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.

Viganola, Domenico, Grant Buckles, Yiling Chen, Pablo Diego-Rosell, Magnus Johannesson, Brian A. Nosek, Thomas Pfeiffer, Adam Siegel, and Anna Dreber. 2021. "Using Prediction Markets to Predict the Outcomes in the Defense Advanced Research Projects Agency's Next-Generation Social Science Programme." Royal Society Open Science 8: 181308.

Abstract

There is evidence that prediction markets are useful tools to aggregate information on researchers’ beliefs about scientific results including the outcome of replications. In this study, we use prediction markets to forecast the results of novel experimental designs that test established theories. We set up prediction markets for hypotheses tested in the Defense Advanced Research Projects Agency’s (DARPA) Next Generation Social Science (NGS2) programme. Researchers were invited to bet on whether 22 hypotheses would be supported or not. We define support as a test result in the same direction as 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 with the null hypothesis). In addition to betting on this binary outcome, we asked participants to bet on the expected effect size (in Cohen’s d) for each hypothesis. Our goal was to recruit at least 50 participants that signed up to participate in these markets. While this was the case, only 39 participants ended up actually trading. Participants also completed a survey on both the binary result and the effect size. We find that neither prediction markets nor surveys performed well in predicting outcomes for NGS2.

Working Papers


Buckles, Grant, Pablo Diego-Rosell, Alex Gil, Jessica Harlan, Erik Jones, Ellyn Maese, and Rick Thomas. "Dynamic Strategy Selection under Bounded Rationality in Constrained Optimization Problems." Stage 1 Registered Report under review.

Abstract

This research constructs, tests, and validates an applied cognitive model of decision-making under bounded rationality. A machine-assisted review of the scientific literature identified models of decision-making, strategy selection, learning, and foraging to explain dynamic strategy selection under bounded rationality. These models were synthesized and aggregated into a Conceptualization–Experimentation–Reflection (CER) model. A virtual urban search and rescue (USAR) experimental paradigm implemented within a Minecraft task environment in a laboratory setting will evaluate the CER model. Participants will conduct USAR missions with experimental manipulations of complexity, knowledge, and other task variables. A robust understanding of human decision-making in USAR strategy selection will enable identification of sub-optimal decisions and develop support systems to augment human capabilities and improve survival rates. Our model will inform the development of Theory of Mind capabilities for AI agents supporting human teams in search optimization problems.

Gainous, Jason, Kevin M. Wagner, Grant Buckles, and Alex Detenber. "Democratic Context, Digital Consumption, and Trust in Government: A Global Examination." Presented at the 2020 American Political Science Association Annual Meeting.

Abstract


In this research, we review how digital information consumption influences views of democracy. Particularly, we consider how digital information consumption structures citizen trust in government, and how the relationship may be influenced based on a country-level democratic context. We use the Varieties of Democracy Institute (V-Dem) principles of democracy criteria: liberal, participatory, deliberative, and egalitarian to differentiate states and consider the influence of digital information consumption in 128 different national systems. We find that the participatory democracy variation is the most likely to moderate the relationship between digital information consumption and trust in government. The relationship between digital consumption and trust was generally negative and this relationship was strongest in moderate democracies as opposed to countries that were either minimally or strongly participatory. Ultimately, we found that digital information's effect on trust in government is dependent on context, not just in government type, but also in the level of existing trust in the country.

Edgerton, Jared and Grant Buckles. "Forecasting rare events: Two-stage classification of terrorist, civil, and coups." Presented at the International Methods Colloquium.

Abstract


Predicting civil wars, terrorist attacks, and coup attempts remains a primary goal of policy makers. In turn, a more precise understanding of where these political events are likely to happen could greatly benefit the United States and its allies, as well as help protect civilians. In the present analysis, we demonstrate a new framework for forecasting rare events. Specifically, we employ a new two-stage framework and permutation upsampling to identify countries which are more likely to experience terrorism. The presented two-stage framework uses novel micro-level data gathered by the Gallup World Poll initiative. The Gallup World Poll survey asks respondents over 200 questions relating to health outcomes, views on governance, economic security, among others. Through this process, we improve on existing machine learning classification processes for predicting rare events.