Appreciation to Referees, 2021

Vicki Bier, the Editor-in-Chief of Decision Analysis, would like to thank the referees who generously provide expert counsel and guidance on a voluntary basis. Without them, the journal could not function. The following list acknowledges those individuals who acted as referees for papers considered during calendar year 2021.

A Characterization of Lexicographic Preferences

This paper characterizes lexicographic preferences over alternatives that are identified by a finite number of attributes. Our characterization is based on two key concepts: a weaker notion of continuity called “mild continuity” (strict preference order between any two alternatives that are different with respect to every attribute is preserved around their small neighborhoods) and an “unhappy set” (any alternative outside such a set is preferred to all alternatives inside). Three key aspects of our characterization are as follows: (i) we use continuity arguments; (ii) we use the stepwise approach of looking at two attributes at a time; and (iii) in contrast with the previous literature, we do not impose noncompensation on the preference and consider an alternative weaker condition.

D Minus Months: Strategic Planning for Weather-Sensitive Decisions

General Eisenhower’s decisions to postpone and, one day later, to launch the “D-Day” invasion of Normandy are a gripping illustration of sequential decisions under uncertainty, suitable for any introductory decision analysis class. They’re also the archetypal example of weather-sensitive decision making using a forecast. This paper develops a framework for analyzing weather-sensitive decisions with a focus on the less-familiar strategic decisions that determine how forecasts are produced and what operational alternatives are available so that decision makers can extract value from forecasts. We tell the story of the decisions made in the months before D-Day regarding how to set up the forecasting process and the myriad decisions implicating nation-level resources that prepared Allied forces not just to invade, but to hold open that decision until the last possible hour so that Eisenhower and his staff could use the critical forecasts. Finally, we overview the current state of the weather-forecasting enterprise, the current challenges of interest to decision analysts, and what this means for decision analysts seeking opportunities to help the weather enterprise improve forecasts and to help operational decision makers extract more value from modern weather forecasts.

Modeling Ethical and Operational Preferences in Automated Driving Systems

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.

Cutoff Threshold Decisions for Classification Algorithms with Risk Aversion

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.

Bundling Decisions for Selling Multiple Items in Online Auctions

Fueled by the widespread use of the internet, more and more ordinary people have now become merchandise sellers who sell their own possessions, such as antique collections and limited souvenirs, to buyers who are interested in such goods via online auctions. This study examines the decision making related to the bidding strategies used in online auctions by both sellers and buyers. When selling goods for which there is a limited supply, sellers consider whether to sell the single homogenous items in multiple, simultaneous auctions or all the items in a single auction. Moreover, when selling heterogeneous but associated goods, sellers may decide to bundle the items for sale or not with an aim of increasing the potential buyers’ willingness to make a purchase. We investigate the effects that various factors related to the bidding strategies used in online auctions, such as the base price and duration of the auction determined by the seller and the bidding price decided by the buyer, have on the seller’s profit, and the utilities of both parties are considered to derive the equilibrium solutions. This study contributes to the literature by proposing an online auction framework that focuses more on individual sellers selling a limited quantity of items with an aim to establish a favorable online auction for both sellers and buyers and earn more profits for sellers. The results show that the base prices and direct purchase prices should be unestablished to achieve the most attractive characteristics of online auctions, which would encourage more buyers to freely place bids. As a result, the bidding items would have more chances to be eventually obtained by the buyer who places the highest bid, which, thus, maximizes the seller’s profit.

Preference–Approval Structures in Group Decision Making: Axiomatic Distance and Aggregation

Preference–approval structure combines the preference information of both ranking and approval, which extends the ordinal preference model by incorporating two categories of choice alternatives, that is, acceptable (good) and unacceptable (bad), in the preference modeling process. In this study, we present some axioms that imply the existence of a unique distance function of preference–approval structures. Based on theoretical analysis and simulation experiments, we further study a preferences aggregation model in the group decision-making context based on the proposed axiomatic distance function. In this model, the group preference is defined as a preference–approval structure that minimizes the sum of its distances to all preference–approval structures of individuals in the group under consideration. Particularly, we show that the group preference defined by the axiomatic distance–based aggregation model has close relationships with the simple majority rule and Cook and Seiford’s ranking.