Preference Uncertainty Research Articles

Decision Analysis of Game Behavior for the Fresh Food Industry: Potential Entrants, Incumbents and Consumers

This paper conducts a decision analysis of game behavior for the fresh food industry, including potential entrants, incumbents and consumers. Potential entrants are the key research object in the analysis of game behavior. The Stackelberg model, considering the uncertainty of market, consumer preference, freshness and greenness of fresh products, is used to analyze the best entry strategies for potential entrants. This paper then establishes an evolutionary game model to further analyze the most optimal entry mode of potential entrants. The combination of two game models can help potential entrants determine the optimal price, output, product freshness, greenness and sales channels.

Associations Between Telemedicine Use Barriers, Organizational Factors, and Physician Perceptions of Care Quality.

Introduction: Evaluating physician perceptions of telemedicine use and its impact on care quality among physician providers is critical to sustaining telemedicine programs, given the uncertainty of reimbursement policy, preferences, inadequate training, and technical difficulties. Physicians reported technical barriers to effectively practicing integrated medicine using telemedicine as patient volumes increased during the pandemic. The objective of this work was to examine whether perceived practice barriers and facilitators were associated with physician respondents' perceptions of telemedicine care quality compared with in-person care. Methods: This cross-sectional study analyzed the 2021 National Electronic Health Record Survey. The sample comprised 1,857 nonfederally employed physicians (weighted n = 403,013) delivering integrated patient care. Of those physicians, 1,630 (weighted n = 346,646) reported providing care through telemedicine. We reported frequencies and percentages of reported practice characteristics. Generalized ordinal logistic regressions examined relationships between practice factors and care quality for telemedicine care. Results: Most of the sample (n = 1,630) were male (66.1%), >50 years of age (66.1%), and worked in a single location (73.5%). A total of 70% of respondents reported that patients had difficulty using telemedicine platforms, and 64% reported limitations in patients' access to technology. Most respondents indicated having provided quality care to some extent (45%) and to a great extent (26%) during telemedicine visits compared to in-person visits. Associations between barriers, facilitators, and care quality perceptions were positive, underscoring resiliency in telemedicine programs among practices. Conclusion: Care modalities and the organizational, environmental, and personal facilitators drive quality perceptions among physicians. Perceived fit and usability determine perceptions of care quality for providers integrating telemedicine into their practice.

Ketamine Analgo-sedation for Mechanically Ventilated Critically Ill Adults: A Rapid Practice Guideline from the Saudi Critical Care Society and the Scandinavian Society of Anesthesiology and Intensive Care Medicine.

This Rapid Practice Guideline (RPG) aimed to provide evidence‑based recommendations for ketamine analgo-sedation (monotherapy and adjunct) versus non-ketamine sedatives or usual care in adult intensive care unit (ICU) patients on invasive mechanical ventilation (iMV) and to identify knowledge gaps for future research. The RPG panel comprised 23 multinational multidisciplinary panelists, including a patient representative. An up-to-date systematic review and meta-analysis constituted the evidence base. The Grading Recommendations, Assessment, Development, and Evaluation approach, and the evidence-to-decision framework were used to assess the certainty of evidence and to move from evidence to decision/recommendation. The panel provided input on the balance of the desirable and undesirable effects, certainty of evidence, patients' values and preferences, costs, resources, equity, feasibility, acceptability, and research priorities. Data from 17 randomized clinical trials (n=898) and 9 observational studies (n=1934) were included. There was considerable uncertainty about the desirable and undesirable effects of ketamine monotherapy for analgo-sedation. The evidence was very low certainty and downgraded for risk of bias, indirectness, and inconsistency. Uncertainty or variability in values and preferences were identified. Costs, resources, equity, and acceptability were considered varied. Adjunctive ketamine therapy had no effect on mortality (within 28 days) (relative risk [RR] 0.99; 95% confidence interval [CI] 0.76 to 1.27; low certainty), and may slightly reduce iMV duration (days) (mean difference [MD] -0.05 days; 95% CI -0.07 to -0.03; low certainty), and uncertain effect on the cumulative dose of opioids (mcg/kg/h morphine equivalent) (MD -11.6; 95% CI -20.4 to -2.7; very low certainty). Uncertain desirable effects (cumulative dose of sedatives and vasopressors) and undesirable effects (adverse event rate, delirium, arrhythmia, hepatotoxicity, hypersalivation, use of physical restraints) were also identified. A possibility of important uncertainty or variability in patient-important outcomes led to a balanced effect that favored neither the intervention nor the comparison. Cost, resources, and equity were considered varied. The RPG panel provided two conditional recommendations and suggested (1) against using ketamine as monotherapy analgo-sedation in critically ill adults on iMV when other analgo-sedatives are available; and (2) using ketamine as an adjunct to non-ketamine usual care sedatives (e.g., opioids, propofol, dexmedetomidine) or continuing with non-ketamine usual care sedatives alone. Large-scale trials should provide additional evidence.

