The Social Circle Heuristic: Fast and Frugal Decisions Based on Small Samples

The Social Circle Heuristic: Fast and Frugal Decisions Based on Small Samples Thorsten Pachur (pachur@mpib-berlin.mpg.de) Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Lentzeallee 94 14195 Berlin, Germany Jorg Rieskamp (rieskamp@mpib-berlin.mpg.de) Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Lentzeallee 94 14195 Berlin, Germany Ralph Hertwig (ralph.hertwig@unibas.ch) University of Basel, Department of Psychology, Missionsstrasse 60/62 4055 Basel, Switzerland Abstract Whereas reliance on information from one’s proximal social environment for generalizing about the population has often been associated with erroneous judgments, this information is often valuable and can be exploited for making accurate inferences. The social circle heuristic is a judgment mechanism in which the content and structure of people’s social networks are used for making inferences about frequencies in the population in a paired comparison task. Because the heuristic has a stopping rule, judgments generated by it will often be based on small samples sizes. In this paper we present experimental evidence that shows both that the social circle heuristic can compete with a more thorough strategy, and that people actually apply it. Samples as Reflections of the Environment Scarcity of information is one of the central properties of everyday decision making. For many judgment problems in the real world, neither direct knowledge of the to-be-judged values nor complete knowledge of all relevant facts that might help predict the correct value are available. Instead, inferences have to be made under uncertainty, based on information that is more or less predictive of the criterion. What processes underlie people’s inferences in such situations? Recent approaches to judgment under uncertainty that acknowledge the bounded rationality of humans have advanced the notion of fast and frugal decision making (Gigerenzer, Todd, & The ABC Research Group, 1999). The heuristics proposed by this program are based on Brunswik’s (1955) idea that judgments are made on the basis of cues that are probabilistically related to the target criterion. As Gigerenzer et al. (1999) have shown, such mechanisms can be astonishingly accurate despite using only a limited amount of information. Typically, fast and frugal heuristics rely on cues that are qualitatively different from the criterion (e.g., considering whether or not there is rent control in a city to predict which of two cities has a higher homeless rate). In the case of frequency judgments, however, the target criterion can be inferred by sampling instances of it from a population. For example, which first name occurs more often in the population: Martin or Simon? Or, does bladder cancer or renal cancer have a higher annual incidence rate? For these inference problems concerned with environmental frequencies, it is possible that—rather than accessing proximal cues—samples consisting of instances of the criterial event are drawn from the proximal environment. As such, samples can also serve as “keys to assessing the distal environment” (Fiedler, 2000, p. 661), and in the absence of direct knowledge about the environment, these “reflections” of the environment are used to infer its latent properties. That humans use proximal samples when making inferences about the entire population has been argued in various forms. For instance, in one interpretation of the availability heuristic (Tversky & Kahneman, 1973; see also Sedlmeier, Hertwig, & Gigerenzer, 1998), frequencies in the environment are judged by accumulating easily accessible instances of the target event (e.g., Lichtenstein, Slovic, Fischhoff, Layman, & Combs, 1978). Judgment phenomena such as the false consensus effect (Ross, Green, & House, 1977) or the optimistic bias (Weinstein, 1980), have been attributed to the employment of the availability heuristic, and the use of such a small-sample-based heuristic has therefore been associated with the fallibility and irrationality of human decision making. In contrast, we ask how humans can achieve fairly accurate judgments in spite of the scarcity and cognitive boundedness they have to face in the real world, and examine what processes contribute to this achievement (cf. Krueger & Funder, in press). Elaborating on the idea of sample-based judgments, we propose and test a simple heuristic for paired comparisons that exploits frequency information in one of people’s most proximal environment: their social network. The Social Environment as Sample Space People’s inferences have been shown to be strongly sensitive to information in their social environment. Prominent examples are attitude formation (e.g., Fishbein & Ajzen, 1975), conformity behavior (Hirshleifer, 1995; Latane, 1981) or risk frequency judgments (Benjamin, Dougan, & Buschena, 2001; Hertwig, Pachur, &