Sunday, April 23, 2017

New understandings of populism

It is apparent, on this first round of the presidential elections in France, that we urgently need to understand better the dynamics and causes of radical populism in democratic polities. What is populism? Why does it have such virulence in the current moment as a political movement? What roles do racism, xenophobia, resentment, and economic fear play in the readiness of ordinary citizens in Europe and America to support radical populist candidates and platforms?

The topic has been the subject of research by very talented investigators over the past twenty years. Several recent books are especially relevant in the current moment. Particularly relevant are Cas Mudde and Cristobal Rovira Kaltwasser's Populism: A Very Short Introduction; Jan-Werner Muller's What Is Populism?; and a recent collection by Social Europe edited by Henning Meyer, Understanding the Populist Revolt. Taken together, the three sources provide an excellent basis for thinking further about the nature of radical populism.

Mudde and Kaltwasser argue that populism differs from other political umbrella terms (socialism, fascism) in one important respect: it is less specific in identifying a well defined ideological program. It is, in their words, "an essentially contested concept". Here are a few of their central ideas:
A more recent approach considers populism, first and foremost, as a political strategy employed by a specific type of leader who seeks to govern based on direct and unmediated support from their followers. It is particularly popular among students of Latin American and non-Western societies. The approach emphasizes that populism implies the emergence of a strong and charismatic figure, who concentrates power and maintains a direct connection with the masses. (kl 677-680)
Beyond the lack of scholarly agreement on the defining attributes of populism, agreement is general that all forms of populism include some kind of appeal to “the people” and a denunciation of “the elite.” Accordingly, it is not overly contentious to state that populism always involves a critique of the establishment and an adulation of the common people. More concretely, we define populism as a thin-centered ideology that considers society to be ultimately separated into two homogeneous and antagonistic camps, “the pure people” versus “the corrupt elite,” and which argues that politics should be an expression of the volonté générale (general will) of the people. (kl 700-705)
This means that populism can take very different shapes, which are contingent on the ways in which the core concepts of populism appear to be related to other concepts, forming interpretative frames that might be more or less appealing to different societies. Seen in this light, populism must be understood as a kind of mental map through which individuals analyze and comprehend political reality. It is not so much a coherent ideological tradition as a set of ideas that, in the real world, appears in combination with quite different, and sometimes contradictory, ideologies. (kl 713-717)
A common thread of populist rhetoric is that the movement is "anti-elitist" and that it speaks on behalf of "the people". Elites, according to populist leaders, have dominated policy and captured the benefits of society; "the people" have been left behind by elites who care nothing for their wellbeing. These tropes make perfect interpretive sense of Trumpism -- the campaign's attack on the media, scientists, politicians, and universities, its virulent personal attacks against Hillary Clinton, and its efforts to divide "the real Americans" from others -- immigrants, Mexicans, Muslims, Jews, and urban dwellers. And this is the most important point: by claiming to speak uniquely for "the people", there is an implicit openness to authoritarianism in populist politics.

So what is "not-Populism"? What is a political ideology and movement that falls outside the populist rubric? They identify pluralism as the main rival:
Pluralism is the direct opposite of the dualist perspective of both populism and elitism, instead holding that society is divided into a broad variety of partly overlapping social groups with different ideas and interests. Within pluralism diversity is seen as a strength rather than a weakness. Pluralists believe that a society should have many centers of power and that politics, through compromise and consensus, should reflect the interests and values of as many different groups as possible. Thus, the main idea is that power is supposed to be distributed throughout society in order to avoid specific groups— be they men; ethnic communities; economic, intellectual, military or political cadres, etc.— acquiring the capacity to impose their will upon the others. (kl 733-738)
Mudde and Kaltwasser pay close attention to what seems like the most important current problem: mobilization around populist political agendas.
By mobilization we mean the engagement of a wide range of individuals to raise awareness of a particular problem, leading them to act collectively to support their cause. Overall, three types of populist mobilization can be identified: personalist leadership, social movement, and political party. (kl 1246-1248)
They highlight three kinds of mechanisms of mobilization: social movements, charismatic leaders, and local grassroots organizations. (See an earlier post on work by McAdam and Kloos on racialized social movements in the United States; link.)

