] ABMs can cater to diverse purposes, e.g., description, explanation, prediction, theoretical exploration, illustration, etc. (Edmonds et al., 2019).

Wijermans, N., Scholz, G., Paolillo, R., Schröder, T., Chappin, E., Craig, T. and Templeton, A. (2022) Models in Social Psychology and Agent-Based Social simulation - an interdisciplinary conversation on similarities and differences. Review of Artificial Societies and Social Simulation, 4 Oct 2022. https://rofasss.org/2022/10/04/models-in-spabss/

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.


[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.


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


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