Disputes between evidential decision theory and causal decision theory have continued for decades, and many theorists state dissatisfaction with both alternatives. Timeless decision theory (TDT) is an extension of causal decision networks that compactly represents uncertainty about correlated computational processes and represents the decisionmaker as such a process. This simple extension enabl...

We describe a framework for specifying conditional desires “desire CY by E degrees if ,8” and evaluating preference queries “would you prefer ~1 over ~2 given $” under uncertainty. We refine the semantics presented in (Tan & Pearl 1994) to allow conditional desires to be overridden by more specific desires in the database. Within this framework, we also enable consideration of surprising worlds...

A new laboratory experiment is designed to identify the best theories for describing decisions under risk. The experimental design has two noteworthy features: a representative sample of binary choice problems (for fair comparison across theories) and a lottery set with a small number of outcomes and probabilities (for ease of non-parametric estimation). We find that a simple heuristic, rank-de...

In real decision situations a decision maker (DM) is often confronted with the problem that the information which is necessary for constructing a classical decision model is not available, or the cost for getting this information seems too high. Subsequently, the DM abstains from constructing a decision model; the DM fears that this model is not an authentic image of the real problem. Fuzzy set...

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The topic of this article is decision procedures for satisfiability modulo theories (SMT) of arbitrary quantifier-free formulæ. We propose an approach that decomposes the formula in such a way that its definitional part, including the theory, can be compiled by a rewrite-based firstorder theorem prover, and the residual problem can be decided by an SMT-solver, based on the Davis-Putnam-Logemann...

In most contemporary approaches to decision making, a decision problem is described by a sets of states and set of outcomes, and a rich set of acts, which are functions from states to outcomes over which the decision maker (DM) has preferences. Most interesting decision problems, however, do not come with a state space and an outcome space. Indeed, in complex problems it is often far from clear...

Classical decision theory models behaviour in terms of utility maximisation where utilities represent rational preference relations over outcomes. However, empirical evidence and theoretical considerations suggest that we need to go beyond this framework. We propose to represent goals by higherorder functions or operators that take other functions as arguments where the max and argmax operators...