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Human decision-making involves the coordinated activity of multiple brain areas, acting in concert, to enable humans to make choices. Most decisions are carried out under conditions of uncertainty, where the desired outcome may not be achieved if the wrong decision is made. In these cases, humans deliberate before making a choice. The neural dynamics underlying deliberation are unknown and intracranial recordings in clinical settings present a unique opportunity to record high temporal resolution electrophysiological data from many (hundreds) brain locations during behavior. Combined with dynamic systems modeling, these allow identification of latent brain states that describe the neural dynamics during decision-making, providing insight into these neural dynamics and computations. Results show that the neural dynamics underlying risky decisions, but not decisions without risk, converge to separate subspaces depending on the subject’s preferred choice and that the degree of overlap between these subspaces declines as choice approaches, suggesting a network level representation of evidence accumulation. These results bridge the gap between regression analyses and data driven models of latent states and suggest that during risky decisions, deliberation and evidence accumulation toward a final decision are represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice.
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The Wisdom of Model Crowds
A wide body of empirical research has revealed the descriptive shortcomings of expected value and expected utility models of risky decision making. In response, numerous models have been advanced to predict and explain people’s choices between gambles. Although some of these models have had a great impact in the behavioral, social, and management sciences, there is little consensus about which model offers the best account of choice behavior. In this paper, we conduct a large-scale comparison of 58 prominent models of risky choice, using 19 existing behavioral data sets involving more than 800 participants. This allows us to comprehensively evaluate models in terms of individual-level predictive performance across a range of different choice settings. We also identify the psychological mechanisms that lead to superior predictive performance and the properties of choice stimuli that favor certain types of models over others. Moreover, drawing on research on the wisdom of crowds, we argue that each of the existing models can be seen as an expert that provides unique forecasts in choice predictions. Consistent with this claim, we find that crowds of risky choice models perform better than individual models and thus provide a performance bound for assessing the historical accumulation of knowledge in our field. Our results suggest that each model captures unique aspects of the decision process and that existing risky choice models offer complementary rather than competing accounts of behavior. We discuss the implications of our results on theories of risky decision making and the quantitative modeling of choice behavior. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis.
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- Award ID(s):
- 1847794
- PAR ID:
- 10394341
- Date Published:
- Journal Name:
- Management Science
- Volume:
- 68
- Issue:
- 5
- ISSN:
- 0025-1909
- Page Range / eLocation ID:
- 3635 to 3659
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/mnsc.2021.4090
