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.

The place for thick theories of the actor in philosophy

image: Bruegel, The Dutch Proverbs (1559)

When philosophers of the social sciences take seriously the importance of individual action within the social realm, we often look in the direction of methodological individualism and the methods of “aggregation dynamics”. That is, agent-centered theorists are usually interested in finding ways of climbing the upward strut of Coleman’s boat through efforts at modeling the interactive dynamics of purposive individuals and the social outcomes they produce. This leads to an interest in applying microeconomics, game theory, or agent-based modeling as ways of discovering the aggregate consequences of a certain theory of the actor (purposive, calculating, strategic rationality). We then get a ready basis for accounting for causal relations in the social world; the medium of causal powers and effects is the collection of purposive actors who live in social relationships and institutions with a fairly homogeneous form of agency.

This is a perfectly valid way of thinking about social causation and explanation. But the thrust of the argument for thick descriptions of actors, coming from microsociologists, ethnomethodologists, and historical sociologists, is that the abstractions associated with thin theories of the actor (goal-directed behavior driven by rational-self-interest) are often inadequate for understanding real social settings. If we attach weight to sociologists like Goffman and Garfinkel, some of the most interesting stuff is happening along the bottom edge of Coleman’s boat — the interactions among socially situated individuals. So how should we think about the challenge of incorporating a richer theory of the actor into the project of supporting an adequate set of ideas about social inquiry and social explanation?

One approach is to simply acknowledge the scientific relevance and importance of research into the mentality of real social actors. This approach accepts the point that we cannot always make use of the very sparse assumptions of thin theories of the actor if we want to understand social phenomena like the politics of hate, the rise of liberal democracy, or the outbreak of ethnic violence. We can then attempt to address the theoretical and methodological problems associated with research into more nuanced understanding of historically and socially situated persons in specific circumstances involving the phenomena of interest. We can give attention to fields like cultural sociology and ethnography and attempt to offer support for those research efforts. This approach also permits the possibility of attempting to formulate a conception of social explanation that fits thick theories of the actor.

This approach seems to lead most naturally to a conception of explanation that is more interpretive than causal, and it suggests that the hard work of social research will go into the effort to find evidence permitting the researcher to form a theory of the attitudes, beliefs, and mental frameworks of the actors involved in the social setting of interest. The example of Robert Darnton’s study of “The Great Cat Massacre” illustrates the value and difficulty of this kind of inquiry (link). And it highlights the crucial role that concrete historical and documentary evidence play in the effort (link). At the same time, the explanations offered are almost inevitably particular to the case, not generalizable.

Is there an approach to social explanation that makes use of a thick theory of the actor but nonetheless aspires to providing largescale social explanations? Can thick theories of the actor, and rich accounts of agency in specific circumstances, be incorporated into causal theories of specific kinds of social organization and change? Can we imagine a parallel masterpiece to Coleman’s Foundations of Social Theory, which incorporates the nuances of thick sociology and points towards a generalizing sociology?

Yes and no. Yes, in that important and large-scale works of comparative historical sociology depend directly on analysis of the thick mentalities of the actors who made up great events — e.g. Mann on fascism (demobilized soldiers), Steinmetz on German colonialism (professional administrators), Frank Dobbin on French technology planning (technocrats), or Charles Sabel on Italian mechanical culture (machinists versus engineers). And this kind of social research depends upon its own kind of generalization — the claim to identify a cultural type that was current in a given population at a certain time. This is the project of discovering a historically concrete mentalité (link). But no, if we think the primary mode of social explanation takes the form of system models demonstrating the genealogy of this social characteristic or that.

This sounds a bit like the heart of the methodenstreit of the last century, between historicists and nomological theorists. Does the social world admit of generalizing explanations (nomothetic), or is social explanation best understood as particular and historically situated (idiographic)? Fortunately we are not forced to choose. Both kinds of explanation are possible in the social realm, and some problems are more amenable to in approach or the other. Only the view that insists on the unity of science find this dilemma unacceptable. But for a methodological pluralist, this is a perfectly agreeable state of affairs.

Mechanisms according to analytical sociology

One of the distinguishing characteristics of analytical sociology is its insistence on the idea of causal mechanisms as the core component of explanation. Like post-positivists in other traditions, AS theorists specifically reject the covering law model of explanation and argues for a “realist” understanding of causal relations and powers: a causal relationship between x and y exists solely insofar as there exist one or more causal mechanisms producing it generating y given the occurrence of x. Peter Hedström puts the point this way in Dissecting the Social:

A social mechanism, as defined here, is a constellation of entities and activities that are linked to one another in such a way that they regularly bring about a particular type of outcome. (kl 181)

A basic characteristic of all explanations is that they provide plausible causal accounts for why events happen, why something changes over time, or why states or events co-vary in time or space. (kl 207)

The core idea behind the mechanism approach is that we explain not by evoking universal laws, or by identifying statistically relevant factors, but by specifying mechanisms that show how phenomena are brought about. (kl 334)

A social mechanism, as here defined, describes a constellation of entities and activities that are organized such that they regularly bring about a particular type of outcome. (kl 342)

So far so good. But AS ads another requirement about causal mechanisms in the social realm that is less convincing: that the only real or credible mechanisms are those involving the actions of individual actors. In other words, causal action in the social world takes place solely at the micro level. This assumption is substantial, non-trivial, and seemingly dogmatic. 

