Notes and Communications “A DECISION THEORY EXAMPLE IN FOOTBALL”: A COMMENT*

Janssen and Daniel analyzed the choice between a one- or a two-point conversion for a particular game situation in college football. Their decision criteria was maximum expected utility based on a von Neumann-Morgenstern utility function defined over the games outcomes. An alternative approach based on a stochastic dominance criterion is presented that does not rely on knowledge of the relative importance of tying vs. winning; rather, it relies on a notion of consistency in the sequential problem.     

Preference-Order Recursion for Finding Relevant Pure,Admissible and Optimal Statistical Decision Functions*

A preference-order recursion algorithm for obtaining a relevant subset of pure, admissible (non-dominated, efficient) decision functions which converges towards an optimal solution in statistical decision problems is proposed. The procedure permits a decision maker to interactively express strong binary preferences for partial decision functions at each stage of the recursion, from which an imprecise probability and/or utility function is imputed and used as one of several pruning mechanisms to obtain a reduced relevant subset of admissible decision functions or to converge on an optimal one. The computational and measurement burden is thereby mitigated significantly, for example, by not requiring explicit or full probability and utility information from the decision maker. The algorithm is applicable to both linear and nonlinear utility functions. The results of behavioral and computational experimentation show that the approach is viable, efficient, and robust.     

Stochastic and Intransitive Behavior in a State-Preference Model of Asset Choice*

Most models of investor behavior assume a time-state independent utility function and result in a deterministic solution where a given set of inputs uniquely specifies the decision. In contrast, a state preference model using a time-state dependent utility function is derived in this paper. The model allows the investment choice decision to be analyzed in a game theoretic context. The general solution is a mixed strategy which allows for a probabilistic interpretation of the decision. The approach presented in this paper can accommodate anomalies such as intransitivity of preference and satisficing as rational behavior. An example of a possible implementation is given along with interpretations of the outcomes.     

ASSESSMENT OF MULTIATTRIBUTED MEASURABLE VALUE AND UTILITY FUNCTIONS VIA MATHEMATICAL PROGRAMMING*

A major restriction on the use of decision analysis in practice is the frequent difficulty of determining a decision maker's multiattribute utility function. The assessment process can be complex and tedious and generally involves: (1) identifying relevant independence conditions, (2) assessing conditional utility functions, (3) assessing scaling constants, and (4) checking for consistency. Some of the assessment and modeling complexities encountered include an assessor's inability to respond in a quantitatively meaningful and consistent way to hypothetical gambles and an analyst's problem in selecting an appropriate functional form that accurately characterizes the conditional utility assessments. A simplified procedure that mitigates these difficulties is proposed. This procedure facilitates the determination of scaling constants by obtaining (via mathematical programming) a multiattributed measurable value function which is converted to a multiattributed utility function. The methodology can be developed advantageously to produce an interactive software package for use as an assessment aid.     

A PENSION DECISION MODEL FOR THE EMPLOYER*

Recently, the Financial Accounting Standards Board (FASB) reevaluated accounting for pension plans. The issue is emotional and highly political in nature. The FASB attempted to justify its approach on the basis of measuring economic activity, but it failed to provide much in the way of analytical support. This paper provides a managerial decision model and an economic basis for the existence of pension plans. A pension plan is described as a cost-saving, risk-sharing, incentive contract. The analysis is developed using agency theory. The model presented here meets three suggested objectives of an employer: 1. Maximization of utility through the maximization of profit 2. Ability to conform the risk characteristics of an employment contract to the risk characteristics of the employer 3. Diversification of the risk inherent in the employment contract Profit is maximized by producing cost savings associated with employee tenure and loyalty. Sharing cost savings with employees (i.e., offering a pension plan) meets the above objectives. The employer determines the optimal sharing rate for the expected cost savings. An examination of the employer's underlying decision process reveals implications for pension plan accounting which generally are consistent with and support the FASB's Statement of Financial Accounting Standards No. 87 [5].     

INFLUENCE OF MISSING ATTRIBUTES ON SELECTING A BEST MULTIATTRIBUTED ALTERNATIVE

Alternatives involving many factors arc difficult to evaluate because of multiple underlying competing objectives. If evaluation is based on an incomplete set of factors, and if the purpose of the evaluation is to select a single overall best alternative, inferior alternatives may be selected with surprising frequency and/or severe negative impact. At the same time, sensitivity analysis of evaluation scores based on statistical criteria can easily mask the impact and the frequency of selection of inferior alternatives. In this paper, criteria appropriate to reflect the decision impact are developed and both the frequency and impact of the selection of inferior alternatives are demonstrated empirically. Previous studies based on statistical criteria [1] [9] indicated minimal impact on overall evaluation and selection. This paper demonstrates that high statistical criterion values coexist with frequent and/or serious errors of selection.     

