Search Results (98)

This paper examines manipulation games through the lens of Machiavellianism, a psychological theory. It analyzes manipulation dynamics using principles like hierarchical perspectives, exploitation tactics, and the absence of conventional morals to interpret interpersonal interactions. Manipulators intersperse unethical behavior within their typical conduct, deploying deceptive tactics before resuming a baseline demeanor. The proposed solution leverages Lyapunov theory to establish and maintain Stackelberg equilibria. A Lyapunov-like function supports each asymptotically stable equilibrium, ensuring convergence to a Nash/Lyapunov equilibrium if it exists, inherently favoring no-regret strategies. The existence of an optimal solution is demonstrated via the Weierstrass theorem. The game is modeled as a three-level Stackelberg framework based on Markov chains. At the highest level, manipulators devise strategies that may not sway middle-level manipulated players, who counter with best-reply strategies mirroring the manipulators’ moves. Lower-level manipulators adjust their strategies in response to the manipulated players to sustain the manipulation process. This integration of stability analysis and strategic decision-making provides a robust framework for understanding and addressing manipulation in interpersonal contexts. A numerical example focusing on the oil market and its regulations highlights the findings of this work. Full article

The multi-armed bandit (MAB) problem is a foundational model for sequential decision-making under uncertainty. While MAB has proven valuable in applications such as clinical trials and online advertising, traditional formulations have limitations; specifically, they struggle to handle three key real-world scenarios: (1) when decisions must follow a hierarchical structure (as in autonomous systems where high-level strategy guides low-level actions); (2) when there are constraints at multiple levels of decision-making (such as both system-wide and component-level resource limits); and (3) when available actions depend on previous choices or context. To address these challenges, we introduce the hierarchical constrained bandits (HCB) framework, which extends contextual bandits to incorporate both hierarchical decisions and multilevel constraints. We propose the HC-UCB (hierarchical constrained upper confidence bound) algorithm to solve the HCB problem. The algorithm uses confidence bounds within a hierarchical setting to balance exploration and exploitation while respecting constraints at all levels. Our theoretical analysis establishes that HC-UCB achieves sublinear regret, guarantees constraint satisfaction at all hierarchical levels, and is near-optimal in terms of achievable performance. Simple experimental results demonstrate the algorithm’s effectiveness in balancing reward maximization with constraint satisfaction. Full article

Background: Conventional pelvic exenteration (PE) comprises the removal of all or most central pelvic organs and is established in clinical practise. Previously, tumours involving bone or lateral sidewall structures were deemed inoperable due to associated morbidity, mortality, and poor oncological outcomes. Recently however high-complexity PE is increasingly described and is defined as encompassing conventional PE with the additional resection of bone or pelvic sidewall structures. This observational cohort study aimed to assess surgical outcomes, health-related quality of life (HrQoL), decision regret, and costs of high-complexity PE for more advanced tumours not treatable with conventional PE. Methods: High-complexity PE data were retrieved from a prospectively maintained quaternary database. The primary outcome was overall survival. Secondary outcomes were perioperative mortality, disease control, major morbidity, HrQoL, and health resource use. For cost–utility analysis, a no-PE group was extrapolated from the literature. Results: In total, 319 cases were included, with 64 conventional and 255 high-complexity PE, and the overall survival was equivalent, with medians of 10.5 and 9.8 years (p = 0.52), respectively. Local control (p = 0.30); 90-day mortality (0.0% vs. 1.2%, p = 1.00); R0-resection rate (87% vs. 83%, p = 0.08); 12-month HrQoL (p = 0.51); and decision regret (p = 0.90) were comparable. High-complexity PE significantly increased overall major morbidity (16% vs. 31%, p = 0.02); and perioperative costs (GBP 37,271 vs. GBP 45,733, p < 0.001). When modelled against no surgery, both groups appeared cost-effective with incremental cost-effectiveness ratios of GBP 2446 and GBP 5061. Conclusions: High-complexity PE is safe and feasible, offering comparable survival outcomes and HrQoL to conventional PE, but with greater morbidity and resource use. Despite this, it appears cost-effective when compared to no surgery and palliation. Full article

