ABM models for the COVID-19 pandemic

In an earlier post I mentioned that agent-based models provide a substantially different way of approaching the problem of pandemic modeling. ABM models are generative simulations of processes that work incrementally through the behavior of discrete agents; so modeling an epidemic using this approach is a natural application.

In an important recent research effort Gianluca Manzo and Arnout van de Rijt have undertaken to provide an empirically calibrated ABM model of the pandemic in France that pays attention to the properties of the social networks that are found in France. They note that traditional approaches to the modeling of epidemic diseases often work on the basis of average population statistics. (The draft paper is posted on ArXiv; link; they have updated the manuscript since posting). They note, however, that diseases travel through social networks, and individuals within a society differ substantially in terms of the number of contacts they have in a typical day or week. This implies intuitively that the transmission of a disease through a population should be expected to be influenced by the social networks found within that population and the variations that exist across individuals in terms of the number of social contacts that they have in a given time period. Manzo and van de Rijt believe that this feature of disease-spread through a community is crucial to consider when attempting to model the progression of the disease. But more importantly, they believe that consideration of contact variation across a population suggests public health strategies that might be successful in reducing the spread of a disease at lower social and public cost.

Manzo offers a general framework for this approach in "Complex Social Networks are Missing in the Dominant COVID-19 Epidemic Models," published last month in Sociologica (link). Here is the abstract for this article:

In the COVID-19 crisis, compartmental models have been largely used to predict the macroscopic dynamics of infections and deaths and to assess different non-pharmaceutical interventions aimed to contain the microscopic dynamics of person-to-person contagions. Evidence shows that the predictions of these models are affected by high levels of uncertainty. However, the link between predictions and interventions is rarely questioned and a critical scrutiny of the dependency of interventions on model assumptions is missing in public debate. In this article, I have examined the building blocks of compartmental epidemic models so influential in the current crisis. A close look suggests that these models can only lead to one type of intervention, i.e., interventions that indifferently concern large subsets of the population or even the overall population. This is because they look at virus diffusion without modelling the topology of social interactions. Therefore, they cannot assess any targeted interventions that could surgically isolate specific individuals and/or cutting particular person-to-person transmission paths. If complex social networks are seriously considered, more sophisticated interventions can be explored that apply to specific categories or sets of individuals with expected collective benefits. In the last section of the article, I sketch a research agenda to promote a new generation of network-driven epidemic models. (31)

Manzo's central concern about what he calls compartmental models (SIR models) is that "the variants of SIR models used in the current crisis context address virus diffusion without modelling the topology of social interactions realistically" (33).

 Manzo offers an interesting illustration of why a generic SIR model has trouble reproducing the dynamics of an epidemic of infectious disease by comparing this situation to the problem of traffic congestion:

It is as if we pretended realistically to model car flows at a country level, and potentially associated traffic jams, without also modelling the networks of streets, routes, and freeways. Could this type of models go beyond recommendations advising everyone not to use the car or allowing only specific fractions of the population to take the route at specific times and days? I suspect they could not. One may also anticipate that many drivers would be highly dissatisfied with such generic and undifferentiated instructions. SIR models currently in use put each of us in a similar situation. The lack of route infrastructure within my fictive traffic model corresponds to the absence of the structure of social interactions with dominant SIR models. (42)

The key innovation in the models constructed by Manzo and van de Rijt is the use of detailed data on contact patterns in France. They make highly pertinent use of a study of close-range contacts that was done in France in 2012 and published in 2015 (Béraud et al link). This study allows for estimation of the frequency of contacts possessed by French adults and children and the extensive variation that exists across individuals. Here is a graph illustrating the dispersion that exists in number of contacts for individuals in the study:

This graph demonstrates the very wide variance that exists among individuals when it comes to "number of contacts"; and this variation in turn is highly relevant to the spread of an infectious disease.

Manzo and van de Rijt make use of the data provided in this COMES-F study to empirically calibrate their agent-based model of the diffusion of the disease, and to estimate the effects of several different strategies designed to slow down the spread of the disease following relaxation of extreme social distancing measures.

The most important takeaway from this article is the strategy that it suggests for managing the reopening of social interaction after the peak of the epidemic. Key to transmission is frequency of close contact, and these models show that a small number of individuals have disproportionate effect on the spread of an infectious disease because of the high number of contacts they have. Manzo and van de Rijt ask the hypothetical question: are there strategies for management of an epidemic that could be designed by selecting a relatively small number of individuals for immunization? (Immunization might take the form of an effective but scarce vaccine, or it might take the form of testing, isolation, and intensive contact tracing.) But how would it be possible to identify the "high contact" individuals? M&R consider two strategies and then represent these strategies within their base model of the epidemic. Both strategies show dramatic improvement in the number of infected individuals over time. The baseline strategy "NO-TARGET" is one in which a certain number of individuals are chosen at random for immunization, and then the process of infection plays out. The "CONTACT-TARGET" strategy is designed to select the same number of individuals for immunization, but using a process that makes it more likely that the selected individuals will have higher-than-average contacts. The way this is done is to select a random group of individuals from the population and then ask those individuals to nominate one of their contacts for immunization. It is demonstrable that this procedure will arrive at a group of individuals for immunization who have higher-than-average numbers of contacts. The third strategy, HUB-TARGET, involves selecting the same number of individuals for treatment from occupations that have high levels of contacts.

The simulation is run multiple times for each of the three treatment strategies, using four different "budgets" that determine the number of individuals to be treated on each scenario. The results are presented here, and they are dramatic. Both contact-sensitive strategies of treatment result in substantial reduction in the total number of individuals infect over the course of 50, 100, and 150 days. And this  in turn translates into substantial reduction of the number of ICU beds required on each strategy.

Here is how Manzo and van de Rijt summarize their findings:

As countries exit the Covid-19 lockdown many have limited capacity to prevent flare-ups of the coronavirus. With medical, technological, and financial resources to prevent infection of only a fraction of its population, which individuals should countries target for testing and tracking? Together, our results suggest that targeting individuals characterized by high frequencies of short-range contacts dramatically improves the effectiveness of interventions. An additional known advantage of targeting hubs with medical testing specifically is that they serve as an early-warning device that can detect impending or unfolding outbreaks (Christakis & Fowler 2010; Kitsak et al. 2010).

This conclusion is reached by moving away from the standard compartmental models that rely on random mixing assumptions toward a network-based modeling framework that can accommodate person-to-person differences in infection risks stemming from differential connectedness. The framework allows us to model rather than average out the high variability of close-contact frequencies across individuals observed in contact survey data. Simulation results show that consideration of realistic close-contact distributions with high skew strongly impacts the expected impact of targeted versus general interventions, in favor of the former.

If these simulation results are indeed descriptive of the corresponding dynamics of spread of this disease through a population of socially connected people, then the research seems to provide an important hint about how public health authorities can effectively manage disease spread in a post-COVID without recourse to the complete shut-down of economic and social life that was necessary in the first half of 2020 in many parts of the world.

