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.

The research university

Where do new ideas, new technologies, and new ways of thinking about the world come from in a modern society? Since World War II the answer to this question has largely been found in research universities. Research universities are doctoral institutions that employ professors who are advanced academic experts in a variety of fields and that expend significant amounts of external funds in support of ongoing research. Given the importance of innovation and new ideas in the knowledge economy of the twenty-first century, it is very important to understand the dynamics of research universities, and to understand factors that make them more or less productive in achieving new knowledge. And, crucially, we need to understand how public policy can enhance the effectiveness of the university research enterprise for the benefit of the whole of society.

Jason Owen-Smith’s recent Research Universities and the Public Good: Discovery for an Uncertain Future is a very welcome and insightful contribution to better understanding this topic. Owen-Smith is a sociology professor at the University of Michigan (itself a major research university with over 1.5 billion dollars in annual research funding), and he brings to his task some of the most insightful ideas currently transforming the field of organizational studies.

Owen-Smith analyzes research universities (RU) in terms of three fundamental ideas. RUs serves as sourceanchor, and hub for the generation of innovations and new ideas in a vast range of fields, from the humanities to basic science to engineering and medicine. And he believes that this triple function makes research universities virtually unique among American (or global) knowledge-producing organizations, including corporate and government laboratories (33).

The idea of the university as a source is fairly obvious: it is the idea that universities create and disseminate new knowledge in a very wide range of fields. Sometimes that knowledge is of interest to a hundred people worldwide; and sometimes it results in the creation of genuinely transformative technologies and methods. The idea of the university as “anchor” refers largely to the stability that research universities offer the knowledge enterprise. Another aspect of the idea of the university as an anchor is the fact that it helps to create a public infrastructure that encourages other kinds of innovation in the region that it serves — much as an anchor tenant helps to bring potential customers to smaller stores in a shopping mall. Unlike other knowledge-centered organizations like private research labs or federal laboratories, universities have a diverse portfolio of activity that confers a very high level of stability over time. This is a large asset for the country as a whole. It is also frequently an asset for the city or region in which it is located.

The idea of the university as a hub is perhaps the most innovative perspective offered here. The idea of a hub is a network concept. A hub is a node that links individuals and centers to each other in ways that transcend local organizational charts. And the power of a hub, and the networks that it joins, is that it facilitates the exchange of information and ideas and creates the possibility of new forms of cooperation and collaboration. Here the idea is that a research university is a place where researchers form working relationships, both on campus and in national networks of affiliation. And the density and configuration of these relationships serve to facilitate communication and diffusion of new ideas and approaches to a given problem, with the result that progress is more rapid. O-S makes use of Peter Galison’s treatment of the simultaneous discovery of the relativity of time measurement by Einstein and Poincaré in Einstein’s Clocks and Poincaré’s Maps: Empires of Time.  Galison shows that Einstein and Poincaré were both involved in extensive intellectual networks that were quite relevant to their discoveries; but that their innovations had substantially different effects because of differences in those networks. Owen-Smith believes that these differences are very relevant in the workings of modern RUs in the United States as well. (See also Galison’s Image and Logic: A Material Culture of Microphysics.)

Radical discoveries like the theory of special relativity are exceptionally rare, but the conditions that gave rise to them should also enable less radical insights. Imagining universities as organizational scaffolds for a complex collaboration networks and focal point where flows of ideas, people, and problems come together offers a systematic way to assess the potential for innovation and novelty as well as for multiple discoveries. (p. 15)

Treating a complex and interdependent social process that occurs across relatively long time scales as if it had certain needs, short time frames, and clear returns is not just incorrect, it’s destructive. The kinds of simple rules I suggested earlier represent what organizational theorist James March called “superstitious learning.” They were akin to arguing that because many successful Silicon Valley firms were founded in garages, economic growth is a simple matter of building more garages. (25)

Rather, O-S demonstrates in the case of the development of the key discoveries that led to the establishment of Google, the pathway was long, complex, and heavily dependent on social networks of scientists, funders, entrepreneurs, graduate students, and federal agencies.

A key observation in O-S’s narrative at numerous points is the futility — perhaps even harmfulness — of attempting to harness university research to specific, quantifiable economic or political goals. The idea of selecting university research and teaching programs on the basis of their ROI relative to economic goals is, according to O-S, deeply futile. The extended example he offers of the research that led to the establishment of Google as a company and a search engine illustrates this point very compellingly: much of the foundational research that made the search algorithms possible had the look of entirely non-pragmatic or utilitarian knowledge production at the time it was funded (chapter 1). (The development of the smart phone has a similar history; 63.) Philosophy, art history, and social theory can be as important to the overall success of the research enterprise as more intentionally directed areas of research (electrical engineering, genetic research, autonomous vehicle design). His discussion of Wisconsin Governor Scott Walker’s effort to revise the mission statement of the University of Wisconsin is exemplary (45 ff.).

Contra Governor Walker, the value of the university is found not in its ability to respond to immediate needs but in an expectation that joining systematic inquiry and education will result in people and ideas that reach beyond local, sometimes parochial, concerns. (46-47)

Also interesting is O-S’s discussion of the functionality of the extreme decentralization that is typical of most large research universities. In general O-S regards this decentralization as a positive thing, leading to greater independence for researchers and research teams and permitting higher levels of innovation and productive collaboration. In fact, O-S appears to believe that decentralization is a critical factor in the success of the research university as source, anchor, and hub in the creation of new knowledge.

