James J. Cochran

Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem

Conjoint analysis is a statistical technique used to elicit partworth utilities for product attributes from consumers to aid in the evaluation of market potential for new products. The objective of the share-of-choice problem (a common approach to new product design) is to find the design that maximizes the number of respondents for whom the new products utility exceeds a specific hurdle (reservation utility). We present an exact branch-and-bound algorithm to solve the share-of-choice problem. Our empirical results, based on several large commercial data sets and simulated data from a controlled experiment, suggest that the approach is useful for finding provably optimal solutions to realistically sized problems, including cases where partworths contain estimation error.

A Cross-Generational Comparison of Motivational Factors in a Sales Career Among Gen-X and Gen-Y College Students

This study is a replication and extension of a study by Castleberry (1990) in which 12 motivational factors related to a sales career were compared across generations. The importance of 12 key motivational factors related to sales positions were rank ordered by 1,390 college students. Respondents represented Gen-X college students from 1990 and Gen-Y students from 2006. The job itself and pay were consistently ranked first and second in both studies for “today” and “ten years in the future.” Gen-Y students rated “hygiene” factors related to job security as significantly more important than did Gen-X students. Conversely, Gen-X students rated “satisfier” factors as significantly more important than did Gen-Y students. Implications for sales managers, recruiters, and educators are provided based on the findings.

You want them to remember? Then make it memorable! Means for enhancing operations research education

In this article I summarize the main points I made in the keynote presentation of the same title I gave at the EURO XXIV conference in Lisbon, Portugal in July of 2010. Each of these points deals in some way with making communications between an operations research professional (academic or practitioner) and a student, client, subordinate, supervisor, or colleague more effective. Furthermore, each point is directly related to some realization (or epiphany) that I have had with regard to communication since I began teaching ORMS in 1984. It is noteworthy that these communications share a common objective; we are trying to facilitate learning. Since I have spent most of my career in academia, my primary emphasis is on communication with students (particularly those enrolled in introductory ORMS courses). However, I have also spent a great deal of time working on operations research problems outside of academia, either as an employee in private industry or as an operations research consultant to corporations and not-for-profit organizations, and I hope as a consequence my discussion is also relevant to those working in the practice of ORMS.

Hierarchical Bayesian Prediction Methods in Election Politics: Introduction and Major Test

The authors build a collection of latent construct models, one per voter, in a representative sample of the voting age population. A given voters model is an algebraic expression of how that person integrates information about candidates and issues to arrive at a vote decision. Modeling individual decisions—called agent-based modeling—avoids aggregation fallacies and yields diagnostic insights unavailable from traditional pre-election polling methods. This article summarizes tests of the predictive accuracy of the method using data from 10 battleground states in the 2004 U.S. presidential election. Results are presented for the popular vote, the Electoral College vote, and within person. (Each persons predicted vote is compared to that persons actual vote obtained from a post-election survey.) Model accuracy is assessed relative to a standard voting intention model and to a meta-poll approach that combines multiple traditional polls over time, within state. The models predictive accuracy compares favorably with other models tested, increasing confidence in its insights about voting behavior. A companion article expands on these diagnostics and reviews applications of the approach to campaign strategy and planning.

Extending Lego® My Simplex

Almost 20 years have passed since Pendegraft described his use of Lego® toys to teach students basic modeling skills and linear programming concepts. Since then, many instructors of operations research including the author and their students have benefited from his contribution. In this article, the author describes a series of extensions he has developed since originally implementing Pendegrafts active learning exercise in the introductory operations courses he teaches. Through these extensions, the author integrates the concepts of sensitivity, integer programming, and LP formulation and extends the modeling experience beyond the original exercise.

A Moment Preserving Finitization Across the Power Series Family of Probability Distributions

Finitization transforms a discrete distribution into a distribution with smaller support of specified size. In special cases finitization preserves moments (moments of the order n finitization coincide with those of the parent distribution). We create a moment preserving finitization method for power series distributions by introducing an alternative representation and showing how to finitize members of this new class in a manner that preserves moments of the parent distribution. We provide results on convolutions and a reproductive property for power series distributions that have been finitized in this manner, and show how these finitized distributions accelerate variate generation in simulation.

Editorial---Editor-in-Chief Search Committee Report: The Digital Future Is Now

School (University of Pennsylvania) be appointed as the next Editor-in-Chief (EIC) of Marketing Science for a term of three years, beginning January 1, 2008. On September 12, 2007 the INFORMS board unanimously accepted our recommendation. Eric graduated from Harvard with a PhD in Statistics in 1994. After brief stints working as a statistician at the Educational Testing Service (ETS) and at DuPont, and as a lecturer in the Statistics Department at Wharton, he joined the Marketing Department at Wharton in July 1996 as an Assistant Professor. He was appointed the K. P. Chao Professor in 2005. Even within a short period of time, he has built up an impressive record of publications, numbering over 50, in marketing, statistics, and education. He has also won numerous awards for both teaching and research, including being a finalist for the Green Award from the Journal of Marketing Research (2004) and the winner of the 2006 Research Committee of

A Survey of Academic Use of Interfaces

We conducted a survey of academically affiliated members of INFORMS to better understand the extent of the current usage of Interfaces. We asked respondents if and how they use Interfaces as a resource in teaching and research, and we also asked a series of questions about their careers and the institutions at which they are employed. Our results show that Interfaces is used mostly in teaching MBA core and elective courses and MS and PhD courses in operations research and operations management. Edelman papers are generally used mostly for teaching and reference in research. In this paper, we describe some differences based on type of academic institution and on whether the respondent is an Interfaces subscriber; we also identify opportunities for Interfaces.

Political engineering: optimizing a U.S. Presidential candidate’s platform

We explore the application of operations research to the problem of defining/refining the political strategy for a candidate in a U.S. Presidential election. We use Hierarchical Bayesian techniques to model criteria used by a stratified random sample of registered voters to evaluate a candidate/platform. We then use the estimated utility parameters as inputs to a model that finds the positions a candidate can take on the salient issues of the election that will optimize expected Electoral College votes conditional on the positions respondents perceive to have been taken by the opposing party’s nominee. This approach is unique in that it (i) considers the value that individual voters associate with various positions the candidates can take on various issues, (ii) considers the chronicity of the electorate’s perceptions of a candidate’s positions on the salient issues, and (iii) yields a solution that will optimize expected Electoral College votes. We demonstrate this model on data collected immediately prior to the 2004 U.S. Presidential election (the most recent U.S. Presidential election not involving any potential candidate for the upcoming 2012 U.S. Presidential election), and we show how these data and the model can also be used to assess the perceived clarity of a candidate’s positions, the sensitivity of a candidate’s support to her/his perceived positions, and the viability of a third party candidate.

Teaching Note---All of Britain Must Be Stoned!

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