Case-based Decision Theory (CBDT) suggests that decisions under ucnertainty are made by analogies to previously-encountered problems. The theory postulates a similarity function over decision problems and a utility functionon outcomes, such that acts are evaluated by a similarity-weighted sum of...

This paper studies some new properties of set functions (and, in particular, "non-additive probabilities" or "capacities") and the Choquet integral with respect to such functions, in the case of a finite domain. We use an isomorphism between non-additive measures on the original space (of states...

The representation of a cooperative transferable utility game as a linear combination of unanimity games may be viewed as an isomorphism between not-necessarily additive set functions on the players space and additive ones on the coalitions space. We extend the unanimity-basis representation to...

We claim that preferences of economic agents cannot be assumed given; rather, they are partly determined by the process of trade in the market, by information about the latter and so forth. In other words, preferences determine actions which, in turn, determine preferences. Thus classical tools...

The representation of knowledge in terms of rules is fraught with theoretical problems, such as the justification of induction, the "right" way to do it, and the revision of knowledg ein face of contradictions. In this paper we argue that these problems, and especiallyt the inconsistency of...

The neo-classical theory of consumer behavior, while a powerful tool, suffers from some well-known flaws. Specifically, it assumes a highly, often unrealistically rational utility-maximizing consumer and sheds little light on the dynamic nature of consumption decisions. In this paper we make...

"Case-Based Decision Theory" is a theory of decision making under uncertainty, suggesting that people tend to choose acts that performed well in similar cases they recall. The theory has been developed from a decision-/game-/economic-theoretical point of view, as a potential alternative to...

We present and axiomatize several update rules for probabilities (and preferences) where there is no unique additive prior. In the context of non-additive probabilities we define and axiomatize Bayesian update rules; in the context of multiple (additive) priors we define maximum likelihood...