The competition and collaboration enabled by decentralized organization, the pluralism and tension created when missions and fields collide, and the complex networks that emerge from knowledge work make universities sources by enabling them to produce new things on an ongoing basis. Their institutional and physical stability prevents them from succumbing to either internal strife or the kinds of ‘creative destruction’ that economist Joseph Schumpeter took to be a fundamental result of innovation under capitalism. (61)

O-S’s discussion of the micro-processes of discovery is particularly interesting (chapter 3). He makes a sustained attempt to dissect the interactive, networked ways in which multiple problems, methods, and perspectives occasionally come together to solve an important problem or develop a novel idea or technology. In O’S’s telling of the story, the existence of intellectual and scientific networks is crucial to the fecundity of these processes in and around research universities.

This is an important book and one that merits close reading. Nothing could be more critical to our future than the steady discovery of new ideas and solutions. Research universities have shown themselves to be uniquely powerful engines for discovery and dissemination of new knowledge. But the rapid decline of public appreciation of universities presents a serious risk to the continued vitality of the university-based knowledge sector. The most important contribution O-S has made here, in my reading, is the detailed work he has done to give exposition to the “micro-processes” of the research university — the collaborations, the networks, the unexpected contiguities of problems, and the high level of decentralization that American research universities embody. As O-S documents, these processes are difficult to present to the public in a compelling way, and the vitality of the research university itself is vulnerable to destructive interference in the current political environment. Providing a clear, well-documented account of how research universities work is a major and valuable contribution.

The mind of government

We often speak of government as if it has intentions, beliefs, fears, plans, and phobias. This sounds a lot like a mind. But this impression is fundamentally misleading. “Government” is not a conscious entity with a unified apperception of the world and its own intentions. So it is worth teasing out the ways in which government nonetheless arrives at “beliefs”, “intentions”, and “decisions”.

Let’s first address the question of the mythical unity of government. In brief, government is not one unified thing. Rather, it is an extended network of offices, bureaus, departments, analysts, decision-makers, and authority structures, each of which has its own reticulated internal structure.

This has an important consequence. Instead of asking “what is the policy of the United States government towards Africa?”, we are driven to ask subordinate questions: what are the policies towards Africa of the State Department, the Department of Defense, the Department of Commerce, the Central Intelligence Agency, or the Agency for International Development? And for each of these departments we are forced to recognize that each is itself a large bureaucracy, with sub-units that have chosen or adapted their own working policy objectives and priorities. There are chief executives at a range of levels — President of the United States, Secretary of State, Secretary of Defense, Director of CIA — and each often has the aspiration of directing his or her organization as a tightly unified and purposive unit. But it is perfectly plain that the behavior of functional units within agencies are only loosely controlled by the will of the executive. This does not mean that executives have no control over the activities and priorities of subordinate units. But it does reflect a simple and unavoidable fact about large organizations. An organization is more like a slime mold than it is like a control algorithm in a factory.

This said, organizational units at all levels arrive at something analogous to beliefs (assessments of fact and probable future outcomes), assessments of priorities and their interactions, plans, and decisions (actions to take in the near and intermediate future). And governments make decisions at the highest level (leave the EU, raise taxes on fuel, prohibit immigration from certain countries, …). How does the analytical and factual part of this process proceed? And how does the decision-making part unfold?

One factor is particularly evident in the current political environment in the United States. Sometimes the analysis and decision-making activities of government are short-circuited and taken by individual executives without an underlying organizational process. A president arrives at his view of the facts of global climate change based on his “gut instincts” rather than an objective and disinterested assessment of the scientific analysis available to him. An Administrator of the EPA acts to eliminate long-standing environmental protections based on his own particular ideological and personal interests. A Secretary of the Department of Energy takes leadership of the department without requesting a briefing on any of its current projects. These are instances of the dictator strategy (in the social-choice sense), where a single actor substitutes his will for the collective aggregation of beliefs and desires associated with both bureaucracy and democracy. In this instance the answer to our question is a simple one: in cases like these government has beliefs and intentions because particular actors have beliefs and intentions and those actors have the power and authority to impose their beliefs and intentions on government.

The more interesting cases involve situations where there is a genuine collective process through which analysis and assessment takes place (of facts and priorities), and through which strategies are considered and ultimately adopted. Agencies usually make decisions through extended and formalized processes. There is generally an organized process of fact gathering and scientific assessment, followed by an assessment of various policy options with public exposure. Final a policy is adopted (the moment of decision).

The decision by the EPA to ban DDT in 1972 is illustrative (link, linklink). This was a decision of government which thereby became the will of government. It was the result of several important sub-processes: citizen and NGO activism about the possible toxic harms created by DDT, non-governmental scientific research assessing the toxicity of DDT, an internal EPA process designed to assess the scientific conclusions about the environmental and human-health effects of DDT, an analysis of the competing priorities involved in this issue (farming, forestry, and malaria control versus public health), and a decision recommended to the Administrator and adopted that concluded that the priority of public health and environmental safety was weightier than the economic interests served by the use of the pesticide.

