This article introduces a new method for eliciting prior distributions from experts. The method models an expert decision-making process to infer a prior probability distribution for a rare event A. More specifically, assuming there exists a decision-making process closely related to A which forms a decision Y, where a history of decisions have been collected. By modelling the data observed to make the historic decisions, using a Bayesian model, an analyst can infer a distribution for the parameters of the random variable Y. This distribution can be used to approximate the prior distribution for the parameters of the random variable for event A. This method is novel in the field of prior elicitation and has the potential of improving upon current methods by using real-life decision-making processes, that can carry real-life consequences, and, because it does not require an expert to have statistical knowledge. Future decision making can be improved upon using this method, as it highlights variables that are impacting the decision making process. An application for eliciting a prior distribution of recidivism, for an individual, is used to explain this method further.
We consider a variant of the hide-and-seek game in which a seeker inspects multiple hiding locations to find multiple items hidden by a hider. Each hiding location has a maximum hiding capacity and a probability of detecting its hidden items when an inspection by the seeker takes place. The objective of the seeker (resp. hider) is to minimize (resp. maximize) the expected number of undetected items. This model is motivated by strategic inspection problems, where a security agency is tasked with coordinating multiple inspection resources to detect and seize illegal commodities hidden by a criminal organization. To solve this large-scale zero-sum game, we leverage its structure and show that its mixed strategies Nash equilibria can be characterized using their unidimensional marginal distributions, which are Nash equilibria of a lower dimensional continuous zero-sum game. This leads to a two-step approach for efficiently solving our hide-and-seek game: First, we analytically solve the continuous game and compute the equilibrium marginal distributions. Second, we derive a combinatorial algorithm to coordinate the players' resources and compute equilibrium mixed strategies that satisfy the marginal distributions. We show that this solution approach computes a Nash equilibrium of the hide-and-seek game in quadratic time with linear support. Our analysis reveals a complex interplay between the game parameters and allows us to evaluate their impact on the players' behaviors in equilibrium and the criticality of each location.
Benefit–cost analyses are critical to support U.S. agencies’ programmatic decision making. These analyses are particularly challenging when one of the benefits is adversary deterrence. This paper presents a framework for calculating the value of deterrence related to countermeasures implemented to mitigate an attack by an adaptive adversary. We offer an approach for partitioning the benefit of countermeasures into three components: (1) threat reduction (deterrence), (2) vulnerability reduction, and (3) consequence mitigation. The benefit of a countermeasure is measured by the expected value of countermeasure implementation (EVCI) attributable to a specific countermeasure. It is based on the concept of expected value of imperfect control, defined as the difference in the expected values of alternatives with and without countermeasures. The EVCI represents all the benefits of implementing the countermeasure and is derived from three sources: (1) changes in attack probability (threat reduction from deterrence), (2) changes in detection probability (vulnerability reduction), and (3) changes in the distribution of attack outcomes (consequence mitigation). We partition the EVCI and estimate the portion attributable to each of these three sources to quantify the unique benefit of each. We provide two applications of the partitioning methodology using examples from the published literature that examine countermeasures designed to protect commercial aircraft against man-portable air defense systems. The proposed framework provides an 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. Funding: This material is based upon work supported by the U.S. Department of Homeland Security under [Grant Award 22STESE00001-02-00]. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. This award was made to Northeastern University and the University of Southern California is a sub-awardee. This work was also supported by the National Science Foundation [Grant 2027296] awarded to Decision Research.
Limited information, time, or capacity may prevent customers from acting as utility maximizers when making purchase decisions. Rather, they would settle for a good enough option; that is, they stop searching and make a purchase as soon as they find an acceptable alternative. We incorporate this behavior in an assortment-optimization problem. Whereas different approaches to modeling customer choice are adopted in assortment planning, all assume customers are utility maximizers. Our work bridges the research streams of assortment planning and bounded rationality, particularly satisficing behavior. In addition, we define a limit for the search budget of customers, in which customers leave without purchase after examining a certain number of items. This assumption brings a new perspective to the assortment-planning literature, enabling us to capture the choice-overload effect. We prove that the firm’s problem of finding the optimal assortment is NP-hard. We further establish certain structural properties of the optimal decision, which allows us to reformulate the model as a mixed-integer program. We analytically derive a tight upper bound on the percentage loss in the firm’s expected profit for small instances when it assumes incorrectly that customers are utility maximizers. For larger instances, we take a numerical approach to determine the loss. Our results indicate that firms offering low-involvement products, among those dealing with satisficing customers, are more likely to face substantial profit loss if they ignore this behavior. Supplemental Material: The e-companion is available at https://doi.org/10.1287/deca.2022.0063 .
