MODELS OF CHOICE BETWEEN MULTIOUTCOME LOTTERIES

Multiplicative-summing decision theories were compared by assessing th eir tit to choice data obtained from 559 subjects, each providing 16-6 0 problems. The problems were 212 pairs of multioutcome monetary lotte ries, including 86 for gains, 86 for losses, and 30 mixed lotteries wi th gains and losses. Lotteries of each pair had the same expected valu e. The fit of each theory to the data was measured by the percent of p roblems with agreement between the observed majority preference and th e lottery with the higher calculated expected utility (or Value); 'per cent concordance' indicates percent of agreement. Parameter fitting sh owed that a linear probability function provided excellent representat ion of the data, and was superior to the rank-dependent weighted proba bility functions of cumulative prospect theory. Modeling is successful when the utility function is S-shaped, concave in the gain and convex in the loss domains, across a wide range of curvatures (from u(x) = x (0.05) to x(0.85)). Surprisingly, the best modeling was with severe cu rvature (u(x) = x(0.05)). Addition of loss aversion to the model and s teeper slopes for losses than for gains provided no or very little inc reases of concordances between theory and data. Conclusions are that f or this data set of choices between multioutcome monetary lotteries, t he most descriptive of the multiplicative-summing theories was a hybri d model which combines the linear probability of classical expected ut ility and a symmetrical S-shaped utility function borrowed from cumula tive prospect theory. A rank-dependent weighted probability function d id not improve the fit of model to data. Variance modeling also descri bed choices very well. (C) 1997 by John Wiley & Sons, Ltd.