Sociological theories typically seek to explain social outcomes such as inequalities, typical behaviours of individuals in different social settings, and social norms. In such theories individuals are the core entities and their actions are the core activities that bring about the social-level phenomena that one seeks to explain. (kl 356)

Although the explanatory focus of sociological theory is on social entities, an important thrust of the analytical approach is that actors and actions are the core entities and activities of the mechanisms explaining plaining such phenomena. (kl 383)

The theory should also explain action in intentional terms. This means that we should explain an action by reference to the future state it was intended to bring about. Intentional explanations are important for sociological theory because, unlike causalist explanations of the behaviourist or statistical kind, they make the act ‘understandable’ in the Weberian sense of the term.’ (kl 476)

Here is a table in which Hedström classifies different kinds of social mechanisms; significantly, all are at the level of actors and their mental states.

The problem with this “action-level” requirement on the nature of social mechanisms is that it rules out as a matter of methodology that there could be social causal processes that involve factors at higher social levels — organizations, norms, or institutions, for example. (For that matter, it also rules out the possibility that some individual actions might take place in a way that is inaccessible to conscious knowledge — for example, impulse, emotion, or habit.) And yet it is common in sociology to offer social explanations invoking causal properties of things at precisely these “meso” levels of the social world. For example:

Each of these represents a fairly ordinary statement of social causation in which a primary causal factor is an organization, an institutional arrangement, or a normative system.

It is true, of course, that such entities depends on the actions and minds of individuals. This is the thrust of ontological individualism (linklink): the social world ultimately depends on individuals in relation to each other and in relation to the modes of social formation through which their knowledge and action principles have been developed. But explanatory or methodological individualism does not follow from the truth of ontological individualism, any more than biological reductionism follows from the truth of physicalism. Instead, it is legitimate to attribute stable causal properties to meso-level social entities and to invoke those entities in legitimate social-causal explanations. Earlier arguments for meso-level causal mechanisms can be found herehere, and here.

This point about “micro-level dogmatism” leads me to believe that analytical sociology is unnecessarily rigid when it comes to causal processes in the social realm. Moreover, this rigidity leads it to be unreceptive to many approaches to sociology that are perfectly legitimate and insightful. It is as if someone proposed to offer a science of cooking but would only countenance statements at the level of organic chemistry. Such an approach would preclude the possibility of distinguishing different cuisines on the basis of the palette of spices and flavors that they use. By analogy, the many approaches to sociological research that proceed on the basis of an analysis of the workings of mid-level social entities and influences are excluded by the strictures of analytical sociology. Not all social research needs to take the form of the discovery of microfoundations, and reductionism is not the only scientifically legitimate strategy for explanation.

(The photo above of a moment from the Deepwater Horizon disaster is relevant to this topic, because useful accident analysis needs to invoke the features of organization that led to a disaster as well as the individual actions that produced the particular chain of events leading to the disaster. Here is an earlier post that explores this feature of safety engineering; link.)

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.

What parts of the social world admit of explanation?

image: John Dos Passos

When Galileo, Newton, or Lavoisier confronted the natural world as “scientists,” they had in mind reasonably clear bodies of empirical phenomena that required explanation: the movements of material objects, the motions of the planets, the facts about combustion. They worked on the hope that nature conformed to a relatively small number of “fundamental” laws which could be discovered through careful observation and analysis. The success of classical physics and chemistry is the result. In a series of areas of research throughout the eighteenth and nineteenth centuries  it turned out that there were strong governing laws of nature — mechanics, gravitational attraction, conservation of matter and energy, electromagnetic propagation — which served to explain a vast range of empirically given natural phenomena. The “blooming, buzzing confusion” of the natural world could be reduced to the operation of a small number of forces and entities.

This finding was not metaphysically or logically inevitable. Nature might have been less regular and less unified than it turned out to be. Natural causes could have fluctuated in their effects and could have had more complex interactions with other causes than has turned out to be the case. Laws of nature might have varied over time and space in unpredictable ways. So the success of the project of the natural sciences is both contingent and breathtakingly powerful. There are virtually no bodies of empirical phenomena for which we lack even a good guess about the underlying structure and explanation of these phenomena; and these areas of ignorance seem to fall at the sub-atomic and the super-galactic levels. 

The situation in the social world is radically different, much as positivistically minded social scientists have wanted to think otherwise. There are virtually no social processes that have the features of predictability and smoothness that are displayed by natural phenomena. Rather, we can observe social processes of unlimited granularity unfolding over time and space, intermingling with other processes; leading sometimes to crashes and exponential accelerations; and sometimes morphing into something completely different.

Imagine that we think of putting together a slow-motion data graphic representing the creation, growth, and articulation of a great city — Chicago, Mexico City, or Cairo, for example. We will need to represent many processes within this graphic: spatial configuration, population size, ethnic and racial composition, patterns of local cooperation and conflict, the emergence and evolution of political authority, the configuration of a transportation and logistics system, the effects of war and natural disaster, the induced transformation of the surrounding hinterland, and the changing nature of relationships with external political powers, to name a few. And within the population itself we will want to track various characteristics of interest: literacy levels, school attendance, nutrition and health, political and social affiliation, gender and racial attitudes and practices, cultural and religious practices, taste and entertainment, and processes of migration and movement. We might think of this effort as a massive empirical project, to provide a highly detailed observational history of the city over a very long period of time. (Cronon’s Nature’s Metropolis: Chicago and the Great West is a treatment of the city of Chicago over the period of about a century with some of these aspirations.) 

