Forming Maximally Diverse Workgroups: An Empirical Study

This work addresses two related important themes in business and business schools today: expanding diversity in the workplace and the increasing reliance on teams as an organizational structure. The paper describes an approach for creating student work groups where the objective is to maximize within group diversity based upon multiple criteria. This approach is an extension of a heuristic-based multiple-criteria decision support system (MCADSS) developed in earlier work (Weitz and Jelassi [1992]); that system was successfully implemented, and is currently in use, at the European Institute of Business Administration (INSEAD) in Fontainebleau, France. The heuristic has been modified here to incorporate a different set of criteria, and to allow for students placing out of core courses. This paper discusses the modified system, its implementation at the Stern School of Business at New York University (NYU), and an empirical experiment evaluating the performance of the system

SOLVING MULTI-CRITERIA ALLOCATION PROBLEMS: A DECISION SUPPORT SYSTEM APPROACH

MCADSS is a multi-criteria allocation decision support system for assisting in the task of partitioning a set of individuals into groups. Based upon multiple criteria, MCADSSâs goal is to maximize the diversity of members within groups, while minimizing the average differences between groups. (The project may be viewed from several perspectives: as a multi-criteria decision making problem, as a "reverse" clustering problem, or as a personnel assignment problem). The system is currently being used to allocate MBA students into sections and study teams at INSEAD, a leading European business school. This paper describes the rationale for MCADSS, design criteria, system methodology, and application results. It also suggests how the approach outlined here might be used for further applications.Information Systems Working Papers Serie

MANAGING EXPERT SYSTEMS: A Framework and Case Study

This paper addresses the problem of managing the development and implementation of a large expert system in an organization. A traditional systems analysis and design methodology is used as a framework to highlight similarities and differences in managing large scale traditional computer based projects and large expert systems. As a non-technical, prescriptive guide, this article focuses on defining at each stage in the project, the tasks to be accomplished, resources required, impact on the organization, likely benefits and potential problems. The case of a large expert system implemented by a multi-national corporation across several European sites is used to clarify and expand upon the management guidelines provided.Information Systems Working Papers Serie

QUANTIFYING THE VALUE OF MODELS AND DATA: A COMPARISON OF THE PERFORMANCE OF REGRESSION AND NEURAL NETS WHEN DATA QUALITY VARIES

Under circumstances where data quality may vary, knowledge about the potential performance of alternate predictive models can enable a decision maker to design an information system whose value is optimized in two ways. The decision maker can select a model which is least sensitive to predictive degradation in the range of observed data quality variation. And, once the "right" model has been selected, the decision maker can select the appropriate level of data quality in view of the costs of acquiring it. This paper examines a real-world example from the field of finance -- prepayments in mortgage-backed securities (MBS) portfolio management -- to illustrate a methodology that enables such evaluations to be made for two modeling alternative: regression analysis and neural network analysis. The methodology indicates that with "perfect data," the neural network approach outperforms regression in terms of predictive accuracy and utility in a prepayment risk management forecasting system (RMFS). Further, the performance of the neural network model is more robust under conditions of data quality degradation.Information Systems Working Papers Serie

COMPARING THE PERFORMANCE OF REGRESSION AND NEURAL NETWORKS AS DATA QUALITY VARIES: A BUSINESS VALUE APPROACH

Under circumstances where data quality may vary (due to inaccuracies or lack of timeliness, for example), knowledge about the potential performance of alternate predictive models can help a decision maker to design a business value-maximizing information system. This paper examines a real-world example from the field of finance to illustrate a comparison of alternative modeling tools. Two modeling alternatives are used in this example: regression analysis and neural network analysis. There are two main results: (1) Linear regression outperformed neural nets in terms of forecasting accuracy, but the opposite was true when we considered the business value of the forecast. (2) Neural net-based forecasts tended to be more robust than linear regression forecasts as data accuracy degraded. Managerial implications for financial risk management of MBS portfolios are drawn from the results.Information Systems Working Papers Serie