Noise traders are a central idea in the modern theory of asset markets, yet there is not a standard model of such agents in contrast to the well-established representation of rational agents as expected utility maximizers. We propose the Hurwicz criterion, a classical criterion in decision analysis for choice under uncertainty, as a foundation for noise traders in asset markets. Hurwicz agents trade on optimism and pessimism and do not trade on information. A binary asset market is introduced with asymmetric information and heterogeneity both in rationality and in ambiguity attitudes. In this environment, noise trader behavior is endogenously positively correlated, the market is more efficient in low sentiment periods, and the favorite-longshot bias holds in equilibrium. The analysis demonstrates that aggregate market properties such as positive trading volume and the favorite longshot bias can be derived from the micro behavior of individual agents that have an axiomatic foundation.
Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeDecision AnalysisVol. 20, No. 2 From the Editor: Belated Recognition for the 2021 Clemen–Kleinmuntz Decision Analysis Best Paper Award Winner and FinalistVicki M. BierVicki M. BierPublished Online:9 May 2023https://doi.org/10.1287/deca.2023.0477The winning paper for 2021 was “Friction and Decision Rules in Portfolio Decision Analysis,” by Gary J. Summers (Summers 2021). To put this paper in context, 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 paper by 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.I congratulate Gary J. Summers on this excellent paper and am especially glad that Decision Analysis was able to help a full-time practitioner bring forth important and insightful ideas in a manner that is compelling to both academics and practitioners.The finalist for 2021 was “Preference-Approval Structures in Group Decision Making: Axiomatic Distance and Aggregation,” by Yucheng Dong, Yao Li, Ying He, and Xia Chen (Dong et al. 2021). Although decision analysis commonly focuses on decision making by an individual or a group acting as an individual, this paper 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.Congratulations to Dong et al. for this excellent and pragmatic contribution to the challenging topic of group decision making.Thanks also to committee members Saurabh Bansal (Pennsylvania State University) and Robert Bordley (University of Michigan) for cochairing the award committee for 2021, and for providing extensive input to the paper descriptions given above. My sincere apologies for this belated recognition to all.ReferencesDong Y, Li Y, He Y, Chen X (2021) Preference–approval structures in group decision making: Axiomatic distance and aggregation. Decision Anal. 18(4):273–295.Link, Google ScholarSummers GJ (2021) Friction and decision rules in portfolio decision analysis. Decision Anal. 18(2):101–120.Link, Google Scholar Previous Back to Top Next FiguresReferencesRelatedInformation Volume 20, Issue 2June 2023Pages 85-185, C2 Article Information Metrics Information Published Online:May 09, 2023 Copyright © 2023, INFORMSCite asVicki M. Bier (2023) From the Editor: Belated Recognition for the 2021 Clemen–Kleinmuntz Decision Analysis Best Paper Award Winner and Finalist. Decision Analysis 20(2):88-88. https://doi.org/10.1287/deca.2023.0477 PDF download
Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeDecision AnalysisVol. 20, No. 2 From the Editors: New Decision Analysis Journal Submission RequirementsVicki M. Bier, Simon French Vicki M. Bier, Simon French Published Online:20 Apr 2023https://doi.org/10.1287/deca.2023.0475Replication is central to the practice of empirical science. Unfortunately, over the last 20 years or so, concerns have grown that replicability is not working as well as it should.1 As a result, INFORMS journals, including Decision Analysis, are improving their policies to support the goal of replication. In the case of Decision Analysis, most submissions do not relate to empirical research. Theoretical, methodological, and discursive submissions are clearly not subject to the same types of replicability requirements. Moreover, many empirical papers reporting case studies and applications are also exempt. It is accepted that prescriptive analyses are designed to evolve the perceptions, understanding, beliefs, values, and judgements