Decision field theory-planning: A cognitive model of planning on the fly in multistage decision making

The world is full of complex environments in which individuals must plan a series of choices to obtain some desired outcome. In these situations, entire sequences of events, including one's future decisions, should be considered before taking an action. Backward induction provides a normative strategy for planning, in which one works backward, deterministically, from the end of a scenario. However, this model often fails to account for human behavior. This article proposes an alternative account, decision field theory-planning (DFT-P), in which individuals plan future choices on the fly through repeated forward-looking mental simulations. As they imagine the possible outcomes of their actions, decision makers simulate their future choices moment to moment. A key prediction of DFT-P is that payoff variability produces noisy simulations and reduces sensitivity to value differences. In two experiments, a robust multistage payoff variability effect was found, with preferences becoming weaker as variability increased. A formal comparison showed that DFT-P provided a good account of people's behavior, while a heuristic model and a flexible version of the backward induction model did not. These results confirm a fundamental prediction of DFT-P, and demonstrate its utility as a tool for understanding how people plan future choices and allocate cognitive resources in multistage decision making.