We develop a new version of prospect theory that employs cumulative rather than separable decision weights and extends the theory in several respects. This version, called cumulative prospect theory, applies to uncertain as well as to risky prospects with any number of outcomes, and it allows

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Prospect Theory for continuous distrbutions

that our framework can help explain the high mean, excess volatility, and predictability of stock returns, as well as their low correlation with consumption growth. The design of our model is influenced by prospect theory and by experimental evidence on how prior outcomes affect risky choice. I.

Rank-dependent utility theory

Prospect Theory is a widely accepted descriptive framework to model decisions under risk. However, it is limited to situations with finitely many outcomes. Moreover, it is discontinuous, i.e., small changes in a lottery can produce large differences in its utility, contrary to experiments

Abstract: Original and cumulative prospect theory differ in the composition rule used to combine the probability weighting function and the value function. We test these composition rules by performing a novel test. We apply estimates of prospect theory’s weighting and value function obtained from

such regulation is then sketched lo provide elemenls of a theory of supply of regulation. A variety of empirical evidence and illustration is also presented. W The state—the machinery and power of the state—is a potential resource or threat to every industry in the society. With its power to prohibit or compel

The study of computational agents capable of rational behaviour has received a great deal of attention in recent years. Theoretical formalizations of such agents and their implementations have proceeded in parallel with little or no connection between them. This paper explores a particular type of rational agent, a BeliefDesire -Intention (BDI) agent. The primary aim of this paper is to integrate (a) the theoretical foundations of BDI agents from both a quantitative decision-theoretic perspective and a symbolic reasoning perspective; (b) the implementations of BDI agents from an ideal theoretical perspective and a more practical perspective; and (c) the building of large-scale applications based on BDI agents. In particular, an air-traffic management application will be described from both a theoretical and an implementation perspective.

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