*.    *.    *

Here is a very interesting set of simulations by Grant Sanderson of the spread of infectious disease on YouTube (link). The video is presented with truly fantastic graphics allowing sophisticated visualization of the dynamics of the disease under different population assumptions. Sanderson doesn't explain the nature of the simulation, but it appears to be an agent-based model with parameters representing probability of infection through proximity. It is very interesting to look at this simulation through the eyes of the Manzo-van de Rijt critique: this model ignores exactly the factor that Manzo and van de Rijt take to be crucial -- differences across agents in number of contacts and the networks and hubs through which agents interact. This is reflected in the fact that every agent is moving randomly across space and every agent has the same average probability of passing on infection to those he/she encounters.

Thinking about pandemic models

One thing that is clear from the pandemic crisis that is shaking the world is the crucial need we have for models that allow us to estimate the future behavior of the epidemic. The dynamics of the spread of an epidemic are simply not amenable to intuitive estimation. So it is critical to have computational models that permit us to project the near- and middle-term behavior of the disease, based on available data and assumptions.

Scott Page is a complexity scientist at the University of Michigan who has written extensively on the uses and interpretation of computational models in the social sciences. His book, The Model Thinker: What You Need to Know to Make Data Work for You, does a superlative job of introducing the reader to a wide range of models. One of his key recommendations is that we should consider many models when we are trying to understand a particular kind of phenomenon. (Here is an earlier discussion of the book; link.) Page contributed a very useful article to the Washington Post this week that sheds light on the several kinds of pandemic models that are currently being used to understand and predict the course of the pandemic at global, national, and regional levels ("Which pandemic model should you trust?"; (link). Page describes the logic of "curve-fitting" models like the Institute for Health Metrics and Evaluation (IHME) model as well as epidemiological models that proceed on the basis of assumptions about the causal and social processes through which disease spreads. The latter attempt to represent the process of infection from infected person to susceptible person to recovered person. (Page refers to these as "microfoundational" models.) Page points out that all models involve a range of probable error and missing data, and it is crucial to make use of a range of different models in order to lay a foundation for sound public health policies. Here are his summary thoughts:

All this doesn’t mean that we should stop using models, but that we should use many of them. We can continue to improve curve-fitting and microfoundation models and combine them into hybrids, which will improve not just predictions, but also our understanding of how the virus spreads, hopefully informing policy. 

Even better, we should bring different kinds of models together into an “ensemble.” Different models have different strengths. Curve-fitting models reveal patterns; “parameter estimation” models reveal aggregate changes in key indicators such as the average number of people infected by a contagious individual; mathematical models uncover processes; and agent-based models can capture differences in peoples’ networks and behaviors that affect the spread of diseases. Policies should not be based on any single model — even the one that’s been most accurate to date. As I argue in my recent book, they should instead be guided by many-model thinking — a deep engagement with a variety of models to capture the different aspects of a complex reality. (link)

Page's description of the workings of these models is very helpful for anyone who wants to have a better understanding of the way a pandemic evolves. Page has also developed a valuable series of videos that go into greater detail about the computational architecture of these various types of models (link). These videos are very clear and eminently worth viewing if you want to understand epidemiological modeling better.

Social network analysis is crucial to addressing the challenge of how to restart businesses and other social organizations. Page has created "A Leader's Toolkit For Reopening: Twenty Strategies to Reopen and Reimagine", a valuable set of network tools and strategies offering concrete advice about steps to take in restarting businesses safely and productively. Visit this site to see how tools of network analysis can help make us safer and healthier in the workplace (link). 

Another useful recent resource on the logic of pandemic models is Jonathan Fuller's recent article "Models vs. evidence" in Boston Review (link). Fuller is a philosopher of science who undertakes two tasks in this piece: first, how can we use evidence to evaluate alternative models? And second, what accounts for the disagreements that exist in the academic literature over the validity of several classes of models? Fuller has in mind essentially the same distinction as Page does, between curve-fitting and microfoundational models. Fuller characterizes the former as "clinical epidemiological models" and the latter as "infectious disease epidemiological models", and he argues that the two research communities have very different ideas about what constitutes appropriate use of empirical evidence in evaluating a model. Essentially Fuller believes that the two approaches embody two different philosophies of science with regard to computational models of epidemics, one more strictly empirical and the other more amenable to a combination of theory and evidence in developing and evaluating the model. The article provides a level of detail that would make it ideal for a case study in a course on the philosophy of social science.

Joshua Epstein, author of Generative Social Science: Studies in Agent-Based Computational Modeling, gave a brief description in 2009 of the application of agent-based models to pandemics in "Modelling to Contain Pandemics" (link). Epstein describes a massive ABM model of a global pandemic, the Global-Scale Agent Model (GSAM), that attempted to model the spread of the H1N1 virus in 1996. Here is a video in which Miles Parker explains and demonstrates the model (link). 

Another useful resource is this video on "Network Theory: Network Diffusion & Contagion" (link), which provides greater detail about how the structure of social networks influences the spread of an infectious disease (or ideas, attitudes, or rumors).

My own predilections in the philosophy of science lean towards scientific realism and the importance of identifying underlying causal mechanisms. This leaves me more persuaded by the microfoundational / infectious disease models than the curve-fitting models. The criticisms that Nancy Cartwright and Jeremy Hardie offer in Evidence-Based Policy: A Practical Guide to Doing It Better of the uncritical methodology of randomized controlled trials (link) seem relevant here as well. The IHME model is calibrated against data from Wuhan and more recently northern Italy; but circumstances were very different in each of those locales, making it questionable that the same inflection points will show up in New York or California. As Cartwright and Hardie put the point, "The fact that causal principles can differ from locale to locale means that you cannot read off that a policy will work here from even very solid evidence that it worked somewhere else" (23). But, as Page emphasizes, it is valuable to have multiple models working from different assumptions when we are attempting to understand a phenomenon as complex as epidemic spread. Fuller makes much the same point in his article:

Just as we should embrace both models and evidence, we should welcome both of epidemiology’s competing philosophies. This may sound like a boring conclusion, but in the coronavirus pandemic there is no glory, and there are no winners. Cooperation in society should be matched by cooperation across disciplinary divides. The normal process of scientific scrutiny and peer review has given way to a fast track from research offices to media headlines and policy panels. Yet the need for criticism from diverse minds remains.

ABM fundamentalism

I've just had the singular opportunity of participating in the habilitation examination of Gianluca Manzo at the Sorbonne, based on his excellent manuscript on the relevance of agent-based models for justifying causal claims in the social sciences. Manzo is currently a research fellow in sociology at CNRS in Paris (Centre National de la Recherche Scientifique), and is a prolific contributor to analytical sociology and computational social science. The habilitation essay is an excellent piece of work and I trust it will be published as an influential monograph. Manzo has the distinction of being expert both on the philosophical and theoretical debates that are underway about social causation and an active researcher in the field of ABM simulations. Pierre Demeulenaere served as a generous and sympathetic mentor. The committee consisted of Anouk Barberousse, Ivan Ermakoff, Andreas Flache, Olivier Godechot, and myself, and reviewer comments and observations were of the highest quality and rigor. It was a highly stimulating session.

