nmin Jia

Simulation techniques for the sensitivity analysis of multi-criteria decision models

This paper presents a simulation approach for high dimensional sensitivity analysis of the weights of multi-criteria decision models. This approach allows simultaneous changes of the weights and generates results that can easily be analyzed statistically to provide insights into multi-criteria model recommendations. In this study we consider three cases: no information, order information, and partial information regarding the weights. Our approach also allows investigation of sensitivity to the form of multi-criteria decision models. The simulation procedures we propose can also be used to aide in the actual decision process, particularly when the task is to select a subset of superior alternatives.

Attribute weighting methods and decision quality in the presence of response error: A simulation study

This paper uses a simulation approach to investigate how different attribute weighting techniques affect the quality of decisions based on multiattribute value models. The weighting methods considered include equal weighting of all attributes, two methods for using judgments about the rank ordering of weights, and a method for using judgments about the ratios of weights. The question addressed is: How well does each method perform when based on judgments of attribute weights that are unbiased but subject to random error? To address this question, we employ simulation methods. The simulation results indicate that ratio weights were either better than rank order weights (when error in the ratio weights was small or moderate) or tied with them (when error was large). Both ratio weights and rank order weights were substantially superior to the equal weights method in all cases studied. Our findings suggest that it will usually be worth the extra time and effort required to assess ratio weights. In cases where the extra time or effort required is too great, rank order weights will usually give a good approximation to the true weights. Comparisons of the two rank-order weighting methods favored the rank-order-centroid method over the rank-sum method.

A Multiattribute Utility Analysis of Alternatives for the Disposition of Surplus Weapons-Grade Plutonium

This paper describes an application of multiattribute utility theory to support the selection of a technology for the disposition of surplus weapons-grade plutonium by the Department of Energy (DOE). This analysis evaluated 13 alternatives, examined the sensitivity of the recommendations to the weights and assumptions, and quantified the potential benefit of the simultaneous deployment of several technologies. The measures of performance that were identified through the creation of a hierarchy of objectives helped to organize the information collected during the evaluation process, and the results of the analysis were presented to DOE on several occasions. This analysis supported the final DOE recommendation to pursue a strategy of the parallel development of two of the most preferred technologies.

Enabling e-transactions with multi-attribute preference models

Abstract This paper describes potential applications of multi-attribute preference models (MAPM) in e-commerce and offers some guidelines for their implementation. MAPM are methodologies for modeling complex preferences that depend on more than one attribute or criterion, and include multi-attribute utility theory, conjoint analysis, and the Analytic Hierarchy Process. There are numerous examples of applications in e-commerce that would benefit from the acquisition of information regarding the preferences of a consumer, a customer, an advice seeker, or a decision maker. Here, the focus is on applications of MAPM models in B2C and B2B websites, where preferences of consumers are assessed for the purpose of identifying products or services that closely match their needs. In this paper, we provide an overview of decision aids with the MAPM approach, emphasizing how the MAPM structure of an individual’s preferences may be assessed. This discussion is illustrated with examples of the use of alternative MAPM assessment approaches that are incorporated in existing websites. We then discuss how MAPM applications should be tailored for success in these environments.

How Much Do You Like It? Within-Alternative Conflict and Subjective Confidence in Consumer Judgments

We seek to reinforce the importance of the notion of within-alternative conflict for consumer research. We replicate our own earlier findings that conflict associated with integrating an alternatives pros and cons influences responses to a judgment task. In the earlier work, we focused on test-retest reliability in judgment; here we extend the work by developing a measure of explicit preference uncertainty using subjective confidence intervals placed around evaluative judgments in consumer purchase contexts. We also extend the prior work by demonstrating an effect of within-alternative conflict on preferences expressed through evaluative ratings. Copyright 2003 by the University of Chicago.

Generalized Disappointment Models

Abstract“Blessed is he who expects nothing, for he shall never be disappointed”—Benjamin FranklinBased on our risk-value framework, this paper presents extensions for the disappointment models that were originally proposed by Bell (1985) and Loomes and Sugden (1986). We provide explicit functional forms for modeling the effect of disappointment on risky choice behavior that generalizes Bells work and lends tractability to the efforts of Loomes and Sugden. Our generalized disappointment models can explain a number of decision paradoxes, and offer additional insights into nonexpected utility preferences based on the intuitive notions of disappointment and risk-value tradeoffs.

A Multiclass Hybrid Production Center in Heavy Traffic

This paper presents an analysis of a single-stage hybrid production system that makes multiple types of products, some of which are made to-order while others are made to-stock. The analysis begins with a formal heavy traffic limit theorem of the production system, which is modeled as a mixed queueing network. Taking insights from the limit theorem, the analysis continues with the development of an approximation procedure. Numerical experiments indicate that this procedure provides good estimates for performance measures such as fill rates and average inventory levels.

Using Attributes to Predict Objectives in Preference Models

Prescriptive decision analysis suggests identifying the fundamental objectives---what the decision maker really cares about---and then constructing a value hierarchy by decomposing these objectives until quantifiable attributes can be identified. In many decision contexts the decision maker is presented with a list of attributes without an opportunity to consider her fundamental objectives. In this paper we explore an approach where a decision maker is given prespecified attributes and then identifies her objectives. She assesses multiattribute models to predict performance levels on each objective and a preference model over these objectives. We use simulation to explore what happens when a decision maker applies this two-step approach to model the relationships between a given set of attributes and her objectives instead of attempting to directly estimate the attribute weights in a choice problem. These simulation results suggest that the explicit consideration of objectives results in less error in expressions of preference than the direct weighting of attributes unless the number of attributes and objectives in the decision context is small.

Preference conditions for utility models: A risk-value perspective

This paper discusses necessary and sufficient preference conditions for utility models basedon a risk-value framework. These conditions provide additional insights into traditionalutility models regarding decision making by risk-value tradeoffs, and can help decisionmakers identify specific functional forms of utility measure in practice.

Axiomatic Measures of Risk and Risk‐Value Models

This article provides a review of risk and risk-value models that lead to decision making by explicitly trading off between risk and value. We begin with a discussion of a preference-dependent measure of risk that is compatible with traditional expected utility theory, and therefore lends itself to an examination of how individuals perceive risky gambles as well as how they choose among them. Further, this risk measure can be the basis for generalizing models of decision making based on the intuitively appealing notion of trading off between risk and value. Based on the two-attribute expected utility axioms, this risk-value framework has much more flexibility in modeling preferences than the traditional single-attribute expected utility theory. Specifically, we describe three useful classes of decision models: moments risk-value models, exponential risk-value models, and generalized disappointment models. These decision models unify two streams of research: one in developing preference models and the other in modeling risk judgments. They can also provide prescriptive guidance for those people who are willing to deviate from the traditional expected utility preference models and make their decisions based on risk-value trade-offs, as in financial decision making. Keywords: risk measure; risk-value model; risk-value trade-off; mean–variance model; utility model