Abstract Inferring latent event frequencies in the environment is a key cognitive function. Models of frequency judgments commonly assume that for such inferences, people rely on instance knowledge (e.g., people in a person's social network) processed in a compensatory fashion. The chapter examines the possible contribution and accuracy of noncompensatory processing of instance knowledge. For that purpose, the chapter extends the notion of ordered and limited search—which has received much attention in cue-based inference—to instance-based inference and propose the boundedly rational social-circle heuristic as one possible model. Unlike the common assumption of compensatory processing in models of instance-based inference, the social-circle heuristic is noncompensatory: it searches the social circles of a person's network for relevant instances sequentially and stops search as soon as a circle discriminates. In computer simulations, the chapter shows that despite its frugality, the social-circle heuristic competes favorably with more complex strategies, especially in environments with a skewed frequency distribution. In two empirical studies, the chapter shows that for predicting people's inferences concerning the relative frequency of real-world events, the heuristic provides a viable alternative to the usual assumption of compensatory instance processing. Finally, the chapter discusses how noncompensatory processing of instance knowledge might account for established regularities in social influence and how the social-circle heuristic relates to norm formation.
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