One element of our conversation was especially enlightening to me. I have written a number of times in Understanding Society and elsewhere about the utility of ABM models, and one line of thought I have developed is a critique of what I have labeled "ABM fundamentalism" -- the view that ABM models are the best possible technique for constructing social explanations for every possible subject in the social sciences (link). This view is expressed in Joshua Epstein's slogan, "If you didn't grow it, you didn't explain it." I maintain that ABM is a useful technique, but only one of many methods appropriate to the problem of constructing explanations of interesting sociological outcomes (link). So I advocate for theoretical and methodological pluralism when it comes to the ABM program.

I asked Gianluca whether he would agree that ABM fundamentalism is incorrect, and was surprised to find that he defends the universal applicability of ABM as a tool to implement any sociological theory. According to him, it is a perfectly general and universal modeling platform that can in principle be applied to any sociological problem. He also made it clear that he does not maintain that the use of ABM methods is optimal for every sociological problem of explanation. His defense of the universal applicability of ABM simulation techniques therefore does not imply that Manzo privileges these techniques as best for every sociological problem. But as a formal matter, he holds that ABM technology possesses the resources necessary to represent any fully specified social theory within a simulation.

The subsequent conversation succeeded in clarifying the underlying source of disagreement for me. What I realized in the discussion that ensued is that I was conflating two things in my label of ABM fundamentalism: the simulation technology and the substantive doctrine of generative social science. Epstein is a generativist, in the sense that he believes that social outcomes need in principle to be generated from a representation of facts about the individuals who make it up (Generative Social Science: Studies in Agent-Based Computational Modeling). Epstein is also an advocate of ABM techniques because they represent a particularly direct way of implementing a generativist explanation. But what Gianluca showed me is that ABM is not formally committed to the generativist dogma, and that an ABM simulation can perhaps incorporate factors at any social level. The insight that I gained, then, is that I should separate the substantive view of generativism from the formal mathematical tools of ABM simulations techniques.

I am still unclear how this would work -- that is, how an ABM simulation might be created that did an adequate job of representing features at a wide range of levels -- actors, organizations, states, structures, and ideologies. For example, how could an ABM simulation be designed that could capture a complex sociological analysis such as Tilly's treatment of the Vendée, with peasants, protests, and merchants, the church, winegrowers' associations, and the strategies of the state? Tilly's historical narrative seems inherently multi-stranded and irreducible to a simulation. Similar points could be made about Michael Mann's comparative historical account of fascisms or Theda Skocpol's analysis of social revolutions.

So there is still an open question for me in this topic. But I think I am persuaded that the fundamentalism to which I object is the substantive premise of generativism, not the formal computational methods of ABM simulations themselves. And if Gianluca is correct in saying that ABM is a universal simulation platform (as a Turing machine is a universal computational device) then the objection is misplaced.

So this habilitation examination in Paris had exactly the effect for me that we would hope for in an academic interaction -- it led me to look at an important issue in a somewhat different way. Thank you, Gianluca!

Computational social science

Is it possible to elucidate complex social outcomes using computational tools? Can we overcome some of the issues for social explanation posed by the fact of heterogeneous actors and changing social environments by making use of increasingly powerful computational tools for modeling the social world? Ken Kollman, John Miller, and Scott Page make the affirmative case to this question in their 2003 volume, Computational Models in Political Economy. The book focuses on computational approaches to political economy and social choice. Their introduction provides an excellent overview of the methodological and philosophical issues that arise in computational social science.

The subject of this book, political economy, naturally lends itself to a computational methodology. Much of political economy concerns institutions that aggregate the behavior of multiple actors, such as voters, politicians, organizations, consumers, and firms. Even when the interactions within and rules of a political or economic institution tion are relatively simple, the aggregate patterns that emerge can be difficult to predict and understand, particularly when there is no equilibrium. It is even more difficult to understand overlapping and interdependent institutions.... Computational methods hold the promise of enabling scholars to integrate aspects of both political and economic institutions without compromising fundamental features of either. (kl 27)

The most interesting of the approaches that they describe is the method of agent-based models (link, link, link). They summarize the approach in these terms:

The models typically have four characteristics, or methodological primitives: agents are diverse, agents interact with each other in a decentralized manner, agents are boundedly rational and adaptive, and the resulting patterns of outcomes comes often do not settle into equilibria.... The purpose of using computer programs in this second role is to study the aggregate patterns that emerge from the "bottom up." (kl 51)

Here is how the editors summarize the strengths of computational approaches to social science.

First, computational models are flexible in their ability to encode a wide range of behaviors and institutions. Any set of assumptions about agent behavior or institutional constraints that can be encoded can be analyzed. 

Second, as stated, computational models are rigorous in that conclusions follow from computer code that forces researchers to be explicit about assumptions. 

Third, while most mathematical models include assumptions so that an equilibrium exists, a system of interacting political actors need not settle into an equilibrium point. It can also cycle, or it can traverse an unpredictable path of outcomes. 

The great strength of computational models is their ability to uncover dynamic patterns. (kl 116)

And they offer a set of criteria of adequacy for ABM models. The model should explain the results; the researcher should check robustness; the model should build upon the past; the researcher should justify the use of the computer; and the researcher should question assumptions (kl 131).

To summarize, models should be evaluated based on their ability to give insight and understanding into old and new phenomena in the simplest way possible. Good, simple models, such as the Prisoner's Dilemma or Nash bargaining, with their ability to frame and shed light on important questions, outlast any particular tool or technique. (kl 139)

A good illustration of a computational approach to problems of political economy is the editors' own contribution to the volume, "Political institutions and sorting in a Tiebout model". A Tiebout configuration is a construct within public choice theory where citizens are permitted to choose among jurisdictions providing different bundles of goods.

In a Tiebout model, local jurisdictions compete for citizens by offering bundles of public goods. Citizens then sort themselves among jurisdictions according to their preferences. Charles M. Tiebout's (1956) original hypothesis challenged Paul Samuelson's (1954) conjecture that public goods could not be allocated efficiently. The Tiebout hypothesis has since been extended to include additional propositions. (kl 2012)

Using an agent-based model they compare different sets of political institutions at the jurisdiction level through which policy choices are made; and they find that there are unexpected outcomes at the population level that derive from differences in the institutions embodied at the jurisdiction level.

Our model departs from previous approaches in several important respects. First, with a few exceptions, our primary interest in comparing paring the performance of political institutions has been largely neglected in the Tiebout literature. A typical Tiebout model takes the political institution, usually majority rule, as constant. Here we vary institutions and measure performance, an approach more consistent with the literature on mechanism design. Second, aside from an example used to demonstrate the annealing phenomenon, we do not explicitly compare equilibria. (kl 2210)

And they find significant differences in collective behavior in different institutional settings.

ABM methodology is well suited to the kind of research problem the authors have posed here. The computational method permits intuitive illustration of the ways that individual preferences in specific settings aggregate to distinctive collective behaviors at the group level. But the approach is not so suitable to the analysis of social behavior that involves a higher degree of hierarchical coordination of individual behavior -- for example, in an army, a religious institution, or a business firm. Furthermore, the advantage of abstractness in ABM formulations is also a disadvantage, in that it leads researchers to ignore some of the complexity and nuance of local circumstances of action that lead to significant differences in outcome.

