The availability bias in social perception and interaction


Book: 
Judgment under Uncertainty: Heuristics and Biases
Authors: 
Taylor, S. E.
Editors: 
Kahneman, D., Slovic, P., & Tversky, A.
Type: 
Category: 
Pages: 
190-200
Year: 
1982
Publisher: 
Cambridge University Press
Place: 
New York
Abstract: 

Every day the social perceiver makes numerous, apparently complex social judgments - Predicting another's behavior, attributing responsibility, categorizing an individual, evaluating anothers, estimating the power or influence of a person, or attributing causality. A central task of social psychology has been to determine how the social perceiver makes these judgments. Until recently, research on this topic was marked by a rationalistic bias, the assumption that judgments are made using thorough, optimal strategies (see, for example, Fischhoff, 1976, for discussion of this point). Errors in judgment were attributed to two sources: (a) accidental errors due to problems with information of which the perceiver was presumably unaware; and (b) errors which resulted from the irrational motives and needs of the perceiver. However, over a period of years, a growing body of evidence suggested not only that people's judgments and decisions are less complete and rational than was thought but that not all errors can be traced to motivational factors. Even in the absence of motives, judgments are often made on the basis of scant data, which are seemingly haphazardly combined and influenced by preconceptions (see, e.g., Dawes, 1976). These findings led to a revised view of the cognitive system. People came to be seen as capacity-limited, capable of dealing with only a small amount of data at a time. Rather than being viewed as a naive scientist who optimizes, the person was said to "satisfice" (Simon, 1957) and use shortcuts that would produce decisions and judgments efficiently and accurately.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education