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Should the family size be used in COVID-19 vaccine prioritization strategy to prevent variants diffusion? A first investigation using a basic ABM

By Gianfranco Giulioni

Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University of Chieti-Pescara, Italy

(A contribution to the: JASSS-Covid19-Thread)

When writing this document, few countries have made significant progress in vaccinating their population while many others still move first steps.

Despite the importance of COVID-19 adverse effects on society, there seems to be too little debate on the best option for progressing the vaccination process after the front-line healthcare personnel has been immunized.

The overall adopted strategies in the front-runner countries prioritize people using their health fragility, and age. For example, this strategy’s effectiveness is supported by Bubar et al. (2021), who provide results based on a detailed age-stratified Susceptible, Exposed, Infectious, Recovered (SEIR) model.

During the Covid infection outbreak, the importance of families in COVID diffusion was stressed by experts and media. This observation motivates the present effort, which investigates if considering family size among the vaccine prioritization strategy can have a role.

This document describes an ABM model developed with the intent of analyzing the question. The model is basic and has the essentials features to investigate the issue.

As highlighted by Squazzoni et al. (2020) a careful investigation of pandemics requires the cooperation of many scientists from different disciplines. To ease this cooperation and to the aim of transparency (Barton et al. 2020), the code is made publicly available to allow further developments and accurate parameters calibration to those who might be interested. (https://github.com/gfgprojects/abseir_family)

The following part of the document will sketch the model functioning and provide some considerations on families’ effects on vaccination strategy.

Brief Model Description

The ABSEIR-family model code is written in Java, taking advantage of the Repast Simphony modeling system (https://repast.github.io/).

Figure 1 gives an overview of the current development state of the model core classes.

Briefly, the code handles the relevant events of a pandemic:

  • the appearance of the first case,
  • the infection diffusion by contacts,
  • the introduction of measures for diffusion limitation such as quarantine,
  • the activation and implementation of the immunization process.

The distinguishing feature of the model is that individuals are grouped in families. This grouping allows considering two different diffusion speeds: fast among family members and slower when contacts involve two individuals from different families.

Figure 1: relationships between the core classes of the ABSEIR-family model and their variables and methods.

It is perhaps worth describing the evolution of an individual state to sketch the functioning of the model.

An individual’s dynamic is guided by a variable named infectionAge. In the beginning, all the individuals have this variable at zero. The program increases the infectionAge of all the individuals having a non zero value of this variable at each time step.

When an individual has contact with an infectious, s/he can get the infection or not. If infected, the individual enters the latency period, i.e. her/his infectionAge is set to 1 and the variable starts moving ahead with time, but s/he is not infectious. Individuals whose infectionAge is greater than the latency period length (ll ) become infectious.

At each time step, an infectious meets all her/his family members and mof randomly chosen non-family members. S/he passes on the infection with probability pif to family members and pof to non-family members. The infection can be passed on only if the contacted individual’s infectionAge equals zero and if s/he is not in quarantine.

The infectious phase ends when the infection is discovered (quarantine) or when the individual recovers i.e., the infectionAge is greater than the latency period length plus the infection length parameter (li).

At the present stage of development, the code does not handle the virus adverse post-infection evolution. All the infected individuals in this model recover. The infectionAge is set at a negative value at recovery because recovereds stay immune for a while (lr). Similarly, vaccination set the individual’s  infectionAge to a (high) negative value (lv).

At the present state of the pandemic evolution it is perhaps useful to use the model to get insights into how the family size could affect the vaccination process’s effectiveness. This will be attempted hereafter.

Highlighting the relevance of families size by an ad-hoc example

The relevance of family size in vaccination strategy can be shown using the following ad-hoc example.

Suppose there are two covid-free villages (say village A and B) whose health authorities are about to start vaccinations to avoid the disease spreading.

Villages are identical in the other aspects except for the family size distribution. Each village has 50 inhabitants, but village A has 10 families with five components each, while village B has two five members families and 40 singletons. Five vaccines arrive each day in each village.

Some additional extreme assumptions are made to make differences straightforward.

First, healthy family members are infected for sure by a member who contracted the virus. Second, each individual has the same number of contacts (say n) outside the family and the probability to pass  on the virus in external contacts is lower than 1. Symptoms take several days before showing up.

Now, the health authority are about to start the vaccination process and has to decide how to employ the available vaccines.

Intuition would suggest that Village B’s health authority should immunize large families first. Indeed, if case zero arrives at the end of the second vaccination day, the spread of the disease among the population should be limited because the virus can be passed on by external contacts only; and the probability of transmitting the virus in external contacts is lower than in the family.

But, should this strategy be used even by village A health authority?

To answer this question, we compare the family-based vaccination strategy with a random-based vaccination strategy. In a random-based vaccination strategy, we expect one members to be immunized in each family at the end of the second vaccination day. In the family-based vaccination strategy, two families are immunized at the end of the second vaccination day. Now, suppose one of the not-immunized citizens gets the virus at the end of day two. It is easy to verify there will be an infected more in the family-based strategy (all the five components of the family) than in the random-based strategy (4 components because one of them was immunized before). Furthermore, this implies that there will be n additional dangerous external contacts in the family-based strategy than in the random-based strategy.

These observations make us conclude that a random vaccination strategy will slow down the infection dynamics in village A while it will speed up infections in village B, and the opposite is true for the family-based immunization strategy.

Some simulation exercises

In this part of the document, the model described above will be used to compare further the family-based and random-based vaccination strategy to be used against the appearance of a new case (or variant) in a situation similar to that described in the example but with a more realistic setting.

As one can easily imagine, the family size distribution and COVID transmission risk in families are crucial to our simulation exercises. It is therefore important to gather real-world information for these phenomena. Fortunately, recent scientific contributions can help.

Several authors point out that a Poisson distribution is a good statistical model representing the family size distribution. This distribution is suitable because a single parameter characterizes it, i.e., its average, but it has the drawback of having a positive probability for zero value. Recently, Jarosz (2020) confirms the Poisson distribution’s goodness for modeling family size and shows how shifting it by one unit would be a valid alternative to solve the zero family size problem.

Furthermore, average family sizes data can be easily found using, for example, the OECD family database (http://www.oecd.org/social/family/database.htm).

The current version of the database (updated on 06-12-2016) presents data for 2015 with some exceptions. It shows how the average size of families in OECD countries is 2.46, ranging from Mexico (3.93) to Sweden (1.8).

The result in Metlay et al. (2021) guides the choice of the infection in the family parameter. They  provide evidence of an overall household infection risk of 10.1%

Simulation exercises consist in parameters sensitivity analysis with respect to the benchmark parameter set reported hereafter.

The simulation initialization is done by loading the family size distribution. Two alternative distributions are used and are tuned to obtain a system with a total number of individuals close to 20000. The two distributions are characterized by different average family sizes (afs) and are shown in figure 2.

Figure 2: two family size distributions used to initialize the simulation. Figures by the dots inform on the frequency of the corresponding size. Black square relates to the distribution with an average of 2.5; red circles relate to the distribution with an average of 3.5

The description of the vaccination strategy gives a possibility to list other relevant parameters. The immunization center is endowed with nv doses of vaccine at each time starting from time tv. At time t0, the state of one of the individuals is changed from susceptible to infected. This subject (case zero) is taken from a family having three susceptibles among their components.

Case zero undergoes the same process as all other following infected individuals described above.

The relevant parameters of the simulations are reported in table 1.

var description values reference
ni number of individuals ≅20000
afs average family size 2.5;3.5 OECD
nv number of vaccine doses available at each time 50;100;150
tv vaccination starting time 1
t0 case zero appearance time 10
ll length of latency 3 Buran et al 2021
li length of infectious period 5 Buran et al 2021
pif probability to infect a family member 0.1 Metlay et al 2021
pof probability to infect a non-family individual 0.01;0.02;0.03
mof number of non-family contacts of an infectious 10

Table 1: relevant parameters of the model.

We are now going to discuss the results of our simulation exercises. We focus particularly on the number of people infected up to a given point in time.

Due to the presence of random elements, each run has a different trajectory. We limit these effects as much as possible to allow ceteris paribus comparisons. For example, we keep the family size distribution equal across runs by loading the distributions displayed in figure 2 instead of using the run-time random number generator. Again, we set the number of non-family contacts (mof) equal for all the agents, although the code could set it randomly at each time step. Despite these randomness reductions, significant differences in the dynamics remain within the same parametrization because of randomness in the network of contacts.

To allow comparisons among different parametrizations in the presence of different evolution, we use the cross-section distributions of the total number of infected at the end of the infection process (i.e. time 200).

Figure 3 reports the empirical cumulative distribution function (ecdf) of several parametrizations. To easily read the figure, we put the different charts as in a plane having the average family size (afs) in the abscissa and the number of available vaccines (nv) in the ordinate. From above, we know two values of afs (i.e. 2.5 and 3.5) and three values of nv (i.e. 50, 100 and 150) are considered. Therefore figure 3 is made up of 6 charts.

Each chart reports ecdfs corresponding to the three different pof levels reported in table 1. In particular, circles denote edcfs for pof = 0.01, squares are for  pof = 0.02 and triangles for  pof = 0.03. At the end, choosing a parameters values triplet (afs, nv, pof), two ecdfs are identified. The red one is for the random-based, while the black one is for the family-based vaccination strategy. The family based vaccination strategy prioritizes families with higher number of members not yet infected.

Figure 3 shows mixed results: the random-based vaccination strategy outperforms the family-based one (the red line is above the balck one) for some parameters combinations while the reverse holds for others. In particular, the random-based tends to dominate the family-based strategy in case of larger family (afs = 3.5) and low and high vaccination levels (nv = 50 and 150). The opposite is true with smaller families at the same vaccination levels. The intermediate level of vaccination provides exceptions.

Figure 3: empirical cumulative distribution function of several parametrizations. The ecdfs is build by taking the number of infected people at period 200 of 100 runs with different random seed for each parametrization.

It is perhaps useful to highlight how, in the model, the family-based vaccination strategy stops the diffusion of a new wave or variant with a significant probability for smaller average family size and low and high vaccination levels (bottom-left and top-left charts) and for large average family size and middle level of vaccination (middle-right chart).

A conclusive note

At present, the model is very simple and can be improved in several directions. The most useful would probably be the inclusion of family-specific information. Setting up the model with additional information on each family member’s age or health state would allow overcoming the “universal mixing assumption” (Watts et al., 2020) currently in the model. Furthermore, additional vaccination strategy prioritization based on multiple criteria (such as vaccinating the families of most fragile or elderly) could be compared.

Initializing the model with census data of a local community could give a chance to analyze a more realistic setting in the wake of Pescarmona et al. (2020) and be more useful and understandable to (local) policy makers (Edmonds, 2020).

Developing the model to provide estimations for hospitalization and mortality is another needed step towards more sound vaccination strategies comparison.

Vaccinating by families could balance direct (vaccinating highest risk individuals) and indirect protection, i.e., limiting the probability the virus reaches most fragiles by vaccinating people with many contacts. It could also have positive economic effects relaunching, for example, family tourism. However, it cannot be implemented at risk of worsening the pandemic.

The present text aims only at posing a question. Further assessments following Squazzoni et al.’s (2020) recommendations are needed.

References

Barton, C.M. et al. (2020) Call for transparency of COVID-19 models. Science, 368(6490), 482-483. doi:10.1126/science.abb8637

Bubar, K.M. et al. (2021) Model-informed COVID-19 vaccine prioritization strategies by age and serostatus. Science 371, 916–921. doi:10.1126/science.abe6959

Edmonds, B. (2020) What more is needed for truly democratically accountable modelling? Review of Artificial Societies and Social Simulation, 2nd May 2020. https://rofasss.org/2020/05/02/democratically-accountable-modelling/

Jarosz, B. (2021) Poisson Distribution: A Model for Estimating Households by Household Size. Population Research and Policy Review, 40, 149–162. doi:10.1007/s11113-020-09575-x

Metlay J.P., Haas J.S., Soltoff A.E., Armstrong KA. Household Transmission of SARS-CoV-2. (2021) JAMA Netw Open, 4(2):e210304. doi:10.1001/jamanetworkopen.2021.0304

Pescarmona, G., Terna, P., Acquadro, A., Pescarmona, P., Russo, G., and Terna, S. (2020) How Can ABM Models Become Part of the Policy-Making Process in Times of Emergencies – The S.I.S.A.R. Epidemic Model. Review of Artificial Societies and Social Simulation, 20th Oct 2020. https://rofasss.org/2020/10/20/sisar/

Watts, C.J., Gilbert, N., Robertson, D., Droy, L.T., Ladley, D and Chattoe-Brown, E. (2020) The role of population scale in compartmental models of COVID-19 transmission. Review of Artificial Societies and Social Simulation, 14th August 2020. https://rofasss.org/2020/08/14/role-population-scale/

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (2020) Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation, 23(2):10. <http://jasss.soc.surrey.ac.uk/23/2/10.html>. doi: 10.18564/jasss.4298


Giulioni, G. (2020) Should the family size be used in COVID-19 vaccine prioritization strategy to prevent variants diffusion? A first investigation using a basic ABM. Review of Artificial Societies and Social Simulation, 15th April 2021. https://rofasss.org/2021/04/15/famsize/


 

Artificial Sociality Manifesto

By Gert Jan Hofstede1*, Christopher Frantz2, Jesse Hoey3, Geeske Scholz4, and Tobias Schröder5

*Corresponding author, 1Information Technology, Wageningen, 2Department of Computer Science, Norwegian University of Science and Technology, 3School of Computer Science, University of Waterloo, 4Institut für Umweltsystemforschung, Universität Osnabrück, 5Potsdam University of Applied Sciences

Table of Contents

Approach

Ambition

With this position paper the authors posit the need for a research area of Artificial Sociality. In brief this means “computational models of the essentials of human social behaviour”; we shall elaborate below. The need for artificial sociality is justified by the encroachment of simulations and knowledge technology, including Artificial Intelligence (AI), into the fabric of our societies. This includes smart devices, biosensors, facial recognition, coordination apps, surveillance apps, search engines, home and care robots, social media, machine learning modules, and agent-based simulation models of socio-ecological and socio-economic systems. It will include many more invasive technologies that will be invented in the coming decades. Artificial sociality is a way to connect human drives and emotions to the challenges our societies face, and the management and policy actions we need to take. In contrast to mainstream AI research, artificial sociality targets the social embeddedness of human behaviour and experience; we could say the collective intelligence of human societies rather than the individual intelligence of single agents. Human sociality has characteristics that differ from other varieties of sociality, while having variation across cultures (Henrich, 2016). In this piece, we concentrate on the incorporation of human sociality into agent-based computational social simulation models as a testbed for the integration of the various elements of artificial sociality.

