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A Reasons-based Framework for Understanding Default Effects

出版	University of California, Irvine, 2011
ISBN	11246777209781124677729
URL	http://books.google.com.hk/books?id=xyB7AQAACAAJ&hl=&source=gbs_api

註釋Default options that automatically enroll people into a program or course of action are more likely than non-enrollment defaults to be viewed as implicit recommendations, influencing how people choose. This dissertation proposes a reasons-based framework to account for such an information asymmetry. Automatic enrollment defaults have the potential to alter an individual's state of affairs --- something happens to you when you fail to decide --- and so they are presumed to require strong justifications. Non-enrollment defaults do not alter an individual's state of affairs, and are presumed to be implemented even in the absence of strong reasons for choosing them. As a result, automatic enrollment defaults are more likely than non-enrollment defaults to provide information relevant to making a default decision. Six experiments find support for this basic framework. Studies 1--3 examine the inferences drawn from default options, and consistently show that participants presume strong justifications for automatic enrollment but usually view non-enrollment as uninformative about a policymaker's reasons. Study 4 demonstrates the causal influence of these inferences: when the default is known to be chosen at random, and therefore uninformative, the impact of the default option disappears. Study 5 demonstrates, in a field experiment setting, that default inferences affect real-world, high-stakes decisions.