Fuzzy semantics have a wide range of applications in life, and especially when expressing people’s evaluation information, it is more specific. As people increasingly prefer to express their personal opinions through media platforms, the opinions of the general public have become an indispensable reference. However, information asymmetry can have a significant impact on the rationality of decision-making. Based on the above considerations, this paper extends bidirectional projection to probabilistic linguistic term sets to preserve the completeness of information as much as possible. The large-scale group decision-making problem under the probabilistic linguistic environment is extended to limited interval values, and a new group decision-making method named IVBPRT-ELECTRE III algorithm (ELECTRE III based on bidirectional projection and regret theory under limited interval-valued probabilistic linguistic term set) is proposed. The method is an extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method based on limited interval-valued probabilistic linguistic term set (l-IVPLTS) bidirectional projection by regret theory approach. Firstly, this involves mining the online text comment information on social media about an emergency and considering the effect of the number of fans, determining the attributes and their initial weights for judging the strengths and weaknesses of the emergency management alternative using the TF-IDF and the $\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{d}2\mathrm{v}\mathrm{e}\mathrm{c}$ technology, and using the entropy value to adjust the initial weight of attributes, not only considering the real opinions of the public, but also combining with the views of experts, making the decision-making alternative selection more scientific and reasonable. Secondly, this paper fills the gap of bidirectional projection under l-IVPLTS environment; then, combining l-IVPLTS bidirectional projection and regret theory to determine the objective weights of experts, combines the differences in individual expertise of experts to obtain the comprehensive weights of experts, and uses the extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method to rank the alternatives. Finally, the feasibility and validity of the provided method is verified through the Yanjiao explosion incident as a case. Full article

The rapid growth of the digital economy has significantly enhanced the convenience of information transmission while reducing its costs. As a result, the participation in social networks (SNs) has surged, intensifying the mutual influence among network participants. To support objective decision-making and gather public opinions within SNs, the research on the consensus-reaching process (CRP) has become increasingly important. However, CRP faces three key challenges: first, as the number of decision-makers (DMs) increases, the efficiency of reaching consensus declines; second, minority opinions and non-cooperative behaviors affect decision outcomes; and third, the relationships among DMs complicate opinion adjustments. To address these challenges, this paper introduces an enhanced CRP mechanism. Initially, the hippopotamus optimization algorithm (HOA) is applied to update the initial community division in Leiden clustering, which accelerates the clustering process, collectively referred to as HOAL. Subsequently, a two-stage opinion adjustment method is proposed, combining minority opinion handling (MOH), non-cooperative behavior management, and dual-fine tuning (DFT) management, collectively referred to as DFT-MOH. Moreover, trust relationships between DMs are directly integrated into both the clustering and opinion management processes, resulting in the HOAL-DFT-MOH framework. The proposed method proceeds by three main steps: (1) First, the HOAL clusters DMs. (2) Then, in the initial CRP stage, DFT manages subgroup opinions with a weighted average to synthesize subgroup perspectives; and in the second stage, MOH addresses minority opinions, a non-cooperative mechanism manages uncooperative behaviors, and DFT is used when negative behaviors are absent. (3) Third, the prospect-regret theory is applied to rank decision alternatives. Finally, the approach is applied to case analyses across three different scenarios, while comparative experiments with other clustering and CRP methods highlight its superior performance. Full article

Decision makers (DMs) are often viewed as autonomous in the majority of multicriteria group decision making (MCGDM) situations, and their psychological behaviors are seldom taken into account. Once more, we are unable to prevent both positive and negative flows of varying alternative preferences due to the nature of attributes or criteria in complicated decision-making problems. However, DMs’ perspectives are likely to affect one another in complicated MCGDM issues, and they frequently use subjective limited rationality while making decisions. The multicriteria quantum decision theory-based group decision making integrating the TODIM-PROMETHEE II strategy under linguistic Z-numbers (LZNs) is designed to overcome the aforementioned problems. In our established technique, the PROMETHEE II controls the positive and negative flows of distinct alternative preferences, the TODIM method manages the experts’ personal regrets over a criterion, and the quantum probability theory (QPT) addresses human cognition and behavior. Because LZNs can convey linguistic judgment and trustworthiness, we provide expert LZNs for their viewpoints in this work. We determine the criterion weights for each expert after first obtaining their respective expert weights. Second, to represent the limited rational behaviors of the DMs, the TODIM-PROMETHEE II approach is introduced. It is employed to determine each alternative’s dominance in both positive and negative flows. Third, a framework for quantum possibilistic aggregation is developed to investigate the effects of interference between the views of DMs. The views of DMs are seen in this procedure as synchronously occurring wave functions that affect the overall outcome by interfering with one another. The model’s efficacy is then assessed by a selection of renewable energy case studies, sensitive analysis, comparative analysis, and debate. Full article

VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective research fields. However, these improvements and extensions only rank the alternatives without classifying them. For this purpose, this text introduces the three-way sequential decisions method and combines it with the VIKOR method to design a three-way VIKOR method that can deal with both ranking and classification. By using the final negative ideal solution (NIS) and the final positive ideal solution (PIS) for all alternatives, the individual regret value and group utility value of each alternative were calculated. Different three-way VIKOR models were obtained by four different combinations of individual regret value and group utility value. In the ranking process, the characteristics of VIKOR method are introduced, and the subjective preference of decision makers is considered by using individual regret, group utility, and decision index values. In the classification process, the corresponding alternatives are divided into the corresponding decision domains by sequential three-way decisions, and the risk of direct acceptance or rejection is avoided by putting the uncertain alternatives into the boundary region to delay the decision. The alternative is divided into decision domains through sequential three-way decisions, sorted according to the collation rules in the same decision domain, and the final sorting results are obtained according to the collation rules in different decision domains. Finally, the effectiveness and correctness of the proposed method are verified by a project investment example, and the results are compared and evaluated. The experimental results show that the proposed method has a significant correlation with the results of other methods, ad is effective and feasible, and is simpler and more effective in dealing with some problems. Errors caused by misclassification is reduced by sequential three-way decisions. Full article

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period. Full article

This study focused on vaccine hesitancy and decision regret about the COVID-19 vaccine among nursing students (BScN and MScN) and Registered Nurses (RNs) in Italy. The primary aim was to describe decision regret and vaccine hesitancy among these groups and to understand what influences vaccine hesitancy. Data were collected through an e-survey conducted from March to June 2024. The Decision Regret Scale and the Adult Vaccine Hesitancy Scale were employed to assess regret and hesitancy levels, assessing trust, concerns, and compliance regarding vaccination. Among the participants, 8.64% were not vaccinated. The results indicated moderate to high levels of decision regret and diverse levels of trust, concerns, and compliance with COVID-19 vaccination. Structural equation modeling revealed that decision regret significantly predicted Trust (R² = 31.3%) and Concerns (R² = 26.9%), with lower regret associated with higher trust and lower concerns about vaccine safety. The number of COVID-19 vaccine boosters was a significant predictor of Trust and Concerns, with more boosters associated with higher trust and lower concerns. MScN students exhibited higher Compliance compared to RNs (R² = 2.9%), highlighting the role of advanced education. These findings suggest that addressing decision regret and providing comprehensive vaccine information could enhance trust and compliance. Full article

Background/Objectives: Cell-free DNA (cfDNA) is a non-invasive prenatal test used to screen for common trisomies (target cfDNA) that can be expanded to assess all autosomal chromosomes (genome-wide cfDNA). As cfDNA testing gains popularity, it is crucial to examine the factors influencing the decision-making process of pregnant individuals when choosing between these two approaches. Methods: In this prospective cohort study, 190 individuals undergoing cfDNA testing for aneuploidy screening, according to the current screening protocol, were allowed to make their own choice between target and genome-wide cfDNA testing. They were asked to complete a first survey at 11–13 weeks, designed to explore their characteristics, preferences, and satisfaction with the prenatal genetic counseling session, as well as a Decisional Conflict Scale. A postnatal survey was administered three months after delivery, including the Decisional Regret Scale and two open questions. Results: 84% of participants opted for genome-wide cfDNA. However, 17% found the decision challenging, and 14% felt that the results might increase anxiety. No significant differences in participant characteristics were found when comparing decisions between genome-wide and target cfDNA. However, significant differences were observed regarding ethnicity (p = <0.001), educational level (p = 0.029), previous cfDNA experience (p = 0.004), and having sufficient information when comparing termination options (p = 0.002). After delivery, only 4% would have changed their decision. Conclusions: Individuals, regardless of their characteristics, prefer genome-wide cfDNA; however, the complexity of the results necessitates enhanced genetic education for prenatal care clinicians. Full article