Other examples of agency decision-making follow a similar pattern. The development of policy concerning science and technology is particularly interesting in this context. Consider, for example, Susan Wright (link) on the politics of regulation of recombinant DNA. This issue is explored more fully in her book Molecular Politics: Developing American and British Regulatory Policy for Genetic Engineering, 1972-1982. This is a good case study of “government making up its mind”. Another interesting case study is the development of US policy concerning ozone depletion; link.

These cases of science and technology policy illustrate two dimensions of the processes through which a government agency “makes up its mind” about a complex issue. There is an analytical component in which the scientific facts and the policy goals and priorities are gathered and assessed. And there is a decision-making component in which these analytical findings are crafted into a decision — a policy, a set of regulations, or a funding program, for example. It is routine in science and technology policy studies to observe that there is commonly a substantial degree of intertwining between factual judgments and political preferences and influences brought to bear by powerful outsiders. (Here is an earlier discussion of these processes; link.)

Ideally we would like to imagine a process of government decision-making that proceeds along these lines: careful gathering and assessment of the best available scientific evidence about an issue through expert specialist panels and sections; careful analysis of the consequences of available policy choices measured against a clear understanding of goals and priorities of the government; and selection of a policy or action that is best, all things considered, for forwarding the public interest and minimizing public harms. Unfortunately, as the experience of government policies concerning climate change in both the Bush administration and the Trump administration illustrates, ideology and private interest distort every phase of this idealized process.

(Philip Tetlock’s Superforecasting: The Art and Science of Prediction offers an interesting analysis of the process of expert factual assessment and prediction. Particularly interesting is his treatment of intelligence estimates.)

Is corruption a social thing?

When we discuss the ontology of various aspects of the social world, we are often thinking of such things as institutions, organizations, social networks, value systems, and the like. These examples pick out features of the world that are relatively stable and functional. Where does an imperfection or dysfunction of social life like corruption fit into our social ontology?

We might say that “corruption” is a descriptive category that is aimed at capturing a particular range of behavior, like stealing, gossiping, or asceticism. This makes corruption a kind of individual behavior, or even a characteristic of some individuals. “Mayor X is corrupt.”

This initial effort does not seem satisfactory, however. The idea of corruption is tied to institutions, roles, and rules in a very direct way, and therefore we cannot really present the concept accurately without articulating these institutional features of the concept of corruption. Corruption might be paraphrased in these terms:

  • Individual X plays a role Y in institution Z; role Y prescribes honest and impersonal performance of duties; individual X accepts private benefits to take actions that are contrary to the prescriptions of Y. In virtue of these facts X behaves corruptly.

Corruption, then, involves actions taken by officials that deviate from the rules governing their role, in order to receive private benefits from the subjects of those actions. Absent the rules and role, corruption cannot exist. So corruption is a feature that presupposes certain social facts about institutions. (Perhaps there is a link to Searle’s social ontology here; link.)

We might consider that corruption is analogous to friction in physical systems. Friction is a factor that affects the performance of virtually all mechanical systems, but that is a second-order factor within classical mechanics. And it is possible to give mechanical explanations of the ubiquity of friction, in terms of the geometry of adjoining physical surfaces, the strength of inter-molecular attractions, and the like. Analogously, we can offer theories of the frequency with which corruption occurs in organizations, public and private, in terms of the interests and decision-making frameworks of variously situated actors (e.g. real estate developers, land value assessors, tax assessors, zoning authorities …). Developers have a business interest in favorable rulings from assessors and zoning authorities; some officials have an interest in accepting gifts and favors to increase personal income and wealth; each makes an estimate of the likelihood of detection and punishment; and a certain rate of corrupt exchanges is the result.

This line of thought once again makes corruption a feature of the actors and their calculations. But it is important to note that organizations themselves have features that make corrupt exchanges either more likely or less likely (link, link). Some organizations are corruption-resistant in ways in which others are corruption-neutral or corruption-enhancing. These features include internal accounting and auditing procedures; whistle-blowing practices; executive and supervisor vigilance; and other organizational features. Further, governments and systems of law can make arrangements that discourage corruption; the incidence of corruption is influenced by public policy. For example, legal requirements on transparency in financial practices by firms, investment in investigatory resources in oversight agencies, and weighty penalties to companies found guilty of corrupt practices can affect the incidence of corruption. (Robert Klitgaard’s treatment of corruption is relevant here; he provides careful analysis of some of the institutional and governmental measures that can be taken that discourage corrupt practices; link, link. And there are cross-country indices of corruption (e.g. Transparency International) that demonstrate the causal effectiveness of anti-corruption measures at the state level. Finland, Norway, and Switzerland rank well on the Transparency International index.)

So — is corruption a thing? Does corruption need to be included in a social ontology? Does a realist ontology of government and business organization have a place for corruption? Yes, yes, and yes. Corruption is a real property of individual actors’ behavior, observable in social life. It is a consequence of strategic rationality by various actors. Corruption is a social practice with its own supporting or inhibiting culture. Some organizations effectively espouse a core set of values of honesty and correct performance that make corruption less frequent. And corruption is a feature of the design of an organization or bureau, analogous to “mean-time-between-failure” as a feature of a mechanical design. Organizations can adopt institutional protections and cultural commitments that minimize corrupt behavior, while other organizations fail to do so and thereby encourage corrupt behavior. So “corruption-vulnerability” is a real feature of organizations and corruption has a social reality.