Generativism

There is a seductive appeal to the idea of a "generative social science". Joshua Epstein is one of the main proponents of the idea, most especially in his book, Generative Social Science: Studies in Agent-Based Computational Modeling. The central tool of generative social science is the construction of an agent-based model (link). The ABM is said to demonstrate the way in which an observable social outcome of pattern is generated by the properties and activities of the component parts that make it up -- the actors. The appeal comes from the notion that it is possible to show how complicated or complex outcomes are generated by the properties of the components that make them up. Fix the properties of the components, and you can derive the properties of the composites. Here is Epstein's capsule summary of the approach:

The agent-based computational model -- or artificial society -- is a new scientific instrument. It can powerfully advance a distinctive approach to social science, one for which the term "generative" seems appropriate. I will discuss this term more fully below, but in a strong form, the central idea is this: To the generativist, explaining the emergence of macroscopic societal regularities, such as norms or price equilibria, requires that one answer the following question: 

The Generativist's Question 

*How could the decentralized local interactions of heterogeneous autonomous agents generate the given regularity?  

The agent-based computational model is well-suited to the study of this question, since the following features are characteristic: [heterogeneity, autonomy, explicit space, local interactions, bounded rationality] (5-6)

And a few pages later:

Agent-based models provide computational demonstrations that a given microspecification is in fact sufficient to generate a macrostructure of interest. . . . To the generativist -- concerned with formation dynamics -- it does not suffice to establish that, if deposited in some macroconfiguration, the system will stay there. Rather, the generativist wants an account of the configuration's attainment by a decentralized system of heterogeneous autonomous agents. Thus, the motto of generative social science, if you will, is: If you didn't grow it, you didn't explain its emergence. (8)

Here is how Epstein describes the logic of one of the most extensive examples of generative social science, the attempt to understand the disappearance of Anasazi population in the American Southwest nearly 800 years ago.

The logic of the exercise has been, first, to digitize the true history -- we can now watch it unfold on a digitized map of Longhouse Valley. This data set (what really happened) is the target -- the explanandum. The aim is to develop, in collaboration with anthropologists, microspecifications -- ethnographically plausible rules of agent behavior -- that will generate the true history. The computational challenge, in other words, is to place artificial Anasazi where the true ones were in 80-0 AD and see if -- under the postulated rules -- the simulated evolution matches the true one. Is the microspecification empirically adequate, to use van Fraassen's phrase? (13)

Here is a short video summarizing the ABM developed under these assumptions:

The artificial Anasazi experiment is an interesting one, and one to which the constraints of an agent-based model are particularly well suited. The model follows residence location decision-making based on ground-map environmental information.

But this does not imply that the generativist interpretation is equally applicable as a general approach to explaining important social phenomena.

Note first how restrictive the assumption is of "decentralized local interactions" as a foundation to the model. A large proportion of social activity is neither decentralized nor purely local: the search for muons in an accelerator lab, the advance of an armored division into contested territory, the audit of a large corporation, preparations for a strike by the UAW, the coordination of voices in a large choir, and so on, indefinitely. In all these examples and many more, a crucial part of the collective behavior of the actors is the coordination that occurs through some centralized process -- a command structure, a division of labor, a supervisory system. And by its design, ABMs appear to be incapable of representing these kinds of non-local coordination.

Second, all these simulation models proceed from highly stylized and abstract modeling assumptions. And the outcomes they describe capture at best some suggestive patterns that might be said to be partially descriptive of the outcomes we are interested in. Abstraction is inevitable in any scientific work, of course; but once recognizing that fact, we must abandon the idea that the model demonstrates the "generation" of the empirical phenomenon. Neither premises nor conclusions are fully descriptive of concrete reality; both are approximations and abstractions. And it would be fundamentally implausible to maintain that the modeling assumptions capture all the factors that are causally relevant to the situation. Instead, they represent a particular stylized hypothesis about a few of the causes of the situation in question.  Further, we have good reason to believe that introducing more details at the ground level will sometimes lead to significant alteration of the system-level properties that are generated.

So the idea that an agent-based model of civil unrest could demonstrate that (or how) civil unrest is generated by the states of discontent and fear experienced by various actors is fundamentally ill-conceived. If the unrest is generated by anything, it is generated by the full set of causal and dynamic properties of the set of actors -- not the abstract stylized list of properties. And other posts have made the point that civil unrest or rebellion is rarely purely local in its origin; rather, there are important coordinating non-local structures (organizations) that influence mobilization and spread of rebellious collective action. Further, the fact that the ABM "generates" some macro characteristics that may seem empirically similar to the observed phenomenon is suggestive, but far from a demonstration that the model characteristics suffice to determine some aspect of the macro phenomenon. Finally, the assumption of decentralized and local decision-making is unfounded for civil unrest, given the important role that collective actors and organizations play in the success or failure of social mobilizations around grievances (link).

The point here is not that the generativist approach is invalid as a way of exploring one particular set of social dynamics (the logic of decentralized local decision-makers with assigned behavioral rules). On the contrary, this approach does indeed provide valuable insights into some social processes. The error is one of over-generalization -- imagining that this approach will suffice to serve as a basis for analysis of all social phenomena. In a way the critique here is exactly parallel to that which I posed to analytical sociology in an earlier post. In both cases the problem is one of asserting priority for one specific approach to social explanation over a number of other equally important but non-equivalent approaches.

Patrick Grim et al provide an interesting approach to the epistemics of models and simulations in "How simulations fail" (link). Grim and his colleagues emphasize the heuristic and exploratory role that simulations generally play in probing the dynamics of various kinds of social phenomena.

Complementarity of thick and thin theories of the actor

There is a range of approaches to the social sciences that fall under the umbrella of "actor-centered" theories (link). The chief fissure among these theories is that between "thin" and "thick" theories of the actor -- theories which provide less or more detail about the mental frameworks and beliefs of the actors being described. The extremes of the two types of theories range from pure rational choice theory to social psychology and ethnography. The two types of theories have complementary strengths and weaknesses. Thin theories, including especially rational choice theory and game theory, make use of a particularly sparse theory of the actor’s decision framework. This approach provides a basis for representing the motives and decisions of actors that can be readily incorporated into powerful techniques of simulation and calculation. Thick theories, including pragmatist and ethnomethodological theories, offer a basis for investigating particular social settings of action in detail, and they provide an in-depth basis for explaining and understanding the choices, judgments, and behavior of the individuals they study. But thick theories are not so readily incorporated into simulation models, precisely because they do not provide abstract, general characterizations of the individual’s action framework.

These comments make the contrast sound like a familiar set of oppositions: nomothetic explanation versus idiographic interpretation; causal explanation versus hermeneutic interpretation. And this in turn suggests that rational choice theory will be good at arriving at generalizations, whereas pragmatist and ethnographic theories will be good at providing satisfying interpretations of the actions of individuals in concrete social and historical circumstances, but not particularly good at providing a basis for general explanations.

The situation is not quite so binary as this suggests, however. A central tool for actor-centered research is set of simulation techniques falling under the rubric of agent-based models. To date ABMs have tended to use thin theories of the actor to represent the players in the simulation. However, it is entirely possible for agent-based models to incorporate substantially greater levels of specificity and granularity about the action frameworks of the individuals in specific circumstances. An ABM can introduce different kinds of agents into a simulation, each of which embodies a specific set of beliefs and modes of reasoning. And it can be argued that this increase in granularity provides a basis for a better simulation of complex social processes involving heterogeneous kinds of actors.

