Clemen-Kleinmuntz Decision Analysis Best Paper Award

The Clemen–Kleinmuntz Decision Analysis Best Paper Award (previously Decision Analysis Special Recognition Award) is awarded annually for the best paper published in the INFORMS journal Decision Analysis in the previous calendar year. The winning paper is announced by March and awarded a plaque by the award committee and a cash prize of $2,000. Funding is provided by an endowment established by the Kleinmuntz Family Foundation.

The criteria for the paper most worthy of special recognition will be:
1) the paper is foundationally based on decision analysis
2) the paper makes an important contribution to theory and/or practice
3) the paper is broadly interesting and influential to a wide portion of the decision analysis community.

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2025 Winner

Ali Abbas, Gordon Hazen
On the Value of Information Across Decision Problems
Volume 22, Issue 1, March 2025

The value of information is a core concept within decision analysis, where the value of seeking additional information on an uncertainty is evaluated. The novelty of the winning entry by Abbas and Hazen is to consider the value of information across different decision problems sharing the same utility function. For the decision maker facing multiple decision problems, or for a company with multiple divisions and seeking to operate with a coherent organizational utility function, the existence of division-specific uncertainties suggests division-specific analyses would be advisable. This work shows that in this context, the method used to quantify the value of information matters. Even when the organizational utility function has a constant risk attitude, expected utility increase for information (EUI) may rank information sources differently at the organizational and divisional levels. However, the buying price for information (BPI) will never produce conflicting rankings. It is only under risk neutrality that EUI will always produce consistent rankings. This argues in favor of BPI and against EUI for ranking information sources in a distributed setting when risk sensitivity matters. The results have implications across a range of applications in which distributed decision-making occurs.

2025 Finalist

N. Onur Bakır
On the Value of the Tail Event Information
Volume 22, Issue 2, June 2025

Rare but high-consequence events pose a challenge in decision analysis. Although tail events often drive expected losses, information about them is limited and can be costly. This paper develops a framework to quantify the value of information that specifically reduces uncertainty in the tails of probability distributions, rather than across the entire distribution. The paper shows when analysts should prioritize collecting tail-focused data instead of refining estimates of more likely outcomes. By extending traditional value-of-information analysis to extreme risks, this work offers guidance for decision makers confronting rare but consequential threats that are present across a range of applications, such as public health, finance, security, environmental studies, and weather-related disasters.

2025 Award Selection Committee:
Andrea Hupman, Manel Baucells, Xuefei Lu, and Jay Simon

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2024 Co-Winner

Sina Ansari, Shakiba Enayati, Raha Akhavan-Tabatabaei, Julie M. Kapp
Curbing the Opioid Crisis: Optimal Dynamic Policies for Preventive and Mitigating Interventions
Volume 21, Issue 3, September 2024

Opioids are a class of drugs that reduce the intensity of pain signals and are commonly prescribed for pain management following an injury. Misuse of prescription opioids can cause dependency on the pill or lead to secondary addictions to heroin or other synthetic opioids such as fentanyl. The U.S. Department of Health and Human Services (HHS) declared a public health emergency in 2017 to tackle the nationwide opioid crisis. This state of emergency remains in effect to this day.

This paper provides practitioners with a tool to effectively address the opioid epidemic and enhance public health by deciding how to allocate their budget to various levels of intervention. It centers on the strategic distribution of resources across diverse interventions aimed at preventing and mitigating the consequences of opioid use disorder (OUD) and overdose occurrences. The paper proposes a decision aid tool built on expected utility theory that feeds into a Markov Decision Process (MDP) model to generate optimal policies upon the current state of the epidemic. A 10-year simulation of the epidemic’s progression is conducted to assess the dynamic efficacy of the proposed decision tool.

The findings reveal that it is optimal to allocate a significant portion of the budget to prevention when the rate of opioid pill acquisition rises, and the methodology results in an average reduction of 29% in total costs compared to the scenario without intervention.