But now what? How can we treat this massive volume of data “scientifically”? And can we aspire to the ambition of showing how these various processes derive from a small number of more basic forces? Does the phenomenon of the particular city admit of a scientific treatment along the lines of Galileo, Newton, or Lavoisier?

The answer is resoundingly no. Such a goal displays a fundamental misunderstanding of the social world. Social things and processes at every level are the contingent and interactive result of the activities of individual actors. Individuals are influenced by the social environment in which they live; so there is no reductionist strategy available here, reducing social properties to purely individual properties. But the key words here are “contingent” and “interactive”. There is no God’s-eye answer to the question, why did Chicago become the metropolis of the central North American continent rather than St. Louis? Instead, there is history — the choices made by early railroad investors and route designers, the availability of timber in Michigan but not Missouri, a particularly effective group of early city politicians in Chicago compared to St. Louis, the comparative influence on the national scene of Illinois and Missouri. These are all contingent and path-dependent factors deriving from the situated choices of actors at various levels of decision making throughout the century. And when we push down into lower levels of the filigree of social activity, we find equally contingent processes. Why did Motown come to dominate musical culture for a few decades in Detroit and beyond? Why did professional football take off but professional soccer did not? Why are dating patterns different in Silicon Valley than Iowa City? None of these questions have law-driven answers. Instead, in every case the answer will be a matter of pathway-tracing, examining the contingent turning points that brought us to the situation in question.

What this argument is meant to make clear is that the social world is not like the natural world. It is fundamentally “historical” (meaning that the present is unavoidably influenced by the past); contingent (meaning that events could have turned out differently); and causally plural (meaning that there is no core set of “social forces” that jointly serve to drive all social change). 

It also means that there is no “canonical” description of the social world. With classical physics we had the idea that nature could be described as a set of objects with mass and momentum; electromagnetic radiation with properties of frequency and velocity; atoms and molecules with fixed properties and forces; etc. But this is not the case with the social world. New kinds of processes come and go, and it is always open to a social researcher to identify a new trend or process and to attempt to make sense of this process in its context. 

I don’t mean to suggest that social phenomena do not admit of explanation at all. We can provide mid-level explanations of a vast range of social patterns and events, from the denuding of Michigan forests in the 1900s to the incidence of first names over time. What we cannot do is to provide a general theory that suffices as an explanatory basis for identifying and explaining all social phenomena. The social sciences are at their best when they succeed in identifying mechanisms that underlie familiar social patterns. And these mechanisms are most credible when they are actor-centered, in the sense that they illuminate the ways that individual actors’ behavior is influenced or generated so as to produce the outcome in question. 

In short: the social realm is radically different from the natural realm, and it is crucial for social scientists to have this in mind as they formulate their research and theoretical ideas.

(I used the portrait of Dos Passos above for this post because of the fragmented and plural way in which he seeks to represent a small slice of social reality in U.S.A. This works better than a single orderly narrative of events framed by the author’s own view of the period.)

Historical vs. sociological explanation

Think of the following matrix of explanatory possibilities of social and historical phenomena:

Vertically the matrix divides between historical and sociological explanations, whereas horizontally it distinguishes general explanations and particular explanations. A traditional way of understanding the distinction between historical and sociological explanations was to maintain that sociological explanations provide generalizations, whereas historical explanations provide accounts for particular and unique situations. Windelband and the historicist school referred to this distinction as that between nomothetic and idiographic explanations (link). It was often assumed, further, that the nomothetic / idiographic distinction corresponded as well to the distinction between causal and interpretive explanations.

On this approach, only two of the cells would be occupied: sociological / general and historical / particular. There are no general historical explanations and no particular sociological explanations.

This way of understanding social and historical explanations no longer has a lot of appeal. “Causal” and “nomological” no longer have the affinity with each other that they once had, and “idiographic” and “interpretive” no longer seem to mutually imply each other. Philosophers have come to recognize that the deductive-nomological model does a poor job of explicating causation, and that we are better served by the idea that causal relationships are established by discovering discrete causal mechanisms. And the interpretive approach doesn’t line up uniquely with any particular mode of explanation.  

So historical and sociological explanations no longer bifurcate in the way once imagined. All four quadrants invoke both causal mechanisms and interpretation as components of explanation.

In fact it is straightforward to identify candidate explanations in the two “vacant” cells — particular sociological explanations and general historical explanations. In Fascists Michael Mann asks a number of moderately general questions about the causes of European fascism; but he also asks about historically particular instances of fascism. Historical sociology involves both singular and general explanations. But likewise, historians of the French Revolution or the English Revolution often provide general hypotheses even as they construct a particular narrative leading to the storming of the Bastille (Pincus, Soboul).

There seem to be two important grounds of explanation that cut across all these variants of explanations of human affairs. It is always relevant to ask about the meanings that participants attribute to actions and social events, so interpretation is a resource for both historical and sociological explanations. But likewise, causal mechanisms are invoked in explanations across the spectrum of social and historical explanation, and are relevant to both singular and general explanations. Or in other words, there is no difference in principle between sociological and historical explanatory strategies. 