The issue of artificial sociality is not new, as we’ll discuss below in the “State of the art” section. Our evolutionary perspective, we feel, offers new possibilities for integrating various strands of research. Our ambition is mainly to find a robust ontology for artificial human sociality, rooted in our actual evolutionary history and allowing to distinguish cultures. We hope that efforts at engineering computational agents and societies can benefit from this work.

Why is sociality so important?

Humans are eusocial

Sociality is a word used across various sciences. Neuroscientist Antonio Damasio makes it a central concept, arguing that it is present in all social creatures, even long predating multicellular organisms (Damasio, 2018). In agreement with Wilson & Holldöbler (Edward O. Wilson & Hölldobler, 2005), Wikipedia defines it in a biological way: “Sociality is the degree to which individuals in an animal population tend to associate in social groups (gregariousness) and form cooperative societies”. The site continues: “The highest degree of sociality recognized by sociobiologists is eusociality. A eusocial taxon is one that exhibits overlapping adult generationsreproductive division of labor, cooperative care of young, (…).” Obviously, this definition holds for humans. We are a eusocial primate species.

Why are we in the world?

A grand question in philosophy is “Why are we in the world?”. Evolutionary biology would answer “because our ancestors reproduced, ever since the beginnings of life”. The next question is “Why did our ancestors reproduce?” Well, they did so because “they were fit, and conquered natural and human-made hazards”. Thirdly, “Why were they fit?” This third “why” question takes us to sociality. Being eusocial gave our ancestors the fitness they needed. It allowed them to cooperate and divide tasks in groups. Millions of years ago, early hominins gathered, hunted, defended themselves, cared for the weak, exchanged goods and foods (G. Hofstede, Hofstede, & Minkov, 2010), chapter 12.

Sociality integrates elements of all possible sciences that are useful in comprehensively modelling human (or non-human) social behaviour, drives, and decision making. It spans from the “what” to the “why” to the “how”. The notion of sociality changes the meaning of the concept of intelligence into something that could be group-level, not individual-level. The most astounding fact about humans is the high degree of social or collective intelligence. Because of the protection it affords, collective intelligence even raises the tolerance for individual ineptness (Diamond, 1999).

Artificial sociality

Artificial sociality is the study of sociality by means of computational modelling. This could take many forms, e.g. social robotics, body-worn devices. In this paper we focus on computational social simulation with a particular focus on sociality. The application to computational social simulation sets purpose and limits to the selection of potentially relevant knowledge. Artificial sociality will be concerned with building blocks and primitives that are chosen so as to be reusable for a multitude of applications. In this sense it is a transformative endeavour. It offers a systematic integration of the existing insulated approaches sponsored by diverse disciplines to understand and analyse the human condition in all its facets. The primitives developed for artificial sociality should have the potential to be used by a great many applied scholars. More importantly, the dedicated integrated treatment of disciplines is increasingly recognised as necessary to produce sufficiently accurate insights, such as the impact of cultural aspects on the assessment of social policy outcomes (Diallo, Shults, & Wildman, 2020). Applications that benefit from a systematic consideration of artificial sociality include models of human collective action in society, in socio-environmental, socio-economical, or socio-technical systems. Typically, these models would be used to support policy making by achieving a better understanding of the dynamics of target systems.

The history of sociality

Early hominins were mentioned above. In the evolution of life, sociality is actually much older than that. To properly appreciate its importance, we’ll present a brief history of sociality.

Sociality is as old as slime moulds, primitive organisms (“Protista”) that are usually monocellular (e.g. Dictyostelium). Slime moulds know collective action and large-scale division of labour. Social insects such as bees and ants are a more familiar case of successful sociality. Among mammals, there are the burrowing mole rats who live in eusocial colonies. These, or similar, life forms are linked to us by an unbroken chain of life. Sociality has an ancient path dependency.

Hominins

Limiting ourselves to the last million years, our hominin ancestors have brought sociality to a new level. In contrast to other primates, humans have not radiated into distinct species, but merged into one genetically closely related pool, with tremendous cultural variation. They did this through a combination of migrating, fighting, spreading of diseases, cross-breeding, and massive copying of inventions. Some of the latter are mastery of fire, language, script, law, agriculture, religion, weapons and money. Our present-day sociality is the outcome of an unbroken chain of reproduction, all the way since the origins of life until today. At present, fission-fusion dynamics happen all the time in all human societies. Divisions between groups of people are deeply gut-felt. They range from stable across generations to ephemeral; but they are not genetically deep, nor absolute. Yet they matter greatly for the behaviour of our policy-relevant systems. Religions, political alliances, trade networks, but also social media hypes and terrorist movements are cases in point.

Victims of reason

In recent centuries, humans have tended to forget that for all our cleverness and symbolic intelligence, humans are also still social mammals with deep relational drives. Our relational drives tell our intelligence what to do, and do so generally without being transparent to us (Haidt, 2012; Kahneman, 2011). A purely cognitive or rational paradigm cannot capture all of these drives. Thus, when trying to understand our collective behaviours, we can be “victims of reason”. To quote Montesquieu: “Le Coeur a ses raisons que la raison ne connaît point” (‘the heart has its reasons unknown to reason’) (Montesquieu, 1979 [1742]). Artificial sociality goes beyond reason, identifying the unknowns of underlying relational motives. Yes, expected profit is an important motive; but it is relational profit that matters, influenced by gut feelings and emotions such as love, hate, pride, shame, envy, loyalties. Financial profit for the individual is just a special case. As theorized eloquently by Mercier and Sperber (Mercier & Sperber, 2017), reason is used by humans for social acceptance far more than it is used for accuracy. Basically, reason is used for arguing and justifying a position in a social group to enhance influence on, and acceptance by, the group.

Watch the lake, not just the ripples

When we create policy, we tend to run from incident to incident, often forgetting to consider the patterns of path dependence linking these incidents. Causal chains of things happening today run backwards into deep history. The French revolution for instance, while seemingly showing limited impact on life nowadays, has changed and shaped the conception of the nation state and of rights that modern citizens comfortably assume to be omnipresent. Similarly, present-day individualism can be traced back to the marriage policies of the medieval catholic church (Henrich, 2020). For both these examples, it stands to reason that even older sources exist, hidden on the unbroken path of history. Across undoubted and transformative change, there is a continuity to history, especially where sociality is concerned. Sociality is about understanding the lake of human nature, in order to better anticipate the ripples on it.

Why artificial sociality?

Fully understanding sociality is vital for our survival. Artificial sociality, by showing sociality in action, can help. Here we propose a list of principles that indicate how vital it is to understand sociality better. Therefore, they justify developing artificial sociality.

  • Systems over disciplines – The earth in the Anthropocene is one system, of which key aspects are ecology, economy, and technology. All of these are known by our intellect. Their development is driven by our sociality. To understand these systems, including human sociality, we need to integrate knowledge across disciplines. This includes both natural and social sciences.
  • Multi-level systems – Grand challenges are multi-level. They are about water, climate, contagious diseases, migration, peace. They involve people and groups in systems combined of natural, institutional and economic subsystems. They have dynamics and feedback cycles, often leading to unanticipated and undesired outcomes. They may or may not be subject to policy, but they are unavoidably subject to sociality.
  • Emotions AND Rationality – In disciplines concerned with modelling human behaviour, there is a tendency to work on the assumption that “we are our brains” (Swaab & Hedley-Prole, 2014). A broad cross-disciplinary perspective, as well as life experience, make it clear that this is not really the case. Sociality has reason for breakfast: we are subject to gut feelings, we are driven, or get carried away, by emotions. Artificial sociality can bring these things to life.
  • Interaction over Individuals – The behaviour of our systems strongly hinges on the sociality of the people in them. Key issues have to do with gut feelings, emotions, trust, communication, hierarchy, group affiliation, power, politics, geopolitics. All of these rest not in the individual but emerge from social interactions
  • Explainability over black boxes – While data-driven modelling experiences great popularity, models purely based on data render limited insight into the conceptual inner workings of a social system and its meaning for a target system (i.e., the social reality it represents). Artificial sociality needs to seek a balance of theory, data, and understanding. Analysing policy without understanding interaction effects limits scientific and practical value.

For whom?

  • Interdisciplinary researchers can use artificial sociality in models for understanding their target systems.
  • Policy makers can create better ideas and policies if they are helped by plausible systemic models of the issues they face and the dynamics those issues exhibit.
  • Citizens can act as policy makers, taking their fate into their hands.
  • Designers of intelligent systems can integrate knowledge about social dynamics.

With whom?

  • All disciplines in the social and life sciences. In order to articulate artificial sociality, all disciplines that study human life can potentially contribute. This ranges across levels of integration: anthropology, artificial intelligence, behavioural biology, behavioural economics, cultural psychology, evolutionary biology, history, neurosciences, psychology, small group behaviour, social geography, social psychology, sociology, system biology…the spirit is one of consilience (Edward O Wilson, 1999).
  • Non-academic stakeholders (e.g., governments, the general public). Not only can participatory approaches help uncover hidden rules and drivers of behaviour, but also can artificial sociality be an educational tool for an enlightened society to raise its self-reflection and awareness of its inner workings.

How?

  • We recognize the integration of various disciplines’ involvements, the diversity of their respective data, theories, concepts and methods, as a challenging endeavour. In many instances we are struck by gaps between involved disciplines, and the ability to integrate data and theory in a systematic manner. Just because one theory is right does not mean that another one is wrong; often, there is complementarity, if one is willing to search for it.
  • Simulation and levels of abstraction. To this end, simulation offers the necessary capabilities, since its approach has the potential to traverse disciplines by offering broad accessibility, modelling at abstraction levels that correspond to the analytical levels within different disciplines (e.g., micro, meso and macro level in sociological research). Its unique ability to afford the systematic integration of theory and data (Tolk, 2015), deductive and inductive reasoning has rendered social simulation as a “third way of doing science” (R. Axelrod, 1997), while available computational resources allow us to explore artificial sociality at scale.
  • Creative spark. Computational simulation requires a design effort that links its various contributions into mechanisms. These constitute an original, interdisciplinary contribution. They can themselves be validated.
  • Disciplinary contributions. Social simulation is conceptually a method embedded in life sciences, complexity, and social-scientific disciplines. Each of our models creates a miniature world. These worlds need all kinds of input from various sources and disciplines.
  • Practicable outputs. Agent-based social simulation typically intends to produce practicable outputs, using theory, data and intuitions as its inputs regardless of their origin (Tolk, 2015) (Edmonds & Moss, 2005). Therefore, social simulation, in particular agent-based modelling, and artificial sociality, should institutionally be fed by many disciplines. All researchers from all disciplines are welcome.
  • Dynamics. Agent-based models are eminently suitable to help understand the dynamics of systems. They allow one to investigate unintended collective patterns arising from individual motives, intra- and intergroup dynamics. In other words, they can link disciplines at different levels of aggregation, from the individual to the society. Sensitivity analysis of these dynamics is an integral part of the method.

State of the art on sociality across disciplines

Research into human behaviour has been carried out for a long time, and in many disciplines. Such research, usable or even intended for modelling, has been picking up in recent decades. It would be presumptuous to try and give a full review of developments. Yet we believe that it is useful to give a brief overview of what is happening in various disciplines. To avoid distracting from our purpose, the details are in the appendix.

What we need from the disciplines

Given our position that every living thing that exists today, has evolved and continues to evolve, we need contributions of various types for making sense of sociality. Let us, for one moment, consider life as a game of chess. In such a model, we need to know the what, the why and the how. In our proposal, these elements will become intertwined.

  • What: the constitutive elements (chessboard, pieces); the starting position, the rules of the game (formal and etiquette).
  • Why: the motivation of the players during a game: typically, this would be “capturing the enemy king”, but other motives could occur. For instance, I might want to lose, for motivating a junior opponent, or win, for challenging him or her.
  • How: the configurations that are meaningful, and sequences of moves that can make these configurations happen. Limitations in players’ skills can reduce the space of possibilities.

These questions also obtain for sociality:

  • What: medical- and neurosciences study our constitutive elements. History, institutional economics and anthropology study what collective behaviours occur in groups of people.
  • Why: evolutionary biology studies the origins of sociality. Psychology studies human motivation today, for instance in leadership, organizational behaviour, clinical -, social- and cultural psychology. Ethology does the same for non-human creatures.
  • How: Sociology tends to describe the how of sociality, for instance patterns, their causes and their sequences. Computational branches in biology, economics, and sociology construct artificial worlds. Simulation gaming, and experimental economics do the same with real people, in artificial incentive structures.

For computational modelling we will need input on all three of these questions. The models will require

  • A “what”: agents in an environment.
  • A “why”: motivation for the agents: drives, urges, goals.
  • A “how”: perceptions and actions for the agents, and coordination of these across space and time. This will lead to emergent pattern.

The three questions are really highly intertwined; we take them apart only for the sake of exposition. Also, the emergent pattern of one branch of science, or of a simulation model, can become the input, taken as given, of another. For instance, some models could investigate the emergence of institutions, norms, or culture; others could use such concepts as input variables.

The take-home message of this section is that our modelling efforts will be best served by an eclectic mode that draws from a broad variety of sciences.

What the disciplines tell us

We shall now attempt a synthesis of work on sociality across disciplines presented in the appendix that are important for the research agenda of artificial sociality. To structure it, we stick to our what, why and how questions. Admittedly, our synthesis is partial; this is done for the sake of purposefulness, not because other perspectives could not have merit.

