A person’s social network constitutes a rich sampling space for informing judgments about social statistics (e.g., the distribution of preferences, risks, or behaviors in the broader social environment). How is this sampling space searched and used to make inferences? This chapter gives an overview on research on the social-circle model, a computational process account of how people make inferences about relative event frequencies. The social-circle model is inspired by the notion of sequential and limited search in models of bounded rationality for multi-attribute decision making. In accord with research on the structure of social memory, the model assumes that social sampling proceeds by sequentially probing a person’s social circles – including oneself, family, friends, and acquaintances – and that search is constrained by a simple stopping rule. The social-circle model has several free parameters that enable it to capture individual differences in the order in which social circles are inspected, in noise during evidence evaluation, and in discrimination thresholds. We provide a step-by-step tutorial for deriving predictions of the social-circle model, review empirical tests of the model, illustrate how the model reflects individual differences in social sampling and differences in sampling across domains, and analyze the ecological rationality of heuristic social sampling.

Social interactions provide a large proportion of the information that people gather on a daily basis. The fundamental question guiding this chapter is whether and how social motivations influence the samples people gather, and how this drives downstream evaluative biases. We begin by highlighting how group-based motivations may influence three different stages of information processing: (1) where and how much information people gather, (2) how people interpret sampled information, and (3) how sampling strategies change recursively over time based on the congeniality of the environment. We then review recent empirical work that tests these possibilities using different social identities and contexts. Across seven studies we found that most participants began sampling from their own group, and that they sampled overall more information from their own group, giving rise to more variable ingroup (relative to outgroup) experiences. We also found that participants asymmetrically integrated their initial experiences into their evaluations based on congeniality: initial positive experiences were integrated into evaluations, whereas initial negative experiences were not. Lastly, we demonstrated that participants adopted different sampling strategies over time when the ingroup was de facto worse, obfuscating real-group differences. Together, we demonstrate that group-based motivations permeate each stage of information sampling, collectively giving rise to biased evaluations. These results unite extant research on sampling and interpretive sources of bias and provide a springboard for future research on sampling behavior across social motivations and contexts.