How do the issues of generalization and particularity arise in the context of causal mechanisms? In several ways. First, explanations based on social mechanisms can take place in both a generalizing and a particular context. We can explain a group of similar social outcomes by hypothesizing the workings of a common causal mechanism giving rise to them; and we can explain a unique event by identifying the mechanisms that produced it in the given unique circumstances. Second, a social-mechanism explanation relies on a degree of lawfulness; but it refrains from the strong commitments of the deductive-nomological method. There are no high-level social regularities. Third, we can refer both to particular individual mechanisms and a class of similar mechanisms. For example, the situation of “easy access to valuable items along with low probability of detection” constitutes a mechanism leading to pilferage and corruption. We can invoke this mechanism to explain a particular instance of corrupt behavior — a specific group of agents in a business who conspire to issue false invoices — or a general fact — the logistics function of a large military organization is prone to repeated corruption. (Sergeant Bilko, we see you!) So mechanisms support a degree of generalization across instances of social activity; and they also depend upon a degree of generalization across sequences of events.
And what about meanings? Human actions proceed on the basis of subjective understandings and motivations. There are some common features of ordinary human experience that are broadly shared. But the variations across groups, cultures, and individuals are very wide, and there is often no substitute for detailed hermeneutic research into the mental frameworks of the actors in specific historical settings. Here again, then, explanations can take the form of either generalized statements or accounts of particular and unique outcomes.
We might say that the most basic difference between historical and sociological explanation is a matter of pragmatics — intellectual interest rather than fundamental logic. Historians tend to be more interested in the particulars of a historical setting, whereas sociologists — even historical sociologists — tend to be more interested in generalizable patterns and causes. But in each case the goal of explanation is to discover an answer to the question, why and how does the outcome occur? And this typically involves identifying both causal mechanisms and human meanings. 


Quantum mental processes?

One of the pleasant aspects of a long career in philosophy is the occasional experience of a genuinely novel approach to familiar problems. Sometimes one’s reaction is skeptical at first — “that’s a crazy idea!”. And sometimes the approach turns out to have genuine promise. I’ve had that experience of moving from profound doubt to appreciation several times over the years, and it is an uplifting learning experience. (Most recently, I’ve made that progression with respect to some of the ideas of assemblage and actor-network theory advanced by thinkers such as Bruno Latour; link, link.)

I’m having that experience of unexpected dissonance as I begin to read Alexander Wendt’s Quantum Mind and Social Science: Unifying Physical and Social Ontology. Wendt’s book addresses many of the issues with which philosophers of social science have grappled for decades. But Wendt suggests a fundamental switch in the way that we think of the relation between the human sciences and the natural world. He suggests that an emerging paradigm of research on consciousness, advanced by Giuseppi Vitiello, John Eccles, Roger Penrose, Henry Stapp, and others, may have important implications for our understanding of the social world as well. This is the field of “quantum neuropsychology” — the body of theory that maintains that puzzles surrounding the mind-body problem may be resolved by examining the workings of quantum behavior in the central nervous system. I’m not sure which category to put the idea of quantum consciousness yet, but it’s interesting enough to pursue further.

The familiar problem in this case is the relation between the mental and the physical. Like all physicalists, I work on the assumption that mental phenomena are embodied in the physical infrastructure of the central nervous system, and that the central nervous system works according to familiar principles of electrochemistry. Thought and consciousness are somehow the “emergent” result of the workings of the complex physical structure of the brain (in a safe and bounded sense of emergence). The novel approach is the idea that somehow quantum physics may play a strikingly different role in this topic than ever had been imagined. Theorists in the field of quantum consciousness speculate that perhaps the peculiar characteristics of quantum events at the sub-atomic level (e.g. quantum randomness, complementary, entanglement) are close enough to the action of neural networks that they serve to give a neural structure radically different properties from those expected by a classical-physics view of the brain. (This idea isn’t precisely new; when I was an undergraduate in the 1960s it was sometimes speculated that freedom of the will was possible because of the indeterminacy created by quantum physics. But this wasn’t a very compelling idea.)

Wendt’s further contribution is to immerse himself in some of this work, and then to formulate the question of how these perspectives on intentionality and mentality might affect key topics in the philosophy of society. For example, how do the longstanding concepts of structure and agency look when we begin with a quantum perspective on mental activity?

A good place to start in preparing to read Wendt’s book is Harald Atmanspacher’s excellent article in the Stanford Encyclopedia of Philosophy (link). Atmanspacher organizes his treatment into three large areas of application of quantum physics to the problem of consciousness: metaphorical applications of the concepts of quantum physics; applications of the current state of knowledge in quantum physics; and applications of possible future advances in knowledge in quantum physics.

Among these [status quo] approaches, the one with the longest history was initiated by von Neumann in the 1930s…. It can be roughly characterized as the proposal to consider intentional conscious acts as intrinsically correlated with physical state reductions. (13)

A physical state reduction is the event that occurs when a quantum probability field resolves into a discrete particle or event upon having been measured. Some theorists (e.g. Henry Stapp) speculate that conscious human intention may influence the physical state reduction — thus a “mental” event causes a “physical” event. And some process along these lines is applied to the “activation” of a neuronal assembly:

The activation of a neuronal assembly is necessary to make the encoded content consciously accessible. This activation is considered to be initiated by external stimuli. Unless the assembly is activated, its content remains unconscious, unaccessed memory. (20)

Also of interest in Atmanspacher’s account is the idea of emergence: are mental phenomena emergent from physical phenomena, and in what sense? Atmanspacher specifies a clear but strong definition of emergence, and considers whether mental phenomena are emergent in this sense:

Mental states and/or properties can be considered as emergent if the material brain is not necessary or not sufficient to explore and understand them. (6)

This is a strong conception in a very specific way; it specifies that material facts are not sufficient to explain “emergent” mental properties. This implies that we need to know some additional facts beyond facts about the material brain in order to explain mental states; and it is natural to ask what the nature of those additional facts might be.

