CiteULike: Tag multi-attribute

Configurable offers and winner determination in multi-attribute auctions

Martin Bichler — 2008-05-06T15:23:48-00:00

European Journal of Operational Research, Vol. 160, No. 2. (16 January 2005), pp. 380-394.

The theory of procurement auctions traditionally assumes that the offered quantity and quality is fixed prior to source selection. Multi-attribute reverse auctions allow negotiation over price and qualitative attributes such as color, weight, or delivery time. They promise higher market efficiency through a more effective information exchange of buyer's preferences and supplier's offerings. This paper focuses on a number of winner determination problems in multi-attribute auctions. Previous work assumes that multi-attribute bids are described as attribute value pairs and that the entire demand is purchased from a single supplier. Our contribution is twofold: First, we will analyze the winner determination problem in case of multiple sourcing. Second, we will extend the concept of multi-attribute auctions to allow for configurable offers. Configurable offers enable suppliers to specify multiple values and price markups for each attribute. In addition, suppliers can define configuration and discount rules in form of propositional logic statements. These extensions provide suppliers with more flexibility in the specification of their bids and allow for an efficient information exchange among market participants. We will present MIP formulations for the resulting allocation problems and an implementation.

Bid expressiveness and clearing algorithms in multiattribute double auctions

Yagil Engel — 2008-05-06T15:11:42-00:00

(2006), pp. 110-119.

Multialternative decision field theory: a dynamic connectionist model of decision making.

RM Roe — 2006-09-28T15:47:40-00:00

Psychological Review, Vol. 108, No. 2. (April 2001), pp. 370-392.

The authors interpret decision field theory (J. R. Busemeyer & J. T. Townsend, 1993) as a connectionist network and extend it to accommodate multialternative preferential choice situations. This article shows that the classic weighted additive utility model (see R. L. Keeney & H. Raiffa, 1976) and the classic Thurstone preferential choice model (see L. L. Thurstone, 1959) are special cases of this new multialternative decision field theory (MDFT), which also can emulate the search process of the popular elimination by aspects (EBA) model (see A. Tversky, 1969). The new theory is unique in its ability to explain several central empirical results found in the multialternative preference literature with a common set of principles. These empirical results include the similarity effect, the attraction effect, and the compromise effect, and the complex interactions among these three effects. The dynamic nature of the model also implies strong testable predictions concerning the moderating effect of time pressure on these three effects.

An agent architecture for multi-attribute negotiation using incomplete preference information

Catholijn Jonker — 2007-09-28T14:19:39-00:00

Autonomous Agents and Multi-Agent Systems, Vol. 15, No. 2. (18 October 2007), pp. 221-252.

Abstract A component-based generic agent architecture for multi-attribute (integrative) negotiation is introduced and its application is described in a prototype system for negotiation about cars, developed in cooperation with, among others, Dutch Telecom KPN. The approach can be characterized as cooperative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a “guessing” heuristic, by which an agent uses the history of the opponent’s bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.

Looking and Weighting in Judgment and Choice,

Douglas Wedell — 2008-05-09T20:25:05-00:00

Organizational Behavior and Human Decision Processes, Vol. 70, No. 1. (April 1997), pp. 41-64.

A sampling model was proposed in which the weight given to a piece of information corresponds to the amount of sampling of that information in either a continuous, discrete or strategic manner. These three sampling processes were related to process tracing measures of initial and additional time per acquisition and frequency of acquisition. The applicability of the sampling model was tested in three experiments in which students uncovered information corresponding to verbal and math aptitude scores of hypothetical applicants and either judged the likelihood of success in a designated major or chose which of a pair of applicants was more likely to succeed in the major. Task focus was manipulated by altering the designated major. In Experiment 1, analysis of judgment data demonstrated large effects of task focus on the weighting of verbal and math scores and corresponding increases in number of acquisitions and time per acquisition on the information receiving more weight. In Experiments 2 and 3, analyses of choice proportions revealed effects of task focus on weight and bias parameters. Looking data in choice provided strong support for two of the stages of processing described by Russo and Leclerc (1994). Initial looks reflected orientation and screening functions and additional looks reflected more evaluative processes. Experiment 3 also explored similarities and differences among groups of participants who were classified as following different identifiable choice strategies.

Multiattribute utility functions, partial information on coefficients, and efficient choice

Karl Mosler — 2007-08-27T22:25:00-00:00

OR Spectrum, Vol. 13, No. 2. (1 June 1991), pp. 87-94.