What

Sociality is not a human invention. It is absolutely central to life on earth, and has been since billions of years, in an unbroken chain of reproduction. Sociality has served to preserve homeostasis in populations, enabling some to reproduce (Damasio, 2018). It is as old as monocellular organisms, many of which are known to coordinate their behaviours in response to external stimuli, particularly at the service of reproduction. Human sociality is special in a few ways (Henrich, 2016). We coordinate in many ways with many people we do not personally know. For achieving joint action, we have basically two mechanisms. In evolutionary terms these are prestige and dominance (Henrich, 2016).  In sociological terms:  status and power (Theodore D. Kemper, 2017). Also, groups of followers are able to curtail the power of leaders. For these functions we evolved intense emotional lives (J. E. Turner, 2007). Emotions are the proximate indicators of our sociality that our organisms provide to us. We’ll return to these issues under “how”.

Selective pressures do not just operate between individuals, but at many levels. There is selective pressure between individuals, human groups, forms of coordination, even ideas. Models can concentrate on any of these levels.

Why

Sociality, in terms of status and power motives in multiple, changing groups, and attending feelings and emotions, is necessary for solving coordination problems, e.g. dividing food, reproducing, bringing up children, or avoiding traffic congestion; and for solving collective action problems and social dilemmas, e.g. selecting a leader, disposing of a dysfunctional leader, or distributing resources across the citizens of a country. This holds in small groups and families with informal social bonds as well as in large groups or societies that rely on formal, depersonalised interaction patterns. Without sociality there can be neither Gemeinschaft (community) nor Gesellschaft (society). Sociality shapes our moral sense.

How

In Humans, sociality develops very early in life, preceding speech and walking. It requires intense care, play, and education during many years; we are a neotenous species, remaining juvenile for many years and even keeping some brain plasticity during adult life. For a baby, the organism has precedence. After just a few months, giving and conferring status becomes important. Between 11 and 19 months, power use develops (Eliot, 2009). During childhood, the social world grows, and various reference groups become distinguished. We learn the dynamics of prestige / status giving and claiming. At puberty, sociality more or less plateaus; just like we speak with the accent of our childhood, we act with its culture. Our hormonal systems are aligned with the dual nature of prestige / status and dominance / power; more on this in the appendix.

This phenomenon of a flexible beginning then stable existence also holds for groups of people. Once formed, societies, groups, organizations and companies, have cultures that tend to remain stable over time, despite many perturbations (Beugelsdijk & Welzel, 2018; G. Hofstede et al., 2010).

Sociality happens. Every action in which several people are present or imagined provides an instance to mutually imprint sociality through status-power dynamics in a world of groups. This ranges from glances and nonverbal involuntary movements, to explicit verbal communication, to social media posts and likes, to elaborate rituals involving prestige and social roles, to coercive acts involving life and death. All of these constitute as many claims for, and accord or refusals of, status; and some of them include power moves.

Groups in society are endlessly variable. They change at various timescales, from life-long to context-dependent and ephemeral. They can be nested or overlapping. Their salience is socially and situationally determined.

Collective results of social acts need not be intended. Much of our societies’ behaviour largely emerges unplanned. A few frequent, archetypical patterns can often be seen in this unplanned system-level behaviour. Agent-based modelling is privileged as a method by allowing to generate these unplanned patterns.

Key theories

There is such a wealth of theoretical work in so many disciplines that even the brief overview above may seem a bit unorganized. Therefore we briefly mention a few of the theories that we’ll most use in our proposal.

  • Kemper’s status – power – reference-group theory of relations. This comprehensive sociological theory also touches on neurobiology and psychology. This makes it compatible with evolutionary theories of human sociality. The appendix has a more elaborate treatment.
  • Heise’s Affect Control Theory (ACT). This theory shares a lot of elements with Kemper’s status – power dynamics but is targeted to small group interactions.
  • Tajfel & Turner’s Social Identity Approach (SIA). This theory elaborates on elements of group and intergroup dynamics, somewhat similar to Kemper’s reference groups.

Work to do in artificial sociality

The synthesis above suggests that sociality is about things that we do, and things that happen between people, in any of the contexts of their lives. Artificial sociality can reproduce sociality using modelling techniques that make life happen: “generative social science” (Epstein, 2006), or, with a newer word, computational social simulation. The task for artificial sociality is first and foremost a modelling task with the ambition to understand sociality-in-action better.

Principles

Ontologically, our perspective is one of consilience. Since there is only one world, findings that align across different sciences are particularly interesting to use in models of sociality. This is the case with the match between neurobiology, emotions, and the status-power theory of relations discussed in the appendix, for example.

Vocabulary

One of our tasks is to generate better understanding and a common vocabulary. At present, many modellers criss-cross the same conceptual space, but with different maps from different reference disciplines.

Open world hypothesis

In order to be able to talk with one another and build shared vocabulary, researchers should maintain an open world hypothesis: if your model differs from mine, then we can talk. What is the difference, is it really a difference, what does that allow or disable? Such discussion allows us to enrich our ontology. It is unrealistic, anyway, to expect everyone to agree. Artificial sociality is heavily loaded with worldview, and people disagree on worldviews. This is actually something that artificial society should help explain; unfortunately, we can predict that such an explanation will not please everyone.

Realms to model

Our sociality operates in a world with non-social elements such as space, time, objects. On a scale from content-based to relational, we can distinguish four realms that need to be modelled.

  1. This means the bio-physical and the institutional world, divorced from what people might feel about it.
  2. Cognitions about content. This includes knowledge, opinions, norms, and values that influences our perspectives on the content realm. They are partially conscious, the less so the more they are shared (and therefore cultural). This realm binds the relational to the non-relational world.
  3. Cognitions about relations. We have ideas about the status (“social importance”) and power that others have, about our own status and power in groups. These are normally unconscious.
  4. Cognitions about our own organism. This includes all kinds of organismic feelings, again often not fully conscious, and may include meta cognitions (e.g. “thinking about thinking”). Emotions link the organism with the relational world, often unconsciously. For instance, an insult is an attack on our status, and may bring the blood to our cheeks.

Artificial sociality requires considering all these elements. To which extent we consider each of them can be case-dependent. Depending on the application, some might have to be further elaborated. It is possible to model only one or several of these realms. For instance, Kemper’s theory posits the organism as one of the relevant reference groups, merging 3. and 4. Hofstede’s GRASP world has only sociality (3.) and no content (Gert Jan Hofstede & Liu, 2020). The general-purpose link from emotion as coherent dynamic social meaning, to content as objects and actions in institutional frames proposed in BayesACT may provide a link between (1.), (2.) and (3.) (Schröder, Hoey, & Rogers, 2016). Ultimately all of the realms will be needed in combination.

Theories and realms

Theories from the social sciences tend to concentrate on a subset of these realms. Table 1 indicates this.

Table 1: theories and realms to model (legend: from – not included, … to +++ central to this theory)

Theory modelled realms
  Content Cognitions
    on content on relations on organism
Affect Control Theory (Heise, 2013) + ++
Reasoned action approach (Fishbein & Ajzen, 2010) + +
Social identity approach (H Tajfel & Turner, 1986) + ++ ++
Status-power theory of relations (Theodore D. Kemper, 2017) +++ +
BayesACT (Schroeder et al., 2016) + + ++

Sources: theory, data, and experience

Models are integration devices, built from a variety of sources. Theory, data, and real-world experience all contribute to the usefulness of models that include artificial sociality (figure 1). The figure positions computational social simulation as a meeting place of these three elements. Different mixtures are possible, depending on the aim of modelling (Edmonds et al., 2019). Models range from purely theory-based ones that can illustrate core concepts, to models developed in participation with stakeholders that reflect real life, to highly complicated, data-fed models that can describe existing data or predict (generalize to) future measurements.

Artificial sociality as we propose it is, in the first instance, a theoretical concept. We believe that it has strong face validity in real life. This is by virtue of the empirical basis and broad scope of the theories involved. Integrating our concepts with data, for instance the never-ending stream of social media data, is a major challenge for the coming years.

Manif - Picture 1

Figure 1. Social simulation as a meeting place of theory, data and real life (Gert Jan Hofstede, 2018).

Model architectures

In artificial sociality we cannot get away with ideas only. Implementations are also needed, and functional computer code. In computer code, all the capabilities of our virtual world and of the agents that populate them, have to be unambiguously specified. This raises the issue of architecture. For instance, do agents have a body, a brain, and a soul? Do groups have common agency, or is that delegated to individuals? If the world is spatial, do we have instinctive reactions to moving objects? Is there “fast and slow” thinking as per many author’s writings  (e.g. (Kahneman, 2011) (Zhu & Thagard, 2002) (Glöckner & Witteman, 2010)?

Currently, a thousand flowers are blooming in the computational modelling of human behaviour. This is a good way to search. We believe that one architecture will not cover all needs; in all likelihood many streams of research will dry up, and we’ll be left with a limited number of rather general-purpose architectures for different purposes. Many existing models and architectures deserve to be taken into account.

State of the art

Artificial sociality, by design, is integrative across its contributing disciplines. Scientists have tried to integrate research on human behaviour and society across disciplines as long as we know. This has, however, become progressively harder as disciplines have branched. Aristotle was still a polymath, but today this is hardly possible any more.

Some attempts that are meaningful for artificial sociality in our view merit mention here.

Conte and Gilbert and their legacy

In social simulation, the concept of sociality was introduced in the nineteen nineties. Psychological computer scientists Kathleen Carley and Allen Newell published their extensive essay “The Nature of the Social Agent” in 1994, in which they proposed that compared with “omniscient” economic agents, social agents have more limited processing capabilities, but a richer social environment. They will turn to socio-cultural clues instead of raw data (Carley & Newell, 1994). Cognitive psychologist Rosaria Conte and sociologist Nigel Gilbert are founders of the notion of “artificial societies” (Gilbert & Conte, 1995). They set out to define artificial sociality as a challenge for computational social simulation. Their reflections were crowded out of the public eye by the advent of the Web, and the increasing ubiquity of data as sources for modelling. Yet computational social modelling has remained focused on human social behaviour.

Flache et al. in a position paper explicitly dedicate their work to Conte, who died prematurely in 2016 (Flache et al., 2017). They plead for more research on the question that Robert Axelrod posed in 1997: “If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear?“(Robert Axelrod, 1997). Flache et al. discuss several models, the currency of which is opinions.

Jager also builds on a statement by Conte when he pleads for “EROS”, or more attention to social psychology in computational social simulation (Wander Jager, 2017). He reviews a number of theories that have been used in social simulation, none of which includes emotions. The most generic of these might be Ajzen’s Theory of Planned Behaviour (the most recent version of which the author calls Reasoned Action Approach (Fishbein & Ajzen, 2010).

Other efforts

Work on active inference and a hierarchical (deep) Bayesian probabilistic view of the mind has led to more integrative models including of interpersonal inference (Moutoussis, Trujillo-Barreto, El-Deredy, Dolan, & Friston, 2014) and culture (Veissière, Constant, Ramstead, Friston, & Kirmayer, 2020). These models consider a long-standing view of human intelligence as being largely predictive rather than descriptive. That is, the mind is set up to seek information, and to interpret evidence, in ways that confirm prior beliefs.

A mid-range approach to sociality is taken by Shults and colleagues. They take domain-directed social scientific theory and develop agent-based models with agents embodying the theory. These tend to contain instantiated sociality elements such as fear. This includes terror management theory (Shults, Lane, et al., 2018) and intergroup dynamics under anxiety (Shults, Gore, et al., 2018).

Some computational modellers have built models of human behaviour suiting their purpose. This includes empathic agents, care robots, and the military. These models include some sociality, without necessarily using that term. Space forbids to deal with them at length. Interesting pointers are (Balke & Gilbert, 2014; Schlüter et al., 2017).

Consumat architecture

An example of an architecture that is appealing because of its simplicity, while including both content and a bit of sociality, is the Consumat (Wander Jager & Janssen, 2012; Wander  Jager, Janssen, & Vlek, 1999). Consumats live in one group or network, not necessarily but possibly in a spatial world, in which they have repeated decisions to take about which they are more or less certain. In addition, they are more or less “happy” based on the outcome of their previous decisions. “Happiness” and “uncertainty” combined determine what they will do: repeat, imitate someone else, deliberate on content issues, or do a more elaborate social comparison. The currency of “happiness” is not further specified, making the Consumat model quite flexible. Embedding fundamental concepts of sociality (e.g., allusions to reference groups and uncertainty), Consumat takes the individual as a unit of concern, rendering it a flexible starting point for richer developments of artificial sociality that have a stronger emphasis on the structure the agent is embedded in. It has found quite a few applications. A more elaborate follow-up effort on Consumat called Humat is now being developed into publications.

FAtiMA

An engineering approach to sociality with considerable fidelity is FAtiMA (Mascarenhas et al., 2021). This open-source toolkit for social agents and robots includes prestige / status dynamics and social emotions. Status dynamics are called “social importance” in FAtiMA (Mascarenhas, Prada, Paiva, & Hofstede, 2013).

GRASP

The GRASP meta-model for sociality (Gert Jan Hofstede, 2019) is an attempt at capturing the bare essentials of sociality: Groups, Rituals, Affiliation, Status, and Power. GRASP is deliberately content-free. Its relational currency is status and power. It is based on the works of Kemper mentioned here, and on Hofstede’s and Minkov’s work on national cultures. Culture modifies the rules of the status-power action choices (G. Hofstede et al., 2010; Gert Jan  Hofstede & Liu, 2019). A showcase model using GRASP, GRASP world (Gert Jan  Hofstede & Liu, 2019; Gert Jan Hofstede & Liu, 2020), pictures the longevity of social groups based on the ease with which agents can leave a group in which they are subjected to power or receive insufficient status. The resulting patterns resemble social dynamics in different cultural environments.

Contextual Action Framework (CAFCA)

The CAFCA meta-model (figure 2) allows to disentangle levels of sociality and context. It was created to add on to Homo economicus models, and allows to classify existing model ontologies. Sociality implies moving to the bottom right of the model. CAFCA shows how far we still have to travel. One could extend it: a relational perspective is not included so far, nor is a multi-group world.

Manif - Picture 2

Figure 2: CAFCA, the Contextual Action Framework (Elsenbroich & Verhagen, 2016).