What factors lead to success in populist mobilization?
For any political actor to be successful, there has to be a demand for her message. Most populist actors combine populism with one or more so-called host ideologies, such as some form of nationalism or socialism. Although populism is often noted as a reason for their success, many electoral studies instead focus exclusively on the accompanying features, such as xenophobia in western Europe or socioeconomic support for disadvantaged groups in Latin America. This is in part a consequence of the lack of available data at the mass level. Empirical studies of populist attitudes are still in their infancy, but they do show that populist attitudes are quite widespread among populations in countries with relevant populist parties (e.g., Netherlands) and social movements (e.g., the United States) as well as in countries with no relevant populist actors (e.g., Chile). (Kindle Locations 2063-2069)
This passage highlights some of the kinds of messages that populists have deployed to support mobilization -- xenophobia and its cousins, and "nation first!" appeals for economic improvement for "the people". Mudde and Kaltwasser highlight the use of mistrust as a political theme -- "elites" are abusing "the people's" interests and needs, the elites cannot be trusted.  Appeals by populist leaders to fear, mistrust, and resentment of others have proven widespread and durable in numerous countries, including the recent presidential campaign in the United States.

A crucially important question before us is why racist and xenophobic attitudes appear to be becoming more common and more readily mobilized, in Europe and in the United States. Why is the rhetoric of division and hate so powerful in today's politics? Mudde and Kaltwasser do not shed much light on this question; indeed, they barely confront the topic. The terms "hate" and "race" do not appear in the book at all. They address the topic of xenophobia more generally (largely in the context of immigration issues). But they do not consider the more basic question: why is hate such a powerful political theme in the politics of extremist populism?

The other two books mentioned above provide more insight into this question, and I will return to them in a subsequent post.

*     *     *

There is today a little bit of good news for everyone concerned about the ascendancy of extremist populist politics in modern democracies. It appears that political novice and moderate candidate Emmanuel Macron has slightly bested far-right populist Marine Le Pen in today's French election results (23.7% vs. 21.8%, with 96% of polls reported). So the final round will involve a run-off election between the two leading candidates, and almost all commentators agree that the advantage in the second round will go to Macron. So the anxiety felt by many around the world that France would follow Great Britain (Brexit) and the United States (Trump) with an unexpected victory for the extreme right populist position is now much abated.

Saturday, April 22, 2017

Complexity and contingency

One of the more intriguing currents of social science research today is the field of complexity theory. Scientists like John Holland (Complexity: A Very Short Introduction), John Miller and Scott Page (Complex Adaptive Systems: An Introduction to Computational Models of Social Life), and Joshua Epstein (Generative Social Science: Studies in Agent-Based Computational Modeling) make bold and interesting claims about how social processes embody the intricate interconnectedness of complex systems.