Ketamine analgo-sedation for mechanically ventilated critically ill adults: A rapid practice guideline from the Saudi Critical Care Society and the Scandinavian Society of Anesthesiology and Intensive Care Medicine.

This Rapid Practice Guideline (RPG) aimed to provide evidence-based recommendations for ketamine analgo-sedation (monotherapy and adjunct) versus non-ketamine sedatives or usual care in adult intensive care unit (ICU) patients on invasive mechanical ventilation (iMV) and to identify knowledge gaps for future research. The RPG panel comprised 23 multinational multidisciplinary panelists, including a patient representative. An up-to-date systematic review and meta-analysis constituted the evidence base. The Grading Recommendations, Assessment, Development, and Evaluation approach, and the evidence-to-decision framework were used to assess the certainty of evidence and to move from evidence to decision/recommendation. The panel provided input on the balance of the desirable and undesirable effects, certainty of evidence, patients' values and preferences, costs, resources, equity, feasibility, acceptability, and research priorities. Data from 17 randomized clinical trials (n = 898) and nine observational studies (n = 1934) were included. There was considerable uncertainty about the desirable and undesirable effects of ketamine monotherapy for analgo-sedation. The evidence was very low certainty and downgraded for risk of bias, indirectness, and inconsistency. Uncertainty or variability in values and preferences were identified. Costs, resources, equity, and acceptability were considered varied. Adjunctive ketamine therapy had no effect on mortality (within 28 days) (relative risk [RR] 0.99; 95% confidence interval [CI] 0.76 to 1.27; low certainty), and may slightly reduce iMV duration (days) (mean difference [MD] -0.05 days; 95% CI -0.07 to -0.03; low certainty), and uncertain effect on the cumulative dose of opioids (mcg/kg/h morphine equivalent) (MD -11.6; 95% CI -20.4 to -2.7; very low certainty). Uncertain desirable effects (cumulative dose of sedatives and vasopressors) and undesirable effects (adverse event rate, delirium, arrhythmia, hepatotoxicity, hypersalivation, use of physical restraints) were also identified. A possibility of important uncertainty or variability in patient-important outcomes led to a balanced effect that favored neither the intervention nor the comparison. Cost, resources, and equity were considered varied. The RPG panel provided two conditional recommendations and suggested (1) against using ketamine as monotherapy analgo-sedation in critically ill adults on iMV when other analgo-sedatives are available; and (2) using ketamine as an adjunct to non-ketamine usual care sedatives (e.g., opioids, propofol, dexmedetomidine) or continuing with non-ketamine usual care sedatives alone. Large-scale trials should provide additional evidence.

A Mathematical Model for Green Leaves Diet Using Ranking of Tridecagonal Fuzzy Numbers

This study presents a new way of ranking transportation options using tridecagonal fuzzy numbers. This approach takes into account factors such as demand, supply, and transportation costs, and aims to provide a more optimized solution for these fuzzy environments. With the rising importance of healthy eating habits, particularly emphasizing green leafy vegetables, devising an effective dietary plan becomes imperative. Traditional approaches often overlook the inherent uncertainty and fuzziness in dietary preferences and nutritional requirements. In this context, tridecagonal fuzzy numbers offer a robust framework to represent and analyze uncertain dietary preferences. Our ultimate goal is to promote a green leaf diet that is both affordable and widely available, while also improving the overall health and well-being of the general public. This research contributes to the advancement of mathematical modelling in nutrition science, offering a valuable tool for promoting healthier dietary choices and lifestyle habits.