We can conclude that in response to Conte’s and Gilbert’s challenge, explicit opinions have received a lot of attention in computational social simulation, but emotions and feelings have not. We believe that this still leaves some phenomena unexplained. Opinions need not always be taken at face value, but can be manifestations of social feelings and emotions, e.g. love for one’s group. Computational agents are still often “autistic”, whereas real people have sociality at their core (Dignum, Hofstede, & Prada, 2014). Sociality can give them “biases”, “perspectives”, or “relational rationality” (Gert Jan Hofstede, Jonker, Verwaart, & Yorke-Smith, 2019) that can be derived from various theories.

Bayesian Affect Control Theory (BayesACT)

BayesACT is a dual process model that unifies decision theoretic deliberative reasoning with intuitive reasoning based on shared cultural affective meanings in a single Bayesian sequential model (Hoey, Schröder, & Alhothali, 2016; Schröder et al., 2016).  Agents constructed according to this unified model are motivated by a combination of affective alignment (intuitive) and decision theoretic reasoning (deliberative), trading the two off as a function of the uncertainty or unpredictability of the situation. The model also provides a theoretical bridge between decision-making research and sociological symbolic interactionism. Bayes ACT is a promising new type of dual process model that explicitly and optimally (in the Bayesian sense) trades off motivation, action, beliefs and utility, and integrates cultural knowledge and norms with individual (rational) decision making processes. Hoey, et al. (in publication: Jesse Hoey, Neil J. MacKinnon, and Tobias Schroeder. Denotative and Connotative Management of Uncertainty: A Computational Dual-Process Model. To appear in Judgement and Decision Making, 16 (2), March 2021.2021) have shown how a component of the model is sufficient to account for some aspects of classic cognitive biases about fairness and dissonance, and have outlined how this new theory relates to parallel constraint satisfaction models.

Proposal: a relational world

We now put forward our own proposal for an architecture, not because we believe this is the only way to go, but in order to give an example of where a more radical take on sociality can lead.

Theory base

Theory versus data

We assume that data provide no more than a partial perspective on the phenomenon they are captured from. Only in concert with a theoretical concept will they attain meaning. For instance, consider today’s vast quantities of data on social media usage. Our communication on social media does not reflect all of our relations. Linking data and Kemper’s theory, we presume that people will use social media to claim status (e.g. show pictures of successes and important rituals), to confer status (e.g. like and follow others), and to use power (e.g. insult high-status others). There are also many relational motives that will not show in social media. People will hide shameful actions (e.g. failing, being exposed); they will protect some of their behaviours from some of their reference groups (e.g. their parents or spouses). People may fear the power of their own government, and stay away from some social media. Often people will seek information and interpret evidence in a way that confirms group acceptance, rather than in a way that confirms facts (Mercier and Sperber, 2009). Which members of a society go on which social media, and just how they select which things to show and which not to, are dependent upon relational dynamics that the data cannot show without help from theory. A theory is needed about the “why” of behaviour.

Building blocks: Complicated vs complex

We are aware of the tension between complicatedness of model structure and complexity of model outcomes (Sun et al., 2016). According to these authors, complex behaviour can be represented either by a model with few simple primitives, or by a very elaborate model. Our intuition is that a bottom-up approach with strong theory base and simple ontology is most promising. An analogy can illustrate this (figure 3). A complicated model architecture tends to be difficult to adapt. The price to pay for a simple, adaptive architecture is abstraction. To build a valid, versatile model with few primitives, just a few types of building blocks could suffice; only, one needs a great many of them.

Manif - Picture 3

Figure 3: giraffe models in Lego. From left to right: 1) Model that is valid but made of a complicated piece; 2) simple model with just 5 different rectangular shapes; 3) more complicated model with 15 different shapes of varied form; 4) simple model with few shapes but many pieces.

From theory to model

Implementing a theory from social science in a computational model is by no means straightforward. Typically, theories leave many elements unspecified. Model designers have to fill the gaps. For instance, the Social Identity Approach (SIA) has been used in computational modelling. It models agents as enacting a particular social role or identity that is context (institutionally) dependent and emotionally meaningful. From reviewing such papers, we learned how difficult it is to model a “complete” social world. We failed to find a single model yet that models SIA to its richness, and can actually be replicated. To accommodate this, a toolbox approach is used by the network project SIAM (SIAM: Social Identity in Agent-based Models, https://www.siam-network.online/), offering a set of formalizations that can then be specified for specific purposes/aims. Still, this is challenging. We believe that interdisciplinary work yields substantial benefits here.

Which theory

In selecting theories to work with, a thousand flowers can blossom. In our case, for creating models with relational agents that have simple ontology but great range, we believe that Kemper’s work, and SIA mentioned before could provide the Lego blocks. Both place individuals (called “agents” in what follows) in a rich world consisting of many groups with salience mechanisms. SIA gives agents both an individual and social identities. Kemper has no self but only reference groups, that is, groups existing in the mind of an individual, not necessarily in the outside world. For Kemper, the organism with its needs and urges is one reference group. Crucially, Kemper additionally gives the agents status and power motives; we believe this to be crucial for social agents. In SIA, agents act upon motives too (such as the need for positive distinctness and self-esteem), while status is achieved through comparison with outgroups. Heise’s Affect Control Theory (ACT) is similar to Kemper here, and more articulate for describing verbal communication; but it works for single groups only. Efforts at broadening ACT to multiple, overlapping and interacting groups, are currently underway  (Hoey & Schröder, 2015).

In what follows we mainly lean on our interpretation of Kemper, as the most generic and simplest of these theories.

What, how and why

The “what” of our relational world consists of individuals and groups. A person can belong to several groups, and not everyone necessarily has the same shared belief about who belongs to which group. Furthermore, there will be an environment with certain affordances; we will come to this later.

Basic rules for the “why” are:

  • What individuals do, is determined by the groups to which they affiliate. Those groups will act as reference groups.
  • People’s choices depend on what they believe their reference groups want them to do.
  • These beliefs are about status and power; they can be about individuals, or about groups.
  • Status beliefs are about the status worthiness of actions, people, and groups; and about appropriate ways of claiming and conferring status.
  • Power beliefs are about the power of people and groups; and about appropriate ways of using power.
  • For obtaining what they want, people can choose between status tactics and power tactics.
  • Status tactics involve claiming and conferring status. As long as conferrals exceed claims, they tend to be pleasant, and create trust. If claims exceed conferrals, people will feel insulted, and power tactics will be used.
  • Power tactics involve coercion and deceit, and tend to lead to resentment and repercussions, except where power is perceived as legitimate.
  • In practice, power use is often couched as status conferral; misunderstandings can also occur.

A model with these primitives would qualify as a GRASP model. The fine print of all of these rules – what is considered appropriate for whom, and in what circumstances – depends upon culture (Gert Jan Hofstede, 2013). This implies that the actual status-power game is quite complex and varied, even though there are few primitives.

The “How” would depend on the context, because the primitives need to be bound to instantiations. Here, the four “elementary forms of sociality” of anthropologist Alan Page Fiske could be useful. This may require a bit of introduction. Fiske, having carried out field studies in various civilizations, came up with four “ elementary forms of sociality” (Fiske, 1992). These are: communal sharing, authority ranking, equality matching and market pricing. Fiske aims with these elementary types to bring unity to the myriad of psychological theories. He says people use these four structures when they “transfer things”, and interestingly, they correspond with four sales in which “things” can be compared: nominal, ordinal, interval and ratio. He comes up with a wide range of issues and situations where the four forms obtain. These are not mutually exclusive: we might use communal sharing in one setting, authority ranking in another, and market pricing in yet another. The balance will depend on the issue or group and on culture.

If we assume that the thing to exchange is social importance or, in Kemper’s sense, status, then the following obtains:

  • Under communal sharing, it is the group, not the individual, that is the unit of status accordance, claiming, and worthiness
  • Under authority ranking, there is a clear hierarchy in social importance, and status accords, as well as power exertion, are asymmetric based on ascription. “Quod licet Iovi not licet bovi” (“What the god Jupiter may do, a cow may not”).
  • Under equality matching, each individual or group is equally worthy, should claim and be accorded the same amount of status.
  • Under market pricing, there is no need for a moral stance, since the market decides.

The likelihood of these four forms is obviously culture-related. In particular, two of Hofstede’s dimensions seem relevant (see figure 4). These forms could directly be used as model mechanisms, or their emergence in agents could be studied based on Hofstede “software of the mind”.

Manif - Picture 4

Figure 4: Likelihood of Fiske’s elementary forms (quadrants) across Hofstede’s dimensions of culture (axes). Market pricing is indifferent to power distance.

Readers are invited to consider current events in their lives, or in the political arena, through a relational lens. Once one distinguishes the silent voice of reference groups, and the dynamics of mutual status and power use, one can also see historical continuity within the relational lives of people, groups, companies, and nations.

Proposed architecture

Figure 4 shows what we propose are key ingredients of our relational architectures for artificial sociality. There is a correspondence between the concepts in the four columns, with the left column reflecting the micro level of individual operation on the level of the organism to operationalise emotions and related individual-centred concepts. To our mind – and put forth in this paper –  the most universal Lego blocks of artificial sociality are relational. In figure 5 we use Tönnies’ term Gemeinschaft for this. Figure 5 shows Kemper’s concepts of status, power and reference groups; but alternatives with similar relational content could be chosen. This relational column is always required. Depending on the application, the concepts in one or more of the other columns are needed. If they are included, they have to be mapped onto them, making status, power and reference groups the basic operational concepts for driving the model’s dynamics. For instance, emphatic agents need to feel and communicate emotions. Social robots need proxemics, i.e. to know the emotional impact of closeness, motion and posture; models that explain phenomena such as tribalism require individual-level concepts in addition to relational conceptions. Speaking to scale, simulations that model social complexity at the societal level, and are concerned with effects of policies require Gesellschaft concepts such as norms and institutions.

Examples for such models include the reaction to imposed behavioural constraints as part of the Covid-19 countermeasures employed throughout nation states – with vastly varying responses based on social structure and influence (expressed in the relational column) and individual motivations of various kinds, including perceived challenges to liberty, economic well-being, etc.  Whatever the variable configuration of sociality elements, we require a conceptual mapping to the physical world, such as the operationalisation in status and power in currencies relevant to the society of concern (e.g., status symbols).

Figure 5 is organised into columns. The leftmost column is organismic on an objective sense, but subjectively perceived. The middle two are intersubjective, continually construed by people in interactions, although things in the Gesellschaft column tend to be perceived by many as objective (Searle, 1995).  The rightmost column is about the physical world, considered objective but often perceived from a subjective, or rather intersubjective, stance.

The impact of this position is that a direct mapping from the physical world to emotions, or from money to behaviour, will not yield versatile models. Data based models without a strong social model of sound theoretical basis using e.g. financial actions to predict future economic behaviour, or past voting to predict future voting, might accommodate specific application cases, but their range of application across cases and time will be limited. More importantly, such models lack the explanatory potential that conceptual models of sociality can offer.

Manif - Picture 5

Figure 5: building block concepts for artificial sociality.

Conclusion

This position paper argues for a biological, relational turn in artificial human sociality. Such a turn will lay a foundation that can reconcile case-specific or discipline-specific model ontologies.

Artificial sociality has the potential to greatly enhance all knowledge technologies that impinge on the social world, including e.g. social robotics and body-worn AI devices.

In this paper we mainly aim to increase the usefulness of computational models of socio- ecological, -economical and -technical systems by tackling their social aspects on a par with the other ones, in a foundational, thorough way.

Many theories, in a great many disciplines, could possibly be used in constructing ontologies for artificial sociality. We provide some pointers and examples. We also present ideas for a “relational world” that could inspire modellers.

There is a lot of work to do.

Appendix: contributions to sociality from various disciplines

The appendix is sorted, admittedly somewhat arbitrarily, according to whether a field of research focuses more on the “What”, the “Why” or the “How” of behaviour. Within those three, the order is alphabetic.

Mainly the “what”

Anthropology

Computational simulations have been made of historic civilizations. In these, simulated populations live in a simulated environment. This requires a mix of historical data and assumptions, in particular about resources and / or social drives. If the various hypotheses that are implemented in the models hold, then the simulations could throw light on historical contingencies, or even reproduce the actual history. A famous example is the “artificial Anasazi” model by Epstein that ”replays” the rise and fall of the Anasazi civilization (Epstein & Axtell, 1996). The agents in this model have no sociality, but are constrained by resources. A recent example is e.g. a model of island colonization based on the concept of gregariousness (Fajardo, Hofstede, Vries, Kramer, & Bernal, 2020).

Another contribution from Anthropology is to study typical patterns of human social organization. The work of Alan Page Fiske is interesting in this respect. Fiske’s, four “ elementary forms of sociality” were mentioned before, in the context of figure 4 (Fiske, 1992). To repeat: communal sharing, authority ranking, equality matching and market pricing.

Institutional Economics

A fundamental feature of humans is our ability to coordinate – at scale, that is. Humans can coordinate on group, societal and global level, both towards shared interests (e.g., emergence of economic and personal liberties in the French revolution; international treaties such as the Whaling convention), but, at times, they also contradict those (e.g., climate change, e.g., (Shivakoti, Janssen, & Chhetri, 2019)). In an attempt to identify the cause of prosperity or demise of societies, New Institutional Economics (North, 1990) integrate the many strands of human behaviour – including the ones outlined above. Rooted in our biology and manifested in our psychology, as humans we possess “minds as social institutions” (Castelfranchi, 2014) that continuously exercise coordination activities. Institutions, here understood as the “integrated systems of rules that structure social interactions” (Hodgson, 2015), or simply “rules of the game” (North, 1990) are the catalysts. They include sophisticated constructs such as written contracts and courts, enabling cooperation at scale (Milgrom, North, & Weingast, 1990); (North, Wallis, & Weingast, 2009), but also informal arrangements for resource governance  (Ostrom, 1990), pointing to opportunities to address social dilemmas, such as the Tragedy of the Commons (Hardin, 1968).