John Holland describes some of the features of behavior of complex systems in these terms in Complexity:
  • self-organization into patterns, as occurs with flocks of birds or schools of fish  
  • chaotic behaviour where small changes in initial conditions (‘ the flapping of a butterfly’s wings in Argentina’) produce large later changes (‘ a hurricane in the Caribbean’)  
  • ‘fat-tailed’ behaviour, where rare events (e.g. mass extinctions and market crashes) occur much more often than would be predicted by a normal (bell-curve) distribution  
  • adaptive interaction, where interacting agents (as in markets or the Prisoner’s Dilemma) modify their strategies in diverse ways as experience accumulates. (p. 5)
In CAS the elements are adaptive agents, so the elements themselves change as the agents adapt. The analysis of such systems becomes much more difficult. In particular, the changing interactions between adaptive agents are not simply additive. This non-linearity rules out the direct use of PDEs in most cases (most of the well-developed parts of mathematics, including the theory of PDEs, are based on assumptions of additivity). (p. 11)
Miller and Page put the point this way:
One of the most powerful tools arising from complex systems research is a set of computational techniques that allow a much wider range of models to be explored. With these tools, any number of heterogeneous agents can interact in a dynamic environment subject to the limits of time and space. Having the ability to investigate new theoretical worlds obviously does not imply any kind of scientific necessity or validity— these must be earned by carefully considering the ability of the new models to help us understand and predict the questions that we hold most dear. (Complex Adaptive Systems, kl 199)
Much of the focus of complex systems is on how systems of interacting agents can lead to emergent phenomena. Unfortunately, emergence is one of those complex systems ideas that exists in a well-trodden, but relatively untracked, bog of discussion. 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. Clearly such notions are important when considering the decentralized systems that are key to the study of complex systems. Here we discuss emergence from both an intuitive and a theoretical perspective. (Complex Adaptive Systems, kl 832)
As discussed previously, we have access to some useful “emergence” theorems for systems that display disorganized complexity. However, to fully understand emergence, we need to go beyond these disorganized systems with their interrelated, helter-skelter agents and begin to develop theories for those systems that entail organized complexity. 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. (Complex Adaptive Systems, kl 987)
And here is Joshua Epstein's description of what he calls "generative social science":
The agent-based computational model— or artificial society— is a new scientific instrument. 1 It can powerfully advance a distinctive approach to social science, one for which the term “generative” seems appropriate. I will discuss this term more fully below, but in a strong form, the central idea is this: To the generativist, explaining the emergence2 of macroscopic societal regularities, such as norms or price equilibria, requires that one answer the following question:  
The Generativist's Question 
*     How could the decentralized local interactions of heterogeneous autonomous agents generate the given regularity?  
The agent-based computational model is well-suited to the study of this question since the following features are characteristics. (5)
Here Epstein refers to the characteristics of heterogeneity of actors, autonomy, explicit space, local interactions, and bounded rationality. And he believes that it is both possible and mandatory to show how higher-level social characteristics emerge from the rule-governed interactions of the agents at a lower level.

There are differences across these approaches. But generally these authors bring together two rather different ideas -- the curious unpredictability of even fairly small interconnected systems familiar from chaos theory, and the idea that there are simple higher level patterns that can be discovered and explained based on the turbulent behavior of the constituents. And they believe that it is possible to construct simulation models that allow us to trace out the interactions and complexities that constitute social systems.

So does complexity science create a basis for a general theory of society? And does it provide a basis for understanding the features of contingency, heterogeneity, and plasticity that I have emphasized throughout? I think these questions eventually lead to "no" on both counts.

Start with the fact of social contingency. Complexity models often give rise to remarkable and unexpected outcomes and patterns. Does this mean that complexity science demonstrates the origin of contingency in social outcomes? By no means; in fact, the opposite is true. The outcomes demonstrated by complexity models are in fact no more than computational derivations of the consequences of the premises of these models. So the surprises created by complex systems models only appear contingent; in fact they are generated by the properties of the constituents. So the surprises produced by complexity science are simulacra of contingency, not the real thing.

Second, what about heterogeneity? Does complexity science illustrate or explain the heterogeneity of social things? Not particularly. The heterogeneity of social things -- organizations, value systems, technical practices -- does not derive from complex system effects; it derives from the fact of individual actor interventions and contingent exogenous influences.

Finally, consider the feature of plasticity -- the fact that social entities can "morph" over time into substantially different structures and functions. Does complexity theory explain the feature of social plasticity? It does not. This is simply another consequence of the substrate of the social world itself: the fact that social structures and forces are constituted by the actors that make them up. This is not a systems characteristic, but rather a reflection of the looseness of social interaction. The linkages within a social system are weak and fragile, and the resulting structures can take many forms, and are subject to change over time.

The tools of simulation and modeling that complexity theorists are in the process of developing are valuable contributions, and they need to be included in the toolbox. However, they do not constitute the basis of a complete and comprehensive methodology for understanding society. Moreover, there are important examples of social phenomena that are not at all amenable to treatment with these tools.

This leads to a fairly obvious conclusion, and one that I believe complexity theorists would accept: that complexity theories and the models they have given rise to are a valuable contribution; but they are only a partial answer to the question, how does the social world work?

Sunday, April 2, 2017

Observation, measurement, and explanation

An earlier post reiterated my reasons for doubting that the social sciences can in principle give rise to general theories that serve to organize and predict the domain of social phenomena. The causes of social events are too heterogeneous and conjunctural to permit this kind of systematic representation.

That said, social behavior and social processes give rise to very interesting patterns at the macro scale. And it is always legitimate to ask what the causes are that produce these patterns. Consider the following graphs. They are drawn very miscellaneously from a range of social science disciplines.

