One way to make inferences about social statistics, such as the frequencies of health risks in the population, is to probe relevant instances in one`s social network. People can infer, for instance, the relative frequency of different diseases by probing how many members of their social network suffer from them. How are such instance-based inferences cognitively implemented? Noncompensatory strategies based on lexicographic and limited search have been extensively examined in the context of cue-based inference. Their role in instance-based inference, by contrast, has received scant attention. We propose the social-circle heuristic as a model of noncompensatory instance-based inference entailing lexicographic and limited search, and test its descriptive and prescriptive implications: To what extent do people rely on the social-circle heuristic? How accurate is the noncompensatory heuristic relative to a compensatory strategy when inferring event frequencies? Two empirical studies show that the heuristic accurately predicts the judgments of a substantial portion of participants. A response time analysis also supports the assumption of lexicographic search: The earlier the heuristic predicted search to be terminated, the faster participants classified as using the social-circle heuristic responded. Using computer simulations to systematically investigate the heuristic`s prescriptive implications, we find that despite its limited search, the heuristic can approximate the accuracy of a compensatory strategy in skewed and in spatially clustered environments both common properties of distributions in real-world social environments. (C) 2013 Elsevier Inc. All rights reserved
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.