The reason this collection of ideas is initially shocking to me is the difference in scale between the sub-atomic level and macro-scale entities and events. There is something spooky about postulating causal links across that range of scales. It would be wholly crazy to speculate that we need to invoke the mathematics and theories of quantum physics to explain billiards. It is pretty well agreed by physicists that quantum mechanics reduces to Newtonian physics at this scale. Even though the component pieces of a billiard ball are quantum entities with peculiar properties, as an ensemble of 10^25 of these particles the behavior of the ball is safely classical. The peculiarities of the quantum level wash out for systems with multiple Avogadro’s numbers of particles through the reliable workings of statistical mechanics. And the intuitions of most people comfortable with physics would lead them to assume that neurons are subject to the same independence; the scale of activity of a neuron (both spatial and temporal) is orders of magnitude too large to reflect quantum effects. (Sorry, Schrodinger’s cat!)

Charles Seife reports a set of fundamental physical computations conducted by Max Tegmark intended to demonstrate this in a recent article in Science Magazine, “Cold Numbers Unmake the Quantum Mind” (link). Tegmark’s analysis focuses on the speculations offered by Penrose and others on the possible quantum behavior of “microtubules.” Tegmark purports to demonstrate that the time and space scales of quantum effects are too short by orders of magnitude to account for the neural mechanisms that can be observed (link). Here is Tegmark’s abstract:

Based on a calculation of neural decoherence rates, we argue that the degrees of freedom of the human brain that relate to cognitive processes should be thought of as a classical rather than quantum system, i.e., that there is nothing fundamentally wrong with the current classical approach to neural network simulations. We find that the decoherence time scales (∼10^−13–10^−20s) are typically much shorter than the relevant dynamical time scales (∼10^−3–10^−1s), both for regular neuron firing and for kinklike polarization excitations in microtubules. This conclusion disagrees with suggestions by Penrose and others that the brain acts as a quantum computer, and that quantum coherence is related to consciousness in a fundamental way. (link)

I am grateful to Atmanspacher for providing such a clear and logical presentation of some of the main ideas of quantum consciousness; but I continue to find myself sceptical. There is a risk in this field to succumb to the temptation towards unbounded speculation: “Maybe if X’s could influence Y’s, then we could explain Z” without any knowledge of how X, Y, and Z are related through causal pathways. And the field seems sometimes to be prey to this impulse: “If quantum events were partially mental, then perhaps mental events could influence quantum states (and from there influence macro-scale effects).”

In an upcoming post I’ll look closely at what Alex Wendt makes of this body of theory in application to the level of social behavior and structure.

Large causes and component causal mechanisms

Image: Yellow River, Qing Dynasty
Image: Free and Slave States, United States 1850

One approach to causal explanation involves seeking out the mechanisms and processes that lead to particular outcomes. McAdam, Tarrow, and Tilly illustrate this approach in their treatment of contentious politics in Dynamics of Contention, and the field of contentious politics is in fact highly suitable to the mechanisms approach. There are numerous clear examples of social processes instantiated in groups and organizations that play into a wide range of episodes of contention and resistance — the mechanics of mobilization, the processes that lead to escalation, the communications mechanisms through which information and calls for action are broadcast, the workings of organizations. So when we are interested in discovering explanations of the emergence and course of various episodes of contention and resistance, it is both plausible and helpful to seek out the specific mechanisms of mobilization and resistance that can be discerned in the historical record.

This is a fairly “micro” approach to explanation and analysis. It seeks to understand how a given process works by looking for the causal mechanisms that underlie it. But not all explanatory questions in the social sciences fall at this level of aggregation. Some researchers are less interested in the micro-pathways of particular episodes and more interested in the abiding social forces and arrangements that influence the direction of change in social systems. For example, Marx declared an explanatory hypothesis along these lines in the Communist Manifesto: “The history of all hitherto existing society is the history of class struggles.” And Michael Mann provides more detailed analysis of world history that encompasses Marx’s hypothesis along with several other large structural factors in The Sources of Social Power (link).

Large social factors at this level include things like the inequalities of power and opportunity created by various property systems; the logic of a competitive corporate capitalist economy; the large social consequences of climate change — whether in the Little Ice Age or the current day; the strategic and military interests of various nations; and the social and economic consequences of ubiquitous mobile computation and communication abilities. Researchers as diverse as Karl Marx, Manuel Castells, Carl von Clausewitz, and William McNeill have sought out causal hypotheses that attempt to explain largescale historical change as the consequence, in part, of the particular configurations and variations of macro factors like these. Outcomes like success in war, the ascendancy of one nation or region over others, the configuration of power and advantage across social groups within modern democracies, and the economic rise of one region over another are all largescale outcomes that researchers have sought to explain as the consequence of other largescale social, economic, and political factors.

