{"pk":38252,"title":"Linking “Micro” to “Macro” Models of State Breakdown to Improve Methods for Political Forecasting","subtitle":null,"abstract":"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,\n \nall 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.","language":"en","license":{"name":"Creative Commons Attribution 4.0","short_name":"CC BY 4.0","text":"Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.","url":"https://creativecommons.org/licenses/by/4.0"},"keywords":[{"word":"instability, conflict, state breakdown, modeling, forcasting"}],"section":"Article","is_remote":true,"remote_url":"https://escholarship.org/uc/item/1xt9k875","frozenauthors":[{"first_name":"Peter","middle_name":"","last_name":"Turchin","name_suffix":"","institution":"University of Connecticut","department":"None"},{"first_name":"Sergey","middle_name":"","last_name":"Gavrilets","name_suffix":"","institution":"University of Tennessee","department":"None"},{"first_name":"Jack","middle_name":"A.","last_name":"Goldstone","name_suffix":"","institution":"","department":"None"}],"date_submitted":"2017-12-30T18:29:46Z","date_accepted":"2017-12-30T18:29:46Z","date_published":"2017-12-31T08:00:00Z","render_galley":null,"galleys":[{"label":"","type":"pdf","path":"https://journalpub.escholarship.org/cliodynamics/article/38252/galley/28791/download/"}]}