2024 Co-Winner

Manel Baucells, Samuel E. Bodily
The Discount Rate for Investment Analysis Applying Expected Utility
Volume 21, Issue 2, June 2024

There is a long-standing intricate relationship at the crossing between finance and decision analysis on how to discount a stream of uncertain cash flows. On the one hand, the finance paradigm suggests risk neutrality with an appropriate discount rate that captures risk preferences and the time value of money. On the other hand, the decision analysis paradigm considers expressing risk aversion through expected utility. To reconcile the two approaches, the winning entry by Baucells and Bodily develops new theory breaking down the problem into three relevant questions, where the selection of the appropriate discount rate plays a central role in the first two, and the third concerns showing the coincidence of the new expected utility present value criterion with the traditional finance expected-NPV-discount-rate recommendation. Baucells and Bodily propose two new methods and provide a series of propositions for if and when conditions that answer these questions. They illustrate the methods with a simple and illuminating example. The work continues the stream of decision analysis research started by authors such as Abbas, Bell, Bickel, Brandao, Clemen, Dyer, Hazen, Howard, Nau, Smith, and many others.

2024 Finalist

Richard S. John, Robin L. Dillon, William J. Burns, Nicholas Scurich
Partitioning the Expected Value of Countermeasures with an Application to Terrorism
Volume 21, Issue 1, March 2024

The efficient use of resources for countermeasures against terrorist actions is critical to keep communities safe and maximize societal value. One major challenge in counter-terrorism decision analysis is to assess the benefits of such countermeasures.in general, and of deterrence in particular. In this paper, the authors suggest an innovative framework to assess the expected value of countermeasures. by partitioning it into three components: threat reduction, vulnerability reduction, and consequence mitigation. The benefit of a countermeasure is measured by the expected value of countermeasure implementation (EVCI) attributable to a specific countermeasure. The paper presents two applications of the partitioning methodology using examples that examine countermeasures designed to protect commercial aircraft against man-portable air defense systems. The proposed framework suggests a useful approach for explicitly accounting separately for deterrence, vulnerability reduction, and consequence mitigation in benefit-cost analyses. It provides quantifiable insights into how countermeasures reduce terrorism risk, as well as risks caused by other malicious agents. Hence, the paper makes an important contribution to the literature on counter-terrorism decision analysis.

2024 Award Selection Committee:
Ali Abbas, Emanuele Borgonovo, and Gilberto Montibeller

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2023 Winner

Gordon Hazen, Emanuele Borgonovo, Xuefei Lu
Information Density in Decision Analysis
Volume 20, Issue 2, June 2023

Sensitivity analysis is used extensively in decision analysis applications to understand whether a decision is close to a tipping point and whether more information should be collected about specific parameters. The existing literature suggests two different approaches for this analysis. The first is based on the value of information, which quantifies the benefit of obtaining data or expert judgment to eliminate or reduce uncertainty. The second is to construct a visualization that illustrates how changes in parameter levels would affect our choice of alternative or the value of its outcome. The winning entry this year develops new theory to combine the strengths of these two approaches in the form of "Information Density," which captures the impact of parameter changes in more detail than could be obtained using value of information calculations. The paper provides a rigorous technical grounding of information density along with helpful examples to support real-world implementation. Therefore, decision analysis practitioners are likely to find the graphical presentation of information density useful for communicating both the value of collecting more information and the magnitude and direction of parameter changes that contribute to that value.

2023 Finalist

Roger Chapman Burk, Richard M. Nehring
An Empirical Comparison of Rank-Based Surrogate Weights in Additive Multiattribute Decision Analysis
Volume 20, Issue 1, March 2023

Multi-attribute decision analysis methods are extensively used to rank alternatives in a wide range of domains. The weights of individual attributes play a crucial role in these methods. However, eliciting these weights from decision makers can be challenging in practice. An extensive body of literature has explored an alternative way to obtain attribute weights: asking decision makers to rank the attributes and then using heuristics to convert these ordinal rankings into cardinal weights. Various heuristics have been discussed in previous literature, and comparisons between them depend on assumptions about the appropriate distribution of "true" attribute weights. This article provides valuable empirical insights by analyzing attribute weight data published in 63 articles across various journals and applications. The findings are significant for practice as they offer clear guidelines for choosing a method to convert ordinal rankings into weights. This research also helps practitioners understand why some conversion methods are more effective than others.