of the participants. A decision analysis therefore cannot be repeated completely afresh. However, we do publish empirical studies of decision behavior conducted under “laboratory conditions,” and case studies can be evaluated using extensive questionnaire and structured-interview results. Therefore, a small group of members of the editorial board have been considering our requirements for submission, peer review, and subsequent publication of such papers.Our discussions recognized that the journal does not have many empirical submissions, so we can begin with relatively “light touch” requirements, note how well they work, and perhaps modify them in a few years in the light of experience. More importantly, much decision analysis research takes place in commercially sensitive areas, so there are issues of intellectual property rights and confidentiality. Accordingly, the journal has recently adopted a new policy aimed at helping to ensure the replicability of research.2“For papers containing significant empirical, observational, or experimental analysis, we strongly recommend sufficient material to be available (either in the paper or in appendices, data-files and codes) to enable the analysis of the data and/or observations to be replicated. Such material should include experimental designs, elicitation protocols and so forth, as well as the raw data and observations. If requested, and recognizing that release of proprietary materials may require non-disclosure agreements, authors using these materials should work with the editors to provide evidence that the paper is accurate, and the conclusions are replicable. On acceptance, the journal would expect that these materials are archived in such a way that future researchers can access them, again recognizing that proprietary requirements may necessitate non-disclosure agreements.”We hope that this contributes to the movement for better peer review and improved replicability.Endnotes1 See https://www.economist.com/leaders/2013/10/21/how-science-goes-wrong; https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2015.00827.x.2 See pubsonline.informs.org/page/deca/submission-guidelines. Back to Top Next FiguresReferencesRelatedInformation Volume 20, Issue 2June 2023Pages 85-185, C2 Article Information Metrics Information Published Online:April 20, 2023 Copyright © 2023, INFORMSCite asVicki M. Bier, Simon French (2023) From the Editors: New Decision Analysis Journal Submission Requirements. Decision Analysis 20(2):85-85. https://doi.org/10.1287/deca.2023.0475 PDF download
Management agencies are tasked with difficult decisions for conservation and management of natural resources. These decisions are difficult because of ecological and social uncertainties, the potential for multiple decision makers from multiple jurisdictions, and the need to account for the diverse values of stakeholders. Decision analysis provides a framework for accounting for these difficulties when making conservation and management decisions. We discuss the benefits of the application of decision analysis for these types of issues and provide insights from three case studies from the Laurentian Great Lakes. These case studies describe applications of decision analysis for decisions within an agency (management of double-crested cormorant), among agencies (response to invasive grass carp), and among agencies and stakeholders (sustainable fisheries harvest management). These case studies provide insight into the ways that decision analysis can be useful for conservation and management of natural resources, but we also highlight future needs for decision making for these resources. In particular, applications of decision analysis for conservation and management would benefit from enhanced integration of both ecological and social science, inclusion of a broader base of stakeholders and rightsholders, and better educational opportunities surrounding decision analysis for undergraduates and graduate students of natural resources management programs. Specific lessons from our experiences include the importance of establishing trust and transparency early through the formation of a working group, collaboratively defining objectives and evaluating uncertainties, risks, and tradeoffs, and implementing participatory modeling processes with an independent facilitator with appropriate quantitative skills.
