Granular perspectives on the Cultural Revolution

It has been observed in earlier posts that there is still a lot we do not understand about China's Cultural Revolution (post, post, post). Why such a sudden and apparently crazed eruption of violence? Why such ultra-radicalism among extremely young adolescents? Why the apparent self-destruction of the Party? How to explain the behavior of Mao and other high Party officials? How much artful politics and how much contagious irrational behavior?

Yiching Wu's The Cultural Revolution at the Margins: Chinese Socialism in Crisis provides an important body of new evidence and perspective on these topics. Wu undertakes to find the perspective of the margin in this study -- the players who were not major national leaders in politics or military, but local activists, cadres, and youth who brought their own, often heterodox, political demands and goals to the struggles. Here is how Wu describes his overriding purpose in the book:

This book is a history of the Cultural Revolution from the perspective of its unruly margins, written with the purpose of better understanding and recuperating a moment of political and ideological possibilities that have been silenced in conventional history and understudied in existing scholarship. Exploring what may be considered a decentered account of the Cultural Revolution, this book attempts to give voices and historical visibility to those otherwise consigned to the peripheries of the movement, where the discontented, disadvantaged, and excluded pressed their demands by creatively exploiting the paralysis of the political order. (xvi)

Wu believes that the currents he uncovers help to explain the surprising twists and turns of events between 1966 and 1976 in China. But they are also important for reasons having to do with China's subsequent development as well. The suppression of the more populist and anti-bureaucratic elements of the CR movement in 1966-1967 prepared the ground for China's turn to market reform and regularization of the economy a decade later.

I argue that the origins of the momentous changes that have radically transformed contemporary Chinese society since the late 1970s and early 1980s can be traced, at least partly, to the height of the Cultural Revolution in 1967–1968, when the turn toward demobilization of the mass movement and restoration of party and state organizations became hardly mistakable. (xvii)

Essentially, if I understand Wu's argument correctly, he believes that the ultra-left strands of the CR were defeated fairly early in the decade of the Cultural Revolution, and the silencing of these voices prepared the ground for a more moderate or even "right-ist" set of policies after the end of the CR period.

I wrote recently of Martin Whyte's critique of the "social volcano" interpretation of contemporary China. Whyte argues that an important survey of public opinion demonstrates that China is not riven by social conflict over inequalities and corruption. Wu plainly believes otherwise:

The state's concern for security may be justified by the ominous reality of proliferating social antagonisms in China. Nationwide, cases of “mass incidents”— a euphemism for protests, riots, and other forms of unrest— escalated from fewer than 10,000 in 1993 to 15,000 in 1997, 32,000 in 1999, 50,000 in 2002, 58,000 in 2003, 74,000 in 2004, and 87,000 in 2005.18 By 2010, the number of protests and riots reportedly had more than doubled again, to 180,000.19 The deep disaffection of those left behind by China's rapid economic development is often rooted in a historical experience obscured by the dominant discourse. The danger posed by the country's socialist and revolutionary past to the current sociopolitical order is evident, as shown in a popular ditty:

Beijing relies on the [Party] Center, Shanghai on its connections, Guangzhou leans on Hong Kong, The drifting population lives by Mao Zedong Thought.20 (6)

Here Wu plainly believes that there is growing unrest and dissatisfaction, potentially relevant to anti-regime mobilization. 

Wu's approach to this material is original and illuminating. He discusses the decade as a "heterogeneous" set of processes that were often independent from the official ideologies and discourses advanced by political leaders and propagandists. Here is an illustrative statement:

The term “margins”  here pertains not only to the actors involved— those who  were disadvantaged or marginalized in Chinese social and political life— but also to the issues and demands that galvanized political contention, practices that went against the grain, and points of view outside the range of the permissible. Often tentative, heterogeneous, and dispersed, these developments  were not marginal in the sense of being trivial or having little political relevance. Although politics at the margins often played no decisive role in determining Red Guard factionalism, it sometimes mobilized tens of thousands of people and occurred in major political centers. (10)

What this comes down to fundamentally is the idea that the CR was not one unified thing, but rather a heterogeneous mix of groups, views, interests, strategies, and passions that cannot be readily described in simple abstract terms. 