Measuring decision confidence

We examine whether the way individuals randomize between options captures their decision confidence. In two experiments in which subjects faced pairs of options (a lottery and a varying sure payment), we allowed subjects to choose randomization probabilities according to which they would receive each option. Separately, we obtained two measures of self-reported confidence - confidence statements and probabilistic confidence - for choosing between the two options. Consistent with the predictions of two theoretical frameworks incorporating preference uncertainty, the randomization probabilities correlated strongly with both self-reported measures (median Spearman correlations between 0.86 to 0.89) and corresponded in absolute levels to probabilistic confidence. This relationship is robust to two exogenous manipulations of decision confidence, where we varied the complexity of the lottery and subjects’ experience with the lottery.

Testing a theory of strategic multi-product choice

This paper tests a theory of strategic multi-product choice (SMPC) using empirical evidence from a large-scale choice experiment, two smaller longitudinal choice experiments, and multi-market panel data. Multi-product choice involves two stages. In the first stage, the consumer chooses a set of substitutable products, where “set” refers to both the variety of alternatives and the quantities of each. In the second stage, the set is consumed. Assuming consumers are strategic, their consumption decisions will consider both the utility of whichever product is selected for consumption and the expected utility (i.e., value) of the set that remains. SMPC therefore requires a dynamic model. We test two such dynamic models in this paper. These models are derived from a basic random utility framework with a stochastic error term for the utility of each product alternative at the moment of consumption. Despite maintaining state variables for the quantity of every alternative, these SMPC dynamic models offer both a value function and optimal consumption policy in closed form. These structures allow us to test for strategic consumption in the second stage and for optimality of the choice sets selected in the first stage. Data from the large-scale choice experiment and the smaller longitudinal choice experiments support strategic consumer decision-making, consistent with SMPC theory. SMPC theory further predicts that the amount of variety consumers select will be higher for lower consumption rates and lower for higher consumption rates. Evidence from panel data of yogurt purchases supports this prediction. While we find that consumption choices are consistent with SMPC theory, they are not consistent with alternative explanations such as variety seeking or diversification bias. Viewed in its entirety, the empirical evidence presented in this paper confirms that both the choice set selected and the way it is consumed are consistent with dynamic models of future preference uncertainty.

Weight Adjustment Framework for Self-Attention Sequential Recommendation

In recent years, sequential recommendation systems have become a hot topic in the field of recommendation system research. These systems predict future user actions or preferences by analyzing their historical interaction sequences, such as browsing history and purchase records, and then recommend items that users may be interested in. Among various sequential recommendation algorithms, those based on the Transformer model have become a focus of research due to their powerful self-attention mechanisms. However, one of the main challenges faced by sequential recommendation systems is the noise present in the input data, such as erroneous clicks and incidental browsing. This noise can disrupt the model’s accurate allocation of attention weights, thereby affecting the accuracy and personalization of the recommendation results. To address this issue, we propose a novel method named “weight adjustment framework for self-attention sequential recommendation” (WAF-SR). WAF-SR mitigates the negative impact of noise on the accuracy of the attention layer weight distribution by improving the quality of the input data. Furthermore, WAF-SR enhances the model’s understanding of user behavior by simulating the uncertainty of user preferences, allowing for a more precise distribution of attention weights during the training process. Finally, a series of experiments demonstrate the effectiveness of the WAF-SR in enhancing the performance of sequential recommendation systems.

