Dynamic Decision Making


The experimental study of decision making has historically focused on simple single-trial judgement or reasoning tasks. However, real world behavior often necessitates online decision making, planning, and sequentially organized behavior. The goal of the proposed symposium is to bring together researchers who are working to understand the cognitive processes underlying this kind of ``dynamic decision making" (defined as tasks or contexts that are structured as a sequence of interdependent decisions). A symposium on this topic is particularly timely since research in the area of dynamic decision making is having a tremendous impact on the field of psychology as a whole. First, researchers are converging on a set of novel computational modeling approaches that explain how decision makers plan sequences of multiple actions, take into account future contingencies, and react in real time to continually changing environmental dynamics. Second, many of the proposed algorithms and models are closely linked to neurobiological correlates (e.g., the recent explosion of research on neurobiology of reinforcement learning). Third, many of the tasks that are being developed for evaluating these models also appear to relate to important individual differences in real-world decision-making. The goal in the symposium is to 1) highlight some of the best work in this area, 2) to facilitate communication between researchers working on these problems from varying perspectives, and 3) to provide an excellent showcase of this area for members of the cognitive science community who may not yet be familiar with this work. The speakers who agreed to participate are all accomplished researchers in this area but each approach the set of problems involved in sequential decision making and learning from a slightly different perspective. The key topics covered include 1) how people plan sequences of actions to accomplish goals (Hotaling, Dimperio, & Busemeyer, Simon & Daw), 2) the underlying neurobiology of sequential decision making and planning (Simon & Daw), 3) how cognitive representations of the task or environment supporting planning and decision-making (Gureckis & Markant, Love & Otto, and Simon & Daw), and 4) how people balance exploration and exploitation in order to arrive at effective decision strategies in an unknown environment (Lee, Zhang, and Steyvers and Gureckis & Markant). In addition to these overlapping psychological themes, the researchers all share a core approach of applying sophisticated computational models to understand human behavior (including Bayesian approaches, reinforcement learning, and Markov Decision Processes).

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