What Science Can do for Democracy

By Tina Eliassi-Rad, Henry Farrell, David Garcia, Stephan Lewandowsky, Patricia Palacios, Don Ross, Didier Sornette, Karim Thébault, & Karoline Wiesner

Humanities and Social Sciences Communications

The Economist recently identified 80 countries whose democracy score declined during the last decade, including the USA and some consolidated European democracies (The Economist Intelligence Unit, 2017). While the question of how democracies arise and how such rise can be facilitated has received much research attention, little is known as yet about how democracies destabilize.Footnote1 The conventional assumption amongst political scientists was that achieving democracy is a one-way ratchet. Only very recently has the question of “democratic backsliding” attracted any research attention (Waldner and Lust, 2018). We argue that insights from complexity science can facilitate the study of democratic processes and institutions and the design of stabilizing policies. The cross-disciplinary approach to political science that we advocate here rests on mathematical models of human societies, built with tools from statistical physics, dynamical systems, complex networks, and game theory (Wiesner et al., 2018). These tools allow scientists to focus on the salient features of the complex system at hand.

It is generally accepted that complex systems defy a one-sentence definition, not least because they are found in all areas of science. However, there are features that most, if not all, complex systems have in common (Ladyman and Wiesner, 2020). All complex systems consist of many, often diverse, elements that self-organize, driven by their many random interactions, into ordered systems that exhibit feedback and nonlinearities, and many of them exhibit forms of nestedness and memory. All complex systems are generally exposed to perturbations from an environment. While being stable against minor perturbations, larger ones can cause regime changes. Many mathematical techniques are in use to model or predict such drastic changes. For example, Sinha and Pan (Sinha and Pan, 2006) model the sudden rises in popularity of particular ideas or products with the Ising model of ferromagnetism. This sociophysics model recovers the long-tailed distributions observed in real social systems such as the outcome of elections and the popularity of movies. The model captures how an agent’s choice can be affected not only by interactions with other agents, but by how well their previous choice allowed them to coordinate with the majority. Another example is the use of renormalization group methods to illustrate the causes of minority opinion spreading (Galam, 2012). In the following, the relevance of some of these features in the context of democracy is discussed.

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Picture: Tim Evanson from Cleveland Heights, Ohio, USA / CC BY-SA (https://creativecommons.org/licenses/by-sa/2.0)

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