For example, a simulation of the political appeal of a nationalistic politician like Donald Trump may benefit by segmenting the electorate into different categories of voters: white nationalists, aging blue-collar workers, anti-globalization young people, .... And the model should represent the fact that actors in these various segments have substantially different ways of making political judgments and actions. So ABM simulations can indeed benefit from greater “thickness” of assumptions about agents. (This was illustrated in the discussion of the Epstein rebellion model earlier; link.)

On the other hand, it is possible to use RCT and DBO theories to illuminate historically particular instances of action -- for example, the analysis of historically situated collective action along the lines of Margaret Levi's review in "Reconsiderations of Rational Choice in Comparative and Political Analysis" (link). These theories can be applied to specific social circumstances and can provide convincing and satisfying interpretations of the reasoning and actions of the agents who are involved. So narrative explanations of social outcomes can be constructed using both thick and thin assumptions about the actors.

Moreover, the explanatory strength of thick theories is not limited to the degree to which they can be incorporated into formal simulations -- what can be referred to as "aggregation dynamics". It is clear that real explanations of important phenomena emerge from research by sociologists like Michele Lamont in Money, Morals, and Manners: The Culture of the French and the American Upper-Middle Class (link), Al Young in The Minds of Marginalized Black Men: Making Sense of Mobility, Opportunity, and Future Life Chances (link), and Erving Goffman in Behavior in Public Places: Notes on the Social Organization of Gatherings (link). We understand better the dynamics of the French professional classes, inner city neighborhoods, and asylums when we read the detailed and rigorous treatments that micro-sociologists provide of these social settings.

What this suggests is that analytical sociology would be well advised to embrace pluralism when it comes to theories of the actor and methods of application of actor-based research. Thick and thin are not logical contraries, but rather complementary ways of analyzing and explaining the social worlds we inhabit.

ABM fundamentalism

image: Chernobyl control room

Quite a few recent posts have examined the power and flexibility of ABM models as platforms for simulating a wide range of social phenomena. Joshua Epstein is one of the high-profile contributors to this field, and he is famous for making a particularly strong claim on behalf of ABM methods. He argues that “generative” explanations are the uniquely best form of social explanation. A generative explanation is one that demonstrates how an upper-level structure or causal power comes about as a consequence of the operations of the units that make it up. As an aphorism, here is Epstein's slogan: "If you didn't grow it, you didn't explain it." 

Here is how he puts the point in a Brookings working paper, “Remarks on the foundations of agent-based generative social science” (link; also chapter 1 of Generative Social Science: Studies in Agent-Based Computational Modeling):

"To the generativist, explaining macroscopic social regularities, such as norms, spatial patterns, contagion dynamics, or institutions requires that one answer the following question:

"How could the autonomous local interactions of heterogeneous boundedly rational agents generate the given regularity?

"Accordingly, to explain macroscopic social patterns, we generate—or “grow”—them in agent models." (1)

And Epstein is quite explicit in saying that this formulation represents a necessary condition on all putative social explanations: "In summary, generative sufficiency is a necessary, but not sufficient condition for explanation." (5).

There is an apparent logic to this view of explanation. However, several earlier posts cast doubt on the conclusion. First, we have seen that all ABMs necessarily make abstractive assumptions about the behavioral features of the actors, and they have a difficult time incorporating "structural" factors like organizations. We found that the ABM simulations of ethnic and civil conflict (including Epstein's own model) are radically over-simplified representations of the field of civil conflict (link).  So it is problematic to assume the general applicability and superiority of ABM approaches for all issues of social explanation.

Second, we have also emphasized the importance of distinguishing between "generativeness" and "reducibility" (link). The former is a claim about ontology -- the notion that the features of the lower level suffice to determine the features of the upper level through pathways we may not understand at all. The latter is a claim about inter-theoretic deductive relationships -- relationships between our formalized beliefs about the lower level and the feasibility of deriving the features of the upper level from these beliefs. But I argued in the earlier post that the fact that A is generated by B does not imply that A is reducible to B. 

So there seem to be two distinct ways in which J. Epstein is over-reaching here: he is assuming that agent-based models can be sufficiently detailed to reproduce complex social phenomena like civil unrest; and second, he is assuming without justification that only reductive explanations are scientifically acceptable.

Consider an example that provides an explanation of a collective behavior that has explanatory weight, that is not generative, and that probably could not be fully reproduced as an ABM.  A relevant example is Charles Perrow's analysis of technology failure as a consequence of organizational properties (Normal Accidents: Living with High-Risk Technologies). An earlier post considered these kinds of examples in more detail (link). Here is my summary of organizational approaches to the explanation of the incidence of accidents and system safety:

However, most safety experts agree that the social and organizational characteristics of the dangerous activity are the most common causes of bad safety performance. Poor supervision and inspection of maintenance operations leads to mechanical failures, potentially harming workers or the public. A workplace culture that discourages disclosure of unsafe conditions makes the likelihood of accidental harm much greater. A communications system that permits ambiguous or unclear messages to occur can lead to air crashes and wrong-site surgeries. (link)

I would say that this organizational approach is a legitimate schema for social explanation of an important effect (the occurrence of large technology failures). Further, it is not a generativist explanation; it does not originate in a simplification of a particular kind of failure and demonstrate through iterated runs that failures occur X% of the time. Rather, it is based on a different kind of scientific reasoning, based on causal analysis grounded in careful analysis and comparison of cases. Process tracing (starting with a failure and working backwards to find the key causal branches that led to the failure) and small-N comparison of cases allows the researcher to arrive at confident judgments about the causes of technology failure. And this kind of analysis can refute competing hypotheses: "operator error generally causes technology failure", "poor technology design generally causes technology failure", or even "technological over-confidence causes technology failure". All these hypotheses have defenders; so it is a substantive empirical hypothesis to argue that certain features of organizational deficiency (training, supervision, communications processes) are the most common causes of technological accidents.

Other examples from sociology could be provided as well: Michael Mann's explanation of the causes of European fascism (Fascists), George Steinmetz's explanation of variations in the characteristics of German colonial rule (The Devil's Handwriting: Precoloniality and the German Colonial State in Qingdao, Samoa, and Southwest Africa), or Kathleen Thelen's explanation of the persistence and change in training regimes in capitalist economies (How Institutions Evolve: The Political Economy of Skills in Germany, Britain, the United States, and Japan). Each is explanatory, each identifies causal factors that are genuinely explanatory of the phenomena in question, and none is generativist in Epstein's sense. These are examples drawn from historical sociology and institutional sociology; but examples from other parts of the disciplines of sociology are available as well.

I certainly believe that ABMs sometimes provide convincing and scientifically valuable explanations. The fundamentalism that I'm taking issue with here is the idea that all convincing and scientifically valuable social explanations must take this form -- a much stronger view and one that is not well supported by the practice of a range of social science research programs.