A key thread in Wu's construction is the toxic role that was played in the early years of Red Guard activism by the CCP's class classification system. Individuals, including especially children, were classified as red or black, heroes or monsters, depending on the occupational status of their fathers and the record of political behavior associated with their families. So a system of hereditary privilege based on bloodlines of proletarian purity was created favoring the sons and daughters of Party and military cadres, and creating permanent and sometimes deadly discrimination against the children of "bourgeois" or "rightist" fathers. (Wu notes that this pattern of privilege continues into the twenty-first century; an astonishing percentage of China's contemporary billionaires are princelings.) And Wu believes that this flattened system had deeply destructive consequences in the decade of turmoil that ensued.

One of the most gripping stories in the book is Wu's careful uncovering of the (short) life and writings of Yu Luoke, an industrial apprentice whose reasoned essay "On Class Origins" on the Communist impropriety of the hereditary privilege created by the classification system eventually found readership in the millions. (Yu was executed at the age of 26 in the presence of 100,000 spectators for the crime of having promulgated these counter-revolutionary ideas; 92.) As Wu presents the case, Yu's analysis was precise, rigorous, and theoretically informed; and it laid the basis for a more liberal understanding of universal rights in Chinese society. 

The theme of heterogeneity carries through to Wu's analysis of the social composition of various segments of activists within the CR. This is particularly evident in his treatment of the Shanghai January Storm (January 1967). Workers were a critical part of this important episode; but workers were not a homogeneous group in Shanghai or elsewhere. Rather, there were different ideologies, grievances, and interests across various groups of workers:
In Shanghai, the sources of socioeconomic discontents were highly diverse, and the lines of demarcation were multiple: between workers employed in state enterprises and those in collective sectors, between senior and junior workers, between permanent and temporary workers, and so on.... One of the most important divisions in Shanghai was that between regular, permanent workers and the vast semiproletarian workforce consisting of temporary and contract workers. (101-102)

Wu demonstrates how important it is to provide a more granular analysis of the various groups of the "proletariat" who became activists in Shanghai -- a lesson that conforms well with current thinking within theories of contentious politics more generally.

There is much more of interest in The Cultural Revolution at the Margins: Chinese Socialism in Crisis. But this brief discussion serves to illustrate a more general point as well: the history of China's Cultural Revolution is not yet complete, 38 years after the death of Mao. Yiching Wu has made another important step forward in filling out that history. 

Social mechanisms and ABM methods

One particularly appealing aspect of agent-based models is the role they can play in demonstrating the inner workings of a major class of social mechanisms, the group we might refer to as mechanisms of aggregation. An ABM is designed to work out how a field of actors of a certain description, in specified kinds of interaction, lead through time to a certain kind of aggregate effect. This class of mechanisms corresponds to the upward strut of Coleman's boat. This is certainly a causal story; it is a generative answer to the question, how does it work?

However, anyone who thinks carefully about causation will realize that there are causal sequences that occur only once. Consider this scenario: X occurs, conditions Ci take place in a chronological sequence, and Y is the result. So X caused Y through the causal steps instigated by Ci. We wouldn't want to say the complex of interactions and causal links associated with the progress of the system through Ci as a mechanism linking X to Y; rather, this ensemble is the particular (in this case unique) causal pathway from X to Y. But when we think about mechanisms, we generally have in mind the idea of "recurring causal linkages", not simply a true story about how X caused Y in these particular circumstances. In other words, for a causal story to represent a mechanism, it needs to be a story that can be found to hold in an indefinite number of cases. Mechanisms are recurring complexes of causal sequences.