Neurobiology and endocrinology

A model of sociality is more valid to the extent that it fits the evidence about our bodies. This includes the brain of course, with e.g. its mirror neurons that are a vehicle for empathy, but also older physiological systems such as the sympathetic (fear and anger) and parasympathetic (well-being) nerve system and the digestive system (all kinds of impulses, e.g. mediated by our gut microbiome). The recent semantic pointer theory of emotions (Kajic, Schroeder, Stewart, & Thagard, 2019) capitalizes on the mathematical apparatus of Affect Control Theory discussed above to embed the sociality of affective experience into neurobiological mechanisms through a neurocomputational simulation model.

Tönnies’ Gemeinschaft and Gesellschaft

A fundamental sociological theme that structures the arena of social behaviour is the dialectic between different forms of social organisation that represent anchor points for an integrated artificial sociality, namely Gemeinschaft (community) and Gesellschaft (society), introduced by (Tönnies, 1963 [1887]), and subsequently popularised by Weber. This distinction was part of an extended debate in early sociology about the core concepts of societal structure, where Gemeinschaft captures the characterisation of social ties observable in a social setting as primarily based on personal relationships, enacted roles and associated values as present in prototypical peasant societies prevalent at the time. Any interaction in those societies was based on what Tönnies referred to as natural will (“Wesenwille”) exhibited by members. Gesellschaft, in contrast, reflects the depersonalised counterpart in which individuals act in indirect form based on assigned roles, formal rules, processes and values, stereotypical structures associated with urban societies. Fittingly, Tönnies characterised motivations for any such interaction driven by rational will (“Kürwille”) encoded in the role individuals exhibit.

Likened to Durkheim’s differentiation between mechanical vs. organic solidarity (Durkheim, 1984), the concepts are stereotypical for the themes and worldviews that structured debate at the time. Instead of drawing on the particularities of either variant of this duality[1], they bear essentials that still apply to group dynamics found in modern societies.

Where behaviour is structured and planned, leading agents to create rules, react to imposed policy or enforce such, the representation of socio-institutional dynamics are of concern. While building and relying on concepts such as status and roles identified in the Gemeinschaft conception, concepts such as rules and governance structures extend beyond neurobiological and psychological bases of group formation, but are the mechanisms that lead to depersonalised coordination structures characteristic for the Gesellschaft interpretation of society. Doing so, models of artificial sociality can resemble the characteristics of real-world societies, including “growing” the complexity arising from systemic interdependencies of actors, roles and resources, and reflect the non-linearity of behavioural outcomes we can observe at scale.

Mainly the “why”

Behavioural biology

Behavioural biology has studied social behaviour of all kinds of animal, including those that resemble us very much. Frans de Waal stands out for his extensive studies about dominance, politics, reconciliation and pro-sociality among primate (Waal, 2009). Chimpanzees and bonobos in particular can teach us a lot about the sociality of Homo sapiens. Like chimps, we have bands of males fighting one another and dominating females. Like bonobos, we have female solidarity, social sexuality, and male reluctance to use their physical superiority.

Evolutionary biology

Our stress on the deep historic continuity of life in an unbroken chain of reproduction under variation implies that we see evolutionary biology as the mother of the social sciences. Our perspective owes to the work of authors such as De Waal, who concluded his discussion of morality in all kinds of animals, particularly primates, as follows: “We seem to be reaching a point at which science can wrest morality from the hands of philosophers” (Waal, 1996).

Evolutionary psychologist Turner argued that emotions have become much more important in humans than in other species, because we do not limit our contacts to either one predictable set of others, or an anonymous mass  (J. E. Turner, 2007). We needed to find a relational compass. Our expressive faces and gestures, and our open faces, developed for that purpose.

Clinical psychology

Clinical psychologist Abraham Maslow gave us the famous model of human needs, by observing his patients and seeing an overarching pattern (Maslow, 1970). This model is antithetical to Homo economicus. The problem with it is that it is hard to operationalise. A more proximate concept in human drives is emotions (Frijda, 1986). Emotions have been used quite a bit in computational social simulation, e.g. the cognitive synthesis of emotions in the OCC model (Ortony, Clore, & Collins, 1998). This has been used as underpinning of empathic computational agents (Dias, Mascarenhas, & Paiva, 2016).

Leadership psychology

The psychology of leadership naturally touches upon sociality. For instance, Van Vugt et al assert “leadership has been a powerful force in the biological and cultural evolution of human sociality” (Van Vugt & von Rueden, 2020). Human groups faced with problems of coordination and collective action turn to leadership for achieving collective agency. Different contexts have led to different leadership styles.  Leaders can base their role on dominance (coercion), or on prestige (voluntary deference), and people still turn to more dominant leaders in times of stress.

Cultural psychology

Cultural psychology adds a comparative perspective to leadership psychology, showing that leadership styles and follower styles are co-dependent and have historical continuity across generations (G. Hofstede et al., 2010). It is also a discipline in its own right, and it shows how all of social psychology is in fact culture-dependent (Smith, Bond, & Kagitcibasi, 2006).

Social Psychology: Social Identity approach

A set of theories useful for modelling group behaviour and intergroup relations are presented in the Social Identity approach (SIA). SIA refers to the combination of Social Identity Theory (H Tajfel, 1982; H Tajfel & Turner, 1986) and Self-Categorization Theory (Reicher, Spears, & Haslam, 2010; J. C. Turner, Hogg, Oakes, Reicher, & Wetherell, 1987).

SIA proposes that social identification is a fundamental basis for collective behaviour, as people derive a significant part of their concept of self from the social groups they belong to (H. Tajfel, 1978; J. C. Turner et al., 1987). When a person’s identity as a group member becomes salient in a particular context, this affects who is seen as being an ingroup member versus someone outside of the group. When a social identity is salient, group membership becomes an important factor for individual beliefs and behaviour – what is important for the group becomes important for the individual. Moreover, groups have their own social norms and expected behaviours. For instance, thinking as members of collectives changes helping behaviour, as we are more likely to provide help to ingroup members (Levine, Prosser, Evans, & Reicher, 2005).

We deem SIA particularly well suited to model sociality, as it spans from the why (motives) to the how (e.g., saliency of social identities that impact on behavior, dynamics between groups), and connects the micro level of individuals with the macro level of groups, groups in groups, all the way up to societies. SIA has been used in social simulation to address diverse research questions from Sociology, opinion dynamics, Environmental Sciences and more (for two qualitative reviews see (Kopecky, Bos, & Greenberg, 2010; Scholz, Eberhard, Ostrowski, & Wijermans, 2021 (in press)). However, up to now there is no standard formalization, and formalizations found vary widely.

Mainly the “how”

Computational biology

Simulations include work of emergent patterns occurring in swarms and fish schools, based on simple positioning rules that fish and birds use while moving. A seminal contribution in the field of behavioural biology was made by the DomWorld model that showed, among other things, how spatial configurations in primate groups could emerge from dominance interactions (Charlotte K. Hemelrijk, 2000; Charlotte K Hemelrijk, 2011). Here, the contribution of a behavioural theory involving dominance and fear was crucial. The swarm and Domworld models also are instances of agent-based models. i.e. computational simulation models in which individuals live in a spatial world. These models have heterogeneity and path dependence, just like real historical developments.

Computational sociology

Sociologists have been at the origin of artificial sociality – avant la lettre. In 1971, mathematical sociologists Sakoda and Schelling published models showing self-organization in societies resulting in unintended, but robust collective patterns. The history of these models was recently traced by (Hegselmann, 2017). Computational sociologists have followed in their tracks, helped by the advent of simulation software (Hegselmann & Flache, 1998) (Deffuant, Carletti, & Huet, 2013). Recent computational models of this kind include emotions and their spread (Schweitzer & Garcia, 2010).

Development psychology

Developmental psychologists show how, during infancy, childhood and puberty, people acquire a more varied concept of the social world. For instance, rough-and-tumble play peaks in boys at the onset of adolescence (G.J. Hofstede, Dignum, Prada, Student, & Vanhée, 2015); among Dutch adolescents a nested set of reference groups develops, and girls are more prosocial overall than boys in a dictator game (Groep, Zanolie, & Crone, 2019; Güroglu, Bos, & Crone, 2014).

Economics

Economics came up with the concept of the profit-maximizing Homo economicus, useful as a standard with which to compare actual human behaviour, in contexts where “profit” can be defined. Not all contexts are like that, which is why behavioural economist Richard Thaler predicted that “Homo economicus will become more emotional” (Thaler, 2000). Experiments in behavioural economics and game theory have now shown that people have relational motives that moderate their actions, and often lead to “non-rational” behaviour that may be heavily culturally biased (Henrich et al., 2005). This is an important finding, because if the pleasantly simple Homo economicus model does not hold in reality, then what is the alternative?

Human motivation: Heise’s Affect Control Theory

Sociologists have also studied universals of human social motivations, either in small groups (Heise, 2013) or more generically (Theodore D. Kemper, 2017).

Heise posited Affect Control Theory, a relational theory on how people in small groups maintain relations. According to Affect Control Theory, every concept has not only a denotative meaning but also an affective meaning, or connotation, that varies along three dimensions:[1] evaluation – goodness versus badness, potency – powerfulness versus powerlessness, and activity – liveliness versus torpidity. His work has recently been elaborated upon in social simulation (Heise, 2013) and combined with decision theoretic (rational) reasoning models (Hoey et al., 2018).

Human motivation: Kemper’s relational world

Kemper, who worked with Heise sometimes, developed a model of human drives that is similar but less operationalized, and wider in scope. It distinguishes two major dimensions, derived empirically, having to do with coerced versus voluntary compliance: power, and status. Kemper’s word “status” is thus not a measure of power, but in a sense the opposite: it is a measure of not needing power. It has been dubbed “social importance” which captures the meaning but is lengthy (Mascarenhas et al., 2013). Readers will recognize these dimensions as the leadership styles named dominance and prestige in the above, and the connotations of goodness and powerfulness in Heise’s theory. Kemper used these two concepts to underpin a generic theory of emotions, to be discussed further down. He extended his idea into a “status-power theory of relations” involving also group life (Theodore D Kemper, 2011). Recently, he wrote a concise version of his theories that is amenable to computational modelling (Theodore D. Kemper, 2017). In a nutshell, his theory posits that all people live in a status-power relational world. Status comes in many currencies. It implies love, respect, attention, applause, financial rewards, sexual favours, or a thousand other things large and small. People strive to attain these things by “claiming status”, through actions, nonverbal behaviours, clothes, appearance, hobbies, exploits, or vested in formal roles. This position paper, for instance, constitutes a status claim by its authors, in the currency of scientific credibility.

People thus strive for status. Yet they are not just selfish, but also motivated by love and affection to “confer status” upon others they deem worthy, or even upon heroes, symbols, deities, or groups. One person’s status worthiness is another one’s motive for conferring status. Status is thought to be a key driving factor in sustainable/durable inequality (Ridgeway, 2019).

When status claims fail, or when love is unrequited, people could respond by sadness, or by anger. In the latter case they might try to obtain the denied items by coercion, “power”. How to play the status-power game in life is something that people learn in their childhood, in a conjunction of “nature and nurture”. The fine print of the status-power game is cultural. For instance, some societies put a lot of value on power as a source of status, others do not; some societies divide status worthiness equally across people, others do not.

Two scientists who took their work and linked it to other disciplines could form an important source of inspiration for advances in sociality. They are Theodore Kemper and Antonio Damasio.

Socio-psycho-neurology: Kemper

Sociologist Theodore D. Kemper was mentioned above. He proposed a “Social interactional theory of emotions” that explicitly integrates socio-physiology of emotions, including work on the fit between neurophysiology and his own status-power model of relations (Theodore D Kemper, 1978). This is known in the literature as the “autonomic specificity hypothesis”, and Kemper’s theory supported it strongly, by linking neurotransmitters of the sympathetic nervous system with unpleasant events involving status loss (noradrenaline) and subjection to power (epinephrine). Acetylcholine, released by the parasympathetic nervous system, was associated with fulfilled status and power needs.

Kemper’s work was reviewed by sociologists with awe and admiration, but also with disbelief (Fine, 1981). It went largely forgotten. Recent work lends support to the specificity hypothesis once more, but without using Kemper’s theory, or integrating the findings across disciplines (McGinley & Friedman, 2017). Obviously, Kemper was ahead of his time. We believe his work is still innovative and important for the way in which it links neurobiology and sociology. According to Kemper, emotions tell their bearer whether survival is being facilitated (well-being signifies adequate status and power) or threatened (depression and fear signify reduced status or threat of others’ power) by events. Emotions are felt by individuals, carried by hormones, but induced by social situations involving relations between people. This is not to say that artificial sociality should include neurobiology. The importance of Kemper’s work is that it links disciplines operating at different levels of analysis, and shows the neurological roots of status and power motives.

Neuroscience: Damasio

Neuroscientist Damasio (2018) covers similar ground as Kemper does, but approaching from the opposite direction. Having noticed in his career that people are driven by more than their brains, he investigates the role of “feelings” in human cultural activity. Feelings, for Damasio, include avoidance of pain and suffering, and the pursuit of well-being and pleasure. They are more bodily, and less articulate, than emotions. For instance, “ache” is a feeling, “shame” is an emotion; feelings and emotions often co-occur. Damasio finds that feelings are not a new invention of evolutionary history, but are manifest in any single-cellular organism. He argues that any organism must maintain homeostasis of its inner environment in order to stay alive. “Feelings are the mental expressions of homeostasis” (ibid., p.6). Since all of our ancestors in the billion-years evolutionary history have had to maintain homeostasis in order to reproduce, “homeostasis, acting under the cover of feeling, is the functional thread that links early life-forms to the extraordinary partnership of bodies and nervous systems [of ourselves]”. Feelings are a primitive, powerful mechanism: we feel with our skins and our guts. Brains are just the latest addition to the organismic arsenal for maintaining homeostasis.

Damasio then turns to the social world: “In their need to cope with the human heart in conflict, in their desire to reconcile the contradictions posed by suffering, fear, anger, and the pursuit of well-being, humans turned to wonder and awe and discovered music making, painting, dancing and literature. They continued their efforts by creating the often beautiful and sometimes frayed epics that go by such names as religious belief, philosophical enquiry, and political governance.” (ibid., p. 8).

