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By Bhowmick D., Davison A.C.

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Extra info for A Laplace mixture model for identification of differential expression in microarray experiments (200

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Despite this, the power function offers the best overall fit of the data. 1 Main Effects There is increasing risk aversion with increasing probability level. 8 refer to the probability of the larger outcome, which for losses is $0. The results confirm our earlier finding for the loss domain that preference for actuarially fair insurance (and hence risk aversion) increases monotonically as the probability of a zero loss increases (Hershey and Schoemaker, 1980). One explanation for this increase in risk aversion is a nonlinear probability weighting function, f(p) , which induces increasingly risk-averse responses for increasing p.

The CE-PE discrepancy is greatest for low probabilities, getting smaller as the probability increases. They show that this pattern can arise from an anchoring and adjustment model where the response is a weighted average of varying starting points and a target utility function. In this paper, we seek to verify the interaction between response mode effects and reference probability since Johnson and Schkade used procedures that differed from our initial designs. For example, they used standard gambles in which both gamble outcomes were nonzero; we use simple gambles in which one outcome is zero.

The F#1-F#3 interaction, significant in problem #2, indicates that frame and presentation taken together have a crucial impact on decision-making. 63). This suggests that, dispersion being equal, information intake procedures generate the interactive effect. When "pre-packed" information is provided (Le. in global modality), frame effects are quite significantly weakened; in dimensional procedures, where subjects CONTEXTUAL EFFECTS AND THE INFLUENCE OF THE FRAME ON DECISION MAKING 31 themselves decide on the value of a given option after processing data pertaining to it, frame influence is maintained.

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