Natural Sample Spaces and Judgment Under Uncertainty

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ABSTRACT Judging the probability of uncertain events is essential for effective decision making. Yet people make many systemic errors in their intuitive judgments. These errors impede important decisions that people make on a daily basis, such as medical diagnosis, political and legal policy-making, and planning for the future. The specific aims of the research are to evaluate and explore the implications of a conceptual framework developed by Gavanski and Hui (1992) for understanding the mental processes that underlie judgements of probability. Gavanski and Hui have argued that, contrary to previous theories, people are not necessarily bad statisticians, in that their judgment strategies are not fundamentally incompatible with statistical principles. However, they are sometimes bad samplers, in that they may access inappropriate sets of information from memory for making their judgments. Both accurate and inaccurate judgments can be understood according to what sets of information people access. This research will (a) test some basic assumptions underlying the framework, (b) apply the framework to understanding the processes by which cognitive organization (i.e., the categorization of information in memory) influence people's ability to judge various probabilities, (c) evaluate novel predictions of the framework for conditions under which people will make either accurate or inaccurate judgments, and (d) use the framework to account for and clarify the processes underlying some established judgment phenomena. The long term objective of the research is to provide an understanding of how people judge probability and when and why they make judgment errors. In addition, the research will lead to the identification of tasks particularly likely to evoke judgment errors and to the development of techniques for improving people's judgments and decisions.