Detecting the Local Maximum: A Satisficing Heuristic

Detecting the Local Maximum: A Satisficing Heuristic Yanlong Sun (suny@bgnet.bgsu.edu) Ryan D. Tweney (tweney@bgnet.bgsu.edu) Department of Psychology, Bowling Green State University Bowling Green, OH 43403 USA Abstract In a simulated yard sale task, participants were asked to sell a series of objects, each of which would attract three customers making a randomly determined offer. Participants were told to maximize the total take from the sale. The analysis of the data revealed that high-performing naive participants were using a strategy that made them relate the current event to the seemingly irrelevant preceding events. We argue that this strategy is consistent with Simon’s (1982) notion of “satisficing heuristic”, which accounted for both partic ipants’ limited computation capacity and the task environment. Introduction Intuitive predictions and probabilistic judgments are often used as tasks to evaluate people’s performance in judgment and decision-making research, and a common scheme is to collect incorrect predictions and misjudgments by “setting up a ‘trap’ that subjects would fall into if they were using a particular heuristic” (W. Goldstein & Hogarth, 1996, p.26). In this type of research, predictions derived from probability theory are often used as an objective criterion, and violations of the normative models are labeled as biased or irrational. Tversky and Kahneman’s “heuristics and biases” program has been the most influential in this field. They suggested that intuitive predictions and judgments are often mediated by a small number of distinctive mental operations, which they called judgmental heuristics. “These heuristics … are often useful but they sometimes lead to characteristic errors or biases” (Kahneman & Tversky, 1996, p.582). For example, people’s tendency to use a small sample of preceding events to evaluate an overall process was attributed to a “representativeness” bias (Tversky & Kahneman, 1973). This bias has been used to account for many cognitive behaviors, such as the tendency to see streaks in random sequences (Gilovich, Vallone & Tversky, 1985), and failure to “acquire a proper notion of regression” (Tversky & Kahneman, 1973). In a recent study on gambling behaviors, Thaler & Johnson (1990) concluded that “current choices are often evaluated with the knowledge of the outcomes which have preceded them, (but) such knowledge can often be a handicap” (p.643). However, the heuristics and biases research program has recently been controversial, partly because “biases” sometimes appear highly adaptive. Thus, Tweney & Doherty (1983) argued that confirmatory tendencies (“confirmation biases”) can be adaptive when hypotheses are relatively new and untested. Further, in an extensive series of studies, Gigerenzer and his colleagues (e.g. 1991, 1994, Gigerenzer & Todd, 1999) found evidence which led them to strongly disagree with Kahneman and Tversky. They argued that many seemingly naive “fast and frugal heuristics” are adaptive in an uncertain environment. Similarly, Kareev, et al., suggested that the limited capacity of working memory (hence the use of small samples) could actually help the early detection of covariation since small samples of correlated variables are highly skewed (Kareev, 1995; Kareev, Lieberman & Lev, 1997). The present study followed Simon’s (1982) notion of “bounded rationality”, which takes into account both people’s limited computation capacity, and the structure of task environments. Our findings suggest that under circumstances when the precise prediction derived from statistics or probability theory is not the only criterion, heuristics based on a small sample size can be valuable. With a satisficing strategy that only needs to “look for a satisfactory alternative” (Simon, 1982, p.295), naive participants were able to effectively accomplish the goal of the task, based on the evaluation of a few preceding events. Recognizing the Maximum of a Sequence The statistical properties of sequential lists of evidence have long been of interest to mathematicians. The dowry problem (or the secretary problem) is a classic example in the dynamic programming literature, one analyzed by Cayley in 1875 (see Ferguson, 1989). As a mathematical problem, the dowry problem is difficult to solve, requiring advanced mathematical knowledge and problem solving ability. Obviously, few, if any, people are likely to work out the exact stopping point mathematically in an everyday life situation when a similar problem is encountered. Instead, without complicated calculations, a player might need to use “common sense” to make decisions. The present study adopted a simplified version of the problem – a simulated “yard-sale” task – to test how naive people evaluate preceding events and make decisions when facing sequential events generated by an unknown process. Participants were asked to sell a series of objects in a simulated yard sale. Each object attracted three potential buyers, each of whom came at a different time and made a