An MCDM Approach for Evaluating Construction-Related Risks using a Combined Fuzzy Grey DEMATEL Method

There is a need for more research into prioritizing project risks based on a sound technique due to the complicated and disorganized character of this stage. The project risk management process typically begins with the identification of critical hazards. This study presents a Grey Fuzzy Decision-Making Trial and Evaluation Laboratory (FGDEMATEL) approach to prioritize potential causes of project risks within Multi-Criteria Decision-Making (MCDM). This framework organizes the numerous risks using the Risk Breakdown Structure (RBS) of the Project Management Institute (PMI). The risk information used in this analysis comes mostly from the views and choices of project experts. Grey theory, which takes language phrases for preference collections and translates them into numerical intervals, is responsible for controlling uncertainty and variance in experts' preferences. As each expert has unique skills and experiences, it evaluates the significance of their opinions using a fuzzy number system that incorporates three dimensions. In the end, the FGDEMATEL approach devised a method to rank various project risks.

Live Game Attendance and the Desire for (Un)Certain Game Outcomes in Football: Evidence From the German 2. Bundesliga

Abstract: This study aims to analyze fans’ demand for live football matches in the German 2. Bundesliga, focusing on match-outcome uncertainty. To examine the decision to attend sporting events, a fixed effects regression and the Tobit model were used to test the uncertainty of game outcomes and reference-dependent preferences with loss aversion. The estimated fans’ demand for attending live football matches is represented by the logged attendance of 2,442 matches from the 2010/2011 to the 2017/2018 seasons of the 2. Bundesliga. Our findings indicate that fans prefer certain game outcomes over uncertain ones. In other words, reference-dependent preferences with loss aversion dominate the desire for close competition. We estimated our main model using various proxies of uncertainty as robustness checks, and the results confirmed our findings.

Envy-Free House Allocation under Uncertain Preferences

Envy-freeness is one of the most important fairness concerns when allocating items. We study envy-free house allocation when agents have uncertain preferences over items and consider several well-studied preference uncertainty models. The central problem that we focus on is computing an allocation that has the highest probability of being envy-free. We show that each model leads to a distinct set of algorithmic and complexity results, including detailed results on (in-)approximability. En route, we consider two related problems of checking whether there exists an allocation that is possibly or necessarily envy-free. We give a complete picture of the computational complexity of these two problems for all the uncertainty models we consider.

Explaining uncertainty in women's fertility preferences

People's fertility preferences are often considered an important determinant of fertility. What is often neglected in studies of preferred fertility, is the uncertainty that people may have about their preferences. In this study, using data on Dutch women through the Longitudinal Internet studies for the Social Sciences (collected early 2018), we examined women's fertility preferences and asked detailed questions about the certainty of these preferences. We also examined whether women agreed with their partner on preferred family size, and to what extent partner (dis)agreement shaped uncertainty. We show that Dutch women expressed much uncertainty about their fertility preferences, with only one-third feeling strongly about their preferences. Uncertainty strongly increased when women preferred higher numbers of children, whereas already having children reduced it. Women who wanted no children were most certain about their preference. Higher preferred family sizes also led to more disagreement with the partner about these preferences, and greater partner disagreement, in turn, led to more uncertainty. These findings imply that people are more likely to downgrade their fertility preferences than to increase them, as women are more certain about their preferences for lower numbers of children and are more open to family sizes below than above their preferred choice. Partner disagreement is often resolved by not having (more) children, lowering realised fertility. Hence, these findings provide another explanation for why many people have fewer children than desired.

Planning location of parcel lockers using group Analytic Hierarchy Process in Spherical Fuzzy environment

Parcel lockers have emerged as an effective solution to address inefficiencies in last-mile delivery. However, the selection of an appropriate location plays a crucial role in ensuring optimal service and enhanced logistic performance. To address this challenge, this work aims to define the best location for parcel lockers in the capital of Ireland, Dublin by conducting a novel decision-making model. The Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) has been adopted to deal with the uncertainty in the decision maker preferences, while the Euclidean Distance-Based Aggregation Method (EDBAM) has been conducted to aggregate the decision makers preferences. The proposed integrated model is an applicable approach to elude AHP deterministic disadvantages, moreover, it is the first application of SF-AHP and EDBAM to select parcel lockers’ location. The findings, including the identification of the top three optimal locations, namely “Post office,” “On commercial area,” and “High population density area,” along with their respective weight scores, provide valuable insights for policymakers making strategic decisions regarding parcel locker placements in Dublin city.

