Dynamic Decision Making and Race Games

Frequent criticism of dynamic decision making research pertains to the overly complex nature of the decision tasks used in experimentation. To address such concerns, we study dynamic decision making with respect to a simple race game, which has a computable optimal strategy. In this two-player race game, individuals compete to be the first to reach a designated threshold of points. Players alternate rolling a desired quantity of dice. If the number one appears on any of the dice, the player receives no points for his turn; otherwise, the sum of the numbers appearing on the dice is added to the player's score. Results indicate that although players are influenced by the game state when making their decisions, they tend to play too conservatively in comparison to the optimal policy and are influenced by the behavior of their opponents. Improvement in performance was negligible with repeated play. Survey data suggests that this outcome could be due to inadequate time for learning or insufficient player motivation. However, some players approached optimal heuristic strategies, which perform remarkably well. 1. Introduction A great deal of our understanding of judgment and decision making comes from a body of research that examines the dysfunctional consequences and systematic biases of adopting heuristics or “rules of thumb” in decision making. For example, decision makers (DMs) are known for ignoring base rate information, failing to revise opinions, having unwarranted confidence, and harboring hindsight biases, to name a few [1]. This seems to imply that humans are fairly incompetent beings [2]. Yet while this appears to be true in controlled settings, it is not so in real life. Toda points out that “man drives a car, plays complicated games, and organizes society” [1]. So why is there such a disconnect between experimentation and real-world phenomena? While it is clear that people do make mistakes and can, under certain situations, exhibit systematic deviations from rational predictions, this research is criticized for concentrating on discrete incidents often lacking any form of meaningful feedback [1]. Critics contend that judgment is best viewed as a continuous and interactive process that enables DMs to cope with their environment. The claim, made by Jungermann, is that decision makers who appear biased or error-prone in the short run may be quite effective in continuous or natural environments that allow for feedback and periodic adjustment in decision making [3]. Research in dynamic decision making (DDM) is well suited to advance our understanding