Complexity and the Limits of Revolution: What Will Happen to the Arab Spring?

The recent social unrest across the Middle East and North Africa has deposed dictators who had ruled for decades. While the events have been hailed as an “Arab Spring” by those who hope that repressive autocracies will be replaced by democracies, what sort of regimes will eventually emerge from the crisis remains far from certain. Here we provide a complex systems framework, validated by historical precedent, to help answer this question. We describe the dynamics of governmental change as an evolutionary process similar to biological evolution, in which complex organizations gradually arise by replication, variation and competitive selection. Different kinds of governments, however, have differing levels of complexity. Democracies must be more systemically complex than autocracies because of their need to incorporate large numbers of people in decision-making. This difference has important implications for the relative robustness of democratic and autocratic governments after revolutions. Revolutions may disrupt existing evolved complexity, limiting the potential for building more complex structures quickly. Insofar as systemic complexity is reduced by revolution, democracy is harder to create in the wake of unrest than autocracy. Applying this analysis to the Middle East and North Africa, we infer that in the absence of stable institutions or external assistance, new governments are in danger of facing increasingly insurmountable challenges and reverting to autocracy. 1 ar X iv :1 21 2. 30 41 v1 [ ph ys ic s. so cph ] 1 3 D ec 2 01 2 Revolutions can greatly alter societies. Learning about their potential outcomes is important for both participants and policymakers. If we can identify patterns across all revolutions, past unrest may inform our understanding of present crises. Doing so is especially critical as the upheaval across the Middle East and North Africa continues to unfold. For a revolution to be successful it must do more than depose or alter the current government; it must also create a new government in line with the intentions of the revolutionaries. A framework that can provide insight into outcomes is therefore necessary in order to evaluate whether a given revolution is likely to be successful. The literature on revolutions has dealt with a wide variety of revolutionary events and outcomes. The conditions cited to explain the outcomes of revolution are themselves very diverse and include, among others, the effect of violence on leader selection [1, 2], the dynamics of large organizations [3], the slow nature of state-building [4], the rational decisions of participants [5], elite incentives [6], and leader characteristics [7]. The definition of revolution ranges widely, from great “social” revolutions that reshape society [8] to lesser “political” ones that only change leadership [9]. The diversity of definitions and the range of conditions and factors affecting revolutionary outcomes make it difficult to imagine a general theory that could identify the consequences of present unrest. As broad and deep as this literature is, it has been criticized by the sociologist Charles Tilly for failing to treat revolutions as “complex but lawful phenomena” such as floods or traffic jams. Rather than sui generis events, he argues that they are part of a spectrum of social change, with no natural boundaries isolating them from the course of human events. Thus, any definition considering revolutions to be ontologically distinctive is inherently limited. In this vein, the recent literature is concerned with the specifics of power of groups and institutions that are able to achieve long-range goals [10]. Here we construct a theory of governmental change from the perspective of complex systems, which can be used to explain and perhaps anticipate the outcomes of revolutions. By embedding social systems in the much broader context of complex systems, we are not limited by the available data on states to formulate hypotheses and frame relevant insights. The available societal data can then serve to validate the mapping of general mathematical principles onto social processes, rather than testing the principles themselves—just as the more commonly applied statistical methods are applicable to physical, biological and social systems and must be used correctly in each case. The focus of our analysis is on the complex