Stochastic representation decision theory: How probabilities and values are entangled dual characteristics in cognitive processes

Humans are notoriously bad at understanding probabilities, exhibiting a host of biases and distortions that are context dependent. This has serious consequences on how we assess risks and make decisions. Several theories have been developed to replace the normative rational expectation theory at the foundation of economics. These approaches essentially assume that (subjective) probabilities weight multiplicatively the utilities of the alternatives offered to the decision maker, although evidence suggest that probability weights and utilities are often not separable in the mind of the decision maker. In this context, we introduce a simple and efficient framework on how to describe the inherently probabilistic human decision-making process, based on a representation of the deliberation activity leading to a choice through stochastic processes, the simplest of which is a random walk. Our model leads naturally to the hypothesis that probabilities and utilities are entangled dual characteristics of the real human decision making process. It derives two previously postulated features of prospect theory (Kahneman and Tversky, 1979): the inverse S-shaped subjective probability as a function of the objective probability and risk-seeking behaviour in the loss domain. It also predicts observed violations of stochastic dominance (Birnbaum and Navarrete, 1998) while it does not when the dominance is “evident”. Our theory, which offers many more predictions for future tests, has strong implications for psychology, economics and artificial intelligence.