Review-based recommendation under preference uncertainty: An asymmetric deep learning framework

Online reviews are one of the most trusted resources for inferring customer needs and understanding consumer decision-making behavior. This study attempts to integrate textual reviews and user-item ratings to improve recommendation performance. To achieve this goal, we propose a deep neural network model. Specifically, the proposed model applies a review-level aggregation strategy to learn user preferences while using an aspect-based document-level aggregation strategy to learn item representation. In this process, we introduce two attention modules at the review and aspect levels, respectively. The review-level attention is used to learn the user preferences that are most related to the target item. The aspect-level attention attempts to learn the item's aspect features that users are most concerned about. In addition, we design a latent stochastic attention mechanism based on the probabilistic generative mechanism, to model the user preference uncertainty. For evaluation, we conduct extensive experiments on several real-world datasets. Using several state-of-the-art methods as comparisons, we find that the proposed model can significantly improve rating predictive power in the context of the recommendation system. Based on ablation experiments, we find that the enhanced predictive power benefits from the preference uncertainty and the attention mechanism. From the qualitative analysis, we suggest that the proposed model can yield many interpretable results.

An Improved Similarity-based Fuzzy Group Decision Making Model through Preference Transformation and K-Means Clustering Algorithm

Group decision making plays a crucial role in organizational and community contexts, facilitating the exchange of expert opinions to arrive at effective decisions. The concept of preference, reflecting an individual's subjective evaluation of criteria or alternatives, forms a foundational element in this process. This study focuses on transforming non-fuzzy preferences, such as preference ordering and utility functions, into fuzzy preference relations (FPR) to address the uncertainty and uniformity inherent in expert preferences. To further enhance decision-making, we assess and visualize the similarity among the experts' uniform preferences. Integrating the K-means clustering algorithm into the fuzzy group decision making model allows for the predetermination of an appropriate number of groups based on the available alternatives. By aggregating individual preferences, we present a final ranking of alternatives. The enhanced methodology, as demonstrated through comparative analysis, showcases its ability to yield positive benefits when applied to decision-making applications.

On the value of multi-echelon inventory management strategies for perishable items with on-/off-line channels

The progress of digitization makes the integration of online and offline sales channels increasingly necessary for retailers. Multichannel and omnichannel multi-echelon networks are gradually more common in responding to customer demands, but their complexity makes the optimization of replenishment and item allocation policies among different channels challenging, especially if products have a short shelf life, as in the case of food retailers, where customer behavior (e.g., first-/last- in-first-out selection) also plays a role. It is not always possible to solve this problem exactly and heuristics are required. We propose a dynamic model and jointly optimize allocation and replenishment policies in the case of perishable goods with stochastic demand, uncertainty in customer selection preferences, and fixed lead times. We study complexity and structure of optimal policies. Furthermore, we explore several intuitive generalizations of base-stock policies over multi-echelon networks, analyzing the effect that potential correlations and imbalances in demand volumes across channels generate on the heuristics and identifying the pros and cons of such solutions. Results show that inventory-pooling effects in multi-echelon models for perishable items are often combined with the allocation of fresher products to offline channels. Generalizations of the well-known constant-order or base-stock policies can be a viable solution that generates benefits and increases system flexibility. They advantageously leverage negative channel correlation, but in the case of unbalanced demand distributions, increased offline demand can impoverish the quality of some heuristics.

