Rationally Selected Utility – A New Theory of Riskless Choice

Traditional prescriptive decision theories from Pascal’s Expected Value to Expected Utility have struggled to explain inconsistent human choices, leading to purely descriptive models like prospect theory. This paper introduces a novel approach to understand why these behaviors occur, reconciling these observed irrationalities with a simple rational objective, specifically in the domain of riskless choice when choosing between options with certain outcomes. Here, we propose that the human nervous system, faced with limited cognitive capacity (a finite representational precision for the subjective values of rewards), optimally selects a task-appropriate utility function to either maximize long-run average earnings or to minimize errors when choosing between certain outcomes. We argue that the utility functions psychologists and economists have measured empirically are tools, sculpted by knowledge of the choice environment's probabilistic structure and the decision-maker's precision level, for optimally achieving a rational objective. Our results show that almost every experimentally observed utility function can be described as a near-optimal tool for achieving a maximization of objective earnings, a goal abandoned since Bernoulli. We quantify the specific gains that result when a chooser decides to add more cognitive capacity when facing a choice and find that these gains are quite small as long as the utility function is rationally selected. Our research provides a novel framework that offers a testable and foundational explanation for why human and animal decision-makers evolved the puzzling range of utility functions and risk attitudes that have been observed empirically.