Computational Decision Science
Decisions, almost by definition, link our thoughts to our actions. Our research uses computational models to characterize this critical link by forcing us to specify the mental processes (i.e., memory, learning, or reward evaluation) involved in making a decision, the environments those choices take place in, and the interaction between the person and the environment. By taking this approach, we develop a better understanding of how the mind works and formulate mathematical models to help individuals, groups, and organizations make better decisions. We have three tracks of work in this area: Deliberation, Choice Environments, and Translational Modeling.
Hertwig, R., Pleskac, T. J., Pachur, T., & Center for Adaptive Rationality. (2019). Taming Uncertainty. MIT Press. https://doi.org/10.7551/mitpress/11114.001.0001
How do people form a belief or a preference? We have investigated this question from many different angles from perceptual decisions to economic decisions to confidence judgments to probabilistic forecasts. Across these domains, we have shown a similar deliberation process is at work where samples of information are sequentially sampled about the object or event in question and accumulated over time. Our understanding of this evidence accumulation process is precise enough that, in controlled laboratory settings, we can predict the choices people will make, the time it will take to make them, and the confidence they will have in them. Going forward, one challenge we want to take on is to move these models out of the lab and use them to predict behavior under much more complex conditions such as geopolitical and economic events.
Kvam, P. D., Pleskac, T. J., Yu, S., & Busemeyer, J. R. (2015). Interference effects of choice on confidence: Quantum characteristics of evidence accumulation. Proceedings of the National Academy of Sciences, 112(34), 10645–10650. https://doi.org/10.1073/pnas.1500688112
Yu, S., Pleskac, T. J., & Zeigenfuse, M. D. (2015). Dynamics of postdecisional processing of confidence. Journal of Experimental Psychology: General, 144(2), 489–510. https://doi.org/10.1037/xge0000062
Zeigenfuse, M. D., Pleskac, T. J., & Liu, T. (2014). Rapid decisions from experience. Cognition, 131(2), 181–194. https://doi.org/10.1016/j.cognition.2013.12.012
Pleskac, T. J. (2012). Comparability Effects in Probability Judgments. Psychological Science, 23(8), 848-854. https://doi.org/10.1177/0956797612439423
Pleskac, T. J., & Busemeyer, J. R. (2010). Two-stage dynamic signal detection: A theory of choice, decision time, and confidence. Psychological review, 117(3), 864–901. https://doi.org/10.1037/A0019737
The decisions people make are shaped as much by their own psychological processes as the choice environments they make their decisions in. The question, then, is what are the critical properties of these choice environments, and how are these structures used to make decisions? We have been working to understand how people use the relationship between risks and rewards to make decisions. This has led us to study why the inverse relationship between risks and rewards is so prevalent (it isn’t simply due to economic forces), and how people use this relationship to make inferences about the chances of different outcomes.
Pleskac, T. J., Conradt, L., Leuker, C., & Hertwig, R. (2021). The ecology of competition: A theory of risk–reward environments in adaptive decision making. Psychological Review, 128(2), 315–335. https://doi.org/10.1037/rev0000261
Leuker, C., Pachur, T., Hertwig, R., & Pleskac, T. J. (2018). Exploiting risk–reward structures in decision making under uncertainty. Cognition, 175, 186–200. https://doi.org/10.1016/j.cognition.2018.02.019
Pleskac, T. J., & Hertwig, R. (2014). Ecologically rational choice and the structure of the environment. Journal of Experimental Psychology: General, 143(5), 2000-2019. https://doi.org/10.1037/xge0000013
Pleskac, T. J. (2007). A signal detection analysis of the recognition heuristic. Psychonomic Bulletin & Review, 14(3), 379–391. https://doi.org/10.3758/BF03194081.
Often, computational models in psychology are used to understand behavior in specific laboratory tasks. We are interested in translating these computational models from the laboratory into tools for identifying and improving problematic decision making. In this area, we have worked to use computational models to identify decision making deficits among real-world risk takers like drug users, to identify critical events or shocks that lead students to quit school, and more recently understand a police officer’s decision to shoot and the role a suspect’s race can play in the decision.
Johnson, D., Cesario, J., & Pleskac, T. J. (2018). How prior information and police experience impacts decisions to shoot. Journal of Personality & Social Psychology, 115(4), 601–623. https://doi.org/10.1037/ pspa0000130
Pleskac, T. J., Cesario, J., & Johnson, D. J. (2018). How race affects evidence accumulation during the decision to shoot. Psychonomic Bulletin & Review, 25, 1301–1330. https://doi.org/10.3758/s13423-017-1369-6
Pleskac, T. J., Keeney, J., Merritt, S. M., Schmitt, N., & Oswald, F. L. (2011). A detection model of college withdrawal. Organizational Behavior and Human Decision Processes, 115(1), 85–98. https://doi.org/ 10.1016/j.obhdp.2010.12.001
Pleskac, T. J. (2008). Decision making and learning while taking sequential risks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(1), 167-185. https://doi.org/10.1037/0278-73188.8.131.52
Wallsten, T. S., Pleskac, T. J., & Lejuez, C. W. (2005). Modeling Behavior in a Clinically Diagnostic Sequential Risk-Taking Task. Psychological Review, 112(4), 862–880. https://doi.org/10.1037/0033-295X.112.4.862