Summary The expected utility analysis of decision under risk needs information on the alternatives and on the decision maker's preferences which in many practical situations are difficult to obtain. This paper presents a procedure for choosing between multiattribute risky alternatives when the probabilities of outcomes are known, the utility function is general multilinear (i.e., can be decomposed into sums and products of univariate utility functions), and there is some partial information on univariate utilities (viz. increasingness) and arbitrary partial information on the scaling coefficients. Pairwise comparisons in the set of alternatives yield a subset which is efficient under the given partial information. Additive and multiplicative utility functions are special cases of the multilinear one. The paper gives particular attention to linear partial information (LPI) on coefficients, which is obtained by standard assessment procedures. The approach can be combined with dominance procedures which use other partial information as LPI on probabilities.

The Bargaining Problem

John Nash — 2005-02-11T11:33:49-00:00

Econometrica, Vol. 18, No. 2. (1950), pp. 155-162.

A new treatment is presented of a classical economic problem, one which occurs in many forms, as bargaining, bilateral monopoly, etc. It may also be regarded as a nonzero-sum two-person game. In this treatment a few general assumptions are made concerning the behavior of a single individual and of a group of two individuals in certain economic environments. From these, the solution (in the sense of this paper) of the classical problem may be obtained. In the terms of game theory, values are found for the game.

Measurable Multiattribute Value Functions

James Dyer — 2007-08-27T22:15:53-00:00

Operations Research, Vol. 27, No. 4. (1979), pp. 810-822.

This paper presents a theory of measurable multiattribute value functions. Measurable value functions are based on the concept of a "preference difference" between alternatives and provide an interval scale of measurement for preferences under certainty. We present conditions for additive, multiplicative, and more complex forms of the measurable multiattribute value function. This development provides a link between the additive value function and multiattribute utility theory.

Materials selection and multi-attribute utility analysis

Richard Roth — 2007-08-27T19:53:08-00:00

Journal of Computer-Aided Materials Design, Vol. 1, No. 3. (1 October 1994), pp. 325-342.

Multi-attribute utility analysis (MAUA) has emerged as a powerful tool for materials selection and evaluation. An operations research technique, MAUA has been used in a wide range of engineering areas, of which materials science and engineering is one of the more recent. Utility analysis affords a rational method of materials selection which avoids many of the fundamental logical difficulties of many widely used alternative approaches. However, MAUA has traditionally been used in materials selection problems only, in which there is certainty regarding the attribute levels of the alternatives. For many new technologies this is not the case. Another operations research technique, subjective probability assessment (SPA), can be used to address this issue. SPA makes it possible to measure a probabilistic distribution describing the confidence of the decision maker in the levels of attributes for which there is a high degree of uncertainty. These probability distributions can be used in conjunction with MAUA to provide a consistent framework for making materials selection decisions. Furthermore, the use of these techniques extends beyond the problem of materials selection into the more speculative areas of materials competitiveness and market demand in cases involving new, unproven technologies.

Elements of Multi-Bayesian Decision Theory

S Weerahandi — 2007-09-17T19:00:47-00:00

The Annals of Statistics, Vol. 11, No. 4. (1983), pp. 1032-1046.

This work provides the elements of a framework for multi-Bayesian statistical decision theory. Solution concepts and criteria are presented. The relationship to Wald's theory is discussed. And two criteria for assessing group decision procedures are defined. One is based on the idea of subsampling the group, and it is found that among the proposed solution concepts only Nash's solution is optimal under subsampling as well. The other assumes the group is itself a sample from a superpopulation, and this yields an analogue of Wald's theory where the elicitation of the priors becomes part of the experimental process. Results on admissibility, minimaxity and so on found in Wald's classical theory become directly applicable in the new setting.

Limitations of Exemplar Models of Multi-Attribute Probabilistic Inference

Robert Nosofsky — 2008-04-02T20:47:05-00:00

Journal of Experimental Psychology: Learning, Memory and Cognition, Vol. 33, No. 6. (1 November 2007), pp. 999-1019.

Observers were presented with pairs of objects varying along binary-valued attributes and learned to predict which member of each pair had a greater value on a continuously varying criterion variable. The predictions from exemplar models of categorization were contrasted with classic alternative models, including generalized versions of a “take-the-best” model and a weighted-additive model, by testing structures in which interactions between attributes predicted the magnitude of the criterion variable. Under typical training conditions, observers showed little sensitivity to the attribute interactions, thereby challenging the predictions from the exemplar models. In a condition involving highly extended training, observers eventually learned the relations between the attribute interactions and the criterion variable. However, an analysis of the observers' response times for making their paired-comparison decisions also challenged the exemplar model predictions. Instead, it appeared that most observers recoded the interacting attributes into emergent configural cues. They then applied a set of hierarchically organized rules based on the priority of the cues to make their decisions.