Or in other words, the over-reach of the ABM camp comes down to this: the claims of exclusivity and general adequacy of the simulation-based approach to explanation. ABM fundamentalists claim that only simulations from units to wholes will be satisfactory (exclusivity), and they claim that ABM simulations can always be designed for any problem that are generally adequate to grounding an explanation (general adequacy). Neither proposition can be embraced as a general or universal claim. Instead, we need to recognize the plurality of legitimate forms of causal reasoning in the social sciences, and we need to recognize, along with their strengths, some of the common weaknesses of the ABM approach for some kinds of problems.

ANT-style critique of ABM

A short recent article in the Journal of Artificial Societies and Social Simulation by Venturini, Jensen, and Latour lays out a critique of the explanatory strategy associated with agent-based modeling of complex social phenomena (link). (Thanks to Mark Carrigan for the reference via Twitter; @mark_carrigan.) Tommaso Venturini is an expert on digital media networks at Sciences Po (link), Pablo Jensen is a physicist who works on social simulations, and Bruno Latour is -- Bruno Latour. Readers who recall recent posts here on the strengths and weaknesses of ABM models as a basis for explaining social conflict will find the article interesting (link). VJ&L argue that agent-based models -- really, all simulations that proceed from the micro to the macro -- are both flawed and unnecessary. They are flawed because they unavoidable resort to assumptions about agents and their environments that reduce the complexity of social interaction to an unacceptable denominator; and they are unnecessary because it is now possible to trace directly the kinds of processes of social interaction that simulations are designed to model. The "big data" available concerning individual-to-individual interactions permits direct observation of most large social processes, they appear to hold.

Here are the key criticisms of ABM methodology that the authors advance:

• Most of them, however, partake of the same conceptual approach in which individuals are taken as discrete and interchangeable 'social atoms' (Buchanan 2007) out of which social structures emerge as macroscopic characteristics (viscosity, solidity...) emerge from atomic interactions in statistical physics (Bandini et al. 2009). (1.2)
• most simulations work only at the price of simplifying the properties of micro-agents, the rules of interaction and the nature of macro-structures so that they conveniently fit each other. (1.4)
• micro-macro models assume by construction that agents at the local level are incapable to understand and control the phenomena at the global level. (1.5)

And here is their key claim:

• Empirical studies show that, contrarily to what most social simulations assume, collective action does not originate at the micro level of individual atoms and does not end up in a macro level of stable structures. Instead, actions distribute in intricate and heterogeneous networks than fold and deploy creating differences but not discontinuities. (1.11) 

This final statement could serve as a high-level paraphrase of actor-network theory, as presented by Latour in Reassembling the Social: An Introduction to Actor-Network-Theory. (Here is a brief description of actor-network theory and its minimalist social ontology; link.)

These criticisms parallel some of my own misgivings about simulation models, though I am somewhat more sympathetic to their use than VJ&L. Here are some of the concerns raised in earlier posts about the validity of various ABM approaches to social conflict (link, link):

• Simulations often produce results that appear to be artifacts rather than genuine social tendencies.
• Simulations leave out important features of the social world that are prima facie important to outcomes: for example, quality of leadership, quality and intensity of organization, content of appeals, differential pathways of appeals, and variety of political psychologies across agents.
• The factor of the influence of organizations is particularly important and non-local.
• Simulations need to incorporate actors at a range of levels, from individual to club to organization.

And here is the conclusion I drew in that post:

• But it is very important to recognize the limitations of these models as predictors of outcomes in specific periods and locations of unrest. These simulation models probably don't shed much light on particular episodes of contention in Egypt or Tunisia during the Arab Spring. The "qualitative" theories of contention that have been developed probably shed more light on the dynamics of contention than the simulations do at this point in their development.

But the confidence expressed by VJ&L in the new observability of social processes through digital tracing seems excessive to me. They offer a few good examples that support their case -- opinion change, for example (1.9). Here they argue that it is possible to map or track opinion change directly through digital footprints of interaction (Twitter, Facebook, blogging), and this is superior to abstract modeling of opinion change through social networks. No doubt we can learn something important about the dynamics of opinion change through this means.

But this is a very special case. Can we similarly "map" the spread of new political ideas and slogans during the Arab Spring? No, because the vast majority of those present in Tahrir Square were not tweeting and texting their experiences. Can we map the spread of anti-Muslim attitudes in Gujarat in 2002 leading to massive killings of Muslims in a short period of time? No, for the same reason: activists and nationalist gangs did not do us the historical courtesy of posting their thought processes in their Twitter feeds either. Can we study the institutional realities of the fiscal system of the Indonesian state through its digital traces? No. Can we study the prevalence and causes of official corruption in China through digital traces? Again, no.

In other words, there is a huge methodological problem with the idea of digital traceability, deriving from the fact that most social activity leaves no digital traces. There are problem areas where the traces are more accessible and more indicative of the underlying social processes; but this is a far cry from the utopia of total social legibility that appears to underlie the viewpoint expressed here.

So I'm not persuaded that the tools of digital tracing provide the full alternative to social simulation that these authors assert. And this implies that social simulation tools remain an important component of the social scientist's toolbox.

John von Neumann and stochastic simulations

source: Monte Carlo method (Wikipedia)

John von Neumann was one of the genuine mathematical geniuses of the twentieth century. A particularly interesting window onto von Neumann's scientific work is provided by George Dyson in his  book, Turing's Cathedral: The Origins of the Digital Universe. The book is as much an intellectual history of the mathematics and physics expertise of the Princeton Institute for Advanced Study as it is a study of any one individual, but von Neumann plays a key role in the story. His contribution to the creation of the general-purpose digital computer helped to lay the foundations for the digital world in which we now all live.

There are many interesting threads in von Neumann's intellectual life, but one aspect that is particularly interesting to me is the early application of the new digital computing technology to the problem of simulating large complex physical systems. Modeling weather and climate were topics for which researchers sought solutions using the computational power of first-generation digital computers, and the research needed to understand and design thermonuclear devices had an urgent priority during the war and post-war years. Here is a description of von Neumann's role in the field of weather modeling in designing the early applications of ENIAC  (P. Lynch, "From Richardson to early numerical weather prediction"; link):

John von Neumann recognized weather forecasting, a problem of both great practical significance and intrinsic scientific interest, as ideal for an automatic computer. He was in close contact with Rossby, who was the person best placed to understand the challenges that would have to be addressed to achieve success in this venture. Von Neumann established a Meteorology Project at the Institute for Advanced Study in Princeton and recruited Jule Charney to lead it. Arrangements were made to compute a solution of a simple equation, the barotropic vorticity equation (BVE), on the only computer available, the ENIAC. Barotropic models treat the atmosphere as a single layer, averaging out variations in the vertical. The resulting numerical predictions were truly ground-breaking. Four 24-hour forecasts were made, and the results clearly indicated that the large-scale features of the mid-tropospheric flow could be forecast numerically with a reasonable resemblance to reality. (Lynch, 9)

image: (link, 10)

A key innovation in the 1950s in the field of advanced computing was the invention of Monte Carlo simulation techniques to assist in the invention and development of the hydrogen bomb. Thomas Haigh, Mark Priestley, and Crispin Rope describe the development of the software supporting Monte Carlo simulations in the ENIAC machine in a contribution to the IEEE Annals of the History of Computing (link). Peter Galison offers a detailed treatment of the research communities that grew up around these new computational techniques (link). Developed first as a way of modeling nuclear fission and nuclear explosives, these techniques proved to be remarkably powerful for allowing researchers to simulate and calculate highly complex causal processes. Here is how Galison summarizes the approach:

