Proof in financial economics reveals how investors (incoming buyers) in a rationally priced market perceive a higher expected utility investment when the payoff from the risky asset has higher variance. The asset’s lower market price more than compensates for its higher risk. Because better information tends to reduce payoff variance, investors can prefer that markets be less informed—that is the paradox. The obvious retort is that less information can mean ill-informed prices and investments, and consequent losses. To grow wealth, investors prefer that the market is sufficiently well informed to allow accurate parameter estimation but not so well informed that there is little remaining payoff variance and hence no attractive investment opportunity. The “ideal investment” is a bet on a fair coin, because the probability of winning is not only “known,” it is known to be 0.5, leaving the payoff from the bet with maximum possible variance.

This research considers Bayesian decision-analytic approaches toward the traversal of an uncertain graph. Namely, a traveler progresses over a graph in which rewards are gained upon a node’s first visit, and costs are incurred for every edge traversal. The traveler knows the graph’s adjacency matrix and the starting position but does not know the rewards and costs. The traveler is a Bayesian who encodes his beliefs about these values using a Gaussian process prior and who seeks to maximize his expected utility over these beliefs. Adopting a decision-analytic perspective, we develop sequential decision-making solution strategies for this coupled information-collection and network-routing problem. We show that the problem is NP-hard and derive properties of the optimal walk. These properties provide heuristics for the traveler’s problem that balance exploration and exploitation. We provide a practical case study focused on the use of unmanned aerial systems for public safety and empirically study policy performance in myriad Erdös–Rényi settings. Funding: This work was supported by the Office of Naval Research [Grant 6000012277] and the Air Force Office of Scientific Research [Grant 21RT0867].

Many practitioners consider decision analysis as a sociotechnical discipline, with probability, utility, and trade-offs as the core components of a model that enables an accountable decision maker experiencing a sense of unease about the present to explore different assumptions about the future and develop a plan about the way forward for the organization. The decision analyst acts as a process consultant, working with the decision maker and key players as a problem solver and applying any of five structural and five content ingredients of decision analysis in building a requisite model that is sufficient in form and content to resolve the problem while acting as a transitional object, which holds and contains the decision maker’s unease and anxiety about the future. Ten social skills that enable the decision analyst to serve as a process consultant are explained. Six case studies representing problem types for evaluating options, allocating resources, bargaining and negotiating, choosing and deciding, managing risk, and revising opinion demonstrate the many ways that their acting as a transitional object enables exploring the future. Funding: This work was supported by Facilitations Ltd.

This paper investigates a product distributor’s incentive to share private consumer preference information with a farming cooperative that possesses the capability for quality improvement. Although conventional view holds that in the presence of the investment spillover effect, downstream information sharing surely occurs when the upstream investment cost is low enough, our results show that (due to the decreasing marginal effect of quality investment) the product distributor voluntarily commits to sharing information only when the quality investment cost is in a medium range. And this ex ante sharing behavior can be induced through a precontracted side payment when the quality cost slightly deviates from the range in which voluntary sharing occurs. Given the challenge of predicting consumer preference for perennial crops, we examine the product distributor’s incentives for ordering and ex post sharing under upfront quality improvement, finding that the distributor can partially disclose the acquired information even facing a potential mismatch between supply and demand. An equilibrium ordering scheme is proposed whereby the distributor prearranges transportation capacity. Interestingly, under delayed information acquisition, partially sharing information is more effective in motivating the farming cooperative to invest more at the outset than not sharing at all. Conflict of Interest Statement: The authors declared that they have no conflicts of interest to this work. Funding: This work was supported by Natural Science Foundation of Shandong Province [ZR2021MA079, ZR2021MA088]; National Natural Science Foundation of China [11271175].

This study distinguishes enterprises into high- and low-type categories based on enterprise value and cost efficiency, examining their strategic behaviors in three investment timing games: move simultaneously, the high-type enterprise moves first, and the low-type enterprise moves first. By comparing the three games, we find that both types of enterprises would always exert more effort in the sequential game than in the simultaneous game, and the later-move advantage makes both types of enterprises prefer to become the follower in the game. We also find that the enhanced cost efficiency advantage or enterprise value gap possessed by the high-type enterprise would widen the effort gap between the two types of enterprises, and enhance the low-type enterprise’s incentive to be the follower. Moreover, the existence of system vulnerability not only causes both types of enterprises to reduce their security effort that generates free-riding behaviors but also can first discourage and then encourage enterprises from moving in advance. We further propose a liability-based mechanism to tackle the free-riding problem. We reveal an exact optimal liability coefficient, whether in the simultaneous or sequential game and find that the high-type enterprise should undertake more compensation when its dominant position becomes more obvious and the low-type enterprise could therefore undertake decreased liability. Last, we extend the model to multiple enterprises and show that the results are robust. Funding: This work was supported by the Shanghai Science and Technology Development Foundation [Grant 24692107700]; the Humanities and Social Science Fund of Ministry of Education of China [Grant 23YJA630100]; Fundamental Research Funds for the Central Universities; DHU Distinguished Young Professor Program; and the Shanghai Social Science Foundation [Grant 2022ZGL009]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2024.0190 .

Frugal materialism is a tendency of consumer demand to become more elastic in product durability in response to a tightening budget constraint. This paper proposes a value function-based model of frugal materialism and establishes a close theoretical link between frugal materialism and the slope of value satiation as defined in the decision analytic literature; for both their absolute and relative versions, frugal materialism and increasing value satiation are nearly equivalent to each other. Only some shapes of the value function are thus compatible with frugal materialism, and the shape restrictions involve concepts familiar from models of decision making under risk, even though the proposed model of frugal materialism does not involve any risk. Under the additional assumption of relative risk neutrality, the demand for risky assets of a frugally materialistic consumer should exhibit well-known behavioral patterns associated with increasing risk aversion, and there is only a narrow possibility for frugal materialism to coexist with precautionary saving behavior.

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].

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.