Exercising government’s will

Since the beginning of the industrial age the topic of regulation of private activity for the public good has been essential for the health and safety of the public. The economics of externalities and public harms are too powerful to permit private actors to conduct their affairs purely according to the dictates of profit and private interest. The desolation of the River Irk described in Engels’ The Condition of the Working-Class in England in 1844 was powerful evidence of this dynamic in the nineteenth century, and need for the protection of health and safety in the food industry, the protection of air and water quality, and establishment of regulations ensuring safe operation of industrial, chemical, and nuclear plants became evident in the middle of the twentieth century. (Of course it goes without saying that our current administration no longer concedes this point.)

A fundamental problem for understanding the mechanics of government is the question of how the will and intentions of government (policies and regulatory regimes) are conveyed from the sites of decision-making to the behavior of the actors whom these policies are meant to influence.

The familiar principal-agent problem designates precisely this complex of issues. Applying a government policy or regulation requires a chain of behaviors by multiple agents within an extended network of governmental and non-governmental offices. It is all too evident that actors at various levels have interests and intentions that are important to their choices; and blind obedience to commands from above is not a common practice within any organization. Instead, actors within an office or bureau have some degree of freedom to act strategically with regard to their own preferences and interests. What, then, are the arrangements that the principal can put in place that makes conformance by the agent more complete?

Further, there are commonly a range of non-governmental entities and actors who are affected by governmental policies and regulations. They too have the ability to act strategically in consideration of their preferences and interests. And some of the actions that are available to non-governmental actors have the capacity to significantly influence the impact and form of various governmental policies and regulations. The corporations that own nuclear power plants, for example, have an ability to constrain and deflect the inspection schedules to which their properties are subject through influence on legislators, and the regulatory agency may be seriously hampered in its ability to apply existing safety regulations.

This is a problem of social ontology: what kind of thing is a governmental agency, how does it work internally, and through what kinds of mechanisms does it influence the world around it (firms, criminals, citizens, local government, …)?

Two related ideas about the nature of organizations are relevant in this context. The idea of organizations as “strategic action fields” that is developed by Fligstein and McAdam (A Theory of Fields) fits the situation of a governmental agency. And the earlier work by Michel Crozier and Erhard Friedberg offer a similar account of the strategic action that jointly determines the workings of an organization. Here is a representative passage from Crozier and Friedberg:

The reader should not misconstrue the significance of this theoretical bet. We have not sought to formulate a set of general laws concerning the substance, the properties and the stages of development of organizations and systems. We do not have the advantage of being able to furnish normative precepts like those offered by management specialists who always believe they can elaborate a model of “good organization” and present a guide to the means and measures necessary to realize it. We present of series of simple propositions on the problems raised by the existence of these complex but integrated ensembles that we call organizations, and on the means and instruments that people have invented to surmount these problems; that is to say, to assure and develop their cooperation in view of the common goals.” 

L’acteur et le système, p. 11

(Here are some earlier discussions of these theories; link, link, link.  And here is a related discussion of Mayer Zald’s treatment of organizations; link.)

Also relevant from the point of view of the ontology of government organization is the new theory of institutional logics. Patricia Thornton, William Ocasio, and Michael Lounsbury describe new theoretical developments within the general framework of new institutionalism in The Institutional Logics Perspective: A New Approach to Culture, Structure and Process. Here is how they define their understanding of “institutional logic”:

… as the socially constructed, historical patterns of cultural symbols and material practices, including assumptions, values, and beliefs, by which individuals and organizations provide meaning to their daily activity, organize time and space, and reproduce their lives and experiences. (2)

The institutional logics perspective is a metatheoretical framework for analyzing the interrelationships among institutions, individuals, and organizations in social systems. It aids researchers in questions of how individual and organizational actors are influenced by their situation in multiple social locations in an interinstitutional system, for example the institutional orders of the family, religion, state, market, professions, and corporations. Conceptualized as a theoretical model, each institutional order of the interinstitutional system distinguishes unique organizing principles, practices, and symbols that influence individual and organizational behavior. Institutional logics represent frames of reference that condition actors’ choices for sensemaking, the vocabulary they use to motivate action, and their sense of self and identity. The principles, practices, and symbols of each institutional order differentially shape how reasoning takes place and how rationality is perceived and experienced. (2)

Here is a discussion of institutional logics; link.

So what can we say about the ontology of policy implementation, compliance, and executive decisions? We can say that —

  • it proceeds through individual actors in particular circumstances guided by particular interests and preferences; 
  • implementation is likely to be imperfect in the best of circumstances and entirely ineffectual in other circumstances; 
  • implementation is affected by the strategic non-governmental actors and organizations it is designed to influence, leading to further distortion and incompleteness. 

We can also, more positively, identify specific mechanisms that governments and executives introduce to increase the effectiveness of implementation of their policies. These include —

  • internal audit and discipline functions, 
  • communications and training strategies designed at enhancing conformance by intermediate actors, 
  • periodic purges of non-conformant sub-officials and powerful non-governmental actors, 
  • and dozens of other strategies and mechanisms of conformance.

