Behavioral Bias: The Hot Stove Effect- The Tendency To Rely On Small Samples
Here’s more research on the tendency to rely on small samples of past experience… This is very likely to get traders and investors in trouble.
Abstract (Via Takemi Fujikawa @ Universiti Sains Malaysia)
Experiments have suggested that decisions from experience differ from decisions from description. In experience based decisions, the decision makers often fail to maximize their payoffs. Previous authors have ascribed the effect of under weighing of rare outcomes to this deviation from maximization. In this paper, I re-examine and provide further analysis on the effect with an experiment that involves a series of simple binary choice gambles. In the current experiment, decisions that bear small consequences are repeated hundreds of times, feedback on the consequence of each decision is provided immediately, and decision outcomes are accumulated. The participants have to learn about the outcome distributions through sampling, as they are not explicitly provided with prior information on the payoff structure. The current results suggest that the “hot stove effect” is stronger than suggested by previous research and is as important as the payoff variability effect and the effect of under weighing of rare outcomes in analyzing decisions from experience in which the features of gambles must be learned through a sampling process.
Introduction (Via Takemi Fujikawa @ Universiti Sains Malaysia)
Much attention has been given to the distinction between decisions from description and decisions from experience. In experience-based decisions, people experience difficulty in estimating and understanding uncertainty. Erev and Barron (2005) hypothesised that two main behavioural tendencies determine the effect of rare events on repeated decisions from experience. The first is a tendency to rely on small samples of past experiences (also proposed by Fox & Hadar, 2006). This tendency leads to underweighting of rare events, as most samples are not likely to include the rare events. The second is a tendency to rely on recent experiences. When the information available to the decision makers (DMs) is limited to the obtained payoffs, this tendency leads to the “hot stove effect”, which implies overweighting of the worst outcomes. The hot stove effect was first introduced by Mark Twain with his observation that if a cat jumped on a hot stove, then she would never jump on a hot stove again. However, the cat would never jump even on a cold stove. Coutu (2006) states that the hot stove effect is a fundamental problem of learning that reduces the DMs’ likelihood of repeating decisions that got them in trouble. The hot stove effect implies a bias against a risky alternative in binary experience-based decisions (Denrell & March, 2001). The bias is a product of the tendency to reproduce actions that have been successful and avoid recent actions that have led to poor outcomes