What to Learn from Near Misses: An Inductive Learning Approach to Credit Risk Assessment

This paper presents a new dimension of inductive learning for credit risk analysis based on the specific impact of Type I and Type II credit errors on the accuracy of the learning process. A Dynamic Updating Process is proposed to refine the credit granting decision over time and therefore improve the accuracy of the learning process. The new dimension is tested on credit files of small Belgian businesses. Results indicate an improvement of the learning process in terms of predictive accuracy, stability, and conceptual validity of the final decision tree.     

An Empirical Study of Equivalence Judgments vs. Ratio Judgments in Decision Analysis*

Two commonly used elicitation modes on strength of preference, equivalence and ratio judgments, were compared in an experiment. The result from the experiment showed that ratio judgments were less effective than equivalence judgments. Based on an iterative design for eliciting multiattribute preference structures, equivalence judgments outperformed ratio judgments in estimating single‐attribute measurable value functions, while being nearly more effective than ratio judgments in assessing multiattribute preference structures. The implications of the results from the experiment are that multiattribute decision‐making techniques should take advantage of the decision maker's inclination of making effective equivalence trade‐off judgments, and that useful techniques should be devised to incorporate different commonly used techniques, such as multiattribute utility theory and the Analytic Hierarchy Process, to elicit and consolidate equivalence trade‐off judgments.     

Multiple Attribute Decision Making with Inexact Value-Function Assessment

A major issue in value-function assessment is the possibility of receiving an indefinite response from a decision maker to a question about preferences. The conventional treatment requires a single, definite response from the decision maker. This approach either assumes that response error is zero or accepts the final solution as only approximate, without knowing how approximate it might be. In this paper we examine the issue of indefinite responses, define it more precisely, and present a method for incorporating it into decision making. The method is adaptive in that it is iterative and interactive with the decision maker who, at each iteration, is provided with information regarding the potential advantage of answering more trade-off questions and/or answering questions more carefully to reduce response error. In contrast to other approaches that “force” rationality and decisiveness, we work with irrationality and indecisiveness on the part of the decision maker. The method is demonstrated for the case of concave, increasing value functions.     

Strategic Assessment Model (SAM): A Multiple Criteria Decision Support System for Evaluation of Strategic Alternatives*

The evaluation of strategic alternatives is a particularly difficult task. This difficulty is due to the complexities inherent in the evaluation process and the lack of structured information. The evaluation process must consider a multitude of relevant information from both the internal and external environments of the organization. Various analytical and normative models have helped decision makers utilize large volumes of information in strategic evaluation; however, most of these models have some limitations. We present a multiple criteria decision support system, called strategic assessment model (SAM), that addresses some of the limitations inherent in the existing models. SAM captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. The environmental forces—decomposed into internal, task, general opportunities, and threats—are used along with the analytic hierarchy process (AHP), subjective probabilities, the entropy concept, and utility theory to enhance the decision maker's intuition in evaluating a set of strategic alternatives.     

A Note on Consistency in Utility Assessment*

A methodology for determining a von Neumann-Morgenstern utility function is outlined based on the axioms crucial to such a function. Reconciliation of inconsistent judgments is facilitated using the theory of reciprocal matrices. Numerical measures of the collective divergence of a set of judgments from perfect consistency or coherency are provided.     

An Experimental Study of the Effectiveness of Three Debiasing Techniques*

Subjective probability distributions constitute an important part of the input to decision analysis and other decision aids. The long list of persistent biases associated with human judgments under uncertainy [16] suggests, however, that these biases can be translated into the elicited probabilities which, in turn, may be reflected in the output of the decision aids, potentially leading to biased decisions. This experiment studies the effectiveness of three debiasing techniques in elicitation of subjective probability distributions. It is hypothesized that the Socratic procedure [18] and the devil's advocate approach [6] [7] [31] [32] [33] [34] will increase subjective uncertainty and thus help assessors overcome a persistent bias called “overconfidence.” Mental encoding of the frequency of the observed instances into prespecified intervals, however, is expected to decrease subjective uncertainty and to help assessors better capture, mentally, the location and skewness of the observed distribution. The assessors' ratings of uncertainty confirm these hypotheses related to subjective uncertainty but three other measures based on the dispersion of the elicited subjective probability distributions do not. Possible explanations are discussed. An intriguing explanation is that debiasing may affect what some have called “second order” uncertainty. While uncertainty ratings may include this second component, the measures based on the elicited distributions relate only to “first order” uncertainty.     

