Research

Publications

Buckles, Grant T. 2019. "Internal Opposition Dynamics and Restraints on Authoritarian Control." British Journal of Political Science 49(3): 883-900.

Abstract

Autocrats rely on co-optation to limit opposition mobilization and remain in power. Yet not all opposition parties that pose a threat to their regime are successfully co-opted. This article provides a formal model to show that reliance on activists influences whether an opposition leader receives and accepts co-optation offers from an autocrat. Activists strengthen a party’s mobilization efforts, yet become disaffected when their leader acquiesces to the regime. This dynamic undermines the co-optation of parties with a strong activist base, particularly those with unitary leadership. Activists have less influence over elite negotiations in parties with divided leadership, which can promote collusion with the regime. The results ultimately suggest that party activism can erode authoritarian control, but may encourage wasteful conflicts with the government.

Working Papers

Viganola, Domenico, Grant Buckles, Yiling Chen, Pablo Diego-Rosell, Magnus Johannesson, Brian A. Nosek, Thomas Pfeiffer, Adam Siegel, and Anna Dreber. "Using Prediction Markets to Predict the Outcomes in DARPA’s Next Generation Social Science Program." Stage 1 Registered Report accepted in principle for publication in Royal Society Open Science.

Abstract

There is evidence that prediction markets are useful tools to aggregate information about researchers’ beliefs about scientific results including forecasting the outcome of replications. In this study, we use prediction markets to forecast the results of novel experimental designs that test established theories. We will set up prediction markets for hypotheses tested in DARPA’s Next Generation Social Science (NGS2) program. We will invite researchers to bet on whether 22 hypotheses will be supported or not. We define support as a test result in the same direction as the hypothesized, with a Bayes Factor of at least 10 (i.e. a likelihood of the observed data being consistent with the tested hypothesis that is at least 10 times greater compared to the null hypothesis). In addition to betting on this binary outcome, we will ask participants to bet on the expected effect size (in Cohen’s d) for each hypothesis. We recruit at least 50 participants that sign up to participate in these markets. Participants will also complete a survey on both the binary result and the effect size. Our goals are to elicit peer beliefs about the outcomes of the hypotheses tested in NGS2, and to test if these peer beliefs can predict the outcomes of novel experimental designs rather than replications. This study will increase our knowledge about the predictability of scientific results and the dynamics of hypothesis testing.