An agent-based model serves to demonstrate how a set of actors give rise to a certain aggregate outcome. This is plainly a species of causal argument. But it is possible to apply ABM methods to circumstances that are unique and singular. This kind of ABM model lacks an important feature generally included in the definition of a mechanism-- the idea of recurrence across a number of cases. So we might single out for special attention those ABMs that identify and analyze processes that recur across multiple social settings. Here we might refer, for example, to the "Schelling mechanism" of residential segregation. There are certainly other unrelated mechanisms associated with urban segregation -- mortgage lending practices or real estate steering practices, for example. But the Schelling mechanism is one contributing factor in a range of empirical and historical cases. And it is a factor that works through the local preferences of individual actors.

So this seems to answer one important question: in what ways can ABM simulations be said to describe social mechanisms? They do so when (i) they describe an aggregative process through which a given meso-level outcome arises, and (ii) the sequence they describe can be said to recur in multiple instances of social process.

A question that naturally arises here is whether there are social mechanisms that fall outside this group. Are there social mechanisms that could not be represented by an ABM model? Or would we want to say that mechanisms are necessarily aggregative, so all mechanisms should be amenable to representation by an ABM?

This is a complicated question. One possible response seems easily refuted: there are mechanisms that work from meso level (organizations) to macro level (rise of fascism) that do not invoke the features of individual actors. Therefore there are mechanisms that do not conform strictly to the requirements of methodological individualism. However, there is nothing in the ABM methodology that requires that the actors should be biological individuals. Certainly it is possible to design an ABM representing the results of competition among firms with different behavioral characteristics. This example still involves an aggregative construction, a generation of the macro behavior on the basis of careful specification of the behavioral characteristics of the units.

Another possible candidate for mechanisms not amenable to ABM analysis might include the use of network analysis to incorporate knowledge-diffusion characteristics into analysis of civil unrest and other kinds of social change. It is sometimes argued that there are structural features of networks that are independent of actor characteristics and choices. But given that ABM theorists often incorporate aspects of network theory into their formal representations of a social process, it is hard to maintain that facts about networks cannot be incorporated into ABM methods.

Another candidate is what Chuck Tilly and pragmatist sociologists (Gross, Abbott, Joas) refer to as the "relational characteristics" of a social situation. Abbott puts the point this way: often a social outcome isn't the result of an ensemble of individuals making discrete choices, but rather is a dance of interaction in which each individual's moves both inform and self-inform later stages of the interaction. This line of thought seems effective as a rebuttal to methodological individualism, or perhaps even analytical sociology, but I don't think it demonstrates a limitation of the applicability of ABM modeling. ABM methods are agnostic about the nature of the actors and their interactions. So it is fully possible for an ABM theorist to attempt to produce a representation of the iterative process just described; or to begin the analysis with an abstraction of the resultant behavioral characteristics found in the group.

I've argued here that it is legitimate to postulate meso-to-meso causal mechanisms. Meso-level things can have causal powers that allow them to play a role in causal stories about social outcomes. I continue to believe that is so. But considerations brought forward here make me think that even in cases where a theorist singles out a meso-meso causal mechanism, he or she is still offering some variety of disaggregative analysis of the item to be explained. It seems that providing a mechanism is always a process of delving below the level of the explananda to uncover the underlying processes and causal powers that bring it about.

So the considerations raised here seem to lead to a strong conclusion -- that all social mechanisms can be represented within the framework of an ABM (stipulating that ABM methods are agnostic about the kinds of agents they postulate). Agent-based models are to social processes as molecular biology is to the workings of the cell.

In fact, we might say that ABM methods simply provide a syntax for constructing social explanations: to explain a phenomenon, identify some of the constituents of the phenomenon, arrive at specifications of the properties of those constituents, and demonstrate how the behavior of the constituents aggregates to the phenomenon in question.

(It needs to be recognized that identifying agent-based social mechanisms isn't the sole use of ABM models, of course. Other uses include prediction of the future behavior of a complex system, "what if" experimentation, and data-informed explanations of complex social outcomes. But these methods certainly constitute a particularly clear and rigorous way of specifying the mechanism that underlies some kinds of social processes.)