In recent years, live streaming has become the mainstream way of online shopping in China. As the dominant player and performer in live streaming, streamers play a crucial role in consumers’ purchase decisions. Therefore, this study focuses on the self-presentation behavior of streamers in the context of e-commerce live streaming and explores the mechanism of its influence on consumers’ purchase intention from the perspective of persuasion knowledge. A total of 538 consumers from China participated in this anonymous survey. The results indicate that helpful and empathetic behaviors of streamers can significantly enhance consumers’ purchase intention, while derogatory, exaggerated, and flattering behaviors of streamers can significantly diminish consumers’ purchase intention. Persuasion knowledge played a mediating role and had a significant negative impact on purchase intention, while anticipated inaction regret weakened its effect on purchase intention. Full article

Background: One of several logistics contexts in which pricing decisions are made involves truckload carriers using reverse auctions to bid for prices they want for their transportation services while operating under uncertainty about factors such as their (i) operations costs and (ii) rivals’ bids. This study’s main purpose is to explore humans’ use of fast and frugal heuristics (FFHs) to navigate those uncertainties. In particular, the study clarifies the logic, theoretical underpinnings, and performance of human FFHs. Methods: The study uses behavior experiments as its core research method. Results: The study’s key findings are that humans use rational FFHs, yet, despite the rationality, human decisions yield average profits that are 35% below profits from price optimization models. The study also found that human FFHs yield very unstable outcomes: the FFH coefficient of variation in profit is twice as large as price optimization. Novel contributions inherent in these findings include (a) clarifying connections between spot market auction pricing and behavioral theories and (b) adding truckload spot markets to the literature’s contexts for measuring performance gaps between human FFHs and optimization models. Conclusions: The contributions have implications for practical purposes that include gauging spot pricing decisions made under constraints such as limited access to price optimization tools. Full article

Large-scale group decision-making (LSGDM) involves aggregating the opinions of participating decision-makers into collective opinions and selecting optimal solutions, addressing challenges such as a large number of participants, significant scale, and a low consensus. In real-world scenarios of LSGDM, various challenges are often encountered due to factors such as fuzzy uncertainties in decision information, the large size of decision groups, and the diverse backgrounds of participants. This paper introduces a dual fine-tuning-based LSGDM method using an online review. Initially, the sentiment analysis is conducted on online review data, and the identified sentiment words are graded and quantified into a fuzzy data set to understand the emotional tendencies of the text. Then, the Louvain algorithm is used to cluster the decision-makers. Meanwhile, a method combining Euclidean distances with Wasserstein distances is introduced to accurately measure data similarities and improve clustering performances. During the consensus-reaching process (CRP), a two-stage approach is employed to adjust the scores: to begin with, by refining the scores of the decision representatives via minor-scale group adjustments to generate a score matrix. Then, by identifying the scores corresponding to the minimum consensus level in the matrix for adjustment. Subsequently, the final adjusted score matrix is integrated with the prospect–regret theory to derive the comprehensive brand scores and rankings. Ultimately, the practicality and efficiency of the proposed model are demonstrated using a case study focused on the purchase of solar lamps. In summary, not only does the model effectively extract the online review data and enhance decision efficiency via clustering, but the dual fine-tuning mechanism in the model to improve consensus attainment also reduces the number of adjustment rounds and avoids multiple cycles without achieving the consensus. Full article

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method. Full article

This study investigates the distinctive modeling of regret utility when compared with common utility. I also introduce the interplay between common utility and regret utility. Using this model, I examine the differences in decision making, which encompasses issues such as risk sharing and principal–agent dilemmas. Regret utility is set so that its risk aversion shows common utility’s prudence (i.e., downside risk aversion). This paper reveals, both qualitatively and quantitively and with a concrete model, that regret utility leads to a more balanced and optimal ratio of agent payouts to outputs compared with common utility, meaning when major outputs are kept by principal, there are relatively larger agent payouts, and when major outputs are kept by the agent, there are relatively smaller agent payouts. This means that regret makes a more balanced distribution, and regret utility is more conservative (not biased). In addition, preliminary empirical research was performed in which people were asked risk preference or averseness questions, and their risk averseness was calculated by using the CRRA (Constant Relative Risk Aversion) utility function. The regret condition leads to a more conservative attitude. Furthermore, the regret model can be used in other areas, like in conservative investment portfolio optimization. Full article