These graphs represent many different kinds of social behavior and processes. A few are synchronic -- snapshots of a variable at a moment in time. The graph of India's population age structure falls in this category, as do the graphs of India's literacy rates. Most are diachronic, representing change over time. The majority show an apparent pattern of stochastic change, even in cases where there is also a measurable direction of change indicating underlying persistent causes. Graphs of stock market activity fall in this category, with random variations of prices even during a consistent period of rising or falling prices.

The graph representing the evolution of China's agricultural economy tells an interesting and complicated story. It shows rising productivity in agriculture and (since 1984) a sharp decline in the proportion of the labor force involved in agriculture -- an important cause of China's urban growth and the growth of its internal migrant population. And it shows a long-term decline in the share of the national economy played by agricultural production overall, from about 40% in 1969 to less than 15% in 2005. What these statistics convey is a period of fundamental change in China, in economy, urbanization, and ultimately in politics.

The graph of the composition of the US population is a time series graph that tells a complicated story as well -- a smooth rise in total national population composed of shifting shares of population across the regions of the country. These shifts of population shares across the region's of the country demand historical and causal explanation.

The graph of India's literacy rates over age warrants comment. It appears to give a valid indication of several important social realities -- a persistent gap between men and women of all ages, and lower literacy among older men and women. But the graph also displays variation that can only reflect some sort of artifact from the data collection: literacy rates plummet at the decade and half decade, for both men and women. Plainly there is a problem with the data represented in this graph; nothing could explain a 15% discrepancy in literacy rates between 57-year-old men and 60-year-old men. The same anomalous pattern is evident in the female graph as well. Essentially there are two distinct data series represented here: the decade and half-decade series (low) and the by-year series (high). There is no way of telling from the graph which series should be given greater credibility. The other chart representing state literacy rates is of interest as well. It allows us to see that there are substantial gaps across states in terms of literacy -- Kerala's literacy rate in 1981 is 2.5 times higher than that of Bihar in that year. And some states have made striking progress in literacy between 1981 and 2001 (Arunachal Pradhesh) while other states have shown less proportional increases (Kerala). Here though we can ask whether the order of states on the graph makes sense. The states are ranked from high to low literacy rates. Perhaps it would be more illuminating to group states by regions so it is possible to draw some inferences and comparisons about similarly situated states.

The graph representing grain price correlations across commodities in Qing China demands a different kind of explanation. We need to be able to identify a mechanism that causes prices in different places to converge to a common market price separated by the cost of transport between these places and the relative utilities of wheat, sorghum, and millet. The mechanism is that of mobile price-sensitive traders responding to information about prices in different locations. The map demonstrates the existence of these mechanisms of communication and transportation on the ground. This is a paradigm example of a mechanism-based explanation. (This example comes from Rawski and Li, eds., Chinese History in Economic Perspective (Studies on China).)

The graph representing the rank order of city sizes is perhaps the most intriguing among all of these. There is nothing inherently implausible about a population distributed across five cities of comparable size and a hundred towns of comparable size -- and yet this hypothetical case would display a size distribution radically different from the Zipf law. So what explanation is available to account for the empirical pattern almost universally observed? Various scholars have argued that the regularity is the result of very simple conditions that apply to city growth rates over time, and that the cities in a growing population will come to conform to the Zipf regularity over time  as a simple statistical consequence of size and growth (link). It is an example, perhaps, of what Schelling calls "the inescapable mathematics of musical chairs" (Micromotives and Macrobehavior).

What these examples have in common is that they illustrate two of the key tasks of the social sciences: to measure important social variables over time and space, and to identify the social mechanisms that lead to variation in these variables. There are large problems of methodology and conceptual clarification that need to be addressed in both parts of this agenda. On the side of measurement, we have the problems of arriving at consistent and revealing definitions of economic wellbeing, using incomplete historical sources to reconstruct estimates of prices and wages, and using a range of statistical methods to validate and interpret the results. And on the explanatory side, we are faced with the difficult task of reconstructing social processes and forces in the past that may have powered the changes we are able to document, and with the task of validating the hypotheses we have put forward on the basis of historical evidence.