Most fundamentally we can say that any model of government that postulates frictionless application and implementation of policy is flawed at its core. Such a model overlooks an ontological fundamental about government and other organizations, large and small: that organizational action is never automatic, algorithmic, or exact; that it is always conveyed by intermediate actors who have their own understandings and preferences about policy; and that it works in an environment where powerful non-governmental actors are almost always in positions to blunt the effectiveness of “the will of government”.

 

This topic unavoidably introduces the idea of corruption into the discussion (link, link). Sometimes the contrarian behavior of internal actors derives from private benefits offered them by outsiders influenced by the actions of government. (Hotels in Moscow?) More generally, however, it raises the question of conflicts of commitment, mission, role obligations, and organizational ethics.

Modeling the social

One of the most interesting authorities on social models and simulations is Scott Page. This month he published a major book on this topic, The Model Thinker: What You Need to Know to Make Data Work for You, and it is a highly valuable contribution. The book corresponds roughly to the content of Page’s very successful Coursera course on models and simulations, and it serves as an excellent introduction to many different kinds of mathematical models in the social sciences.

Page’s fundamental premise in the book is that we need many models, and many intellectual perspectives, to make sense of the social world. Mathematical modeling is a way of getting disciplined about the logic of our theories and hypotheses about various processes in the world, including the physical, biological, and social realms. No single approach will be adequate to understanding the complexity of the world; rather, we need multiple hypotheses and models to disentangle the many concurrent causal and systemic processes that are under way at a single time. As Page puts the point:

As powerful as single models can be, a collection of models accomplishes even more. With many models, we avoid the narrowness inherent in each individual model. A many-models approach illuminates each component model’s blind spots. Policy choices made based on single models may ignore important features of the world such as income disparity, identity diversity, and interdependencies with other systems. (2)

Social ontology supports this approach in a fundamental way. The way I would put the point is this: social processes are almost invariably heterogeneous in their causes, temporal characters, and effects. So we need to have a way of theorizing society that is well suited to the forms of heterogeneity, and the many-models approach does exactly that.

Page proposes that there are multiple reasons why we might turn to models of a situation (physical, ecological, social, …): to “reason, explain, design, communicate, act, predict, and explore” (15). We might simplify this list by saying that models can enhance theoretical understanding of complex phenomena (explanation, discovery of truth, exploration of hypotheses) and they may also serve practical purposes involving prediction and control.

 
 
 
Especially interesting are topics taken up in later chapters of the book, including the discussion of network models and broadcast, diffusion, and contagion models (chapters 9-10). These are all interesting because they represent different approaches to a common social phenomenon, the spread of a property through a population (ideas, disease, rebellion, hate and intolerance). These are among the most fundamental mechanisms of social change and stability, and Page’s discussion of relevant models is insightful and accessible.
 
Page describes the constructs he considers as models, or abstract representations analogous to mathematical expressions. But we might also think of them as mini-theories of social mechanisms. Many of these examples illustrate a single kind of process that is found in real social situations, though rarely in a pure form. Games of coordination are a good example (chapter 15): the challenge of coordinating behavior with another purposive actor in order to bring about a beneficial outcome for both is a common social circumstance. Game theory provides an abstract analysis of how coordination can be achieved between rational agents; and the situation is more complicated when we consider imperfectly rational actors.
 
Another distinction that might be relevant in sorting the models that Page describes is that between “micro” and “macro”. Some of the models Page presents have to do with individual-level behavior (and interactions between individuals); whereas others have to do with transitions among aggregated social states (market states, political regimes, ecological populations). The majority of the models considered have to do with individual choice, decision rules, and information sharing — a micro-level approach comparable to agent-based modeling techniques. Several of the systems-dynamics models fall at the macro-end of the spectrum. Page treats this issue with the concept of “granularity”: the level of structure and action at which the model’s abstraction is couched (222).
 
The book closes with two very interesting examples of important social phenomena that can be analyzed using some of the models in the book. The first is the opioid epidemic in the United States, and the second is the last four decades’ rapid increase in economic inequality. Thomas Schelling’s memorable phrase, “the inescapable mathematics of musical chairs”, is relevant to both problems. Once we recognize the changing rates of prescription of opioids, clustering of opioid users, and probability of transitioning from usage to addiction, the explosion of addition rates and mortality is inevitable.
 
Early in the book Page notes the current vogue for “big data” as a solution to the problem of understanding and forecasting large social trends and changes. He rightly argues that the data do not speak for themselves. Instead, it is necessary to bring analytical techniques to bear in order to identify relevant patterns, and we need to use imagination and rigor in creating hypotheses about the social mechanisms that underlie the patterns we discover. The Model Thinker is indeed a model of an approach to analyzing and understanding the complex world of social action and interaction that we inhabit.

Eleven years of Understanding Society

This month marks the end of the eleventh year of publication of Understanding Society. Thanks to all the readers and visitors who have made the blog so rewarding. The audience continues to be international, with roughly half of visits coming from the United States and the rest from UK, the Philippines, India, Australia, and other European countries. There are a surprising number of visits from Ukraine.