SOLVING MULTI-CRITERIA ALLOCATION PROBLEMS: A DECISION SUPPORT SYSTEM APPROACH

MCADSS is a multi-criteria allocation decision support system for assisting in the task of partitioning a set of individuals into groups. Based upon multiple criteria, MCADSSâs goal is to maximize the diversity of members within groups, while minimizing the average differences between groups. (The project may be viewed from several perspectives: as a multi-criteria decision making problem, as a "reverse" clustering problem, or as a personnel assignment problem). The system is currently being used to allocate MBA students into sections and study teams at INSEAD, a leading European business school. This paper describes the rationale for MCADSS, design criteria, system methodology, and application results. It also suggests how the approach outlined here might be used for further applications.Information Systems Working Papers Serie

QUANTIFYING THE VALUE OF MODELS AND DATA: A COMPARISON OF THE PERFORMANCE OF REGRESSION AND NEURAL NETS WHEN DATA QUALITY VARIES

Under circumstances where data quality may vary, knowledge about the potential performance of alternate predictive models can enable a decision maker to design an information system whose value is optimized in two ways. The decision maker can select a model which is least sensitive to predictive degradation in the range of observed data quality variation. And, once the "right" model has been selected, the decision maker can select the appropriate level of data quality in view of the costs of acquiring it. This paper examines a real-world example from the field of finance -- prepayments in mortgage-backed securities (MBS) portfolio management -- to illustrate a methodology that enables such evaluations to be made for two modeling alternative: regression analysis and neural network analysis. The methodology indicates that with "perfect data," the neural network approach outperforms regression in terms of predictive accuracy and utility in a prepayment risk management forecasting system (RMFS). Further, the performance of the neural network model is more robust under conditions of data quality degradation.Information Systems Working Papers Serie

The hypoxia marker CAIX is prognostic in the UK phase III VorteX-Biobank cohort: an important resource for translational research in soft tissue sarcoma

BACKGROUND: Despite high metastasis rates, adjuvant/neoadjuvant systemic therapy for localised soft tissue sarcoma (STS) is not used routinely. Progress requires tailoring therapy to features of tumour biology, which need exploration in well-documented cohorts. Hypoxia has been linked to metastasis in STS and is targetable. This study evaluated hypoxia prognostic markers in the phase III adjuvant radiotherapy VorteX trial. METHODS: Formalin-fixed paraffin-embedded tumour biopsies, fresh tumour/normal tissue and blood were collected before radiotherapy. Immunohistochemistry for HIF-1α, CAIX and GLUT1 was performed on tissue microarrays and assessed by two scorers (one pathologist). Prognostic analysis of disease-free survival (DFS) used Kaplan-Meier and Cox regression. RESULTS: Biobank and outcome data were available for 203 out of 216 randomised patients. High CAIX expression was associated with worse DFS (hazard ratio 2.28, 95% confidence interval: 1.44-3.59, P<0.001). Hypoxia-inducible factor-1α and GLUT1 were not prognostic. Carbonic anhydrase IX remained prognostic in multivariable analysis. CONCLUSIONS: The VorteX-Biobank contains tissue with linked outcome data and is an important resource for research. This study confirms hypoxia is linked to poor prognosis in STS and suggests that CAIX may be the best known marker. However, overlap between single marker positivity was poor and future work will develop an STS hypoxia gene signature to account for tumour heterogeneity

Surface processes recorded by rocks and soils on Meridiani Planum, Mars: Microscopic Imager observations during Opportunity's first three extended missions

The Microscopic Imager (MI) on the Mars Exploration Rover Opportunity has returned images of Mars with higher resolution than any previous camera system, allowing detailed petrographic and sedimentological studies of the rocks and soils at the Meridiani Planum landing site. Designed to simulate a geologist's hand lens, the MI is mounted on Opportunity's instrument arm and can resolve objects 0.1 mm across or larger. This paper provides an overview of MI operations, data calibration, and analysis of MI data returned during the first 900 sols (Mars days) of the Opportunity landed mission. Analyses of Opportunity MI data have helped to resolve major questions about the origin of observed textures and features. These studies support eolian sediment transport, rather than impact surge processes, as the dominant depositional mechanism for Burns formation strata. MI stereo observations of a rock outcrop near the rim of Erebus Crater support the previous interpretation of similar sedimentary structures in Eagle Crater as being formed by surficial flow of liquid water. Well-sorted spherules dominate ripple surfaces on the Meridiani plains, and the size of spherules between ripples decreases by about 1 mm from north to south along Opportunity's traverse between Endurance and Erebus craters