Modeling organizational performance

Organizations do things that we care about. They are generally at least partially designed in order to bring about certain kinds of outcomes, and managers continue to tinker with them to improve them. And we have very good reasons for wanting to be able to measure their performance, to introduce innovations that improve performance, and to measure the improvements that result. These points are true whether we have in mind examples drawn from business, government, or contentious politics.

We might offer a highly abstract description of an organization as an ensemble of --

• actors and motives
• rules of action for the actors
• authority relations
• activities
• inputs
• outputs

Abstractly we can define the quality of the organization in terms of the efficiency and effectiveness with which it brings about its intended outcomes. Consider an organization designed to recruit teenagers into the Peace Corps. The organization requires a certain level of input (money and staff time) to produce a given level out output (let's say 100 adequately qualified recruits). If two organizations are intended to perform this same function but one requires twice the labor time and twice the inputs of the second, we can say that the first organization is inferior to the second on grounds of efficiency. If one organization gives rise to recruits who are of greater likelihood to persist through training, we can say this organization is superior on grounds of effectiveness.

We might represent the activities, inputs, and outputs of the organization in a diagram analogous to a diagram of an industrial process. Here we would represent the functional components within the structure in terms of their inputs and outputs, and we would represent the workings of the organization as a flow of "products" from one component to another. Consider, for example, this diagram of the production process of a Chinese electrical device factory.

We can imagine a highly analogous diagram for the flow of patients through the various service areas of a hospital.

We can readily introduce evaluative characteristics for such a process: for example, efficiency, productivity, quality of product, level of work satisfaction, and profitability. And now we can give a specific definition to the idea of process improvement; it is an innovation to the process that reduces costs with the same level of output (quality and quantity) or improves output with a constant level of cost.

We are now in a position to ask the question of possible "improvements" in the process: are there innovations in the process that will reduce waste, reduce costs, increase quality of output, improve job satisfaction for workers, or increase profitability? Can we rearrange linkages within the process that reduce costs or increase quality? Can we redesign component processes to save energy, time, or inputs? Can we identify factors that lead to worker dissatisfaction and ameliorate them?

An industrial process like this one can be represented with off-the-shelf simulation software (for example, SIMUL8). Each component process is assigned a set of technical characteristics (raw material needs, time of assembly, energy requirements, labor time) and we can run the simulation to measure inputs (raw materials, energy, labor), outputs, and wastage. We can then experiment with various innovations in the process by tweaking the linkages among the components and modifying the components in ways that affect their technical characteristics. These simulation systems are widely used in manufacturing industry, and they are proven to contribute to rapid design and re-design of complex manufacturing processes so as to create workable industrial solutions. (Here is an Autodesk simulation video of a production process simulation.)

Is it possible to treat social organizations in an analogous way? Can a hospital, a labor union, a tax bureaucracy, or a university be represented as a flow of activities and transformations? There are classes of organizations where this approach seems to work well. It would seem that any organization that serves primarily to process information and transactions can be represented in this way. So a hospital fits this framework fairly well: the patient arrives in the ER reception area; information is collected; patient is moved to an ER examining room; doctor evaluates condition and assigns diagnosis; patient receives urgent treatment; patient is assigned to in-patient room; and so forth. By charting out this set of transactions it is possible for an industrial engineer to suggest changes in process to the hospital administrator that will save time, reduce costs, reduce accidents, or improve quality of treatment for the patient.

 

This description operates at the level of the functional and technical characteristics of the functional components of the system. But it is often important to approach organizations in a more granular way, by examining the behaviors of the individuals whose activities make up the technical characteristics of the component processes. Let's suppose that nursing units 1 and 2 have identical duties; but Unit 1 has a higher rate of hospital-born infection than Unit 2. What accounts for the difference? One possibility is that Unit 1 has a lower level of morale among the nurses, leading to a somewhat more careless attitude towards patient treatment. And to understand variations in morale, we need to gather more information about the influences on the working environment as experienced by the two groups of nurses.