The impact of Damasio’s work is to downplay the role of intellect and mind in the shaping of collective behaviours, in favour of feelings. Damasio legitimizes gut feelings as motivators. It does not take much imagination to summarize his picture of feelings as a status-power world in the sense found by Kemper. Having adequate status causes well-being; being confronted with power causes fear. Since the world of feelings and emotions is less complex than the world of ideas, primacy of the former reduces the number of primitives required to model sociality, compared with a “brainy” world.

Damasio and Kemper together lay a strong foundation of consilience to the work of artificial sociality. Both give a central role to the organism, but not to the “self”. Kemper considers the “self” a superfluous notion; he considers the organism, with its feelings, as only one of the many reference groups that influence a person’s actions. Damasio shows that our organism has a life of its own, only some of which reaches our consciousness.

Acknowledgements

We thank the 150 attendants to the Artificial Sociality track at SocSimFest 2021, many of whom made valuable remarks that helped us.

[1] Durkheim puts a stronger emphasis on the stereotypical micro-level mechanisms in both forms of solidarity, such as enforcement mechanisms.

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Shults, F. L., Gore, R., Wildman, W. J., Lynch, C. J., Lane, J. E., & Toft, M. D. (2018). A generative model of the mutual escalation of anxiety between religious groups. Journal of Artificial Societies and Social Simulation, 21(4), 7. http://jasss.soc.surrey.ac.uk/21/4/7.html

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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/


 

Query: How could we make Data Mining tools more useful for Agent-Based modelling?

By Robin Faber

I am currently doing my master thesis in Computer Science at TU Delft on Data Mining (DM) in Agent-Based Simulation. The goal of this thesis is to provide model designers and analysts with DM tools to make the evaluation of models easier.

The main idea is to create a tool in Python that connects with NetLogo to run models, design experiments and obtain and present the output with visualisations. Because Python has many data analytic libraries, it provides tools that NetLogo lacks in terms of data analytics for the output of ABMs. From my understanding, there are some tools in NetLogo such as BehaviorSpace to run experiments, but this is quite basic and produces a text file which still has to be analysed elsewhere. What I would like to do is develop a library in Python that streamlines this whole process of “run model → get output → analyse output”, with a focus on the usability and ease of use to also make it available for people that are not experienced programmers. However, because my background is in Computer Science, I obviously lack some knowledge of what is needed in order for the tool to be useful and usable for an actual model designer.

The three main questions I would like to ask the RofASSS readers are these:

  • Which requirements would you define to make the tool easy to use for non-programmers? (e.g. documentation, GUI, lines of code, data structures)
  • What type of information is important to obtain from a simulation? (e.g variables, locations, agent counts)
  • How should the information obtained from the model output/experiments be presented? (e.g, types of graphs/tables/visualisation)

If you have any questions, comments or would like to schedule a call to discuss this topic, please contact me using the comments facility at the bottom of this post (for comments) or emailing me at: r.j.faber@student.tudelft.nl.


Faber, R.J. (2021) Query: How could we make Data Mining tools more useful for Agent-Based Modelling. Review of Artificial Societies and Social Simulation, 5th February 2021. https://rofasss.org/2021/02/3/dm4abm/


 

How Can ABM Models Become Part of the Policy-Making Process in Times of Emergencies – The S.I.S.A.R. Epidemic Model

By Gianpiero Pescarmona1, Pietro Terna2,*, Alberto Acquadro1, Paolo Pescarmona3, Giuseppe Russo4, and Stefano Terna5

*Corresponding author, 1University of Torino, IT, 2University of Torino, IT, retired & Collegio Carlo Alberto, IT, 3University of Groningen, NL, 4Centro Einaudi, Torino, IT, 5tomorrowdata.io

(A contribution to the: JASSS-Covid19-Thread)

We propose an agent-based model to simulate the Covid-19 epidemic diffusion, with Susceptible, Infected, symptomatic, asymptomatic, and Recovered people: hence the name S.I.s.a.R. The scheme comes from S.I.R. models, with (i) infected agents categorized as symptomatic and asymptomatic and (ii) the places of contagion specified in a detailed way, thanks to agent-based modeling capabilities. The infection transmission is related to three factors: the infected person’s characteristics and the susceptible one, plus those of the space in which contact occurs. The asset of the model is the development of a tool that allows analyzing the contagions’ sequences in simulated epidemics and identifying the places where they occur.

The characteristics of the S.I.s.a.R. model

S.I.s.a.R. can be found at https://terna.to.it/simul/SIsaR.html with information on model construction, the draft of a paper also reporting results, and an online executable version of the simulation program, built using NetLogo. The model includes the structural data of Piedmont, an Italian region, but it can be readily calibrated for other areas. The model reproduces a realistic calendar (e.g., national or local government decisions), via a dedicated script interpreter.

Why another model? The starting point has been the need to model the pandemic problem in a multi-scale way. This was initiated a few months before the publication of new frontier articles, such as Bellomo et al. (2020), so when equation-based S.I.R. models, with their different versions, were predominating.

As any model, also this one is based on some assumptions: time will tell whether these were reasonable hypotheses. Modeling the Covid-19 pandemic requires a scenario and the actors. As in a theatre play, the author defines the roles of the actors and the environment. The characters are not real, they are pre-built by the author, and they act according to their peculiar constraints. If the play is successful, it will run for a long time, even centuries. If not, we will rapidly forget it. Shakespeare’s Hamlet is still playing after centuries, even if the characters and the plot are entirely imaginary. The same holds for our simulations: we are the authors, we arbitrarily define the characters, we force them to act again and again in different scenarios. However, in our model, the micro-micro assumptions are not arbitrary but based on scientific hypotheses at the molecular level, the micro agents’ behaviors are modeled in an explicit and realistic way. In both plays and simulations, we compress the time: a whole life to 2 or 3 hours on the stage. In a few seconds, we run the Covid-19 pandemic spread in a given regional area.

With our model, we move from a macro compartmental vision to a meso and microanalysis capability. Its main characteristics are:

  • scalability: we take in account the interactions between virus and molecules inside the host, the interactions between individuals in more or less restricted contexts, the movement between different environments (home, school, workplace, open spaces, shops, in a second version, we will add transportations and long trips between regions/countries; discotheques; other social aggregation events, as football matches); the movements occur in different parts of the daily life, as in Ghorbani et al. (2020);

the scales are:

    • micro, with the internal biochemical mechanism involved in reacting to the virus, as in Silvagno et al. (2020), from where we derive the critical importance assigned to an individual intrinsic susceptibility related to the age and previous morbidity episodes; the model incorporates the medical insights of one of its co-authors, former full professor of clinical biochemistry, signing also the quoted article; a comment on Lancet (Horton, 2020) consistently signals the syndemic character of the current event: «Two categories of disease are interacting within specific populations—infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and an array of non-communicable diseases (NCDs)»;
    • meso, with the open and closed contexts where the agents behave, as reported above;
    • macro, with the emergent effects of the actions of the agents; this final analysis is a premise to evaluate the costs and benefits of the different intervention policies;
  • granularity: at any level, the interactions are partially random and therefore the final results always reflect the sum of the randomness at the different levels; changing the constraints at different levels and running multiple simulations should allow the identification of the most critical points, i.e., those on which the intervention should be focused.

Contagion sequences as a source of suggestions for intervention policies

All the previous considerations are not exhaustive. The critical point that makes helpful the production of a new model is creating a tool that allows analyzing the contagions’ sequences in simulated epidemics and identifying the places where they occur. We represent each infecting agent as a horizontal segment with a vertical connection to another agent receiving the infection. We represent the second agent via a further segment at an upper layer. With colors, line thickness, and styles, we display multiple data.

As an example, look at Fig.4: we start with two agents coming from the outside, with black as color code (external place), the first one–regular, as reported by the thickness of the segment, starting at day 0 and finishing at day 22–is asymptomatic (dashed line) and infects five agents; the second one–robust, as reported by the thickness of the segment, starting at day 0 and finishing at day 15–is asymptomatic (dashed line) and infects no one; the first of the five infected agents received the infection at home (cyan color) and turns to be asymptomatic after a few days of incubation (dotted line), and so on. Solid lines identify symptomatic agents; brown color refers to workplaces, orange to nursing homes; yellow to schools; pink to hospitals; gray to open spaces. Thick or extra-thick lines refer to fragile or extra-fragile agents, respectively.

This technique enables understanding at a glance how an epidemic episode is developing. In this way, it is easier to reason about countermeasures and, thus, to develop intervention policies. In Figs. 1-4, we can look both at the places where contagions occur and at the dynamics emerging with different levels of intervention. In Fig. 1 we find evidence of the role of the workplaces in diffusing the infection, with a relevant number of infected fragile workers. In Fig. 2, by isolating fragile workers at home, the epidemics seems to finish, but in Fig. 3, we see a thin event (a single case of contagion) that creates a bridge toward a second wave. Finally, in Fig. 4, we see that the epidemic is under control by isolating the workers and any kind of fragile agents. (Please enlarge the on-screen images to see more details).

A scatter graph showing an epidemic with regular containment measures, showing a highly significant effect of workplaces (brown)Figure 1 – An epidemic with regular containment measures, showing a highly significant effect of workplaces (brown)

A scatter graph showing The effects of stopping fragile workers at day 20, with a positive result, but home contagions (cyan) keep alive the pandemic, exploding again in workplaces (brown)

Figure 2 – The effects of stopping fragile workers at day 20, with a positive result, but home contagions (cyan) keep alive the pandemic, exploding again in workplaces (brown)

A scatter graph showing The effects of stopping fragile workers at day 20, with a positive result, but home contagions (cyan) keep alive the pandemic, exploding again in workplaces (brown)

Figure 3 – Same, analyzing the first 200 infections with evidence of the event around day 110 with the new phase due to a unique asymptomatic worker

A scatter graph showing the impoaact of Stopping fragile workers plus any case of fragility at day 15, also isolating nursing homes

Figure 4 – Stopping fragile workers plus any case of fragility at day 15, also isolating nursing homes

Batches of simulation runs

The sequence in the steps described by the four figures is only a snapshot, a suggestion. We need to explore systematically the introduction of factual, counterfactual, and prospective interventions to control the spread of the contagions. Each simulation run–whose length coincides with the disappearance of symptomatic or asymptomatic contagion cases–is a datum in a wide scenario of variability in time and effects. Consequently, we need to represent compactly the results emerging from batches of repetitions, to compare the consequences of each batch’s basic assumptions.

For this purpose, we used blocks of one thousand repetitions. Besides summarizing the results with the usual statistical indicators, we adopted the technique of the heat-maps. In this perspective, with Steinmann et al. (2020), we developed a tool for comparative analyses, not for forecasting. This consideration is consistent with the enormous standard deviation values that are intrinsic to the problem.

Figs. 5-6 provide two heat-maps reporting the duration of each simulated epidemic in the x axis and the number of the symptomatic, asymptomatic, and deceased agents in the y axis. 1,000 runs in both cases.

The actual data for Piedmont, where the curve of the contagions flattened with the end of May, with around 30 thousand subjects, is included in the cell in the first row, immediately to the right of the mode in Fig. 6. In the Fall, a second wave seems possible, jumping into one of the events of the range of events on the right side of the same figure.

Figure 5 – 1000 Epidemics without containment measures (2D histogram of (Symptomatic+Asymptomatic+Deceased against days)

Figure 6 – 1000 Epidemics with basic non-pharmaceutical containment measures, no school in September 2020 (2D histogram of (Symptomatic+Asymptomatic+Deceased against days)

In Table 1 we have a set of statistical indicators related to 1,000 runs of the simulation with the different initial conditions. Cases 1 and 2 are those of Fig. 5 and 6. Then we introduce Case 4, excluding from the workplace workers with health fragilities, so highly susceptible to contagion, with smart work when possible or sick pay conditions. The gain in the reduction of affected people and duration is relevant and increases – in Case 5 – if we leave at home all kinds of fragile people.

Scenarios Total symptomatic Total symptomatic, asymptomatic, deceased Days
1. no control 851.12
(288.52)
2253.48
(767.58)
340.10
(110.21)
2. basic controls, no school in Sep 2020 158.55
(174.10)
416.98
(462.94)
196.97
(131.18)
4. basic controls, stop fragile workers, no schools in Sep 2020 120.17
(149.10)
334.68
(413.90)
181.10
(125.46)
5. basic controls, stop fragile workers & fragile people, nursing-homes isolation, no schools in Sep 2020 105.63
(134.80)
302.62
(382.14)
174.39
(12.82)
7. basic controls, stop f. workers & fragile people, nursing-homes isolation, open factories, schools in Sep 2020 116.55
(130.91)
374.68
(394.66)
195.28
(119.33)

Table 1 – Statistical indicators, limited to the mean and to the standard deviation, reported in parentheses, for a set of experiments; the row numbers are consistent with the paper at https://terna.to.it/simul/SIsaR.html where we report a larger number of simulation experiments

In Case 7, we show that keeping the conditions of Case 5, while opening schools and factories (work places in general), increases in a limited way the adverse events.

A second version

A second version of the model is under development, using https://terna.github.io/SLAPP/, a Python shell for ABM prepared by one of the authors of this note, referring to the pioneering proposal http://www.swarm.org of the Santa Fe Institute.

References

Bellomo, N., Bingham, R., Chaplain, M. A. J., Dosi, G., Forni, G., Knopoff, D. A., Lowengrub,, J., Twarock, R., and Virgillito, M. E. (2020). A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world. arXiv e-prints, art. arXiv:2006.03915, June.

Ghorbani, A., Lorig, F., de Bruin, B., Davidsson, P., Dignum, F., Dignum, V., van der Hurk, M., Jensen, M., Kammler, C., Kreulen, K., et al. (2020). The ASSOCC Simulation Model: A Response to the Community Call for the COVID-19 Pandemic. Review of Artificial Societies and Social Simulation. URL https://rofasss.org/2020/04/25/the-assocc-simulation-model/.

Horton, R. (2020). Offline: Covid-19 is not a pandemic. Lancet (London, England), 396(10255):874. URL https://www.thelancet.com/action/showPdf?pii=S0140-6736%2820%2932000-6.