2023 Award Selection Committee:
Jun Zhuang (Chair), Saurabh Bansal, and Jay Simon.

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2022 Winner

Andrea C. Hupman
Cutoff Threshold Decisions for Classification Algorithms with Risk Aversion
Volume 19, Issue 1, March 2022

Classification algorithms predict the class membership of an unknown record. Methods such as logistic regression or the naïve Bayes algorithm produce a score related to the likelihood that a record belongs to a particular class. A cutoff threshold is then defined to delineate the prediction of one class over another. This paper derives analytic results for the selection of an optimal cutoff threshold for a classification algorithm that is used to inform a two-action decision in the cases of risk aversion and risk neutrality. The results provide insight to how the optimal cutoff thresholds relate to the associated costs and the sensitivity and specificity of the algorithm for both the risk neutral and risk averse decision makers. The optimal risk averse threshold is not reliably above or below the optimal risk neutral threshold, but the relation depends on the parameters of a particular application.The results further show the risk averse optimal threshold is insensitive to the size of the data set or the magnitude of the costs, but instead is sensitive to the proportion of positive records in the data and the ratio of costs. Numeric examples and sensitivity analysis derive further insight. Results show the percent value gap from a misspecified risk attitude increases as the specificity of the classification algorithm decreases.

2022 Finalists

William N. Caballero, Roi Naveiro, David Ríos Insua
Modeling Ethical and Operational Preferences in Automated Driving Systems
Volume 19, Issue 1, March 2022

Whereas automated driving technology has made tremendous gains in the last decade, significant questions remain regarding its integration into society. Given its revolutionary nature, the use of automated driving systems (ADSs) is accompanied by myriad novel quandaries relating to both operational and ethical concerns that are relevant to numerous stakeholders (e.g., governments, manufacturers, and passengers). When considering any such problem, the ADS’s decision-making calculus is always a central component. This is true for concerns about public perception and trust to others regarding explainability and legal certainty. Therefore, in this manuscript, we set forth a general decision-analytic framework tailorable to multitudinous stakeholders. More specifically, we develop and validate a generic tree of ADS management objectives, explore potential attributes for their measurement, and provide multiattribute utility functions for implementation. Given the contention surrounding numerous ethical concerns in ADS operations, we explore how each of the aforementioned components can be tailored in accordance with the stakeholder’s desired ethical perspective. A simulation environment is developed upon which our framework is tested. Within this environment we illustrate how our approach can be leveraged by stakeholders to make strategic trade-offs regarding ADS behavior and to inform policymaking efforts. In so doing, our framework is demonstrated as a practical, tractable, and transparent means of modeling ADS decision making.

Colin Small, J. Eric Bickel
Model Complexity and Accuracy: A COVID-19 Case Study
Volume 19, Issue 4, December 2022

When creating mathematical models for forecasting and decision making,there is a tendency to include more complexity than necessary, in the belief that higher-fidelity models are more accurate than simpler ones. In this paper, we analyze the per-formance of models that submitted COVID-19 forecasts to the U.S. Centers for DiseaseControl and Prevention and evaluate them against a simple two-equation model that isspecified using simple linear regression. Wefind that our simple model was comparablein accuracy to highly publicized models and had among the best-calibrated forecasts.This result may be surprising given the complexity of many COVID-19 models and theirsupport by large forecasting teams. However, our result is consistent with the body ofresearch that suggests that simple models perform very well in a variety of settings.

2022 Award Selection Committee:
Gilberto Montibeller (Chair), Kara Morgan, and Jason Merrick

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2021 Winner

Gary J. Summers
Friction and Decision Rules in Portfolio Decision Analysis
Volume 18, Issue 2, June 2021
https://doi.org/10.1287/deca.2020.0421

The practice of decision analysis typically involves identifying performance objectives and managerial preferences, quantifying uncertainties, and then using them as inputs to assess alternatives. An exhaustive theory-driven literature in decision analysis provides foundations for this practice, assuming that objectives, preferences, and uncertainties are well understood and available to quantify. The award-winning article by Gary J. Summers highlights that this quantification may not be accurate in practice, leading to “friction” in decision-analysis models. This friction can systematically lead to a loss in decision quality. The article provides examples from multiple domains, and new analysis to illustrate this friction. As such, the article underscores the importance of calibrating the inputs to decision-analysis models. It promises to be a springboard for future research on understanding the causes of friction in decision-analysis models, its implications, and mitigation methods.

