Cognitive biases significantly influence decision-making by distorting how individuals perceive and evaluate outcomes over time. This systematic review synthesizes research from various domains, including behavioral economics, psychology, and health, to explore six time-related biases affecting intertemporal judgments and trade-offs. We analyze the underlying mechanisms of each bias, map their interrelationships, and uncover their impacts on both individual choices and societal decisions. Drawing upon empirical evidence, we propose tailored strategies to mitigate the adverse effects of these biases. Our findings contribute to the literature not only by enhancing the understanding of time-related cognitive biases but also by providing practical insights for improving decision-making and policy design aimed at promoting long-term well-being. The review concludes by highlighting critical gaps in the literature and outlining a future research agenda to further investigate and address biases in intertemporal decision-making.

Best-Worst Method (BWM) is a structured approach for eliciting criteria weights in multi-criteria decision-making. This review paper revisits the original BWM formulation and procedural logic, and discusses behavioral motivations for dual anchoring and structured elicitation. It then synthesizes major methodological developments, covering linear and multiplicative formulations, interaction modeling extensions (nonadditive models), Bayesian formulations, group-aggregation models, tradeoff-based elicitation, parsimonious and disaggregation-based variants, sorting extensions, and fuzzy and belief-based treatments of imprecision and epistemic uncertainty. Because the reliability of inferred weights depends on judgment quality, the paper consolidates and compares consistency checking approaches and discusses their implications for practice. Representative application domains are reviewed to illustrate how BWM is deployed in practice. Finally, future research directions are outlined, emphasizing behavioral validation, integration into complete decision pipelines, scalable elicitation support, and cautious human–AI co-production that preserves problem-specific preference meaning. These directions also include transferring BWM's anchored elicitation principle to other preference elicitation approaches, including MACBETH, UTA, and conjoint analysis.

Alignment of core supply chain functions with corporate supply chain strategy is a key success factor for firms. Misalignments can lead to inefficiencies, higher costs, risks, and weaker performance. In particular, misalignment between transportation strategy and supply chain strategy can reduce responsiveness and flexibility, increase risks, and amplify environmental impacts. Although classic supply chain frameworks established the theoretical foundations of supply chain strategy, they treated transportation as a secondary issue and overlooked contextual decision factors such as product density, perishability, product life cycle, resilience, and sustainability. Consequently, a conceptual model that systematically integrates the complexity of the transportation and supply chain strategy alignment is not yet present in the literature. To address this gap, we develop a conceptual framework that introduces four transportation strategies, i.e. Cost-Oriented, Flexibility-Reliant, Modal-Control-Intensive, and Value-Enhanced, mapped against supply chain strategies across thirteen contextual criteria. Using the Best-Worst Method, we supplement the framework with an illustrative survey study of Global Fortune 500 companies. The results indicate that no single transportation strategy is right across all supply chains, but tailor-made service bundles can lead to alignment. Our study extends alignment theory to transportation and offers practical insights for managers of shipper firms and logistics service providers.

Eliciting the weights of attributes is a key step in multi-attribute decision-making methods. The weights usually represent the relative importance of the attributes or the tradeoffs among them in forming a decision. Various weight elicitation methods exist, each based on different assumptions and procedures. Still, many of these methods do not explicitly account for the potential influence of cognitive biases in their design. This study examines the anchoring bias, a well-known cognitive bias, in the weight elicitation step (the Tradeoff procedure) of multi-attribute value theory (MAVT). We developed the following three hypotheses: (i) Using the most important (best) attribute to construct the indifference pairs in the Tradeoff procedure leads to higher weights for the best and worst attributes and lower weights for the other attributes, (ii) using the least important (worst) attribute to construct the indifference pairs in the Tradeoff procedure leads to lower weights for the best and worst attributes and higher weights for the other attributes, and (iii) using both best and worst attributes to construct the indifference pairs (i.e., the best–worst tradeoff: BWT) mitigates the anchoring bias. To test the hypotheses, we conducted an experiment by designing a questionnaire based on MAVT and collected data from 336 participants for a decision problem. The findings indicate that the anchoring bias has a significant impact on the Tradeoff procedure and that the BWT is effective in mitigating this bias.

This study examines the power dynamics and their impact on involvement levels and compromise complexities in B2B joint decision-making within Dutch high-tech firms. Using a five-phase framework, it investigates the asymmetrical participation of co-owning decision-makers in dyadic buyer-supplier relationships and presents a conceptual model to explain how different power sources influence business interactions. A qualitative multiple case study involving Dutch high-tech firms was conducted. The research reveals that power bases significantly determine both involvement levels and the complexities of compromises, showing a positive correlation between involvement and compromise complexity. This study provides guidance for B2B stakeholders, offering diagnostic tools for understanding power structures and strategies to enhance collaborative decision-making. Additionally, the paper recommends governance frameworks and role delineations to improve participation levels, especially for companies with less power. This scholarly work enriches the literature by clarifying the relationship between power bases, involvement levels, and compromise complexities, and extends the application of social power theory to high-tech B2B contexts.

Anchoring bias refers to the human tendency to rely heavily on an initial piece of information when making judgments. This bias has significant implications for decision analysis methods that rely on human judgments. This study examines the influence of anchoring bias in the value function elicitation step of the multiattribute value theory, specifically within the midvalue splitting procedure. We hypothesize that the starting point provided by the analyst during elicitation creates a bias in decision makers’ judgments, leading to distorted value functions and ultimately affecting decision outcomes. We also hypothesize that counter-anchoring and avoiding the use of anchors mitigate the effect of anchoring bias. To test the hypotheses, we designed an experiment and collected data from 320 subjects. The findings show that the starting point in the midvalue splitting procedure could change the attribute-specific value functions and, consequently, the overall value of the alternatives. Additionally, two debiasing strategies, counter-anchoring and avoiding the use of anchors, were found to be effective in reducing the effect of anchoring bias. The implications of this study can extend to other structured value function elicitation methods.