Topics in the past year have been diverse. The most frequent topic is my current research interest, organizational dysfunction and technology failure. Also represented are topics in the philosophy of social science (causal mechanisms, computational modeling), philosophy of history, China, and the politics of hate and division. The post with the largest number of views was “Is history probabilistic?”, posted on December 30, and the least-read post was “The insights of biography”, posted on August 29. Not surprisingly, the content of the blog follows the topics which I’m currently thinking about, including most recently the issue of sexual harassment of women in university settings.

Writing the blog has been a good intellectual experience for me. Taking an hour or two to think intensively about a particular idea — large or small — and trying to figure out what I think about it is genuinely stimulating for me. It makes me think of the description that Richard Schacht gave in an undergraduate course on nineteenth-century philosophy of Hegel’s theory of creativity and labor. A sculptor begins with an indefinite idea of a physical form, a block of stone, and a hammer and chisel, and through interaction with the materials, tools, and hands he or she creates something new. The initial vision, inchoate as it is, is not enough, and the block of stone is mute. But the sculptor gives material expression to his or her visions through concrete interaction with the materials at hand. This is not a bad analogy for the process of thinking and writing itself. It is interesting that Marx’s conception of the creativity of labor derives from this Hegelian metaphor.

This is what I had hoped for when I began the blog in 2007. I wanted to have a challenging form of expression that would allow me to develop ideas about how society and the social sciences work, and I hoped that this activity would draw me into new ideas, new thinking, and new approaches to problems already of interest. This has certainly materialized for me — perhaps in the same way that a sculptor develops new capacities by contending with the resistance and contingency of the stone. There are issues, perspectives, and complexities that I have come to find very interesting that would not have come up in a more linear kind of academic writing.

It is also interesting for me to reflect on the role that “audience” plays for the writer. Since the first year of the blog I have felt that I understood the level of knowledge, questions, and interests that brought visitors to read a post or two, and sometimes to leave a comment. This is a smart, sophisticated audience. I have felt complete freedom in treating my subjects in the way that I think about them, without needing to simplify or reduce the problems I am considering to a more “public” level. This contrasts with the experience I had in blogging for the Huffington Post a number of years ago. Huff Post was a much more visible platform, but I never felt a connection with the audience, and I never felt the sense of intellectual comfort that I have in producing Understanding Society. As a result it was difficult to formulate my ideas in a way that seemed both authentic and original.

So thank you, to all the visitors and readers who have made the blog so satisfying for me over such a long time.

Sexual harassment in academic contexts

Sexual harassment of women in academic settings is regrettably common and pervasive, and its consequences are grave. At the same time, it is a remarkably difficult problem to solve. The “me-too” movement has shed welcome light on specific individual offenders and has generated more awareness of some aspects of the problem of sexual harassment and misconduct. But we have not yet come to a public awareness of the changes needed to create a genuinely inclusive and non-harassing environment for women across the spectrum of mistreatment that has been documented. The most common institutional response following an incident is to create a program of training and reporting, with a public commitment to investigating complaints and enforcing university or institutional policies rigorously and transparently. These efforts are often well intentioned, but by themselves they are insufficient. They do not address the underlying institutional and cultural features that make sexual harassment so prevalent.

The problem of sexual harassment in institutional contexts is a difficult one because it derives from multiple features of the organization. The ambient culture of the organization is often an important facilitator of harassing behavior — often enough a patriarchal culture that is deferential to the status of higher-powered individuals at the expense of lower-powered targets. There is the fact that executive leadership in many institutions continues to be predominantly male, who bring with them a set of gendered assumptions that they often fail to recognize. The hierarchical nature of the power relations of an academic institution is conducive to mistreatment of many kinds, including sexual harassment. Bosses to administrative assistants, research directors to post-docs, thesis advisors to PhD candidates — these unequal relations of power create a conducive environment for sexual harassment in many varieties. In each case the superior actor has enormous power and influence over the career prospects and work lives of the women over whom they exercise power. And then there are the habits of behavior that individuals bring to the workplace and the learning environment — sometimes habits of masculine entitlement, sometimes disdainful attitudes towards female scholars or scientists, sometimes an underlying willingness to bully others that finds expression in an academic environment. (A recent issue of the Journal of Social Issues (link) devotes substantial research to the topic of toxic leadership in the tech sector and the “masculinity contest culture” that this group of researchers finds to be a root cause of the toxicity this sector displays for women professionals. Research by Jennifer Berdahl, Peter Glick, Natalya Alonso, and more than a dozen other scholars provides in-depth analysis of this common feature of work environments.)

The scope and urgency of the problem of sexual harassment in academic contexts is documented in excellent and expert detail in a recent study report by the National Academies of Sciences, Engineering, and Medicine (link). This report deserves prominent discussion at every university.