Silvagno, F., Vernone, A. and Pescarmona, G. P. (2020). The Role of Glutathione in Protecting against the Severe Inflammatory Response Triggered by COVID-19. In «Antioxidants», vol. 9(7), p. 624. http://dx.doi.org/10.3390/antiox9070624.

Steinmann P., Wang J. R., van Voorn G. A., and Kwakkel J. H. (2020). Don’t try to predict covid-19. if you must, use deep uncertainty methods. Review of Artificial Societies and Social Simulation, 17. https://rofasss.org/2020/04/17/deep-uncertainty/.


Pescarmona, G., Terna, P., Acquadro, A., Pescarmona, P., Russo, G., and Terna, S. (2020) How Can ABM Models Become Part of the Policy-Making Process in Times of Emergencies - The S.I.S.A.R. Epidemic Model. Review of Artificial Societies and Social Simulation, 20th Oct 2020. https://rofasss.org/2020/10/20/sisar/


 

Flu and Coronavirus Simulator – A geospatial agent-based simulator for analyzing COVID-19 spread and public health measures on local regions

By Imran Mahmood

A Summary of: Mahmood et al. (2020)

In this paper we reviewed the lessons learned during the development of the ‘Flu and Coronavirus Simulator’ (FACS) and compare our chosen Agent-based Simulation approach with the conventional disease modelling approaches.

FACS provides an open-ended platform for the specification and implementation of the primary components of Agent-Based Simulation (ABS): (i) Agents; (ii) Virtual environment and (iii) Rule-set using a systematic Simulation Development Approach. FACS inherits features of a comprehensive simulation framework from its ancestors: (i) FLEE (Groen & Arabnejad, 2015) and (ii) FabSim3 (Groen & Arabnejad 2014). Where, FLEE mainly specializes in ABS complex dynamics e.g., agent movements; FabSim3 provides the ability to simulate a large population of agents with microscopic details using remote supercomputers. The combination of this legacy code offers numerous benefits including high performance, high scalability, and greater re-usability through a model coupling. Hence it provides an open-ended API for modellers and programmers to use it for further scientific research and development. FACS generalizes the process of disease modelling and provides a template to model any infectious disease. Thus allowing: (i) non-programmers (e.g., epidemiologists and healthcare data scientists) to use the framework as a disease modelling suite; and (ii) providing an open-ended API for modellers and programmers to use it for further scientific research and development. FACS offers a built-in location graph construction tool that allows the import of large spatial data-sets (e.g., Open Street Map), automated parsing and pre-processing of the spatial data, and generating buildings of various types, thus allowing ease in the synthesis of the virtual environment for the region under consideration. FACS provides a realistic disease transmission algorithm with the ability to capture population interactions and demographic patterns e.g., age diversity, daily life activities, mobility patterns, exposure at the street-level or in public transportation, use, or no use of face mask, assumptions of exposure within closed quarters.

We believe our approach has proven to be quite productive in modelling complex systems like epidemic spread in large regions due to ever-changing model requirements, multi-resolution abstraction, non-linear system dynamics, rule-based heuristics, and above all large-scale computing requirements. During the development of this framework, we learned that the real-world abstraction changes more rapidly than in other circumstances. For instance, the concept of social distancing and lockdown scenarios have evolved significantly since early March. Therefore, rapid changes in the ABS model were necessary. Model building in these cases benefits more from using a bottom-up approach like ABS, as opposed to any centralized analytical solution.

References

Groen, D., & Arabnejad, H. (2014). Fabsim3. GitHub. https://github.com/djgroen/FabSim3

Groen, D., & Arabnejad, H. (2015). Flee. GitHub. https:// github.com/djgroen/flee

Mahmood, I.,  Arabnejad, H., Suleimenova, D., Sassoon, I.,  Marshan, A.,  Serrano-Rico, A.,  Louvieris, P., Anagnostou, A., Taylor, S.J.E., Bell, D. & Groen, D. (2020) FACS: a geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions, Journal of Simulation, DOI: 10.1080/17477778.2020.1800422


Mahmood, I. (2020) Flu and Coronavirus Simulator - A geospatial agent-based simulator for analyzing COVID-19 spread and public health measures on local regions. Review of Artificial Societies and Social Simulation, 10th Sept 2020. https://rofasss.org/2020/09/10/facs/


 

The role of population scale in compartmental models of COVID-19 transmission

By Christopher J. Watts1,*, Nigel Gilbert2, Duncan Robertson3, 4, Laurence T. Droy5, Daniel Ladley6and Edmund Chattoe-Brown5

*Corresponding author, 12 Manor Farm Cottages, Waresley, Sandy, SG19 3BZ, UK, 2Centre for Research in Social Simulation (CRESS), University of Surrey, Guildford GU2 7XH, UK, 3School of Business and Economics, Loughborough University, Loughborough, UK, 4St Catherine’s College, University of Oxford, Oxford, UK, 5School of Media, Communication and Sociology, University of Leicester, UK, 6University of Leicester School of Business, University of Leicester, Leicester, LE17RH, UK

(A contribution to the: JASSS-Covid19-Thread)

Compartmental models of COVID-19 transmission have been used to inform policy, including the decision to temporarily reduce social contacts among the general population (“lockdown”). One such model is a Susceptible-Exposed-Infectious-Removed (SEIR) model developed by a team at the London School of Hygiene and Tropical Medicine (hereafter, “the LSHTM model”, Davies et al., 2020a). This was used to evaluate the impact of several proposed interventions on the numbers of cases, deaths, and intensive care unit (ICU) hospital beds required in the UK. We wish here to draw attention to behaviour common to this and other compartmental models of diffusion, namely their sensitivity to the size of the population simulated and the number of seed infections within that population. This sensitivity may compromise any policy advice given.

We therefore describe below the essential details of the LSHTM model, our experiments on its sensitivity, and why they matter to its use in policy making.

The LSHTM model

Compartmental models of disease transmission divide members of a population according to their disease states, including at a minimum people who are “susceptible” to a disease, and those who are “infectious”. Susceptible individuals make social contact with others within the same population at given rates, with no preference for the other’s disease state, spatial location, or social networks (the “universal mixing” assumption). Social contacts result in infections with a chance proportional to the fraction of the population who are currently infectious. Perhaps to reduce the implausibility of the universal mixing assumption, the LSHTM model is run for each of 186 county-level administrative units (“counties”, having an average size of 357,000 people), instead of a single run covering the whole UK population (66.4 million). Each county receives the same seed infection schedule: two new infections per day for 28 days. The 186 county time series are then summed to form a time series for the UK. There are no social contacts between counties, and the 186 county-level runs are independent of each other. Outputs from the model include total and peak cases and deaths, ICU and non-ICU hospital bed occupancy, and the time to peak cases, all reported for the UK as a whole.

Interventions are modelled as 12-week reductions in contact rates, and, in the first experiment, scheduled to commence 6 weeks prior to the peak in UK cases with no intervention. Further experiments shift the start of the intervention, and trigger the intervention upon reaching a given number of ICU beds, rather than a specific time.

Studying sensitivity to population size

The 186 counties vary in their population sizes, from Isles of Scilly (2,242 people) to West Midlands (2.9 million). We investigated whether the variation in population size led to differences in model behaviour. The LSHTM model files were cloned from https://github.com/cmmid/covid-UK , while the data analysis was performed using our own scripts posted at https://github.com/innovative-simulator/PopScaleCompartmentModels .

A graph showing Peak week infections against population size (on a log scale). The peak week looks increasing linear (with the log population scale), but there is a uniform increase in peak week with more seed infections.The figure above shows the results of running the LSHTM model with populations of various sizes, each point being an average of 10 repetitions. The time, in weeks, to the peak in cases forms a linear trend with the base-10 logarithm of population. A linear regression line fitted to these points gives Peak Week = 2.70 log10(Population) – 2.80, with R2 = 0.999.

To help understand this relationship, we then compared the seeding used by the LSHTM team, i.e. 2 infectious persons per day for 28 days, to two forms of reduced seeding, 1 per day for 28 days, and 2 per day for 14 days. Halving the seeding is similar in effect, but not identical to, doubling the population size.

Deterministic versions of other compartmental models of transmission (SIR, SEIR, SI) confirmed the relation between population size and time of occurrence to be a common feature of such models. See the R and Excel files at: https://github.com/innovative-simulator/PopScaleCompartmentModels .

For the simplest, the SI model, the stock of infectious people is described by the logistic function.I(t)=N/(1+exp(-u*C*(t-t*)))Here N is the population size, u susceptibility, and C the contact rate. If I(0)=s, the number of seed infections, then it can be shown that the peak in new infections, I(t*), occurs at timet*=ln(N/s-1)/(u*C)

Hence, for N/s >> 1, the time to peak cases, t*, correlates well with log10N/s.

As well as peak cases, analogous sensitivity was found for the timing of peaks in infections and hospital admissions, and for reaching critical levels, such as the hospital bed capacity as a proportion of the population. In contrast, the heights of peaks, and totals of cases, deaths and beds were constant percentages of population when population size was varied.

Why the unit of population matters

Davies et al. (2020a) make forecasts of both the level of peak cases and the timing of their occurrence. Despite showing that two counties can vary in their results (Davies et al., 2020a, p. 6), and mentioning in the supplementary material some effects of changing the seeding schedule (Davies et al., 2020b, p. 5), they do not mention any sensitivity to population size. But, as we have shown here, given the same number and timing of seed infections, the county with the smallest population will peak in cases earlier than the one with the largest. This sensitivity to population size affects the arguments of Davies et al. in several ways.

Firstly, Davies et al. produce their forecasts for the UK by summing county-level time series. But counties with out-of-sync peaks will sum to produce a shorter, flatter peak for the UK, than would have been achieved by synchronous county peaks. Thus the forecasts of peak cases for the UK are being systematically biased down.

Secondly, timing is important for the effectiveness of the interventions. As Davies et al. note in relation to their experiment on shifting the start time of the intervention, an intervention can be too early or too late. It is too early if, when it ends after 12 weeks, the majority of the population is still susceptible to any remaining infectious cases, and a serious epidemic can still occur. At the other extreme, an intervention can be too late if it starts when most of the epidemic has already occurred.

A timing problem also threatens if the intervention is triggered by the occupancy of ICU beds reaching some critical level. This level will be reached for the UK or average county later than for a small county. Thus the problem extends beyond the timing of peaks to affect other aspects of a policy supported by the model.

Our results imply that an intervention timed optimally for a UK-level, or average county-level, cases peak, as well as an intervention triggered by a UK-level beds occupancy threshold, may be less effective for counties with far-from-average sizes.

There are multiple ways of resolving these issues, including re-scaling seed infections in line with size of population unit, simulating the UK directly rather than as a sum of counties, and rejecting compartmental models in favour of network- or agent-based models. A discussion of the respective pros and cons of these alternatives requires a longer paper. For now, we note that compartmental models remain quick and cheap to design, fit, and study. The issues with Davies et al. (2020a) we have drawn attention to here highlight (1) the importance of adequate sensitivity testing, (2) the need for care when choosing at which scale to model and how to seed an infection, and (3) the problems that can stem from uniform national policy interventions, rather than ones targeted at a more local level.

References

Davies, N. G., Kucharski, A. J., Eggo, R. M., Gimma, A., Edmunds, W. J., Jombart, T., . . . Liu, Y. (2020a). Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. The Lancet Public Health, 5(7), e375-e385. doi:10.1016/S2468-2667(20)30133-X

Davies, N. G., Kucharski, A. J., Eggo, R. M., Gimma, A., Edmunds, W. J., Jombart, T., . . . Liu, Y. (2020b). Supplement to Davies et al. (2020b). https://www.thelancet.com/cms/10.1016/S2468-2667(20)30133-X/attachment/cee85e76-cffb-42e5-97b6-06a7e1e2379a/mmc1.pdf


Watts, C.J., Gilbert, N., Robertson, D., Droy, L.T., Ladley, D and Chattoe-Brown, E. (2020) The role of population scale in compartmental models of COVID-19 transmission. Review of Artificial Societies and Social Simulation, 14th August 2020. https://rofasss.org/2020/08/14/role-population-scale/


 

A Bibliography of ABM Research Explicitly Comparing Real and Simulated Data for Validation

By Edmund Chattoe-Brown

The Motivation

Research that confronts models with data is still sufficiently rare that it is hard to get a representative sense of how it is done and how convincing the results are simply by “background reading”. One way to advance good quality empirical modelling is therefore simply to make it more visible in quantity. With this in mind I have constructed (building on the work of Angus and Hassani-Mahmooei 2015) the first version of a bibliography listing all ABM attempting empirical validation in JASSS between 1998 and 2019 (along with a few other example) – which generates 68 items in all. Each entry gives a full reference and also describes what comparisons are made and where in the article they occur. In addition the document contains a provisional bibliography of articles giving advice or technical support to validation and lists three survey articles that categorise large samples of simulations by their relationships to data (which served as actual or potential sources for the bibliography).

With thanks to Bruce Edmonds, this first version of the bibliography has been made available as a Centre for Policy Modelling Discussion Paper CPM-20-216, which can be downloaded http://cfpm.org/discussionpapers/256.

The Argument

It may seem quite surprising to focus only on validation initially but there is an argument (Chattoe-Brown 2019) which says that this is a more fundamental challenge to the quality of a model than calibration. A model that cannot track real data well, even when its parameters are tuned to do so is clearly a fundamentally inadequate model. Only once some measure of validation has been achieved can we decide how “convincing” it is (comparing independent empirical calibration with parameter tuning for example). Arguably, without validation, we cannot really be sure whether a model tells us anything about the real world at all (no matter how plausible any narrative about its assumptions may appear). This can be seen as a consequence of the arguments about complexity routinely made by ABM practitioners as the plausibility of the assumptions does not map intuitively onto the plausibility of the outputs.

The Uses

Although these are covered in the preface to the bibliography in greater detail, such a sample has a number of scientific uses which I hope will form the basis for further research.