These approaches are not logically incompatible. If we follow along with William McNeill (Plagues and Peoples – Central Role Infectious Disease Plays in World History) and consider the idea that the modern distribution of national power across the globe is a consequence of the vulnerability of various regions to disease, we are fully engaged in the idea that macro factors have macro consequences. But it is also open to us to ask the question, how do these macro factors work at the more granular level? What are the local mechanisms that underlay the dynamics of disease in Southeast Asia, West Africa, or South America? So we can always shift focus upwards and downwards, and we can always look for more granular explanations for any form of social causal influence. And in fact, some historical sociologists succeed in combining both approaches; for example, Michael Mann in his study of fascism (Fascists), who gives attention both to largescale regional factors (the effects of demobilization following World War I) and local, individual-level factors (the class and occupational identities of fascist recruits) (link).

That said, the pragmatics of the two approaches are quite different. And the logic of causal research
appears to differ as well. The causal mechanisms theory of explanation suggests close comparative study of individual cases — particular rebellions, particular episodes of population change, particular moments of change of government. The “large social factor” approach to explanation suggests a different level of research, a research method that permits comparison of large outcomes and the co-variance of putative causal factors. Mill’s methods of causal reasoning appear to be more relevant to this type of causal hypothesis. Theda Skocpol’s study of social revolution in States and Social Revolutions is a case in point (link).

The harder question is this: are the large social factors mentioned here legitimate “causes”, or are they simply placeholders for more granular study of particular mechanisms and pathways? Should reference to “capitalism,” “world trading system,” or “modern European reproductive regime” be expected to disappear in the ideal historical sociology of the future? Or is this “large structure” vocabulary an altogether justified and stable level of social analysis on the basis of which to construct historical and social explanations? I am inclined to believe that the latter position is correct, and that it is legitimate to conceive of social research at a range of levels of aggregation (link, link). The impulse towards disaggregation is a scientifically respectable one, but it should not be understood as replacing analysis at a higher level.

(The illustrations above were chosen to provide examples of historical processes (the silting of waterways and patterns of slaveholding) that admit of explanation in terms of largescale historical factors (climate, geography, and political systems).)

Verisimilitude in models and simulations

Modeling always requires abstraction and simplification. We need to arrive at a system for representing the components of a system, the laws of action that describe their evolution and interaction, and a way of aggregating the results of the representation of the components and their interactions. Simplifications are required in order to permit us to arrive at computationally feasible representations of the reality in question; but deciding which simplifications are legitimate is a deeply pragmatic and contextual question. Ignoring air resistance is a reasonable simplification when we are modeling the trajectories of dense, massive projectiles through the atmosphere; it is wholly unreasonable if we are interested in modeling the fall of a leaf or a feather under the influence of gravity (link).

Modeling the social world is particularly challenging for a number of reasons. Not all social actors are the same; actors interact with each other in ways that are difficult to represent formally; and actors change their propensities for behavior as a result of their interactions. They learn, adapt, and reconfigure; they acquire new preferences and new ways of weighing their circumstances; and they sometimes change the frames within which they deliberate and choose.

Modeling the social world certainly requires the use of simplifying assumptions. There is no such thing as what we might call a Borges-class model — one that represents every feature of the terrain. This means that the scientist needs to balance realism, tractability, and empirical adequacy in arriving at a set of assumptions about the actor and the environment, both natural and social. These judgments are influenced by several factors, including the explanatory and theoretical goals of the analysis. Is the analysis intended to serve as an empirical representation of an actual domain of social action — the effects on habitat of the grazing strategies of a vast number of independent herders, say? Or is it intended to isolate the central tendency of a few key factors — short term cost-benefit analysis in a context of a limited horizon of environmental opportunities, say?

If the goal of the simulation is to provide an empirically adequate reconstruction of the complex social situation, permitting adjustment of parameters in order to answer “what-if” questions, then it is reasonable to expect that the baseline model needs to be fairly detailed. We need to build in enough realism about the intentions and modes of reasoning of the actors, and we need a fair amount of detail concerning the natural, social, and policy environments in which they choose.

The discipline of economic geography provides good examples of both extremes of abstraction and realism of assumptions. At one extreme we have the work of von Thunen in his treatment of the Isolated State, producing a model of habitation, agriculture, and urbanization that reflects the economic rationality of the actors.

At the other extreme we have calibrated agent-based models of land use that build in more differentiated assumptions about the intentions of the actors and the legal and natural environment in which they make their plans and decisions. A very good and up-to-date volume dedicated to the application of calibrated agent-based models in economic geography is Alison Heppenstall, Andrew Crooks, Linda See, and Michael Batty, Agent-Based Models of Geographical Systems. The contribution by Crooks and Heppenstall provides an especially good introduction to the approach (“Introduction to Agent-Based Modelling”). Crook and Heppenstall describe the distinguishing features of the approach in these terms:

To understand geographical problems such as sprawl, congestion and segregation, researchers have begun to focus on bottom-up approaches to simulating human systems, specifically researching the reasoning on which individual decisions are made. One such approach is agent-based modelling (ABM) which allows one to simulate the individual actions of diverse agents, and to measure the resulting system behaviour and outcomes over time. The distinction between these new approaches and the more aggregate, static conceptions and representations that they seek to complement, if not replace, is that they facilitate the exploration of system processes at the level of their constituent elements. (86)