Christened "Monte Carlo" after the gambling mecca, the method amounted to the use of random, numbers (a la roulette) to simulate the stochastic processes too complex to calculate in full analytic glory. But physicists and engineers soon elevated the Monte Carlo above the lowly status of a mere numerical calculation scheme; it came to constitute an alternative reality--in some cases a preferred one--on which "experimentation" could be conducted. (119) 

At Los Alamos during the war, physicists soon recognized that the central problem was to understand the process by which neutrons fission, scatter, and join uranium nuclei deep in the fissile core of a nuclear weapon. Experiment could not probe the critical mass with sufficient detail; theory led rapidly to unsolvable integro-differential equations. With such problems, the artificial reality of the Monte Carlo was the only solution--the sampling method could "recreate" such processes by modeling a sequence of random scatterings on a computer. (120)

The approach that Ulam, Metropolis, and von Neumann proposed to take for the problem of nuclear fusion involved fundamental physical calculations and statistical estimates of interactions between neutrons and surrounding matter. They proposed to calculate the evolution of the states of a manageable number of neutrons as they traveled from a central plutonium source through spherical layers of other materials. The initial characteristics and subsequent interactions of the sampled neutrons were assigned using pseudo-random numbers. A manageable number of sampled spaces within the unit cube would be "observed" for the transit of a neutron (127) (10^4 observations). If the percentage of fission calculated in the sampled spaces exceeded a certain value, then the reaction would be self-sustaining and explosive. Here is how the simulation would proceed:

Von Neumann went on to specify the way the simulation would run. First, a hundred neutrons would proceed through a short time interval, and the energy and momentum they transferred to ambient matter would be calculated. With this "kick" from the neutrons, the matter would be displaced. Assuming that the matter was in the middle position between the displaced position and the original position, one would then recalculate the history of the hundred original neutrons. This iteration would then repeat until a "self-consistent system" of neutron histories and matter displacement was obtained. The computer would then use this endstate as the basis for the next interval of time, delta t. Photons could be treated in the same way, or if the simplification were not plausible because of photon-matter interactions, light could be handled through standard diffusion methods designed for isotropic, black-body radiation. (129)

Galison argues that there were two fairly different views in play of the significance of Monte Carlo methods in the 1950s and 1960s. According to the first view, they were simply a calculating device permitting the "computational physicist" to calculate values for outcomes that could not be observed or theoretically inferred. According to the second view, Monte Carlo methods were interpreted realistically. Their statistical underpinnings were thought to correspond exactly to the probabilistic characteristics of nature; they represented a stochastic view of physics.

King's view--that the Monte Carlo method corresponded to nature (got "back of the physics of the problem") as no deterministic differential equation ever could--I will call stochasticism. It appears in myriad early uses of the Monte Carlo, and clearly contributed to its creation. In 1949, the physicist Robert Wilson took cosmic-ray physics as a perfect instantiation of the method: "The present application has exhibited how easy it is to apply the Monte Carlo method to a stochastic problem and to achieve without excessive labor an accuracy of about ten percent." (146)

This is a very bold interpretation of a simulation technique. Rather than looking at the model as an abstraction from reality, this interpretation looks at the model as a digital reproduction of that reality. "Thus for the stochasticist, the simulation was, in a sense, of apiece with the natural phenomenon" (147).

One thing that is striking in these descriptions of the software developed in the 1950s to implement Monte Carlo methods is the very limited size and computing power of the first-generation general-purpose computing devices. Punch cards represented "the state of a single neutron at a single moment in time" (Haigh et al link 45), and the algorithm used pseudo-random numbers and basic physics to compute the next state of this neutron. The basic computations used third-order polynomial approximations (Haigh et al link 46) to compute future states of the neutron. The simulation described here resulted in the production of one million punched cards. It would seem that today one could use a spreadsheet to reproduce the von Neumann Monte Carlo simulation of fission, with each line being the computed result from the previous line after application of the specified mathematical functions to the data represented in the prior line. So a natural question to ask is -- what could von Neumann have accomplished if he had Excel in his toolkit? Experts -- is this possible?

Modifying an epidemiological model for party recruitment

Here I'll follow up on the idea of using an epidemiological model to capture the effects of political mobilization through organization. One of the sample models provided by the NetLogo library is EpiDEM Basic (link). This model simulates an infectious disease moving through a population through person-to-person contact.

We can adapt this model to a political context by understanding "infection" as "recruitment to the party". I've modified the model to allow for re-infection after an agent has been cured [disaffiliated from the party]. This corresponds to exit and re-entrance into a party or political organization. This leads the model to reach various levels of equilibrium within the population depending on the settings chosen for infectiousness, cure rates, and cure time frames. The video above represents a sample run of my extension of EpiDEM Basic. The graph represents the percentage of the population that have been recruited to the party at each iteration. The infection rate [mobilization success] surges to nearly 100% in the early ticks of the model, but then settles down to a rough equilibrium for the duration of the run. Orange figures are party members, while blue are not members (either because they have never affiliated or they have dis-affiliated).

An important shortcoming in this approach is that it is forced to represent every agent as a "cadre" for the organization as soon as he/she is recruited; whereas on the ground it is generally a much smaller set of professional cadres who serve as the vectors of proselytization for the party. This accounts for the early surge in membership to almost 100%, which then moderates to the 30% level. The initial surge derives from the exponential spread of infection prior to the period in which cures begin to occur. I've referenced this flaw in the realism of the model by calling this a "grassroots" party. On the current settings of recruitment and defection the population stabilizes at about 30% membership in the party. Ideally the model could be further modified to incorporate "infection" by only a specified set of cadres rather than all members.

It seems possible to merge this party-mobilization model with the Epstein model of rebellion (also provided in the NetLogo library), allowing us taking party membership into account as a factor in activation. In other words, we could attempt to model two processes simultaneously: the "infection" of new party members through a contagion model, and the differential activation of agents according to whether they are exposed to a party member or not. This is complicated, though, and there is a simpler way of proceeding: try to represent the workings of the model with an exogenously given number of party cadres. This can be implemented very simply into the Epstein Rebellion model.

As a first step, I introduce party membership as a fixed percentage of population and assume that the threshold for activation is substantially lower for members than non-members. The causal assumption is this: the presence of a party member in a neighborhood increases the threshold for action. The logic of this modification is this: for a given agent, if there is a party member in the neighborhood, then the threshold for action is low; whereas if there is no party member in the neighborhood, the threshold for action is high.

Now run the model with two sets of assumptions: no party members and 1% party members.

Scenario 1: occurrence of mobilization with no party members

Scenario 2: occurrence of mobilization with 1% party members

The two panels represent these two scenarios. As the two panels illustrate, the behavior of the population of agents is substantially different in the two cases. In both scenarios there are sudden peaks of activism (measured on the "Rebellion Index" panel). But those peaks are both higher and more frequent in the presents of a small number of activists. So we might say the model succeeds in illustrating the difference that organization makes in the occurrence of mobilization. A few party activists substantially increase the likelihood of rebellion.