Now let us suppose that we are interested in improving the quality of care (reducing hospital-born infections) in Unit 1. We need to have a hypothesis about what factors are contributing to the behaviors leading to sub-par care. Using this hypothesis we can design an intervention. For example, we might reason that Unit 1 has not yet been renovated and is painted a drab green color; whereas Unit 2 is painted with bright, cheerful colors. If we believe that the color of paint influences mood, we might innovate by repainting Unit 1 and monitoring results. If the rate of HBI remains high, then we have disconfirmed the paint hypothesis; if it falls, we have provided some (weak) support for the paint hypothesis.

This more micro-level perspective on the performance of organizations suggests a different kind of modeling. Here it seems that it would be possible to construct an agent-based model of the individuals who make up an interconnected space within a complex institution like a hospital. If we represent the actors' behavioral characteristics in such a way as to bring "concentration on task" into the simulation, we may be able to demonstrate the effects of low morale on patient safety, based on the interactive behaviors of high-morale or low-morale staff.

Another granular approach is available through the use of general-purpose simulation engines like SimCity to represent the flows and operations of the components of a system. Here is an introduction to the use of SimCity as a way of evaluating the likely consequences of various policy changes; link. Here are several simulations of the economic and demographic effects of mining in Ontario coming from the Social Innovations Simulation project; link.

Finally, there are applied simulation systems based on "discrete event simulation" (DES). Here is a good survey article published in Medical Decision Making describing the application of DES to hospitals; link. The authors describe the approach in these terms:

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article is to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as the wider modeling task force.

These simulation methodologies permit one important capability for the institutional designer: they permit the development of "experiments" in which we evaluate the expected consequences of a given innovation or policy change. And they are most applicable in situations where there are queues of users and flows of products. How will the functioning of the emergency room organization in a large hospital change if the registration process -- and therefore throughput -- is improved? The simulations mentioned here are intended to keep track of the spreading consequences of changes introduced in one or more parts of the system; and, as systems scientists often discover, those consequences are sometimes highly unexpected.

These kinds of approaches have been applied to a range of service organizations -- banks, restaurants, hospitals. Essentially this is the application of the tools of industrial and systems engineering to certain kinds of social organizations, and the experience in these applications has been positive. A more difficult question is whether these simulation techniques can aid in the effort to assess the functioning of more comprehensive and multi-purpose institutions like universities, police departments, or legislatures.

Emergentism and generationism

media: lecture by Stanford Professor Robert Sapolsky on chaos and reduction

Several recent posts have focused on the topic of simulations in the social sciences. An interesting question here is whether these simulation models shed light on the questions of emergence and reduction that frequently arise in the philosophy of the social sciences. In most cases the models I've mentioned are "aggregation" models, in which the simulation attempts to capture the chief dynamics and interaction effects of the units and then work out the behavior and evolution of the ensemble. This is visibly clear when it comes to agent-based models. However, some of the scholars whose work I admire are "complexity" theorists, and a common view within complexity studies is the idea that the system has properties that are difficult or impossible to derive from the features of the units.

So does this body of work give weight to the idea of emergence, or does it incline us more in the direction of supervenience and ontological unit-ism?