The volume also pays a good deal of attention to the problem of validation and testing of simulations. Here is how Manson, Sun, and Bonsal approach the problem of validation of ABMs in their contribution, “Agent-Based Modeling and Complexity”:

Agent-based complexity models require careful and thorough evaluation, which is comprised of calibration, verification, and validation (Manson 2003 ) . Calibration is the adjustment of model parameters and specifications to fit certain theories or actual data. Verification determines whether the model runs in accordance with design and intention, as ABMs rely on computer code susceptible to programming errors. Model verification is usually carried out by running the model with simulated data and with sensitivity testing to determine if output data are in line with expectations. Validation involves comparing model outputs with real-world situations or the results of other models, often via statistical and geovisualization analysis. Model evaluation has more recently included the challenge of handling enormous data sets, both for the incorporation of empirical data and the production of simulation data. Modelers must also deal with questions concerning the relationship between pattern and process at all stages of calibration, verification, and validation. Ngo and See ( 2012 ) discuss these stages in ABM development in more detail. (125)

An interesting current illustration of the value of agent-based modeling in analysis and explanation of historical data is presented by Kenneth Sylvester, Daniel Brown, Susan Leonard, Emily Merchant, and Meghan Hutchins in “Exploring agent-level calculations of risk and return in relation to observed land-use changes in the US Great Plains, 1870-1940” (link). Their goal is to see whether it is possible to reproduce important features of land use in several Kansas counties by making specific assumptions about decision-making by the farmers, and specific information about the changing weather and policy circumstances within which choices were made. 
Here is how Sylvester and co-authors describe the problem of formulating a representation of the actors in their simulation:

Understanding the processes by which farming households made their land-use decisions is challenging because of the complexity of interactions between people and the places in which they lived and worked, and the often insufficient resolution of observed information. Complexity characterizes land-use processes because observed historical behaviors often represent accumulated decisions of heterogeneous actors who were affected by a wide range of environmental and human factors, and by specific social and spatial interactions. (1)

Here is a graph of the results of the Sylvester et al agent-based model, simulating the allocation of crop land across five different crops given empirical weather and rainfall data.

So how well does this calibrated agent-based model do as a simulation of the observed land use patterns? Not particularly well, in the authors’ concluding remarks; their key finding is sobering:

Our base model, assuming profit maximization as the motive for land-use decision making, reproduced the historical record rather poorly in terms of both land use shares and farm size distributions in each township. We attribute the differences to deviations in decision making from profit-maximizing behavior. Each of the subsequent experiments illustrates how relatively simple changes in micro-level processes lead to different aggregate outcomes. With only minor adjustments to simple mechanisms, the pace, timing, and trajectories of land use can be dramatically altered.

However, they argue that this lack of fit does not discredit the ABM approach, but rather disconfirms the behavioral assumption that farmers are simple maximizers of earning. They argue, as sociologists would likely agree, that “trajectories of land-use depended not just on economic returns, but other slow processes of change, demographic, cultural, and ecological feedbacks, which shaped the decisions of farmers before and long after the middle of the twentieth century.” And therefore it is necessary to provide more nuanced representations of actor intentionality if the model is to do a good job of reproducing the historical results and the medium-term behavior of the system.
(In an earlier post I discussed a set of formal features that have been used to assess the adequacy of formal models in economics and other mathematized social sciences (link). These criteria are discussed more fully in On the Reliability of Economic Models: Essays in the Philosophy of Economics.)

(Above I mentioned the whimsical idea of “Borges-class models” — the unrealizable ideal of a model that reproduces every aspect of the phenomena that it seeks to simulate. Here is the relevant quotation from Jorge Borges.

On Exactitude in Science
Jorge Luis Borges, Collected Fictions, translated by Andrew Hurley.

…In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that that vast Map was Useless, and not without some Pitilessness was it, that they delivered it up to the Inclemencies of Sun and Winters. In the Deserts of the West, still today, there are Tattered Ruins of that Map, inhabited by Animals and Beggars; in all the Land there is no other Relic of the Disciplines of Geography.

—Borges quoting Suarez Miranda,Viajes devarones prudentes, Libro IV,Cap. XLV, Lerida, 1658)

Computational models for social phenomena

There is a very lively body of work emerging in the intersection between computational mathematics and various fields of the social sciences. This emerging synergy between advanced computational mathematics and the social sciences is possible, in part, because of the way that social phenomena emerge from the actions and thoughts of individual actors in relationship to each other. This is what allows us to join mathematics to methodology and explanation. Essentially we can think of the upward strut of Coleman’s boat — the part of the story that has to do with the “aggregation dynamics” of a set of actors — and can try to create models that can serve to simulate the effects of these actions and interactions.

source: Hedstrom and Ylikoski (2010) “Causal Mechanisms in the Social Sciences” (link)

Here is an interesting example in the form of a research paper by Rahul Narain and colleagues on the topic of modeling crowd behavior (“Aggregate Dynamics for Dense Crowd Simulation”, link). Here is their abstract:

Large dense crowds show aggregate behavior with reduced individual freedom of movement. We present a novel, scalable approach for simulating such crowds, using a dual representation both as discrete agents and as a single continuous system. In the continuous setting, we introduce a novel variational constraint called unilateral incompressibility, to model the large-scale behavior of the crowd, and accelerate inter-agent collision avoidance in dense scenarios. This approach makes it possible to simulate very large, dense crowds composed of up to a hundred thousand agents at near- interactive rates on desktop computers.

