I am grateful to Cliodynamics for this special issue revisiting the ideas put forth in Revolution and Rebellion in the Early Modern World (Goldstone 1991, 2016) a quarter century ago. The two things that one could hope for in advancing any theory are that it proves capable of being advanced and enriched by other scholars, and that it proves capable of being applied in new ways and to new phenomena that were not anticipated. This issue gives examples of both, and shows how scholars are even now only beginning to tap the possibilities of Demographic Structural Theory (DST) in explaining politics, history, and long-term economic trends.

In this essay, I will tell the story of how demographic structural theory was conceived, relate its early reception among scholars, and comment on the important contributions by other scholars to this special issue.

A Review Essay on Strange Parallels: Southeast Asia in Global Context, c. 800–1830. Volume 2: Mainland Mirrors: Europe, Japan, China, South Asia, and the Islands by Victor Lieberman (Cambridge University Press, 2009).

Three predictive problems bedevil our ability to foresee political crises and state breakdown: (1) how to tell when a previously stable state falls into a situation of hidden but dangerous instability; (2) how to tell, once a certain level of instability has appeared in the form of protests, riots, or regional rebellions, whether chaos will grow and accelerate into revolution or civil war, or if the protests are likely to be contained and dampen out; and (3) how to tell which individuals and groups are likely to be the main source of mobilization for radical movements, and whether opposition networks will link up, grow and spread, or be isolated and contained. Prior work has focused on each of these problems separately. However, all three issues are crucial to understanding and foreseeing conflict dynamics. These issues operate on different time-scales and require separate models. In this article we discuss how better models of each process could be developed and, crucially, integrated with data for a more effective prediction system. A major theoretical challenge for us is to link these different approaches in order to increase their predictive power. A major empirical challenge is to identify data (direct or proxy) that can be used to parameterize, validate, and test our models.