In recent years, the state of Colorado has experienced extreme wildfire events that have degraded forest and watershed health and devastated human communities. With expanding human development and a changing climate, wildfire activity is likely to increase, and wildfire management agencies will be challenged to sustain landscapes and the ecosystem services they provide. A critical element of the United States’ federal-, state-, and local-level multiagency wildfire response is the interagency dispatching system, which facilitates the ordering, mobilization, and tracking of firefighting resources to and from wildfire incidents—a role that is likely to increase in both importance and workload in the future. Given increasing demands, it is worth considering ways to improve efficiencies, capacity, and capability within the current Colorado dispatching system. With this, the Rocky Mountain Coordinating Group (RMCG) and the Rocky Mountain Area Fire Executive Council (RMA-FEC) sought to reorganize the dispatching system, beginning with exploration of changes to dispatching zone boundaries and the number and location of dispatching centers throughout the state. Here we describe a multiyear research–management partnership with the RMCG and RMA-FEC to apply a structured decision-making process to guide this reorganization effort. We highlight the steps used in a participatory process that involved local decision makers and included iteratively revising and clarifying the problem statement, developing objectives and translating them into measurable attributes, building a multiobjective optimization model to generate and compare alternatives, and communicating a recommended alternative that was ultimately adopted. To conclude, we discuss insights from our experience and highlight opportunities for similar work to support efficient wildfire management elsewhere in the United States. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Advance Environmental Sustainability. Funding: This research was supported by the U.S. Department of Agriculture Forest Service.
Species status assessments are used to inform U.S. Fish and Wildlife Service (USFWS) decision making for Endangered Species Act (ESA) classification decisions, recovery planning, and more. The large number of species that require assessment and uncertainty in the data available impede the process of assigning and completing the assessments, which makes creating a multiyear work plan extremely difficult. An optimized triaging system that maximizes the use of the best available information while managing the complex ESA workload and meeting deadlines is necessary. We used a structured decision-making framework to approach the problem with the goal of creating a prioritization tool that would be effective at scheduling assessments, given the best information available and priorities of the USFWS. We collected data on the species awaiting assessment and developed a value function that incorporates existing deadlines, taxonomic uncertainty, controversy of the species, and population and habitat data availability and quality. We used a constrained linear optimization algorithm to maximize the value function and ensure that workload capacity was not exceeded. A comparison of model scenarios indicates that imposed deadlines impact the model more than capacity constraints. Additionally, differential weighting of the metrics significantly affected the outcome of the model. In the future, elicitation of metric weights should be done routinely before the model is run for use in official planning to ensure alignment with current USFWS priorities. Output from this optimization can be used to inform a five-year work plan, allocate resources, and discuss workforce decisions. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Advance Environmental Sustainability. Funding: This work was funded via an inter-agency agreement between the USFWS and the USGS and subsequently by a Research Work Order contract between the USGS and the University of Florida [Grant G21AC00016].
There is a need to achieve sustainable agricultural production to secure food, fiber, and fuel for a growing global population. Climate-smart (CS) actions (no-till and cover crops) can reduce carbon emissions and promote soil organic carbon (SOC) storage. Contemporary voluntary carbon markets provide producers with a monetary incentive to adopt CS actions. However, SOC–yield dynamics under CS actions are not well known, making it difficult for producers to judge whether additional income from carbon credits will offset potential losses to yield income. We designed a SOC–yield framework that captures SOC–yield–income dynamics under traditional (reduced tillage, no cover crops) and CS actions. Using a modified structured decision-making approach, we applied the framework to a case study in which producers aim to increase income by selling carbon credits after adopting CS actions. Specifically, we demonstrated how to balance tradeoffs between yield and carbon credit income that arise from tillage and winter cover crop actions (cereal rye, Secale cereale L. and crimson clover, Trifolium incarnatum L.) in a soybean (Glycine max L.) production system in Mississippi. Results indicated that a producer could minimize losses to net yield income by adopting no-till if already using cover crops. There was also evidence that carbon credit income could offset losses to yield income when adopting CS in place of traditional actions. Identifying risks to yield income and SOC storage can help design carbon neutrality policies that have minimum impact on a producer’s income. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Advance Environmental Sustainability. Funding: This work was supported by the USDA-ARS [Grants 58-0200-0-002 (Advancing Agricultural Research) and 58-6001-8-003] and the USDA National Institute of Food and Agriculture [McIntire Stennis Project 1020959]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0478 .
Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeDecision AnalysisVol. 20, No. 2 From the Editor and Chair of the Award Committee: 2022 Clemen–Kleinmuntz Decision Analysis Best Paper AwardVicki M. Bier, Gilberto Montibeller Vicki M. Bier, Gilberto Montibeller Published Online:9 May 2023https://doi.org/10.1287/deca.2023.0476In this issue, we present the 2022 Clemen–Kleinmuntz Decision Analysis Best Paper Award. The author of the best paper will receive a $2,000 prize. This prize is supported by an endowment established by the Kleinmuntz Family Foundation and administered by INFORMS. The goal of the Best Paper Award is to draw attention to the high quality of work published in the journal and encourage the journal’s continuing growth and success.All papers published during 2022 in the journal were assessed against three main criteria: The paper is foundationally based on Decision Analysis; the paper makes an important contribution to theory and/or practice; and the paper is broadly interesting and influential to a wide portion of the Decision Analysis community. We wish to thank Kara Morgan (of Quant Policy Strategies) and Jason Merrick (of Virginia Commonwealth University), who were members of the award committee, chaired by Gilberto Montibeller (of Loughborough University and the University of Southern California).We are pleased to announce that the winning paper for 2022 is “Cutoff Threshold Decisions for Classification Algorithms with Risk Aversion,” by Andrea C. Hupman (Decision Analysis, vol. 19, no. 1, 2022, pp. 63–78; Hupman 2022). A critical decision in the design of machine learning algorithms for classification is the cutoff threshold for the classes. Andrea has proposed innovative 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 into how the optimal cutoff thresholds relate to the associated costs and the sensitivity and specificity of the algorithm for different types of risk attitudes.The winning paper highlights the important contribution that Decision Analysis can make to machine learning in modeling the kinds of judgments that often are required in such algorithms, employing a clear and explicit normative framework. We congratulate Andrea Hupman for the excellent paper and encourage our community to develop further research on the interface between artificial intelligence and Decision Analysis.Two papers were finalists for the Clemen–Kleinmuntz Decision Analysis Best Paper Award. The first finalist paper was “Modeling Ethical and Operational Preferences in Automated Driving Systems,” by William N. Caballero, Roi Naveiro, and David Ríos Insua (Decision Analysis, vol. 19, no. 1, 2022, pp. 21–43; Caballero et al. 2022). The paper deals with an important development in transportation technology: automated driving systems. The use of these systems, given their revolutionary nature, brings novel challenges related to both operational and ethical concerns that are relevant to numerous stakeholders (e.g., governments, manufacturers, and passengers). When considering any such problem, the decision-making calculus of the automated driving system is always a central component.In the paper, the authors propose a general decision-analytic framework tailorable to distinctive stakeholders involved in the design, regulation, and use of an automated driving system. They developed and validated a generic tree of management objectives for the system, explored potential attributes for their measurement, and provided multiattribute utility functions for implementation. Furthermore, they explored how each of the components can be tailored following the stakeholder’s desired ethical perspective and tested it via a simulated environment. We congratulate William N. Caballero, Roi Naveiro, and David Ríos Insua for the application of decision analysis to this important topic in transportation science.The second finalist paper for the Clemen–Kleinmuntz Decision Analysis Best Paper Award was “Model Complexity and Accuracy: A COVID-19 Case Study,” by Colin Small and J. Eric Bickel (Decision Analysis, vol. 19, no. 4, pp. 354–383; Small and Bickel 2022). There has been intensive modeling effort in epidemiology during the COVID-19 pandemic, with the development of several sophisticated and large-scale predictive models, in the belief that higher-fidelity models are more accurate than simpler ones. This thoughtful paper analyzed the performance of models that submitted COVID-19 forecasts to the U.S. Centers for Disease Control and Prevention and evaluated them against a simple two-equation model specified using simple linear regression. They found that their simple model was comparable in accuracy to highly publicized models and had among the best-calibrated forecasts.This research result may be surprising, given the complexity of many COVID-19 models and their support by large forecasting teams. However, the result is consistent with the body of research that suggests that simple models often perform well in a variety of settings. Even more importantly, the authors emphasize how a decision-making focus can help in developing predictive models that are requisite in supporting policymakers dealing with emerging health threats. The paper is an excellent example of research in Health Decision Analysis and was part of the special issue on this topic