Dynamic prospect theory: Two core decision theories coexist in the gambling behavior of monkeys and humans
Year: 2023
Type: article
Abstract: Research in the multidisciplinary field of neuroeconomics has been driven by two influential theories regarding human economic choice: prospect theory, which describes decision-making under risk, and reinforcement learning theory, which describes learning for decision-making. We hypothesized that these two distinct theories guide decision-making in a comprehensive manner. Here, we propose and test a new decision-making theory under uncertainty that combines these highly influential theories. Collecting many gambling decisions from laboratory monkeys allowed for reliable testing of our hybrid model and revealed a systematic violation of prospect theory’s assumption that probability weighting is static. Using the same experimental paradigm in humans, substantial similarities between monkey and human behavior were described by our hybrid model, which incorporates decision-by-decision learning dynamics of prediction errors into static prospect theory. Our new model provides a single unified theoretical framework for exploring the neurobiological model of economic choice in human and nonhuman primates.
Source: Science Advances
Authors: Agnieszka Tymula, Xueting Wang, Yuri Imaizumi, Takashi Kawai, Jun Kunimatsu, Masayuki Matsumoto, Hiroshi Yamada
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $4,500
Awards: Role of dopamine signals in prefrontal executive functions, G2088570879, G2644592831, Role of the cerebellum in cooperation and empathy, G3442320929, G4133443254, Neural mehcanisms underlying behavioral regulation based on physiological needs: model animal study, G6246161379, G7090037266, Neural basis for value-based decision making and physiological needs, Neuroeconomic foundations of probability and value perception, G8980086550
OpenAlex ID: W4377088084