The study documents the frequency of sexual harassment in academic and scientific research contexts, and the data are sobering. Here are the results of two indicative studies at Penn State University System and the University of Texas System:

The Penn State survey indicates that 43.4% of undergraduates, 58.9% of graduate students, and 72.8% of medical students have experienced gender harassment, while 5.1% of undergraduates, 6.0% of graduate students, and 5.7% of medical students report having experienced unwanted sexual attention and sexual coercion. These are staggering results, both in terms of the absolute number of students who were affected and the negative effects that these  experiences had on their ability to fulfill their educational potential. The University of Texas study shows a similar pattern, but also permits us to see meaningful differences across fields of study. Engineering and medicine provide significantly more harmful environments for female students than non-STEM and science disciplines. The authors make a particularly worrisome observation about medicine in this context:

The interviews conducted by RTI International revealed that unique settings such as medical residencies were described as breeding grounds for abusive behavior by superiors. Respondents expressed that this was largely because at this stage of the medical career, expectation of this behavior was widely accepted. The expectations of abusive, grueling conditions in training settings caused several respondents to view sexual harassment as a part of the continuum of what they were expected to endure. (63-64)

The report also does an excellent job of defining the scope of sexual harassment. Media discussion of sexual harassment and misconduct focuses primarily on egregious acts of sexual coercion. However, the  authors of the NAS study note that experts currently encompass sexual coercion, unwanted sexual attention, and gender harassment under this category of harmful interpersonal behavior. The largest sub-category is gender harassment:

“a broad range of verbal and nonverbal behaviors not aimed at sexual cooperation but that convey insulting, hostile, and degrading attitudes about” members of one gender (Fitzgerald, Gelfand, and Drasgow 1995, 430). (25)

The “iceberg” diagram (p. 32) captures the range of behaviors encompassed by the concept of sexual harassment. (See Leskinen, Cortina, and Kabat 2011 for extensive discussion of the varieties of sexual harassment and the harms associated with gender harassment.)

 

The report emphasizes organizational features as a root cause of a harassment-friendly environment.

By far, the greatest predictors of the occurrence of sexual harassment are organizational. Individual-level factors (e.g., sexist attitudes, beliefs that rationalize or justify harassment, etc.) that might make someone decide to harass a work colleague, student, or peer are surely important. However, a person that has proclivities for sexual harassment will have those behaviors greatly inhibited when exposed to role models who behave in a professional way as compared with role models who behave in a harassing way, or when in an environment that does not support harassing behaviors and/or has strong consequences for these behaviors. Thus, this section considers some of the organizational and environmental variables that increase the risk of sexual harassment perpetration. (46)

Some of the organizational factors that they refer to include the extreme gender imbalance that exists in many professional work environments, the perceived absence of organizational sanctions for harassing behavior, work environments where sexist views and sexually harassing behavior are modeled, and power differentials (47-49). The authors make the point that gender harassment is chiefly aimed at indicating disrespect towards the target rather than sexual exploitation. This has an important implication for institutional change. An institution that creates a strong core set of values emphasizing civility and respect is less conducive to gender harassment. They summarize this analysis in the statement of findings as well:

Organizational climate is, by far, the greatest predictor of the occurrence of sexual harassment, and ameliorating it can prevent people from sexually harassing others. A person more likely to engage in harassing behaviors is significantly less likely to do so in an environment that does not support harassing behaviors and/or has strong, clear, transparent consequences for these behaviors. (50)

So what can a university or research institution do to reduce and eliminate the likelihood of sexual harassment for women within the institution? Several remedies seem fairly obvious, though difficult.

  • Establish a pervasive expectation of civility and respect in the workplace and the learning environment
  • Diffuse the concentrations of power that give potential harassers the opportunity to harass women within their domains
  • Ensure that the institution honors its values by refusing the “star culture” common in universities that makes high-prestige university members untouchable
  • Be vigilant and transparent about the processes of investigation and adjudication through which complaints are considered
  • Create effective processes that ensure that complainants do not suffer retaliation
  • Consider candidates’ receptivity to the values of a respectful, civil, and non-harassing environment during the hiring and appointment process (including research directors, department and program chairs, and other positions of authority)
  • Address the gender imbalance that may exist in leadership circles

As the authors put the point in the final chapter of the report:

Preventing and effectively addressing sexual harassment of women in colleges and universities is a significant challenge, but we are optimistic that academic institutions can meet that challenge–if they demonstrate the will to do so. This is because the research shows what will work to prevent sexual harassment and why it will work. A systemwide change to the culture and climate in our nation’s colleges and universities can stop the pattern of harassing behavior from impacting the next generation of women entering science, engineering, and medicine. (169)

System effects

Quite a few posts here have focused on the question of emergence in social ontology, the idea that there are causal processes and powers at work at the level of social entities that do not correspond to similar properties at the individual level. Here I want to raise a related question, the notion that an important aspect of the workings of the social world derives from “system effects” of the organizations and institutions through which social life transpires. A system accident or effect is one that derives importantly from the organization and configuration of the system itself, rather than the specific properties of the units.

