Dynamic prospect theory - two core decision theories coexist in the gambling behavior of monkeys and humans

Abstract

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.