138,320 research outputs found
The shape of incomplete preferences
Incomplete preferences provide the epistemic foundation for models of
imprecise subjective probabilities and utilities that are used in robust
Bayesian analysis and in theories of bounded rationality. This paper presents a
simple axiomatization of incomplete preferences and characterizes the shape of
their representing sets of probabilities and utilities. Deletion of the
completeness assumption from the axiom system of Anscombe and Aumann yields
preferences represented by a convex set of state-dependent expected utilities,
of which at least one must be a probability/utility pair. A strengthening of
the state-independence axiom is needed to obtain a representation purely in
terms of a set of probability/utility pairs.Comment: Published at http://dx.doi.org/10.1214/009053606000000740 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Reasons and Means to Model Preferences as Incomplete
Literature involving preferences of artificial agents or human beings often
assume their preferences can be represented using a complete transitive binary
relation. Much has been written however on different models of preferences. We
review some of the reasons that have been put forward to justify more complex
modeling, and review some of the techniques that have been proposed to obtain
models of such preferences
Dynamic Non-Bayesian Decision Making
The model of a non-Bayesian agent who faces a repeated game with incomplete
information against Nature is an appropriate tool for modeling general
agent-environment interactions. In such a model the environment state
(controlled by Nature) may change arbitrarily, and the feedback/reward function
is initially unknown. The agent is not Bayesian, that is he does not form a
prior probability neither on the state selection strategy of Nature, nor on his
reward function. A policy for the agent is a function which assigns an action
to every history of observations and actions. Two basic feedback structures are
considered. In one of them -- the perfect monitoring case -- the agent is able
to observe the previous environment state as part of his feedback, while in the
other -- the imperfect monitoring case -- all that is available to the agent is
the reward obtained. Both of these settings refer to partially observable
processes, where the current environment state is unknown. Our main result
refers to the competitive ratio criterion in the perfect monitoring case. We
prove the existence of an efficient stochastic policy that ensures that the
competitive ratio is obtained at almost all stages with an arbitrarily high
probability, where efficiency is measured in terms of rate of convergence. It
is further shown that such an optimal policy does not exist in the imperfect
monitoring case. Moreover, it is proved that in the perfect monitoring case
there does not exist a deterministic policy that satisfies our long run
optimality criterion. In addition, we discuss the maxmin criterion and prove
that a deterministic efficient optimal strategy does exist in the imperfect
monitoring case under this criterion. Finally we show that our approach to
long-run optimality can be viewed as qualitative, which distinguishes it from
previous work in this area.Comment: See http://www.jair.org/ for any accompanying file
Dominance Measuring Method Performance under Incomplete Information about Weights.
In multi-attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative?s intensity of dominance, known as dominance measuring methods. Different dominancemeasuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we useMonte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions orweights represented by fuzzy numbers.Moreover, dominance measuringmethod performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one
- …