  • To identify (and justify) good and bad practice, thus promoting good practice.
  • To identify (and then perhaps fill) gaps in the set of technical tools needed to support validation (for example involving particular sorts of data).
  • To test the feasibility and value of general advice offered on validation to date and refine it in the face of practical challenges faced by analysis of real cases.
  • To allow new models to demonstrably outperform the levels of validation achieved by existing models (thus creating the possibility for progressive empirical research in ABM).
  • To support agreement about the effective use of the term validation and to distinguish it from related concepts (like verification) and potentially unhelpful (for example ambiguous or rhetorically loaded) uses

The Plan

Because of the labour involved and the diversity of fields in which ABM have now been used over several decades, an effective bibliography on this kind cannot be the work of a single author (or even a team of authors). My plan is thus to solicit (fully credited) contributions and regularly release new versions of the bibliography – with new co-authors as appropriate. (This publishing model is intended to maintain the quality and suitability for citation of the resulting document relative to the anarchy that sometimes arises in genuine communal authorship!) All of the following contributions will be gratefully accepted for the next revision (on which I am already working myself in any event)

  • References to new surveys or literature reviews that categorise significant samples of ABM research by their relationship to data.
  • References for proposed new entries to the bibliography in as much detail as possible.
  • Proposals to delete incorrectly categorised entries. (There are a small number of cases where I have found it very difficult to establish exactly what the authors did in the name of validation, partly as a result of confusing or ambiguous terminology.)
  • Proposed revisions to incorrect or “unfair” descriptions of existing entries (ideally by the authors of those pieces).
  • Offers of collaboration for a proposed companion bibliography on calibration. Ultimately this will lead to a (likely very small) sample of calibrated and validated ABM (which are often surprisingly little cited given their importance to the credibility of the ABM “project” – see, for example, Chattoe-Brown (2018a, 2018b).

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

Chattoe-Brown, Edmund (2018a) ‘Query: 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, 11 June. https://rofasss.org/2018/06/11/ecb/

Chattoe-Brown, Edmund (2018b) ‘A Forgotten Contribution: Jean-Paul Grémy’s Empirically Informed Simulation of Emerging Attitude/Career Choice Congruence (1974)’, Review of Artificial Societies and Social Simulation, 1 June. https://rofasss.org/2018/06/01/ecb/

Chattoe-Brown, Edmund (2019) ‘Agent Based Models’, in Atkinson, Paul, Delamont, Sara, Cernat, Alexandru, Sakshaug, Joseph W. and Williams, Richard A. (eds.) SAGE Research Methods Foundations. doi:10.4135/9781526421036836969


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/


 

Basic Modelling Hygiene – keep descriptions about models and what they model clearly distinct

By Bruce Edmonds

The essence of a model is that it relates to something else – what it models – even if this is only a vague or implicit mapping. Otherwise a model would be indistinguishable from any other computer code, set of equations etc (Hesse 1964; Wartofsky 1966). The centrality of this essence makes it unsurprising that many modellers seem to conflate the two.

This is made worse by three factors.

  1. A strong version of Kuhn’s “Spectacles” (Kuhn 1962) where the researcher goes beyond using the model as a way of thinking about the world to projecting their model onto the world, so they see the world only through that “lens”. This effect seems to be much stronger for simulation modelling due to the intimate interaction that occurs over a period of time between modellers and their model.
  2. It is a natural modelling heuristic to make the model more like what it models (Edmonds & al. 2019), introducing more elements of realism. This is especially strong with agent-based modelling which lends itself to complication and descriptive realism.
  3. It is advantageous to stress the potential connections between a model (however abstract) and possible application areas. It is common to start an academic paper with a description of a real-world issue to motivate the work being reported on; then (even if the work is entirely abstract and unvalidated) to suggest conclusions for what is observed. A lack of substantiated connections between model and any empirical data can be covered up by slick passing from the world to the model and back again and a lack of clarity as to what their research achieves (Edmonds & al. 2019).

Whatever the reasons the result is similar – that the language used to describe entities, processes and outcomes in the model is the same as that used for its descriptions of what is intended to be modelled.

Such conflation is common in academic papers (albeit to different degrees). Expert modellers will not usually be confused by such language because they understand the modelling process and know what to look for in a paper. Thus one might ask, what is the harm of a little rhetoric and hype in the reporting of models? After all, we want modellers to be motivated and should thus be tolerant of their enthusiasm. To show the danger I will thus look at an example that talks about modelling aspects of ethnocentrism.

In their paper, entitled “The Evolutionary Dominance of Ethnocentric Cooperation“, Hartshorn, Kaznatcheev & Shultz (2013) further analyse the model described in (Hammond & Axelrod 2006). The authors have reimplemented the original model and extensively analysed it especially the temporal dynamics. The paper is solely about the original model and its properties, there is no pretence of any validation or calibration with respect to any data. The problem is in the language used, because it the language could equally well refer to the model and the real world.

Take the first sentence of its abstract: “Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution“. This sounds like the simulation suggests something about the world we live in – that, as the title suggests, Ethnocentric cooperation naturally dominates other strategies (e.g. humanitarianism) and so it is natural. The rest of the abstract then goes on in the same sort of language which could equally apply to the model and the real world.

Expert modellers will understand that they were talking about the purely abstract properties of the model, but this will not be clear to other readers. However, in this case there is evidence that it is a problem. This paper has, in recent years, shot to the top of page requests from the JASSS website (22nd May 2020) at 162,469 requests over a 7-day period, but is nowhere in the top 50 articles in terms of JASSS-JASSS citations. Tracing where these requests come from, results in many alt-right and Russian web sites. It seems that many on the far right see this paper as confirmation of their Nationalist and Racist viewpoints. This is far more attention than a technical paper just about a model would get, so presumably they took it as confirmation about real-world conclusions (or were using it to fool others about the scientific support for their viewpoints) – namely that Ethnocentrism does beat Humanitarianism and this is an evolutionary inevitability [note 1].

This is an extreme example of the confusion that occurs when non-expert modellers read many papers on modelling. Modellers too often imply a degree of real-world relevance when this is not justified by their research. They often imply real-world conclusions before any meaningful validation has been done. As agent-based simulation reaches a less specialised audience, this will become more important.

Some suggestions to avoid this kind of confusion:

  • After the motivation section, carefully outline what part this research will play in the broader programme – do not leave this implicit or imply a larger role than is justified
  • Add in the phrase “in the model” frequently in the text, even if this is a bit repetitive [note 2]
  • Keep  discussions about the real world in a different sections from those that discuss the model
  • Have an explicit statement of what the model can reliably say about the real world
  • Use different terms when referring to parts of the model and part of the real world (e.g. actors for real world individuals, agents in the model)
  • Be clear about the intended purpose of the model – what can be achieved as a result of this research (Edmonds et al. 2019) – for example, do not imply the model will be able to predict future real world properties until this has been demonstrated (de Matos Fernandes & Keijzer 2020)
  • Be very cautious in what you conclude from your model – make sure this is what has been already achieved rather than a reflection of your aspirations (in fact it might be better to not mention such hopes at all until they are realised)

Notes

  1. To see that this kind of conclusion is not necessary see (Hales & Edmonds 2019).
  2. This is similar to a campaign to add the words “in mice” in reports about medical “breakthroughs”, (https://www.statnews.com/2019/04/15/in-mice-twitter-account-hype-science-reporting)

References

Edmonds, B., et al. (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

Hammond, R. A., N. D. and Axelrod, R. (2006). The Evolution of Ethnocentrism. Journal of Conflict Resolution, 50(6), 926–936. doi:10.1177/0022002706293470

Hartshorn, Max, Kaznatcheev, Artem and Shultz, Thomas (2013) The Evolutionary Dominance of Ethnocentric Cooperation, Journal of Artificial Societies and Social Simulation 16(3), 7. <http://jasss.soc.surrey.ac.uk/16/3/7.html>. doi:10.18564/jasss.2176

Hesse, M. (1964). Analogy and confirmation theory. Philosophy of Science, 31(4), 319-327.

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

de Matos Fernandes, C. A. and Keijzer, M. A. (2020) No one can predict the future: More than a semantic dispute. Review of Artificial Societies and Social Simulation, 15th April 2020. https://rofasss.org/2020/04/15/no-one-can-predict-the-future/

Wartofsky, M. (1966). the Model Muddle – Proposals for an Immodest Realism. Journal Of Philosophy, 63(19), 589-589.


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/


 

The Policy Context of Covid19 Agent-Based Modelling

By Edmund Chattoe-Brown

(A contribution to the: JASSS-Covid19-Thread)

In the recent discussions about the role of ABM and COVID, there seems to be an emphasis on the purely technical dimensions of modelling. This obviously involves us “playing to our strengths” but unfortunately it may reduce the effectiveness that our potential policy contributions can make. Here are three contextual aspects of policy for consideration to provide a contrast/corrective.

What is “Good” Policy?

Obviously from a modelling perspective good policy involves achieving stated goals. So a model that suggests a lower death rate (or less taxing of critical care facilities) under one intervention rather than another is a potential argument for that intervention. (Though of course how forceful the argument is depends on the quality of the model.) But the problem is that policy is predominantly a political and not a technical process (related arguments are made by Edmonds 2020). The actual goals by which a policy is evaluated may not be limited to the obvious technical ones (even if that is what we hear most about in the public sphere) and, most problematically, there may be goals which policy makers are unwilling to disclose. Since we do not know what these goals are, we cannot tell whether their ends are legitimate (having to negotiate privately with the powerful to achieve anything) or less so (getting re-elected as an end in itself).

Of course, by its nature (being based on both power and secrecy), this problem may be unfixable but even awareness of it may change our modelling perspective in useful ways. Firstly, when academic advice is accused of irrelevance, the academics can only ever be partly to blame. You can only design good policy to the extent that the policy maker is willing to tell you the full evaluation function (to the extent that they know it of course). Obviously, if policy is being measured by things you can’t know about, your advice is at risk of being of limited value. Secondly, with this is mind, we may be able to gain some insight into the hidden agenda of policy by looking at what kind of suggestions tend to be accepted and rejected. Thirdly, once we recognise that there may be “unknown unknowns” we can start to conjecture intelligently about what these could be and take some account of them in our modelling strategies. For example, how many epidemic models consider the financial costs of interventions even approximately? Is the idea that we can and will afford whatever it takes to reduce deaths a blind spot of the “medical model?”

When and How to Intervene

There used to be an (actually rather odd) saying: “You can’t get a baby in a month by making nine women pregnant”. There has been a huge upsurge in interest regarding modelling and its relationship to policy since start of the COVID crisis (of which this theme is just one example) but realising the value of this interest currently faces significant practical problems. Data collection is even harder than usual (as is scholarship in general), there is a limit to how fast good research can ever be done, peer review takes time and so on. The question here is whether any amount of rushing around at the present moment will compensate for neglected activities when scholarship was easier and had more time (an argument also supported by Bithell 2018). The classic example is the muttering in the ABM community about the Ferguson model being many thousands of lines of undocumented C code. Now we are in a crisis, even making the model available was a big ask, let alone making it easier to read so that people might “heckle” it. But what stopped it being available, documented, externally validated and so on before COVID? What do we need to do so that next time there is a pandemic crisis, which there surely will be, “we” (the modelling community very broadly defined) are able to offer the government a “ready” model that has the best features of various modelling techniques, evidence of unfudgeable quality against data, relevant policy scenarios and so on? (Specifically, how will ABM make sure it deserves to play a fit part in this effort?) Apart from the models themselves, what infrastructures, modelling practices, publishing requirements and so on do we need to set up and get working well while we have the time? In practice, given the challenges of making effective contributions right now (and the proliferation of research that has been made available without time for peer review may be actively harmful), this perspective may be the most important thing we can realistically carry into the “post lockdown” world.

What Happens Afterwards?

ABM has taken such a long time to “get to” policy based on data that looking further than the giving of such advice simply seems to have been beyond us. But since policy is what actually happens, we have a serious problem with counterfactuals. If the government decides to “flatten the curve” rather than seek “herd immunity” then we know how the policy implemented relates to the model “findings” (for good or ill) but not how the policy that was not implemented does. Perhaps the outturn of the policy that looked worse in the model would actually have been better had it been implemented?

Unfortunately (this is not a typo), we are about to have an unprecedently large social data set of comparative experiments in the nature and timing of epidemiological interventions, but ABM needs to be ready and willing to engage with this data. I think that ABM probably has a unique contribution to make in “endogenising” the effects of policy implementation and compliance (rather than seeing these, from a “model fitting” perspective, as structural changes to parameter values) but to make this work, we need to show much more interest in data than we have to date.

In 1971, Dutton and Starbuck, in a worryingly neglected article (cited only once in JASSS since 1998 and even then not in respect of model empirics) reported that 81% of the models they surveyed up to 1969 could not achieve even qualitative measurement in both calibration and validation (with only 4% achieving quantitative measurement in both). As a very rough comparison (but still the best available), Angus and Hassani-Mahmooei (2015) showed that just 13% of articles in JASSS published between 2010 and 2012 displayed “results elements” both from the simulation and using empirical material (but the reader cannot tell whether these are qualitative or quantitative elements or whether their joint presence involves comparison as ABM methodology would indicate). It would be hard to make the case that the situation in respect to ABM and data has therefore improved significantly in 4 decades and it is at least possible that it has got worse!

For the purposes of policy making (in the light of the comments above), what matters of course is not whether the ABM community believes that models without data continue to make a useful contribution but whether policy makers do.

References

Angus, S. D. and Hassani-Mahmooei, B. (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), 16. doi:10.18564/jasss.2952

Bithell, M. (2018) Continuous model development: a plea for persistent virtual worlds, Review of Artificial Societies and Social Simulation, 22nd August 2018. https://rofasss.org/2018/08/22/mb

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), 128–171. doi:10.1109/tsmc.1971.4308269

Edmonds, B. (2020) What more is needed for truly democratically accountable modelling? Review of Artificial Societies and Social Simulation, 2nd May 2020. https://rofasss.org/2020/05/02/democratically-accountable-modelling/


Chattoe-Brown, E. (2020) The Policy Context of Covid19 Agent-Based Modelling. Review of Artificial Societies and Social Simulation, 4th May 2020. https://rofasss.org/2020/05/04/policy-context/