We analyze the stability of a game-theoretic model of a polymorphic eco-evolutionary system in the presence of human intervention. The goal is to understand how the intensity of this human intervention and competition within the system impact its stability, with cancer treatment as a case study. In this case study, the physician applies anti-cancer treatment, while cancer, consisting of treatment-sensitive and treatment-resistant cancer cells, responds by evolving more or less treatment-induced resistance, according to Darwinian evolution. We analyze how the existence and stability of the cancer eco-evolutionary equilibria depend on the treatment dose and rate of competition between cancer cells of the two different types. We also identify initial conditions for which the resistance grows unbounded. In addition, we adopt the level-set method to find viscosity solutions of the corresponding Hamilton–Jacobi equation to estimate the basins of attraction of the found eco-evolutionary equilibria and simulate typical eco-evolutionary dynamics of cancer within and outside these estimated basins. While we illustrate our results on the cancer treatment case study, they can be generalized to any situation where a human aims at containing, eradicating, or saving Darwinian systems, such as in managing antimicrobial resistance, fisheries management, and pest management. The obtained results help our understanding of the impact of human interventions and intraspecific competition on the possibility of containing, eradicating, or saving evolving species. This will help us with our ability to control such systems.

Evolutionary cancer therapy (ECT) delays or forestalls the progression of metastatic cancer by adjusting treatment based on individual patient and disease characteristics. Clinical implementation of ECT can improve patient outcomes but faces technical and cultural challenges. To address those, we propose a systems approach incorporating systems modeling, problem structuring, and stakeholder engagement. This approach identifies and addresses barriers to implementation, ensuring the feasibility of ECT in clinical practice and enabling better metastatic cancer care.

Despite its recent introduction in literature, the Best–Worst Method (BWM) is among the most well-known and applied methods in Multicriteria Decision-Making. The method can be used to elicit the relative importance (weight) of the criteria as well as to get the priorities of the alternatives on the criteria at hand. In this paper, we will present an extension of the method, namely, the parsimonious Best–Worst-Method (P-BWM) permitting to apply the BWM to get the priorities of the alternatives in case they are in a large number. At first, the Decision-Maker (DM) is asked to give a rating to the alternatives under consideration; after, the classical BWM is applied to a set of reference alternatives to get their priorities used to compute, then, the priorities of all the alternatives under consideration. We propose also a procedure to select reference alternatives, possibly in cooperation with the DM, providing a well-distributed coverage of the rating range. The new proposal requires the DM a fewer number of pairwise comparisons than the original BWM. Another contribution of the paper is related to the comparison between BWM, P-BWM, the Analytic Hierarchy Process (AHP), and the parsimonious AHP in terms of the amount of preference information provided by the DM in each method to apply it. In addition to the standard approach, we propose one alternative way of inferring the priority vectors in BWM and P-BWM based on the barycenter of the space of alternatives priorities compatible with the preferences given by the DM. Finally, an experiment with university students has been conducted to test the new proposal. Results of the experiments show that P-BWM performs better than BWM in terms of capability to represent the DM's preferences and the difference between the results of the two methods is significant from the statistical point of view. The new proposal will permit to use the potentialities of the BWM to get the alternatives’ priorities in real-world decision-making problems where a large number of alternatives must be taken into account.

Consumer goods supply chains are intensifying their efforts to develop and offer green products, in order to seize new business opportunities and improve profitability. A specific type of green products concerns marginal and development cost-intensive green products (MDIGPs), like electric vehicles. As greening these products affects both marginal and development costs, their design presents special challenges, especially within the context of uncertain demand. This paper formulates the joint product pricing-ordering-greening decision problem in the supply chains of MDIGPs and examines the impact of demand uncertainty. A sequential game-theoretic framework is developed, providing analytical expressions of the optimal solutions for the stochastic model. A bargaining game on the wholesale price between supply chain members is proposed to coordinate decisions. We compare the optimal decisions numerically in the stochastic and deterministic cases and find that, although demand uncertainty creates inefficiency in the green supply chain, it might positively impact product greenness and prices. Given the impact of the unit-variable greening costs of MDIGPs, we are able to identify cases where–contrary to common belief–demand uncertainty does not always lead firms to reduce greenness or increase prices.

Joint decision-making is one of the coordination mechanisms to address the inherent complexity of business-to-business (B2B) processes within a supply chain. Joint decision-making can be helpful to define shared goals and objectives, identify supply chain failures and opportunities, and consolidate supply chain success. Parties may benefit directly from a partnership's potential and synergies by collaboratively making decisions. However, specific business conditions need to be in place to enable joint decision-making. This paper investigates how companies in a dyadic relationship arrive at joint and individual supply chain decision-making structure. We examine the drivers, facilitators, and barriers of making joint as well as individual decisions within the supplier-buyer dyad and frame our arguments borrowing perspectives from resource dependency theory, transaction cost economics, collaboration theory, and social exchange theory. The paper presents a case study of Dutch high-tech companies, analysing experiences of supply chain managers via semi-structured interviews. High-tech firms often collaborate and share supply chain decisions due to the high-value capital equipment as well as a shared dependency on highly specific scarce resources. Our study provides new empirical insight into how firms cope with conflicting drivers, facilitators, and barriers in collaborations, controlling their decision-making structure. From the case study, we identify the combinations of facilitators and drivers that tend to promote the existence of joint decisions. We conclude with providing a list of suggestions for decision-makers and future research.