Federico Bianchi takes up this intersection between computational mathematics and social behavior in a useful short paper called “From Micro to Macro and Back Again: Agent-based Models for Sociology” (link). His paper focuses on one class of computational models, the domain of agent-based models. Here is how he describes this group of approaches to social explanation:

An Agent-Based Model (ABM) is a computational method which enables to study a social phenomenon by representing a set of agents acting upon micro-level behavioural rules and interacting within environmental macro-level (spatial, structural, or institutional) constraints. Agent-Based Social Simulation (ABSS) gives social scientists the possibility to test formal models of social phenomena, generating a virtual representation of the model in silico through computer programming, simulating its systemic evolution over time and comparing it with the observed empirical phenomenon. (1) 

 And here is how he characterizes the role of what I called “aggregation dynamics” above:

Solving the complexity by dissecting the macro-level facts to its micro-level components and reconstructing the mechanism through which interacting actors produce a macro-level social outcome. In other words, reconstructing the micro-macro link from interacting actors to supervenient macrosociological facts. (2)

Or in other words, the task of analysis is to provide a testable model that can account for the way the behaviors and interactions at the individual level can aggregate to the observed patterns at the macro level.

Another more extensive example of work in this area is Gianluca Manzo, Analytical Sociology: Actions and Networks. Manzo’s volume proceeds from the perspective of analytical sociology and agent-based models. Manzo provides a very useful introduction to the approach, and Peter Hedstrom and Petri Ylikoski extend the introduction to the field with a chapter examining the role of rational-choice theory within this approach. The remainder of the volume takes the form of essays by more than a dozen sociologists who have used the approach to probe and explain specific kinds of social phenomena.

Manzo provides an account of explanation that highlights the importance of “generating” the phenomena to be explained. Here are several principles of methodology on this topic:

  • P4: in order to formulate the “generative model,” provide a realistic description of the relevant micro-level entities (P4a) and activities (P4b) assumed to be at work, as well as of the structural interdependencies (P4c) in which these entities are embedded and their  activities unfold;
  • P5: in order rigorously to assess the internal consistency of the “generative model” and to determine its high-level consequences, translate the “generative model” into an agent-based computational model;
  • P6: in order to assess the generative sufficiency of the mechanisms postulated, compare the agent-based computational model’s high-level consequences with the empirical description of the facts to be explained (9)

So agent-based modeling simulations are a crucial part of Manzo’s understanding of the logic of analytical sociology. As agent-based modelers sometimes put the point, “you haven’t explained a phenomenon until you’ve shown how it works on the basis of a detailed ABM.” But the ABM is not the sole focus of sociological research, on Manzo’s approach. Rather, Manzo points out that there are distinct sets of questions that need to be investigated: how do the actors make their choices? What are the structural constraints within which the actors exist? What kinds of interactions and relations exist among the actors? Answers to all these kinds of question are needed if we are to be able to design realistic and illuminating agent-based models of concrete phenomena.

Here is Manzo’s summary table of the research cycle (8). And he suggests that each segment of this representation warrants a specific kind of analysis and simulation.

This elaborate diagram indicates that there are different locations within a complex social phenomenon where different kinds of analysis and models are needed. (In this respect the approach Manzo presents parallels the idea of structuring research methodology around the zones of activity singled out by the idea of methodological localism; link.) This is methodologically useful, because it emphasizes to the researcher that there are quite a few different kinds of questions that need to be addressed in order to successfully explain a give domain of phenomena.

The content-specific essays in the volume focus on one or another of the elements of this description of methodology. For example, Per-Olof Wikstrom offers a “situational action theory” account of criminal behavior; this definition of research focuses on the “Logics of Action” principle 4b.

People commit acts of crime because they perceive and choose (habitually or after some deliberation) a particular kind of act of crime as an action alternative in response to a specific motivation (a temptation or a provocation). People are the source of their actions but the causes of their actions are situational. (75)

SAT proposes that people with a weak law-relevant personal morality and weak ability to exercise self-control are more likely to engage in acts of crime because they are more likely to see and choose crime as an option. (87)

Wikstrom attempts to apply these ideas by using a causal model to reproduce crime hotspots based on situational factors (90).

The contribution of Gonzalez-Bailon et al, “Online networks and the diffusion of protest,” focuses on the “Structural Interdependency” principle 4c.

One of the programmatic aims of analytical sociology is to uncover the individual-level mechanisms that generate aggregated patterns of behaviour…. The connection between these two levels of analysis, often referred to as the micro-macro link, is characterised by the complexity and nonlinearity that arises from interdependence; that is, from the influence that actors exert on each other when taking a course of action. (263)

Their contribution attempts to provide a basis for capturing the processes of diffusion that are common to a wide variety of types of social behavior, based on formal analysis of interpersonal networks.

Networks play a key role in diffusion processes because they facilitate threshold activation at the local level. Individual actors are not always able to monitor accurate the behavior of everyone else (as global thresholds assume) or they might be more responsive to a small group of people, represented in their personal networks. (271)

They demonstrate that the structure of the local network matters for the diffusion of an action and the activation of individual actors.

In short, Analytical Sociology: Actions and Networks illustrates a number of points of intersection between computational mathematics, simulation systems, and concrete sociological research. This is a very useful effort as social scientists attempt to bring more complex modeling tools to bear on concrete social phenomena.

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