What are some examples of system effects? Consider these phenomena:

  • Flash crashes in stock markets as a result of automated trading
  • Under-reporting of land values in agrarian fiscal regimes 
  • Grade inflation in elite universities 
  • Increase in product defect frequency following a reduction in inspections 
  • Rising frequency of industrial errors at the end of work shifts 

Here is how Nancy Leveson describes systems causation in Engineering a Safer World: Systems Thinking Applied to Safety:

Safety approaches based on systems theory consider accidents as arising from the interactions among system components and usually do not specify single causal variables or factors. Whereas industrial (occupational) safety models and event chain models focus on unsafe acts or conditions, classic system safety models instead look at what went wrong with the system’s operation or organization to allow the accident to take place. (KL 977)

Charles Perrow offers a taxonomy of systems as a hierarchy of composition in Normal Accidents: Living with High-Risk Technologies:

Consider a nuclear plant as the system. A part will be the first level — say a valve. This is the smallest component of the system that is likely to be identified in analyzing an accident. A functionally related collection of parts, as, for example, those that make up the steam generator, will be called a unit, the second level. An array of units, such as the steam generator and the water return system that includes the condensate polishers and associated motors, pumps, and piping, will make up a subsystem, in this case the secondary cooling system. This is the third level. A nuclear plan has around two dozen subsystems under this rough scheme. They all come together in the fourth level, the nuclear plant or system. Beyond this is the environment. (65)

Large socioeconomic systems like capitalism and collectivized socialism have system effects — chronic patterns of low productivity and corruption in the latter case, a tendency to inequality and immiseration in the former case. In each case the observed effect is the result of embedded features of property and labor in the two systems that result in specific kinds of outcomes. And an important dimension of social analysis is to uncover the ways in which ordinary actors pursuing ordinary goals within the context of the two systems, lead to quite different outcomes at the level of the “mode of production”. And these effects do not depend on there being a distinctive kind of actor in each system; in fact, one could interchange the actors and still find the same macro-level outcomes.

Here is a preliminary effort at a definition for this concept in application to social organizations:

A system effect is an outcome that derives from the embedded characteristics of incentive and opportunity within a social arrangement that lead normal actors to engage in activity leading to the hypothesized aggregate effect.

Once we see what the incentive and opportunity structures are, we can readily see why some fraction of actors modify their behavior in ways that lead to the outcome. In this respect the system is the salient causal factor rather than the specific properties of the actors — change the system properties and you will change the social outcome.

 

When we refer to system effects we often have unintended consequences in mind — unintended both by the individual actors and the architects of the organization or practice. But this is not essential; we can also think of examples of organizational arrangements that were deliberately chosen or designed to bring about the given outcome. In particular, a given system effect may be intended by the designer and unintended by the individual actors. But when the outcomes in question are clearly dysfunctional or “catastrophic”, it is natural to assume that they are unintended. (This, however, is one of the specific areas of insight that comes out of the new institutionalism: the dysfunctional outcome may be favorable for some sets of actors even as they are unfavorable for the workings of the system as a whole.)

 
Another common assumption about system effects is that they are remarkably stable through changes of actors and efforts to reverse the given outcome. In this sense they are thought to be somewhat beyond the control of the individuals who make up the system. The only promising way of undoing the effect is to change the incentives and opportunities that bring it about. But to the extent that a given configuration has emerged along with supporting mechanisms protecting it from deformation, changing the configuration may be frustratingly difficult.

Safety and its converse are often described as system effects. By this is often meant two things. First, there is the important insight that traditional accident analysis favors “unit failure” at the expense of more systemic factors. And second, there is the idea that accidents and failures often result from “tightly linked” features of systems, both social and technical, in which variation in one component of a system can have unexpected consequences for the operation of other components of the system. Charles Perrow describes the topic of loose and tight coupling in social systems in Normal Accidents; 89 ff,)

Social mobility disaggregated

 

There is a new exciting and valuable contribution from the research group around Raj Chetty, Nathan Hendren, and John Friedman, this time on the topic of neighborhood-level social mobility. (Earlier work highlighted measures of the impact on social mobility contributed by university education across the country. This work is presented on the Opportunity Insights website; link, link. Here is an earlier post on that work; link.) In the recently released work Chetty and his colleagues have used census data to compare incomes of parents and children across the country by neighborhood of birth, with the ability to disaggregate by race and gender, and the results are genuinely staggering. Here is a report on the project on the US Census website; link. The interactive dataset and mapping app are provided here (link). The study identifies neighborhoods of origin; characteristics of parents and neighborhoods; and characteristics of children.

Here are screenshots of metropolitan Detroit representing the individual incomes of the children (as adults) based on their neighborhoods of origin for all children, black children, and white children. (Of course a percentage of these individuals no longer live in the original neighborhood.) There are 24 outcome variables included as well as 13 neighborhood characteristics, and it is possible to create maps based on multiple combinations of these variables. It is also possible to download the data.

Children born in Highland Park, Michigan earned an average individual income as adults in 2014-15 of $18K; children born in Plymouth, Michigan earned an average individual income as adults of $42K. It is evident that these differences in economic outcomes are highly racialized; in many of the tracts in the Detroit area there are “insufficient data” for either black or white individuals to provide average data for these sub-populations in the given areas. This reflects the substantial degree of racial segregation that exists in the Detroit metropolitan area. (The project provides a special study of opportunity in Detroit, “Finding Opportunity in Detroit”.)

This dataset is genuinely eye-opening for anyone interested in the workings of economic opportunity in the United States. It is also valuable for public policy makers at the local and higher levels who have an interest in improving outcomes for children in poverty. It is possible to use the many parameters included in the data to probe for obstacles to socioeconomic progress that might be addressed through targeted programs of opportunity enhancement.

(Here is a Brookings description of the social mobility project’s central discoveries; link.)

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