title: Framing Effects: Dynamics and Task Domains
abstract: The author examines the mechanisms and dynamics of framing effects in risky choices across three distinct task domains (i.e., life-death, public property, and personal money). The choice outcomes of the problems presented in each of the three task domains had a binary structure of a sure thing vs a gamble of equal expected value; the outcomes differed in their framing conditions and the expected values, raging from 6000, 600, 60, to 6, numerically. It was hypothesized that subjects would become more risk seeking, if the sure outcome was below their aspiration level (the minimum requirement). As predicted, more subjects preferred the gamble when facing the life-death choice problems than facing the counterpart problems presented in the other two task domains. Subjects' risk preference varied categorically along the group size dimension in the life-death domain but changed more linearly over the expected value dimension in the monetary domain. Framing effects were observed in 7 of 13 pairs of problems, showing a positive frame-risk aversion and negative frame-risk seeking relationship. In addition, two types of framing effects were theoretically defined and empirically identified. A bidirectional framing effect involves a reversal in risk preference, and occurs when a decision maker's risk preference is ambiguous or weak. Four bidirectional effects were observed; in each case a majority of subjects preferred the sure outcome under a positive frame but the gamble under a negative frame. In contrast, a unidirectional framing effect refers to a preference shift due to the framing of choice outcomes: A majority of subjects preferred one choice outcome (either the sure thing or the gamble) under both framing conditions, with positive frame augmented the preference for the sure thing and negative frame augmented the preference for the gamble. These findings revealed some dynamic regularities of framing effects and posed implications for developing predictive and testable models of human decision making.
authors.affiliation: University of South Dakota
authors.initials: XT
authors.lastname: Wang