2021 Finalist

Yucheng Dong, Yao Li, Ying He, Xia Chen
Preference–Approval Structures in Group Decision Making: Axiomatic Distance and Aggregation
Volume 18, Issue 4, December 2021
https://doi.org/10.1287/deca.2021.0430

While decision analysis commonly focuses on decision making by an individual or a group acting as an individual, the paper by Dong et al. (2021) focuses on group decision making. It combines two popular approaches for aggregating individual preferences, ranked voting and approval voting, which have compensating strengths and weaknesses: Ranked voting leverages the preference ranking central to the decision-analysis approach, but is subject to strategic manipulation (where individuals misrepresent their preferences). Approval voting is immune to strategic manipulation, but does not provide the complete ranking required by decision analysis. The article shows that combining them leads to superior performance. Future empirical research and practice is likely to significantly benefit from the rigorous foundational treatment provided by this article.

2021 Award Selection Committee:
Saurabh Bansal and Robert Bordley (co-chairs)

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2020 Winner

Jeffery L. Guyse, L. Robin Keller, Candice H. Huynh
Valuing Sequences of Lives Lost or Saved Over Time: Preference for Uniform Sequences
Volume 17, Issue 1, March 2020
https://doi.org/10.1287/deca.2019.0397

Many policy decisions are based on the valuation of loss of lives over multi-period horizons. The winning paper provides an important contribution on this front by identifying behavioral anomalies that deviate from the normative standard discounting model. It uses laboratory experiments to show that individuals prefer uniform sequences of lives lost, rather than conforming to standard time discounting which is agnostic of such uniformity or other trends. This discovery has important implications for future decision analysis theory development and applications in medicine, public health, and environmental policies. First, it shows that alternative discounting models are required to assess alternatives with different sequences of loss of life. Second, more descriptive research is needed to better understand how individuals assess fatality rates. We congratulate the authors for this excellent paper, both for its rigor and its important practical implications.

2020 Finalist

Jay Simon, Donald Saari, L. Robin Keller
Interdependent Altruistic Preference Models
Volume 17, Issue 3, September 2020
https://doi.org/10.1287/deca.2020.0411

In many social settings, individuals seek to accommodate others’ preferences and welfare when assessing alternatives, the representation of altruistic behavior in the DA literature so far has lacked a full axiomatization and is complicated by possibly interdependent preferences. The runner up paper provides a technical development to represent these altruistic preferences. This development has several strengths. It provides for tractable value functions which would enable other researchers to adopt them in various domains and contexts. Furthermore, these functions can be elicited using previously known DA protocols. Illustrated examples in the paper also provide exemplars for other researchers to use these value functions. Our congratulations to the authors on the development of this elegant theory and in doing that resolving a long-standing hurdle in the decision analysis literature.

2020 Award Selection Committee:
Saurabh Bansal (chair), Andrea Hupman, and Gilberto Montibeller

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2019 Winner

Yuyu Fan, David V. Budescu, David Mandel, Mark Himmelstein
Improving Accuracy by Coherence Weighting of Direct and Ratio Probability Judgments
Volume 16, Issue 3, September 2019
https://doi.org/10.1287/deca.2018.0388

Since the early work of Bob Winkler, there has been considerable research on how to combine expert probability assessments of an event into an improved probability. Typically probabilities are weighted based on the expert's credibility and past historical performance. More recent work finds evidence that an expert's probability assessment should also be weighted based on coherence; i.e, based on the degree to which probability assessments are consistent with the rules of probability. This paper extends that work to distributions of continuous events, with different partitioning and more generic weighting methods. The authors also looked at whether coherence weights derived from one set of items would be effective with a different set of test items. They close with suggestions for future research. The members of the award-selection committee hope that this award encourages further work in this important area.

2019 Finalists

Mark Schneider , Cary Deck, Mikhael Shor, Tibor Besedeš, Sudipta Sarangi
Optimizing Choice Architectures
Volume 16, Issue 1, March 2019
https://doi.org/10.1287/deca.2018.0379

Manel Baucells, Rakesh K. Sarin
The Myopic Property in Decision Models
Volume 16, Issue 2, June 2019
https://doi.org/10.1287/deca.2018.0384

2019 Award Selection Committee:
Andrea Hupman (chair), Robert Bordley, John Butler, and Eva Regnier

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2018 Winner

Bin Mai, Shailesh Kulkarni
When Hackers Err: The Impacts of False Positives on Information Security Games
Volume 15, Issue 2, June 2018
https://doi.org/10.1287/deca.2017.0363