An Empirical Estimation of the Investor Loss Function Associated with the Use of Analysts' Forecasts of Earnings

The aggregate investor loss function associated with the use of analysts' forecasts of earnings expresses the relation between loss and prediction error. Prior research suggested that the shape of this loss function should provide guidance in decision rules for investors. This study empirically estimates the investor loss function and finds an asymmetry in the relation between loss and analysts' prediction error. For overestimates of earnings, loss is a quadratic function of error; while for underestimates of earnings, loss is a linear function of error.     

COMPARATIVE EVALUATION OF INFORMATION UNDER TWO BUSINESS OBJECTIVES

Information economics models evaluate the value of information under the assumption that decision makers wish to maximize their expected payoff. This assumption has been criticized for not being realistic enough since decision makers usually consider more than one business objective and might be satisficers rather than optimizers. This paper attempts to apply an information economics model to decision situations where two business performance criteria, expected payoff and risk, are considered. In order to overcome the difficulty of unknown trade-off between the two criteria, one criterion is used as an objective to be optimized, while the other is set as a constraint. This may be interpreted as a combination of optimizing and satisficing approaches. It is shown how an information system can be evaluated in terms of both expected payoff and risk. The model suggests the trade-off between the two criteria as an additional trait of an information system. In the last part of the paper, a numerical example illustrates how a comparative evaluation of information structures is performed when risk minimization and expected payoff maximization are concurrently used as business performance criteria.     

A COMPARISON OF INFORMATION STRUCTURES FOR A RIGID DECISION RULE CASE

The information economics model of individual decision assumes that decision makers are capable of instantly modifying their decision rules in response to signal changes from the information structure. Individuals, however, often tend to stick to rigid decision rules, particularly when they are trained to respond with conditioned reactions, such as to a traffic light. This article modifies the individual decision model for such cases. It discusses probabilistic information structures as well as the deterministic information function. It results in an ordering of information structures that differs from the ordering imposed by the Blackwell Theorem. When deterministic information functions are considered, an ordering that is finer than the prevailing one is proposed. Transformations between deterministic and probabilistic information functions and the relationship between the proposed model and the previous theory are also discussed.     

AHP DESIGN ISSUES FOR LARGE-SCALE SYSTEMS

The analytic hierarchy process (AHP) is a decision analysis technique that uses judgments from a group of relevant decision makers and hierarchical decomposition to derive a set of ratio-scaled utility measures for decision alternatives. This paper addresses a number of design issues involved in the implementation of AHP for large-scale systems. Specifically, it describes the use of incomplete experimental designs for simplifying data-collection tasks. The effects of reducing the size of the hierarchy through attribute deletion and the effects of including identical attributes on any given level also are evaluated.     

THE TAX LITIGATION DECISION: AN ANALYSIS OF THE SMALL CLAIMS DIVISION OF THE U.S. TAX COURT*

One of the more difficult but intriguing problems in the tax field is the decision of when (and how) to settle tax disputes with the Internal Revenue Service (IRS) and when (and how) to litigate. Research in this area of decision making is limited and has concentrated on the probability of winning in the judicial system. This paper examines the tax litigation decision for suits in the Small Claims Division of the U.S. Tax Court. Four examples are presented which involve varying degrees of information regarding the likelihood of a settlement and differing risk attitudes. The maximum amount the taxpayer should be willing to spend in pursuing litigation is derived for each case.     

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described.     

A Credit Limit Decision Model for Inventory Floor Planning and Other Extended Trade Credit Arrangements*

This paper analyzes the dealership credit limit problem in terms of the valuation of a Markov process of cash flows with sequential credit decisions over an infinite planning horizon. The formulation distinguishes between the upper bound on credit applicable at the account formation stage and the upper bound applicable to periodic reorders. The result is a closed form solution to the problem which serves as a criterion function for approving or denying credit on a customer-by-customer basis. Data for a sample of manufacturing firms are employed to estimate typical ranges for criterion function parameters. Upper bounds on credit limits are then calculated and graphically presented for median parameter values as well as for values at the 5th and 95th percentiles for the sample data. Finally, an empirical study is conducted of actual trade credit extended by firms. The results support the hypothesis that the variables in the decision model are important determinants of the amount of trade credit outstanding.