in the December 2022 issue of Decision Analysis (Long et al. 2022). Dillon et al. (2023) also cite Small and Bickel (2022) in regard to the potential and challenges of Decision Analysis in supporting policymaking during future pandemics. We congratulate Colin Small and J. Eric Bickel for the creative and thought-provoking paper for health security decision making.Concluding this letter, we would like to thank all the authors that published papers in 2022 in the journal and were considered for the award. We encourage our Decision Analysis community to continue submitting high-quality manuscripts that can be strong contenders for the Clemen–Kleinmuntz Decision Analysis Best Paper Award in future years.ReferencesCaballero WN, Naveiro R, Insua DR (2022) Modeling ethical and operational preferences in automated driving systems. Decision Anal. 19(1):21–43.Link, Google ScholarDillon RL, Bier VM, John, RS, Althenayyan A (2023) Closing the gap between decision analysis and policy analysts before the next pandemic. Decision Anal. 20(2):109–132Abstract, Google ScholarHupman AC (2022) Cutoff threshold decisions for classification algorithms with risk aversion. Decision Anal. 19(1):63–78.Link, Google ScholarLong EF, Montibeller G, Zhuang J (2022) Health decision analysis: Evolution, trends, and emerging topics. Decision Anal. 19(4):255–264.Link, Google ScholarSmall C, Bickel JE (2022) Model complexity and accuracy: A COVID-19 case study. Decision Anal. 19(4):354–383.Link, Google Scholar Previous Back to Top Next FiguresReferencesRelatedInformation Volume 20, Issue 2June 2023Pages 85-185, C2 Article Information Metrics Information Published Online:May 09, 2023 Copyright © 2023, INFORMSCite asVicki M. Bier, Gilberto Montibeller (2023) From the Editor and Chair of the Award Committee: 2022 Clemen–Kleinmuntz Decision Analysis Best Paper Award. Decision Analysis 20(2):86-87. https://doi.org/10.1287/deca.2023.0476 PDF download
More and more decision-making problems are being solved by groups. Collective intelligence is the ability of groups to perform well when solving complex problems. Thus, it is important to encourage collective intelligence to emerge from groups. In this study, we explore how two critical characteristics of groups, that is, group structure and individual knowledge in groups, influence the emergence of collective intelligence. To do this, we propose a measure for group structure using the collaboration network of a group and a measure for the distribution of individual knowledge in groups. Group structure is measured based on the intensities of links and whether the network is hierarchical or flat. The distribution of individual knowledge is measured from the perspective of whether group information is shared or unique. Social interactions among group members and individual changes in opinion are modeled based on a simulation technique. We find that unbalanced information distribution undermines group performance, whereas group structure can modify the effect of information distribution. We also find that groups with broadly distributed knowledge are good at solving complex problems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72171158, 71771156 and 71971145].
Conservation translocations, intentional movements of species to protect against extinction, have become widespread in recent decades and are projected to increase further as biodiversity loss continues worldwide. The literature abounds with analyses to inform translocations and assess whether they are successful, but the fundamental question of whether they should be initiated at all is rarely addressed formally. We used decision analysis to assess northern leopard frog reintroduction in northern Idaho, with success defined as a population that persists for at least 50 years. The Idaho Department of Fish and Game was the decision maker (i.e., the agency that will use this assessment to inform their decisions). Stakeholders from government, indigenous groups, academia, land management agencies, and conservation organizations also participated. We built an age-structured population model to predict how management alternatives would affect probability of success. In the model, we explicitly represented epistemic uncertainty around a success criterion (probability of persistence) characterized by aleatory uncertainty. For the leading alternative, the mean probability of persistence was 40%. The distribution of the modelling results was bimodal, with most parameter combinations resulting in either very low (<5%) or relatively high (>95%) probabilities of success. Along with other considerations, including cost, the Idaho Department of Fish and Game will use this assessment to inform a decision regarding reintroduction of northern leopard frogs. Conservation translocations may benefit greatly from more widespread use of decision analysis to counter the complexity and uncertainty inherent in these decisions. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Advance Environmental Sustainability. Funding: This work was supported by the Wilder Institute/Calgary Zoo, the U.S. Fish and Wildlife Service [Grant F18AS00095], the NSF Idaho EPSCoR Program and the National Science Foundation [Grant OIA-1757324], and the Hunt Family Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0472 .
Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful. Funding: This research was partially supported by the U.S. Department of Homeland Security through the Center for Accelerating Operational Efficiency at Arizona State University.
This paper presents the results of four lottery-type experiments that investigate the effects of incentive structures on decision-making under uncertainty. We compare choices made with and without incentives, with fixed targets, with binary targets, and with four-outcome targets that are discretized from a logistic distribution. The results of the behavioral experiments (i) validate theoretical findings of utility functions induced by fixed and uncertain targets. Further, the behavioral results show that (ii) individuals’ choices are indeed affected by incentive structures, which we quantify by several deviation measures. (iii) Defined consistency measures show that choices under uncertain targets become less consistent as the number of uncertain target outcomes increases. The results of these experiments provide insights into the effects of setting incentive structures on decision-making behavior.
The Triangular and PERT (Program Evaluation Review Technique) distribution probability density functions are commonly used in decision and risk analyses. These distributions are popular because they are each specified by only three points (two support bounds and the mode) that are believed to be easy to assess from experts or data. In this paper, we carefully analyze how close the Triangular and PERT distributions are to other distributions sharing the same support and mode and show that the errors induced by the Triangular and PERT distributions are significant. We further show that distributions that are characterized by the median tend to provide a better fit than do those that are characterized by the mode. Funding: This research was supported by the Equinor Fellows Program and the Operating System 2.0 research program developed by the Construction Industry Institute.
Information value has been proposed and used as a probabilistic sensitivity measure, the idea being that uncertain parameters having higher information value are precisely those to which an optimal decision is more sensitive. In this paper, we study the notion of information density as a graphical complement to information value analysis, one that augments an information value calculation with associated directions of information gain. We formally examine mathematical details absent from its earlier presentation that guarantee information density exists and is well posed and describe its relationship to alternate measures of information value. We present its application in the context of a realistic case study and discuss the associated insights.
Bidders’ bidding behavior is analyzed in first price sealed-bid (FPSB) auctions using an adversarial risk analysis (ARA) framework. However, using nonstrategic play and level-k thinking solution concepts, modeling is performed by assuming only two bidders. Also, the aleatory and concept uncertainties have not been yet taken into account by using an ARA framework for these auctions. In this paper, we apply an ARA approach to model bidders’ bidding behavior in a more realistic way for FPSB auctions. We assume n bidders that may have different wealth and heterogeneous risk behaviors. We use nonstrategic play and level-k thinking solution concepts, and we take into account aleatory uncertainty in addition to epistemic uncertainty. Finally, concept uncertainty is taken into account to find ARA solutions for these auctions. We also provide numerical examples to illustrate our methodology.
Cybersecurity planning supports the selection of and implementation of security controls in resource-constrained settings to manage risk. Doing so requires considering adaptive adversaries with different levels of strategic sophistication in modeling efforts to support risk management. However, most models in the literature only consider rational or nonstrategic adversaries. Therefore, we study how to inform defensive decision making to mitigate the risk from boundedly rational players, with a particular focus on making integrated, interdependent planning decisions. To achieve this goal, we introduce a modeling framework for selecting a portfolio of security mitigations that interdict adversarial attack plans that uses a structured approach for risk analysis. Our approach adapts adversarial risk analysis and cognitive hierarchy theory to consider a maximum-reliability path interdiction problem with a single defender and multiple attackers who have different goals and levels of strategic sophistication. Instead of enumerating all possible attacks and defenses, we introduce a solution technique based on integer programming and approximation algorithms to iteratively solve the defender’s and attackers’ problems. A case study illustrates the proposed models and provides insights into defensive planning. Funding: A. Peper and L. A. Albert were supported in part by the National Science Foundation [Grant 2000986].
