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 .

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.

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.

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 performance of models that submitted COVID-19 forecasts to the U.S. Centers for Disease Control and Prevention and evaluate them against a simple two-equation model that is specified using simple linear regression. We find that our simple model was comparable in 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 their support by large forecasting teams. However, our result is consistent with the body of research that suggests that simple models perform very well in a variety of settings. History: This paper has been accepted for the Decision Analysis Special Issue on Emerging Topics in Health Decision Analysis. Funding: This work was supported by Sandia National Laboratories [Cockrell School of Engineering’s Engineering Foundation Endowed Professorship No. 1].

Information resources have been shared to promote the business operations of firms. However, the connection of business information sharing interfaces between firms has increased the attack surface and created opportunities for the hacker. We examine the benefits and risks of business information sharing for firms who exert security efforts against a strategic hacker that launches attacks subjectively. We show that two kinds of security efforts, security investment and security knowledge sharing, act as strategic substitutes when the business-sharing degree is low and act as strategic complements otherwise. Besides, the strategic hacker is not always aggressive, who will give up launching attack activities when the business-sharing degree is relatively low. Moreover, as a specific characteristic in the security domain, the risk interdependency first enhances and then suppresses both firms’ security investments and the hacker’s attack effort, which causes a free-riding problem for two firms. Then, two coordination mechanisms, an investment-based mechanism and liability-based mechanism, are proposed to help firms coordinate their strategies to reach socially optimal security levels. Last, we extend the main model to three cases to make our model more general. This paper provides the first evidence to assess the security risks exacerbated by business information sharing while considering a strategic hacker. Some management insights to managers for making security decisions are provided.

In the backdrop of the opioid epidemic, opioid prescribing has distinct medical and social challenges. Overprescribing contributes to the ongoing opioid epidemic, whereas underprescribing yields inadequate pain relief. Moreover, opioids have serious adverse effects including tolerance and increased sensitivity to pain, paradoxically inducing more pain. Prescribing trade-offs are recognized but not modeled in the literature. We study the prescribing decisions for chronic, acute, and persistent pain types to minimize the cumulative pain that incorporates opioid adverse effects (discomfort and dependence) and the risk of tolerance or hypersensitivity (THS) developed with opioid use. After finding closed-form solutions for each pain type, we analytically investigate the sensitivity of acute pain prescriptions and examine policies on incorporation of THS, patient handover, and adaptive treatments. Our analyses show that the role of adverse effects in prescribing decisions is as critical as that of the pain level. Interestingly, we find that the optimal prescription duration is not necessarily increasing with the recovery time. We show that not incorporating THS or information curtailment at patient handovers leads to overprescribing that can be mitigated by adaptive treatments. Last, using real-life pain and opioid use data from two sources, we estimate THS parameters and discuss the proximity of our model to clinical practice. This paper has a pain management framework that leads to tractable models. These models can potentially support balanced opioid prescribing after their validation in a clinical setting. Then, they can be helpful to policy makers in assessment of prescription policies and of the controversy around over- and underprescribing. History: This paper has been accepted for the Decision Analysis Special Issue on Emerging Topics in Health Decision Analysis. Funding: This work was supported by the National Institutes of Health [Grants K08 EB022631, R24 DA055306, R33 DA046085, and U24 NS115708]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2021.0447 .

We provide an adversarial risk analysis framework for batch acceptance problems in which a decision maker relies exclusively on the size of the batch to accept or reject its admission to a system, albeit being aware of the presence of an opponent. The adversary acts as a data-fiddler attacker perturbing the observations perceived by the decision maker through injecting faulty items and/or modifying the existing items to faulty ones. We develop optimal policies against this combined attack strategy and illustrate the methodology with a review spam example.

Cyberattacks constitute a major threat to most organizations. Beyond financial consequences, they may entail multiple impacts that need to be taken into account when making risk management decisions to allocate the required cybersecurity resources. Experts have traditionally focused on a technical perspective of the problem by considering impacts in relation with the confidentiality, integrity, and availability of information. We adopt a more comprehensive approach identifying a broader set of generic cybersecurity objectives, the corresponding set of attributes, and relevant forecasting and assessment models. These are used as basic ingredients for decision support in cybersecurity risk management.