The Choice of Depreciation Method Under Uncertainty*

This study presents a framework for choosing between depreciation methods when future cash flows from operations are not assumed known with certainty but only in probabilistic terms. Specifically, the accelerated depreciation method and the straight-line depreciation method are compared and mathematical conditions are derived for the depreciation method that should be adopted in different circumstances and under different tax systems. It is shown that, contrary to conventional wisdom, the straight-line depreciation method is the preferred method for lowering the company's present value of tax liability in various realistic situations.     

A Stochastic,Psychometric Valuation Model for Personnel Selection Systems*

This paper describes a method for the valuation of personnel selection systems that is based on contributions from accounting, utility theory, psychometric techniques, Markov processes, labor economics, and present-value analysis. The model developed here can be used by managers to assess the usefulness of proposed selection procedures. It extends previous work in the area by including both the stochastic nature of the employment process and the time value of the associated costs and benefits.     

A Comparison of Arbitration Procedures for Risk‐Averse Disputants*

We propose an arbitration model framework that generalizes many previous quantitative models of final offer arbitration, conventional arbitration, and some proposed alternatives to them. Our model allows the two disputants to be risk averse and assumes that the issue(s) in dispute can be summarized by a single quantifiable value. We compare the performance of the different arbitration procedures by analyzing the gap between the disputants' equilibrium offers and the width of the contract zone that these offers imply. Our results suggest that final offer arbitration should give results superior to those of conventional arbitration.     

Insignificant Betas and the Efficacy of the Sharpe Diagonal Model for Portfolio Selection*

When both practitioners and theorists apply Sharpe's diagonal model [15] to simplify the portfolio selection problem, they assume that the entire covariation structure of each stock (i.e., with all other stocks) is captured in that stock's covariance with the market (or β). Furthermore, it is well known that the selection algorithm itself has a marked tendency to select stocks with the lowest βs, ceteris paribus. When a stock's β is statistically indistinguishable from zero, it is an empirical issue whether the market model is (a) less appropriate for that particular stock relative to those with statistically significant βs; or is (b) a viable model in that the covariance of this stock's rate-of-return with all other stocks' rates-of-return vanishes. The objective of this paper is to distinguish empirically between (a) and (b), and to propose a heuristic which will improve the ex-post performance of the diagonal model. The possible benefits of this heuristic are also demonstrated in a rigorous statistical framework.     

Testing Prospect Theory in a Deterministic Multiple Criteria Decision-Making Environment*

Prospect theory by Kahneman and Tversky [7] is tested in a deterministic multiple criteria decision-making context. In two experiments conducted in classroom settings subjects made pairwise preference comparisons of condominiums for sale. The results of the experiments indicate that the traditional value model did not explain the subjects' revealed preferences as well as the prospect model. We conclude that prospect theory is a reasonable model of choice for many individuals in such a context.     

Dynamic Pricing and Demand Estimation for a Single Unique Asset*

We address the situation of a firm that needs to dispose of a large, expensive asset (e.g., car, machine tool, earth mover, turbine, house, airplane), with or without a given deadline (and either known or unknown to the buyer). If a deadline exists, the asset is salvaged at a known value which may be zero, or even negative if there is a disposal cost. The asset has a known holding cost and may also have an initial nominal (undiscounted) price. The question is how, if at all, the price should be discounted as time progresses to maximize the expected proceeds. We use a dynamic recursion where each decision stage can be optimized based on classic economic monopoly pricing theory with a demand intensity function estimated from sales data, and show that the model is well‐behaved in the sense that the optimal price and optimal expected revenue monotonically decline as the deadline approaches. We test the model by comparing its optimal price pattern to the official pricing policy practiced at a used‐car dealer. We then extend the model to situations where the buyer knows the seller's deadline and thus may alter his behavior as the deadline approaches.     

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].     

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.     

Risk,Ambiguity, and Decision Choice: Some Additional Evidence*

This paper extends prior research by jointly assessing the roles of risk attitude and tolerance for ambiguity in predicting choice. An experiment examined the effects of these variables on decisions made in four different scenarios. The four scenarios (treatment combinations) were generated by manipulating risk and ambiguity into two levels (high and low). The context was defined in terms of a sample size selection problem. The second issue explored was the effect of attitudes toward risk and ambiguity on decision confidence. The results indicate that (1) both risk attitude and ambiguity intolerance determined choice behavior, (2) the roles of these individual attitudes depend on the levels of the two treatment variables of risk and ambiguity, (3) the presence of ambiguity accentuates the perception of risk in individual subjects, and (4) decision makers who are less risk averse, and have more tolerance for ambiguity, display greater confidence in their choice. The paper discusses some of the managerial implications of the results.     

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.     