John Miller and Scott Page provide an accessible framework within which to consider these kinds of problems in Complex Adaptive Systems: An Introduction to Computational Models of Social Life. They look at certain kinds of social phenomena as constituting what they call "complex adaptive systems," and they try to demonstrate how some of the computational tools developed in the sciences of complex systems can be deployed to analyze and explain complex social outcomes. Here is how they characterize the key concepts:

Adaptive social systems are composed of interacting, thoughtful (but perhaps not brilliant) agents. (kl 151)

Page and Miller believe that social phenomena often display "emergence" in a way that we can make sense of. Here is the umbrella notion they begin with:

The usual notion put forth underlying emergence is that individual, localized behavior aggregates into global behavior that is, in some sense, disconnected from its origins. Such a disconnection implies that, within limits, the details of the local behavior do not matter to the aggregate outcome. (kl 826)

And they believe that the notion of emergence has "deep intuitive appeal". They find emergence to be applicable at several levels of description, including "disorganized complexity" (the central limit theorem, the law of large numbers) and "organized complexity" (the behavior of sand piles when grains have a small amount of control).

Under organized complexity, the relationships among the agents are such that through various feedbacks and structural contingencies, agent variations no longer cancel one another out but, rather, become reinforcing. In such a world, we leave the realm of the Law of Large Numbers and instead embark down paths unknown. While we have ample evidence, both empirical and experimental, that under organized complexity, systems can exhibit aggregate properties that are not directly tied to agent details, a sound theoretical foothold from which to leverage this observation is only now being constructed. (kl 976)

Organized complexity, in their view, is a substantive and important kind of emergence in social systems, and this concept plays a key role in their view of complex adaptive systems.

Another -- and contrarian -- contribution to this field is provided by Joshua Epstein. His three-volume work on agent-based models is a fundamental text book for the field. Here are the titles:

Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science
Growing Artificial Societies: Social Science From the Bottom Up
Generative Social Science: Studies in Agent-Based Computational Modeling

Chapter 1 of Generative Social Science provides an overview of Epstein's approach is provided in "Agent-based Computational Models and Generative Social Science", and this is a superb place to begin (link). Here is how Epstein defines generativity:

Agent-based models provide computational demonstrations that a given microspecification is in fact sufficient to generate a macrostructure of interest.... 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. (42)

Epstein describes an extensive attempt to model a historical population using agent-based modeling techniques, the Artificial Anasazi project (link). This work is presented in Dean, Gumerman, Epstein, Axtell, Swedlund, McCarroll, and Parker, "Understanding Anasazi Culture Change through Agent-Based Modeling" in Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes. The model takes a time series of fundamental environmental, climate, and agricultural data as given, and he and his team attempt to reconstruct (generate) the pattern of habitation that would result. Here is the finding they arrive at:

Generativity seems to be directly incompatible with the idea of emergence, and in fact Epstein takes pains to cast doubt on that idea.

I have always been uncomfortable with the vagueness--and occasional mysticism--surrounding this word and, accordingly, tried to define it quite narrowly.... There, we defined "emergent phenomena" to be simply "stable macroscopic patterns arising from local interaction of agents." (53)

So Epstein and Page both make use of the methods of agent based modeling, but they disagree about the idea of emergence. Page believes that complex adaptive systems give rise to properties that are emergent and irreducible; whereas Epstein doesn't think the idea makes a lot of sense. Rather, Epstein's view depends on the idea that we can reproduce (generate) the macro phenomena based on a model involving the agents and their interactions. Macro phenomena are generated by the interactions of the units; whereas for Page and Miller, macro phenomena in some systems have properties that cannot be easily derived from the activities of the units.

At the moment, anyway, I find myself attracted to Herbert Simon's effort to split the difference by referring to "weak emergence" (link):

... reductionism in principle even though it is not easy (often not even computationally feasible) to infer rigorously the properties of the whole from knowledge of the properties of the parts. In this pragmatic way, we can build nearly independent theories for each successive level of complexity, but at the same time, build bridging theories that show how each higher level can be accounted for in terms of the elements and relations of the next level down. (Sciences of the Artificial 3rd edition 172)

This view emphasizes the computational and epistemic limits that sometimes preclude generating the phenomena in question -- for example, the problems raised by non-linear causal relations and causal interdependence. Many observers have noted that the behavior of tightly linked causal systems may be impossible to predict, even when we are confident that the system outcomes are the result of "nothing but" the interactions of the units and sub-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.