Research Interests and Working Papers
This page describes papers that have been presented
at meetings or are not yet published but are available for distribution.
Before citing any of the papers that you obtain here, please contact
me at
{clemen -at- duke -dot- edu}
to find out whether a newer version is available or has been published.
All files are in pdf format unless otherwise noted.
Table of Contents
On the Choice of Baselines in Multiattribute Portfolio Analysis: A Cautionary Note
By Robert T. Clemen and James E. Smith
Aug, 2009.
Abstract: In multiattribute portfolio optimization, a decision maker must evaluate a number of projects on multiple dimensions and then select the set of projects that optimizes the portfolio's overall value. In this note, we discuss the importance of establishing an appropriate baseline score for not doing a project in multiattribute portfolio analysis. We believe that practitioners often implicitly assume that not doing a project results in the worst possible score on all attributes. We argue that this assumption is often inappropriate and may lead to incorrect recommendations.
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Psychological and organizational factors influencing decision process innovation: The role of perceived threat to managerial power
by Kelly E. See and Robert T. Clemen
September, 2005.
Abstract: Organizations often face complex choices involving uncertainty, trade-offs, and broad consequences, but responding to such situations in rational ways can be hampered by individual decision makers' cognitive limitations. The framework of decision analysis (DA) provides a unified collection of analytical decision-making tools and procedures that are designed to help managers cope with difficult decisions, yet little is known about what influences the ability of firms to innovate with respect to decision-making practices. This paper investigates factors that facilitate and impede adoption of decision process innovations . Integrating individual-level theories of technology acceptance and managerial innovation with organization-level theories of innovation, we present the results of a multilevel empirical survey of 160 senior managers from a variety of organizations. Our survey incorporates measures of individual psychological perceptions, organization structure, and environmental context. We find support for many of the variables that have previously been found to predict innovation, namely attitudes toward the innovation, organizational culture, degree of centralization, and concerns for legitimacy in the institutional environment. Furthermore, we examine a previously unexplored individual-level issue in innovation research, perceived threat to managerial value and control, and find that a key barrier to decision process innovation is the tendency for managers to perceive such innovations as threats to their own value, discretion, and control. This impediment to innovation is mitigated by highly formalized organizational structures, presumably because such structures are characterized by strict rules and hierarchy that are perceived to preserve authority and power.
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Debiasing expert overconfidence: A Bayesian calibration model
by R. T. Clemen and K. C. Lichtendahl, Jr.
June, 2002. Presented at PSAM6, San Juan, Puerto Rico
Abstract: In a decision and risk analysis, experts may provide subjective probability distributions that encode
their beliefs about future uncertain events. For continuous variables, experts often provide these judgments in the form
of quantiles of the distribution (e.g., 5th, 50th, and 95th percentiles). Psychologists have shown, though, that such subjective
distributions tend to be too narrow, representing overconfidence on the part of the expert. We propose an approach for modeling
and debiasing expert overconfidence. Based on past performance data (previous assessments and realizations for a number of
uncertain variables), and using Bayesian methods to update prior distributions on the model parameters, we show how our model
can be used to debias expert probabilities. We develop and demonstrate both a single-expert model and a multiple-expert hierarchical
model.
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For other working papers related to decision analysis and education, check out: Decision Making in
High Schools: A Curriculum-Development Project. |