Structure of Uncertainty and Decision Making: An Experimental Investigation*

Decisions in the real world usually involve imprecise information or uncertainty about the precesses by which outcomes may be determined. This research reports the results of a laboratory experiment which examined whether the structure of uncertainty, namely, both the center and the range of the probability distribution describing the uncertainty, is an important determinant of choice. Specifically, it examines how the uncertainty of audit by the Internal Revenue Service of income tax returns affects taxpayers' decisions about intentional noncompliance. The context is relevant as almost nothing is known about how taxpayers assess detection risks using the probability information they have. The study focuses on intentional noncompliance. The factors affecting it are distinct and separate from those affecting unintentional noncompliance. Other factors that affect intentional tax noncompliance, such as risk, tax rates, and penalty rates, were controlled in the experiment. It was hypothesized that the lower the mean and the lesser the range (ambiguity) of the perceived audit probability, the greater the international noncompliance. As hypothesized, the analysis indicates that both the mean and the range of the perceived audit probability rate affect intentional noncompliance, though the effect of ambiguity is greater at a relatively higher level of mean. This result suggests that the strength of the information describing an uncertain event is captured better by both the mean and the range of the uncertainty than either of those components singly.     

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.     

Estimation of Nonlinear Models in the Presence of Measurement Error*

Techniques used in decision sciences and business research to estimate interactions between latent variables are limited in controlling for measurement error. This article uses a latent structure modeling approach that substantially controls for measurement error in nonlinear relationships. The results of this technique are compared to the results obtained applying hierarchical regression analysis and the impact of measurement error is assessed. The paper provides a unique assessment of the validity of the multi-attribute attitude model. The validity of the multiplicative rule in the model is supported.     

Inference in a Binary Response Model with Applications to Data Analysis*

Standard errors of the coefficients of a logistic regression (a binary response model) based on the asymptotic formula are compared to those obtained from the bootstrap through Monte Carlo simulations. The computer intensive bootstrap method, a nonparametric alternative to the asymptotic estimate, overestimates the true value of the standard errors while the asymptotic formula underestimates it. However, for small samples the bootstrap estimates are substantially closer to the true value than their counterpart derived from the asymptotic formula. The methodology is discussed using two illustrative data sets. The first example deals with a logistic model explaining the log-odds of passing the ERA amendment by the 1982 deadline as a function of percent of women legislators and the percent vote for Reagan. In the second example, the probability that an ingot is ready to roll is modelled using heating time and soaking time as explanatory variables. The results agree with those obtained from the simulations. The value of the study to better decision making through accurate statistical inference is discussed.     

Sensitivity Analysis for Dependent Variables*

Decision analysts use sensitivity analysis to identify influential variables, to determine which input variables to model stochastically, and to characterize scenarios that could affect a change in the rank ordering of the alternatives. A frequently recommended sensitivity analysis technique is “one‐way” sensitivity analysis, which determines a variable's influence by the degree to which the objective function changes as that variable is varied while all other variables are held fixed. Disadvantages of one‐way analysis are that it measures the influence of only one variable at a time and it assumes independence among the input variables. Clearly, however, there are situations when dependencies exist among the input variables that could possibly affect the sensitivity analysis results. This research develops a strategy that incorporates dependence relations among the input variables into the sensitivity analysis using rank correlations. Only decision problems with a finite number of alternatives and continuous state variables are considered.     

A Two-Period Repeated Game Advertising Investment Model for Oligopolistic Markets with an Application to the Beer Industry*

This paper proposes a simple analytical model of advertising competition in oligopoly markets. The widely used log-log sales response function underlies the model specification. Advertising carryover effects are assumed to persist for one period following the period in which the expenditure occurs. Firms are assumed to be engaged in a repeated competitive game in which in every period advertising levels are set such that they maximize current and next period (i.e., two-period) profits. A Nash equilibrium solution is sought for the game. Compared with previous empirical studies of advertising competition in a game theoretic framework, the proposed model offers the following advantages: (1) oligopoly, not duopoly, markets are analyzed; (2) industry sales is allowed to vary over time as a function of advertising expenditures; (3) non-zero discount rates are used for the players. An empirical application is provided using data from the beer market on sales and advertising expenditures of Anheuser-Busch and Miller Brewing. Comparisons are provided with policies that ignore the dependence of next period profits on current advertising levels, reaction function strategies and spending levels obtained from a market share game. Extension of the model formulation to multiple marketing instruments is briefly discussed.     

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.     

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.     

Data and Model Management in a Generalized MCDM-DSS*

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.     

Optimal Portfolio Selection and Cutoff Rates of Security Performance: A Multi-Index Case*

The present study performs portfolio analysis using a multi-index model in the diagonal form. In a mean-variance framework, an alternative solution to a portfolio optimization problem is derived, providing analytical and computational improvements. This leads to a proof of a crucial functional property of cutoff rates of security performance in the solution, thus providing formal justification for a nonranking procedure of optimal portfolio selection. The robustness of the above functional property, and hence the nonranking procedure, is demonstrated numerically when the underlying normality assumption of security returns is replaced by a more general assumption of stable Paretian distributions.