5,506 research outputs found
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
An Efficient Protocol for Negotiation over Combinatorial Domains with Incomplete Information
We study the problem of agent-based negotiation in combinatorial domains. It
is difficult to reach optimal agreements in bilateral or multi-lateral
negotiations when the agents' preferences for the possible alternatives are not
common knowledge. Self-interested agents often end up negotiating inefficient
agreements in such situations. In this paper, we present a protocol for
negotiation in combinatorial domains which can lead rational agents to reach
optimal agreements under incomplete information setting. Our proposed protocol
enables the negotiating agents to identify efficient solutions using
distributed search that visits only a small subspace of the whole outcome
space. Moreover, the proposed protocol is sufficiently general that it is
applicable to most preference representation models in combinatorial domains.
We also present results of experiments that demonstrate the feasibility and
computational efficiency of our approach
Revisiting Norm Optimization for Multi-Objective Black-Box Problems: A Finite-Time Analysis
The complexity of Pareto fronts imposes a great challenge on the convergence
analysis of multi-objective optimization methods. While most theoretical
convergence studies have addressed finite-set and/or discrete problems, others
have provided probabilistic guarantees, assumed a total order on the solutions,
or studied their asymptotic behaviour. In this paper, we revisit the
Tchebycheff weighted method in a hierarchical bandits setting and provide a
finite-time bound on the Pareto-compliant additive -indicator. To the
best of our knowledge, this paper is one of few that establish a link between
weighted sum methods and quality indicators in finite time.Comment: submitted to Journal of Global Optimization. This article's notation
and terminology is based on arXiv:1612.0841
Potential Optimality of Pareto Optima
AbstractIn this paper the notion of potential optimality without an assumption that a value function exists is used to investigate multicriterial optimization problems. Our results show that the notions of potential optimality and strong Pareto optimality (weak Pareto optimality, properly Pareto optimality) are equivalent for special forms of objective functions which are increasing with respect to strong Pareto relation (weak Pareto relation)
Dominance intensity measure within fuzzy weight oriented MAUT: an application
We introduce a dominance intensity measuring method to derive a ranking of alternatives to deal with incomplete information in multi-criteria decision-making problems on the basis of multi-attribute utility theory (MAUT) and fuzzy sets theory. We consider the situation where there is imprecision concerning decision-makers’ preferences, and imprecise weights are represented by trapezoidal fuzzy
weights.The proposed method is based on the dominance values between pairs of alternatives. These values can be computed by linear programming, as an additive multi-attribute utility model is used to rate the alternatives. Dominance values are then transformed into dominance intensity measures, used to rank the alternatives under consideration. Distances between fuzzy numbers based on the generalization of the left and right fuzzy numbers are utilized to account for fuzzy weights.
An example concerning the selection of intervention strategies to restore an aquatic ecosystem contaminated by radionuclides illustrates the approach. Monte Carlo simulation techniques have been used to show that the proposed method performs well for different imprecision levels in terms of a hit ratio and a rank-order correlation measure
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