Models of planning in dynamic decision making

Although most research into risky decision making has focused on simple scenarios – where isolated choices are made independent of one another – many important decisions in life play out across sequences of interdependent events and actions. Despite the ubiquity and importance of such decision problems, we know relatively little about how people manage the complexities of dynamic, multistage decisions. Our work combines techniques from two lines of research to investigate how people handle the challenges of dynamic decision making (DDM). From the econometric tradition we rely on a family of truth-and-error models that can be used to estimate the distribution and stability of preference profiles, and the presence of errors. In a complementary analysis, we use cognitive modeling to investigate the psychological processes underlying DDM. Decision Field Theory-Planning provides a unified framework for testing competing hypotheses about how people collect information and plan for the future. Results from both sets of analyses identify distinct groups of individuals. We discuss the behavioral and cognitive factors distinguishing groups from one another, including degree of planning, biased information sampling, and strategy shifts. We highlight the strengths and weaknesses of each modeling approach, and examine where their results support conflicting conclusions.