Multi-criteria group decision-making method based on total distance and BWM with spatial information in Hesitant Pythagorean fuzzy environment

The processing method of fuzzy information is a critical element in multi-criteria group decision-making (MCGDM). The hesitant Pythagorean fuzzy set (HPFS) has a higher capacity in express the uncertainty of human inherent preference. A composite weighted mathematical programming model with prospect theory and best-worst method (BWM) is proposed to solve the uncertainty of criterion weight acquisition and decision-makers (DMs) psychological behavior under the HPF environment. The decision-making process is as follows: Firstly, a novel spatial distance measurement method is designed which considers the extension space of HPFSs space by five parameters under the HPF environment. Secondly, the optimal criteria weights model minimizes the total distance between the alternatives and the HPF positive ideal solution (HPFPIS), as well as minimizes the consistency ratio of BWM. Thirdly, we propose the prospect decision matrix by the prospect theory and optimal weights, then use the ordered weighted average operator under the normal distribution to calculate the weight of DMs and rank the decision alternatives. Finally, an example is illustrated here, sensitivity and reliability, and comparative analysis are conducted to verify the effectiveness of the proposed method.

Dominance Effects in the Wild

Abstract In real-world marketplaces, one may encounter an alternative that is inferior to another one in the assortment. While the presence of such seemingly irrelevant inferior alternatives should ostensibly have no influence on consumers’ decisions, an extensive literature using stylized lab experiments has found that, surprisingly, their presence matters. Specifically, the dominance effect suggests that the presence of an inferior alternative shifts consumer’s preferences toward the alternative made to be superior. However, null results in some recent lab studies and a lack of real-world evidence call into question whether, when, and how the effect exists. In this work, we find clear evidence that dominance matters in the wild. We also identify an important moderator for the dominance effect—preference uncertainty—and test it in both a real-world marketplace for digital freelance services and a lab experiment. Further, we find evidence for additional moderators that help explain how the effect works, including the count of dominated alternatives and the magnitude of dominance, consistent with a perceptual mechanism. This work is the first to use consequential field data to shed light on when and why the dominance effects occur, with implications for marketers, choice architects, user interface designers, and policymakers.

Sequential versus Simultaneous: The Variety-Seeking in Gift Giving

Gift giving is an exchange that occurs since the beginning of civilization. It has a social, economic, and communication role (Belk, 1976). It is a complex act that involves many agents and factors, like gift giver and gift receiver preferences, the relationship between them, and the context where the gift is exchanged. The critical point in gift giving is to elevate receiver preferences without losing identity and keep a focus on the relationship (Liu, Dallas, & Fitzsimons, 2019). Nowadays, gift giving represents an essential part of Brazilian economics and culture (CNDL, 2019; 2020). This paper has an objective to understand how the offer framing effect, which suggests that the way a product is offered to a decision-maker and the number of choices this person performs, may impact the decision made (Mittelman, Andrade, Chattopadhyap, & Brendl, 2014), and how the social closeness between givers and receivers affects variety-seeking in gift giving situations. Through an experimental study, this paper found that social closeness influences variety-seeking, when givers and receivers are closer (distant) the choices are less (more) varied. It is also found that the relation between social closeness and variety-seeking depends on the way the choice is done (sequential or simultaneous) since this relation only happened when the choice was not sequential. Furthermore, preference uncertainty is a possible explanation for the main relation.

Assessing whether decisions are more sensitive to preference or prediction uncertainty with a value of information approach

In many decisions, we are not only uncertain about the predicted outcomes of decision alternatives but also about stakeholder preferences regarding these outcomes. Further information collection may reduce uncertainties, but is costly. We present and apply a framework to identify the most decisive uncertainties and prioritize data collection efforts based on value of information (VoI) sensitivity analysis. Preference uncertainty is usually not explicitly considered in VoI analysis or in standard utility theory. Based on the expected expected utility (EEU) concept, we consider uncertain predictions and preferences jointly in decisions and subsequent VoI analysis. We focus on the expected value of partially perfect information (EVPPI) and adapt a fast, given-data algorithm for estimating this metric. The framework is motivated by complex environmental decision problems and we apply it to a hypothetical multi-criteria decision regarding coral reef management with conflicting stakeholder perspectives. The results show that better understanding of stakeholder positions can be as relevant as improving system understanding. For one perspective, preference model parameters had the highest EVPPI, while for another predictive uncertainties of the reef system attributes were more relevant. For two perspectives, the decision was largely insensitive. By considering predictive and preferential uncertainty on an equal footing in VoI analysis, we open up possibilities to design data collection for decision support processes more efficiently.