Abstract
In this paper institutions are treated as stabilized sets of expectations, an approach that encourages investigation of how cultural formations, political regimes, global financial arrangements, and other institutions can be both reliable and yet also subject to sudden and sometimes catastrophic transformations. We examine conditions that make political cascades, or tipping, more or less likely. We report findings from computer-assisted agent-based modelling experiments designed to test Timur Kuran’s preference falsification model for explaining the possibility, but rare occurrence of, revolutionary political cascades. Since it run on a computer, our operationalized model of Kuran’s theory is a necessarily precise and elaborated refinement of the incompletely specified version presented by Kuran. Our purpose is to go beyond his explanation for why political cascades can occur, albeit rarely, to explore the conditions that make them more or less likely. Our specific focus is on the impact of the amount of knowledge about the state of the entire system possessed by citizens with stronger or weaker inclinations to publicly express their anti-regime sentiments. The “zone of knowledge” of individuals is an unexamined variable whose importance is unrecognized but implied by Kuran’s analysis. We find that with some reasonable but crucial refinements Kuran’s preference falsification theory works to explain the pattern observed in the political world of rare but sweeping cascades of; that the amount of knowledge individuals have about the behavior of the population is crucial to shaping the probability of a cascade; and that variation in the myopia of “early followers” is considerably more important for determining the likelihood and comprehensiveness of sudden political transformations than the influence of first movers or the contribution of other plausible factors.
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Notes
Max Weber’s classic definition of the state is derived from his more fundamental point that no institution can exist “in a sociologically relevant sense” apart from “a probability that certain kinds of meaningfully oriented social action will take place” (Weber 1922, p. 38). This conception of institutions has become standard in political science (e.g. North 1981, pp. 17–21). On the concept of “emergent properties” (Holland 1998).
Wejnert (2002) and Dobbin et al. (2007) draw heavily on Rogers's work and show the field still organized by an accumulation of data about how very specific factors associated with new products or populations can affect the likelihood, rapidity, and completeness of diffusion. However, this vast literature has had little impact on social scientists, in part because it has not coped effectively with the “curse of dimensionality” (Achen 2002) (see also De Marchi 2005, p. 35). That is, the interactive implications of dozens of separate variables have been identified, but without a theoretical or methodological approach for testing their potency or for moving beyond description and post hoc analyses toward probability prediction or systematic explanation of patterns of diffusion outcomes.
Lustick and Miodownik (2004).
Lustick (2002). The software is available at http://ps-i.sourceforge.net/.
Concerning the problem of titrating complexity in agent-based models see Lustick and Miodownik (2009).
See Grimm et al. (2006).
Experiments have not yet been run with landscapes with closed edges, but the more general question raised by the shape of the landscape is explored by the “density” variable, which changes the topology of the toroidal landscape with variable amounts of isolation between scattered groups.
For technical reasons we used nine agent classes in our model rather than ten. This is an irrelevant difference.
Replication of our experiments can be accomplished by downloading the PS-I modeling platform at SourceForge, Version 4.0.5 at http://ps-i.sourceforge.net/. A manual to guide use of the platform is available at http://www.polisci.upenn.edu/ps-i/publications/PSIManual.pdf. Scripts (*.scp) and model platform file (*.mdl) as well as replication files of the statistical analysis are available here: https://bit.ly/2TUm2UL.
We also ran models with a threshold of 90% (N = 136) but these were substantially the same as a threshold of 80% hence these are not reported here.
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Lustick, I.S., Miodownik, D. When do institutions suddenly collapse? Zones of knowledge and the likelihood of political cascades. Qual Quant 54, 413–437 (2020). https://doi.org/10.1007/s11135-019-00883-9
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DOI: https://doi.org/10.1007/s11135-019-00883-9