2018 Finalist

Robin L. Dillon, William J. Burns, Richard S. John
Insights for Critical Alarm-Based Warning Systems from a Risk Analysis of Commercial Aviation Passenger Screening
Volume 15, Issue 3, September 2018
https://doi.org/10.1287/deca.2018.0369

2018 Award Selection Committee:
Victor Richmond Jose (chair), Eva Regnier, and Matthias Seifert

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2017 Winner

Eike Nohdurft, Elisa Long, Stefan Spinler
Was Angelina Jolie Right? Optimizing Cancer Prevention Strategies Among BRCA Mutation Carriers
Volume 14, Issue 3, September 2017
https://doi.org/10.1287/deca.2017.0352

2017 Finalist

Christopher C. Hadlock, J. Eric Bickel
Johnson Quantile-Parameterized Distributions
Volume 14, Issue 1, March 2017
https://doi.org/10.1287/deca.2016.0343

2017 Award Selection Committee:
Emanuele Borgonovo (chair), Victor Richmond Jose, and L. Robin Keller

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2016 Winner

Samuel E. Bodily
Reducing Risk and Improving Incentives in Funding Entrepreneurs
Volume 13, Issue 2, June 2016
http://dx.doi.org/10.1287/deca.2015.0326

2016 Finalists

David M. Blum, M. Elisabeth Paté-Cornell
Probabilistic Warnings in National Security Crises: Pearl Harbor Revisited
Volume 13, Issue 1, March 2016
http://dx.doi.org/10.1287/deca.2015.0321

Venkata R. Prava, Robert T. Clemen, Benjamin F. Hobbs, Melissa A. Kenney
Partition Dependence and Carryover Biases in Subjective Probability Assessment Surveys for Continuous Variables: Model-Based Estimation and Correction
Volume 13, Issue 1, March 2016
http://dx.doi.org/10.1287/deca.2015.0323

2016 Award Selection Committee:
Eva Regnier (chair), Emanuele Borgonovo, and Janne Kettunen

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2015 Winner

Manel Baucells, Antonio Villasís
Equal Tails: A Simple Method to Elicit Utility Under Violations of Expected Utility
Volume 12, Issue 4, December 2015
http://dx.doi.org/10.1287/deca.2015.0320

2015 Finalist

Otso Massala, Ilia Tsetlin
Search Before Trade-offs Are Known
Volume 12, Issue 3, September 2015
http://dx.doi.org/10.1287/deca.2015.0313

2015 Award Selection Committee:
Robin Dillon-Merrill (chair), Debarun Bhattacharjya, and Eva Regnier

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2014 Winner

Nelson Lau, Yakov Bart, J. Neil Bearden, Ilia Tsetlin
Exploding Offers Can Blow Up in More Than One Way
Volume 11, Issue 3, September 2014
http://dx.doi.org/10.1287/deca.2014.0297

2014 Finalist

Ying He, James S. Dyer, John C. Butler
Decomposing a Utility Function Based on Discrete Distribution Independence
Volume 11, Issue 4, December 2014
http://dx.doi.org/10.1287/deca.2014.0302

2014 Award Selection Committee:
Jason Merrick (chair), Eva Regnier, and Yael Grushka-Cockayne

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2013 Winner

Stephen C. Hora, Benjamin R. Fransen, Natasha Hawkins, Irving Susel
Median Aggregation of Distribution Functions
Volume 10, Issue 4, December 2013
http://dx.doi.org/10.1287/deca.2013.0282

2013 Finalist

Manel Baucells, Rakesh K. Sarin
Determinants of Experienced Utility: Laws and Implications
Volume 10, Issue 2, June 2013
http://dx.doi.org/10.1287/deca.2013.0270

2013 Award Selection Committee:
Jason Merrick (chair), Casey Lichtendahl, and Kevin McCardle

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2012 Winner

Kenneth C. Lichtendahl, Jr., Samuel E. Bodily
Multiplicative Utilities for Health and Consumption
Volume 9, Issue 4, December 2012
http://dx.doi.org/10.1287/deca.1120.0248

2012 Finalist

Steven A. Lippman, Kevin F. McCardle
Embedded Nash Bargaining: Risk Aversion and Impatience
Volume 9, Issue 1, March 2012
http://dx.doi.org/10.1287/deca.1110.0224

2012 Award Selection Committee:
Robin-Dillon Merrill (chair), Jack Soll, Barry Cobb, and Matthias Seifert