Or does it? Probably not.

The modifications introduced here are very simple, and they succeed in addressing a primary concern I raised in an earlier post about the original version of Epstein's model: the fact that it does not take the presence of organization into account as a causal factor in civil unrest. But the realism of the model is still low. For example, the Rebellion model is specifically intended to capture the relationship between cops and agents. But it is not interactive in the other way in which rebellious behavior spreads: the process in which rising density of activation in a neighborhood increases the probability of activation for each individual. In other words, neither the original implementation nor this simple extension allows introduction of the spatial dimensions of mobilization and civil unrest (aside from the original random location of party activists).

But most fundamentally, the extension I've presented here is still a highly abstract representation of the workings of organizations in the context of civil unrest and mobilization. I've boiled the workings of a political organization down to a single effect: if a neighborhood is exposed to a party cadre, the individuals in that neighborhood are substantially more likely to become active. And the model behaves accordingly; there is more activism when there are more cadres. But we can't really interpret this as the derivation of a social effect from an independent set of assumptions; rather, the implementation of the idea of organization simply assumes the fact that cadres amplify activation by others in the neighborhood. In other words, the model is built to embody the effect I was expecting to see.

This exercise makes a couple of points. First, agent-based models have the virtue of being very explicit about the logic of action that is represented. So it is possible for anyone to review the code and to modify the assumptions, or to introduce factors that perhaps should be considered. (NetLogo is particularly welcoming to the non-expert in this regard, since it is easy to go back and forth between the code and the graphical representation of the model.)

But second, no one should imagine that agent-based models reproduce reality. Any ABM is implemented by (1) codifying one or more assumptions about the factors that influence a given collective phenomenon, and (2) codifying the rules of action for the kinds of agents that are to be represented. Both kinds of assumption require extreme abstraction from the reality of a social setting, and therefore models can almost invariably be challenged for a lack of realism. It is hard for me to see how an agent-based model might be thought to be explanatory of a complex social reality such as the Cairo uprising.

Modeling organizational recruitment

One defect of the ABMs considered in the prior post about the emergence of civil conflict is that they do not incorporate the workings of organizations into the dynamics of mobilization. And yet scholars like Tilly (Dynamics of Contention) and Bianco (Peasants without the Party: Grassroots Movements in Twentieth Century China) make it clear that organizations are critical to the development and scope of mobilization of a populace. So a model of civil conflict needs to be able to incorporate the effects of organizations in the mobilization and activation of large groups of individual agents. Here I will explore what we might want from an ABM that incorporates organizations.

Ideally I would like to see a model that incorporates:

• NxN individual actors (50x50 in the diagram above, or 2,500 agents)
• M organizations with different characteristics competing for membership among the actors
• A calculation of "uprising behavior" based on the net activation of a threshold percentage of actors in a circumscribed region

How might organizations be introduced into an agent-based model of social contention? I can imagine two quite different approaches. (A) We might look at organizations as higher-level agents within the process. As each organization works its way through the population it gains or loses members; and this affects individual behavior and the geographical distribution of activated agents. This would be an attempt to directly model the mechanism of mobilization through organizational mobilization. (B) Another possible and simpler approach is to represent organizations as environmental factors, analogous to disease vectors, which percolate through the population of first-order agents and alter their behavior. Let's consider both. 

(A) Organizations as meso-level agents. The first approach requires that we provide rules of behavior for both kinds of agents, and recognize that the two processes (organizational recruitment and individual action) may influence each other iteratively. Organizations compete for members and strive to create collective action in support of their agendas. Membership in an organization influences the individual actor by increasing activation. And increasing membership influences the functioning of the organization.

Individual actors gain organizational properties when they are recruited to one of the organizations. Suppose that individual actors have these properties (largely drawn from the Epstein model):

• grievance level
• risk aversiveness
• income level
• salience of ethnicity for identity 
• location
• Organization-driven properties of activation
• derived: level of activation (probability of involvement in response to an appeal from the organization)

If we want to model organizations as agents, then we need to specify their properties and action rules as well. We might begin by specifying that organizations have properties that affect their actions and their ability to recruit:

• content of political agenda / call to action
• perceived effectiveness
• real effectiveness
• number of cadres devoted to mobilization effort

For a simulation of inter-group conflict, we would like to include two ethnic groups, and one or more organizations competing within each group.

Mobilization occurs at the individual level: actors receive invitations to membership sequentially, and they respond according to the net effect of their current characteristics. Once an actor has affiliated, he/she remains susceptible to appeals from other organizations, but the susceptibility is reduced.

Membership in an organization affects an individual's level of engagement in a set of grievance issues and his/her propensity for action. Individuals may express their organizational status at a range of levels of activism:

• highly engaged 
• moderately engaged
• disengaged 

The model calculates each agent's behavior as a function of grievance, risk, appeal, location, and organizational influence.

This approach suggests development of two stages of simulation: first a simulation of the competition of two organizations within a group; and second, a simulation of the individual-level results of calls to action by multiple organizations involving a specified distribution of organizational affiliations.

(B) Organizations as infection vectors. A simpler approach is to represent the various organizations as contagious diseases that have differential infection rates depending on agent properties, and differential effects on behavior depending on which "infection" is present in a given agent. Presumably the likelihood of infection is influenced by whether the individual has already been recruited by another organization; this needs to be represented in the rules governing infection. It also implies that there is a fair amount of path dependence in the simulation: the organization that starts first has an advantage over competitors.

It seems it would be possible to incorporate a disease mechanism into the Epstein model to give a role for organizations in the occurrence of civil unrest.

Now imagine running the model forward with two types of processes occurring simultaneously. The organizations recruit members iteratively and the activation status of each individual is calculated on each tick of the model. At each tick every individual has a membership status with respect to the organizations ("infections"), and each has an activation level (low, medium, high). When a concentration of, say, 40% of agents are activated to a high level in a region of a given size, this constitutes an episode of uprising / ethnic violence / civil unrest.

Two fundamental questions arise about this hypothetical simulation. First, is the simulation assumption that "organizational mobilization is like an infectious disease" a reasonable one? Or does organizational mobilization have different structural and population dynamics than the spread of a disease? For example, diseases percolate through direct contact; perhaps organizational mobilization has more global properties of diffusion. And second, does the resulting simulation give rise to patterns that have realistic application to real processes of social contention? Do we learn something new about social contention and mobilization by incorporating the additional factor of "organization" in this way that the Epstein model by itself does not reveal?

(It should be noted that organizations are a peculiar kind of agent. They have properties that are characteristic of "complex adaptive systems": they are supra-individual, they are influenced by the actors they touch, and they influence the behavior of the actors they touch. So the behavioral properties of an organization perhaps should not be specified exogenously.)

(NetLogo is a sophisticated modeling package that permits researchers to develop small and medium-sized agent-based models, and it provides a number of relevant examples of simulations that are of interest to social scientists (link). Particularly interesting for the current purposes are a simulation of the Epstein model of rebellion discussed earlier (link) and an implementation of an AIDS contagion model that could be considered as a platform for modeling the spread of an organization or a set of ideas as well (link). Here is the link for NetLogo:
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.)