Robert F. Bordley

Queue Spillovers in Transportation Networks with a Route Choice

This paper examines different traffic phenomena that occur when drivers have to navigate a network in which queues backup past diverge intersections. In particular, it looks at the bottleneck capacity of a network and how reducing it to below a critical level can resume the attractiveness of an alternative route, thus preventing the system from reaching the saturation point. The author concludes that the time-dependent traffic assignment problem with physical queues is chaotic in nature and that it may be impossible to obtain input data with the required accuracy to make reliable predictions of cumulative output flows on severely congested networks.

Multiattribute Preference Analysis with Performance Targets

This paper develops an approach based on performance targets to assess a preference function for a multiobjective decision under uncertainty. This approach yields preference functions that are strategically equivalent to conventional multiattribute utility functions, but the target-oriented approach is more natural for some classes of decisions. In some situations, the target-oriented preference conditions are analogous to reliability theory conditions for series or parallel failure modes in a system. In such cases, reinterpreting the conditions using reliability concepts can be useful in assessing the preference function. The target-oriented approach is also a generalization of common forms of goal programming. The approach has particular applicability for resource allocation decisions where the outcome of the decision is significantly determined by the actions of other stakeholders to the decision, such as new product development or decision making in a controversial regulated environment.

Determining the Appropriate Depth and Breadth of a Firm's Product Portfolio

Some firms have broad product lines; others have lean product lines. To determine the appropriate number of entries in a specific firms product line, the author develops a model that balances the benefits of increased revenue from a broad product line against production and engineering costs. Two innovations were central in the development of the model: (1) redefining how products are scored on various product attributes so that attribute scores vary normally across the population of products and (2) redefining how the number of entries in a product portfolio is calculated in order to discount the significance of entries that are highly similar to existing products. The author also introduces the notion of a centroid time to more easily adjust sales and total development costs for product life cycle and investment life cycle effects. These redefinitions enable a firms profit to be modeled as a simple function of its effective number of product entries, the effective number of competitors entries, the total sales in the segment, variable profits adjusted for capacity constraints, and product development costs. This leads to a simple expression for the profit-maximizing number of effective entries, both when competitor portfolios are fixed and when competitors dynamically adjust their portfolios. The author illustrates how to estimate and apply the model on a realistic example.

Quantum Mechanical and Human Violations of Compound Probability Principles: Toward a Generalized Heisenberg Uncertainty Principle

A key central tenet of decision theory is that decomposing an uncertain event into sub-events should not change the overall probability assigned to that uncertain event. As we show, both quantum physics and behavioral decision theory appear to systematically violate this principle in very similar ways. These results suggest that the structuring phase of decision analysis-which specifies how various events are decomposed-helps shape the subjective probabilities which will ultimately be assigned to those events.

A Pragmatic Method for Evaluating Election Schemes through Simulation

This article combines ideas from ethics, social choice, and political theory to develop a simulation method for assessing the desirability of different voting schemes in different situations. I use the method to evaluate six well-known election systems. My results are intuitive. I find that approval voting seems to be a good voting scheme for mass elections.

R&D project selection versus R&D project generation

Much of the technical literature emphasizes R&D project selection. This paper reviews the authors experience with R&D project selection. In his view, this experience suggests that the emphasis should be on the generation of high-quality R&D projects through effective communication of corporate priorities, implementation issues, and related technical efforts.

A Decision-Analytic Approach to Reliability-Based Design Optimization

Reliability-based design optimization is concerned with designing a product to optimize an objective function, given uncertainties about whether various design constraints will be satisfied. However, the widespread practice of formulating such problems as chance-constrained programs can lead to misleading solutions. While a decision-analytic approach would avoid this undesirable result, many engineers find it difficult to determine the utility functions required for a traditional decision analysis. This paper presents an alternative decision-analytic formulation that, although implicitly using utility functions, is more closely related to probability maximization formulations with which engineers are comfortable and skilled. This result combines the rigor of decision analysis with the convenience of existing optimization approaches.

Reformulating decision theory using fuzzy set theory and Shafer's theory of evidence

Abstract Utilities and probabilities in decision theory are usually assessed by asking individuals to indicate their preferences between various uncertain choices. In this paper, we argue that (1) The utility of a consequence can be assessed as the membership function of the consequence in the fuzzy set ‘ satisfactory ’. (2) The probability of an event, instead of being directly assessed, should be inferred from the evidence associated with that event. The degree of evidence is quantified using Shaferian basic probability assignments. In addition, we use the Heisenberg Uncertainty Principle to argue for a change in one of the technical assumptions underlying decision theory. As a result of this change, some kinds of evidence will be observable in certain experiments but unobservable in others. Since probabilities are defined over the potential outcomes of an experiment, they will only be defined over some, but not all, the evidence. As a result, the probabilities associated with different experiments could be inconsistent. This formulation emphasizes the importance of new distinctions (and not just new information) in updating probabilities. We argue that this formulation addresses many of the observed empirical deviations between decision theory and experiment. It also addresses the anomalies of quantum physics. We close with a brief discussion of directions for further research.

Integrating Gap Analysis and Utility Theory in Service Research

Conventional utility theory models customer preferences in terms of actual performance and does not use benchmarks. But empirical work in gap analysis shows that customer preferences clearly depend on the disparity between performance and some benchmark. To resolve this apparent discrepancy between theory and experiment, this article shows that a simple reinterpretation of utility makes utility a function of the uncertainty-discounted gap between actual performance and a benchmark. The author interprets the benchmark as reflecting customer product expectations. The resulting formulation is used to derive a consumer choice model in which customer choice depends on how perceived performance compares to expectations and on customer uncertainty about performance and expectations. In this model, increasing information on a product or service tends to increase its sales if its performance is above customer expectations and to decrease its sales if its performance is below customer expectations.

An Intransitive Expectations-Based Bayesian Variant of Prospect Theory

Psychological experiments have established that the classical expected utility model appears descriptively inadequate. Viscusis prospective reference theory attempts to reconcile the expected utility model with many of these experiments by supposing that individuals have prior expectations about the utility they can expect to get from lottery payoffs. Bayesian theory then implies that individuals revise lottery probabilities in light of these prior expectations before choosing among lotteries so as to maximize expected utility. But Viscusis theory cannot account for nonmonotonic or intransitive behavior. This article develops an extension of Viscusis model with correlated prior beliefs that does account for nonmonotonic and intransitive behavior.