A Bounded Rationality Account of Wishful Thinking

A Bounded Rationality Account of Wishful Thinking Rebecca Neumann (beckieneumann@gmail.com) Cognitive Science Program, University of California, Berkeley, CA 94720 USA Anna N. Rafferty (rafferty@cs.berkeley.edu) Computer Science Division, University of California, Berkeley, CA 94720 USA Thomas L. Griffiths (tom griffiths@berkeley.edu) Department of Psychology, University of California, Berkeley, CA 94720 USA Abstract People tend towards wishful thinking, in which they overesti- mate the probability of favorable outcomes and underestimate the probability of unfavorable outcomes. Many explanations for this phenomenon focus on its irrationality. We explore whether wishful thinking could actually help people make bet- ter decisions given that they have limited cognitive resources. We consider a situation in which multiple decisions must be made over a period of time, where the consequences of these decisions are not fully determined. We model this situation as a Markov decision process, and incorporate limited cognitive resources by varying the amount of time in the future that the agent considers the consequences of its decisions. Through simulations, we show that with limited cognitive resources, this model can exhibit better performance by incorporating a bias towards wishful thinking. This advantage occurs across a range of decision-making environments, suggesting that the same effect could be applicable to many real life scenarios. Keywords: rational process models; Markov decision pro- cesses Introduction People tend to overestimate the probability that their pre- ferred outcomes will occur and to underestimate the proba- bility of non-preferred outcomes (e.g., Camerer & Lovallo, 1999; Larwood & Whittaker, 1977; Lyles & Thomas, 1988; Svenson, 1981; Weinstein, 1980). This “wishful think- ing” or optimistic bias occurs in a variety of situations, from estimating the likelihood of a desired candidate win- ning an election (Babad, 1997) to predicting one’s future salary (Weinstein, 1980). This phenomenon seems irrational: people have unrealistic expectations, and these expectations could lead to risky choices. Most explanations of wishful thinking have focused on it as an irrational cognitive bias. While wishful thinking appears to be detrimental, we ex- plore whether this bias could be a rational strategy given peo- ple’s limited cognitive resources. Determining the best de- cision in a given situation requires considering all possible outcomes and their long range consequences. Yet, consid- ering all of these possibilities is computationally intractable. Wishful thinking might help to compensate for the fact that people cannot fully evaluate the long term consequences of their actions. Reinforcement learning models for how artifi- cial agents should make decisions have shown that employ- ing optimistic beliefs when faced with actions with unknown consequences can lead to improved performance (Kaelbling, 1993). Similarly, using optimistic confidence bounds when exploring a search tree to choose actions can lead to improved agent performance in practice (e.g. Munos & Teytaud, 2006), although theoretical bounds show that this effect is not guar- anteed (Coquelin & Munos, 2007). We use Markov decision processes to determine whether these advantages for optimism in exploring actions with un- known consequences might also hold for the problem of choosing actions when one has limited cognitive resources. Using a simple decision making situation in which an agent must make choices about whether to keep or quit its job over a period of time, we examine how an optimistic bias affects the agent’s performance when it can only consider the con- sequences of its actions for a limited period of time into the future. The limited horizon that agents consider mimics peo- ple’s limited computational resources. In our simulations, we find that agents with moderate optimism perform better than agents with no bias when the horizon is relatively small com- pared to the true time period over which agents can act. We begin by describing existing theories for the wishful thinking bias, and background about the Markov decision processes that we use to model human action planning. We then demonstrate how we model limited computational re- sources by restricting the horizon that agents consider when choosing actions. Through four simulations, we determine under what environmental and computational constraints an optimistic bias improves performance. Background We briefly review existing theories about the wishful thinking bias and provide an overview of Markov decision processes. Wishful Thinking Several theories of wishful thinking have been proposed that aim to explain why this bias occurs and how prevalent it is, although there remain significant gaps in the theories ac- counting for the phenomenon (Krizan & Windschitl, 2007). Ego-utility theory suggests that the bias protects the agent’s self image, and thus should only occur in situations that af- fect self image. For example, most people estimate that they are better drivers than the average person (Svenson, 1981), and students believe they will have fewer health problems than their peers (Weinstein, 1980). In contrast to this the- ory, the strategic theory asserts that the bias should occur in a broader range of situations, and that it can be tempered by in- centives for accuracy (Akerlof & Dickens, 1982). However,