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Industry 5.0 builds upon the advancements of Industry 4.0 by shifting its focus from pure automation to a more human-centric approach. This research aims to identify and model the key enablers contributing to the transitional shift from Industry 4.0 to Industry 5.0. The paper introduces a novel theory-driven framework, utilizing diverse methodologies such as Exploratory Factor Analysis (EFA), Fuzzy Set Theory (FST), Evidential Reasoning Algorithm (ERA), Expected Utility Theory (EUT), and Risk Profiling. The framework concentrates on evaluating the significance of these enablers during the transition and prioritizing them from multiple perspectives. The proposed model generates a continuum of scenarios illustrating the relative importance of resilience, sustainability, and the human-centric dimensions of Industry 5.0. The research identifies several risks associated with these enablers. A comprehensive risk profiling analysis is conducted to classify the risks according to predefined parameters. It is observed that, as the importance perspective changes, some risks exhibit robustness in their severity, while others are sensitive to slight variations in parameters. This study utilizes risk profiling based on dynamic changes in the perception of risk assessment. The risk profiling approach aids decision-makers in effectively planning actions to address the various risks associated with the transition from Industry 4.0 to Industry 5.0. [ABSTRACT FROM AUTHOR]

The evolution of cancer cells parallels species evolution in numerous ways. Variations arise and spread under the pressure of competition between cancer cells. Current investigations of tumor evolution echo earlier debates between biologists. These include the role of non-Darwinian mechanisms, the contribution of neutral evolution, and life history dynamics. The trade-off between proliferation and metastasis is the most well-studied application of life history theory to cancer evolution. This article briefly introduces some parallels between cancer and species evolution, focusing on the life history of evolving tumors. Next, we review evidence from simulation and experimental studies supporting task specialization and trade-offs in cancer. We also cover recent work on inferring tumor tasks from data. We then turn to the implications of multi-tasking and the utility of the theory in explaining critical aspects of tumor heterogeneity. Finally, we discuss some of the criticism and future directions of this research topic. [ABSTRACT FROM AUTHOR]

Mathematical modeling and numerical simulation have a considerable presence in the vast universe of engineering disciplines, given their usefulness in explaining, comprehending, and simulating phenomena and processes with which engineers are in contact in their daily creative and problem‐solving work. For this reason, engineering study programs have at least one course dedicated to dealing with the mathematical modeling of dynamic systems as an essential complement to subsequent courses such as automatic control, structural dynamics, and mechanical vibrations. Nowadays, many technological tools illustrate the applications of mathematical modeling interactively through experiments that offer an incomparable motivation to the students to corroborate with real‐world examples, the utility and veracity of the theory presented to them in the classroom and that in many occasions seems lacking utility and direct relation with the world in which they develop. Based on those mentioned above, this paper presents an example of applying the Laplace transform in modeling physical systems, using a second‐order circuit attached to an Arduino Due board in conjunction with the Jupyter Notebook environment. The numerical and experimental results can be obtained through three optional kernels: Python, Octave, or MATLAB®. For educational purposes, the resulting computer application was presented to undergraduate students of Mechatronics Engineering as an illustrative complement to two courses entitled Signals and Systems Analysis, part of the second semester, and Mathematical Modeling, part of the fifth semester. [ABSTRACT FROM AUTHOR]

Individual evacuation decision making has been studied for multiple decades mainly using theory-based approaches, such as random utility theory. This study aims to bridge the research gap that no studies have adopted data-driven approaches in modeling the compliance of hurricane evacuees with government-issued evacuation orders using survey data. To achieve this, we conducted a survey in two coastal metropolitan regions of Florida (Jacksonville and Tampa) during the 2020 Atlantic hurricane season. After preprocessing survey data, we employed three supervised learning algorithms with different complexities, namely, multinomial logistic regression, random forest, and support vector classifier, to predict evacuation decisions under various hypothetical hurricane threats. We found that the evacuation decision is mainly determined by people's perception of hurricane risk regardless of whether the government issued an order; COVID-19 risk is not a major factor in evacuation decisions but influences the destination type choice if an evacuation decision is made. Additionally, past and future evacuation destination types were found to be highly correlated. After comparing the algorithms for predicting evacuation decisions, we found that random forest can achieve satisfactory classification performance, especially for certain categories or when some categories are merged. Finally, we presented a conceptual optimization model to incorporate the data-driven modeling approach for evacuation behavior into a government-led evacuation planning framework to improve the compliance rate. [ABSTRACT FROM AUTHOR]

BACKGROUND: Genetic improvement of crop varieties requires significant investment. Therefore, varieties must be developed to suit a broad range of breeding targets, such as yield and suitability to rainfall zones, farm management practices and quality traits. In the case of breeding for disease resistance, breeders need to consider the value of genetic improvement relative to other disease management strategies and the dynamics of pathogen genetic and phenotypic diversity. This study uses a benefit–cost analysis framework to assess the economic value of fungicide management and crop genetic improvement in disease resistance for Australian chickpea varieties. RESULTS: When assessing the likelihood of growers switching to new crop varieties with improved genetic resistance to disease, the simulation results reveal that adopting these varieties yielded higher net benefit values compared to implementing current fungicide strategies across all rainfall zones. On average, the increase in net benefit varied between 2.6% and 3.5%. Conversely, when we examined the scenario involving modifying the current fungicide strategy, we observed that, on average, switching from the current fungicide management strategy to one which involved additional fungicides was beneficial in about 73% of the cases. CONCLUSION: Our analysis reveals the importance of factors such as commodity prices, production costs, disease‐related variables and risk aversion in determining the economic benefits of adopting new crop protection strategies. Furthermore, the research reveals the need for accessible information and reliable data sources when evaluating the benefits of new agricultural technologies. This would assist growers in making informed and sustainable disease management decisions. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the "11th Five-Year Plan" and "12th Five-Year Plan" periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin's overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the "11th Five-Year Plan" and "12th Five-Year Plan" periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin. [ABSTRACT FROM AUTHOR]

Assertions of pervasive inefficiency in the behavior and organization of developing agriculture are found to be based on unsound methodologies. Models apparently based on expected utility theory are theoretically flawed and use highly restrictive assumptions that make them largely irrelevant for explaining actual decisions. When a more appropriate model is applied to the case of the green revolution in the Philippines, the hypothesis that loss aversion impedes adoption of new technology is rejected. Common assertions about the inefficiency of agricultural institutions are also found wanting. The risk-bearing theory of share-tenancy, which is thought to imply inefficiency, cannot explain observed tenant shares. Once the disadvantages of fixed-lease contracts are recognized, sharing is plausibly second-best efficient. The purported inefficiency implied by the inverse relationship between farm size and yield per hectare also dissipates once the endogeneity of farm size is accounted for. Inasmuch as efficiency can explain the stylized facts of behavior and organization in developing agriculture, policy recommendations based on inappropriate theory and misplaced exogeneity should be viewed with skepticism. [ABSTRACT FROM AUTHOR]

Copyright of Investigación Económica is the property of Universidad Nacional Autonoma de Mexico, Facultad de Economia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present an optimization planning method for enhancing power quality in integrated energy systems in large-building microgrids by adjusting the sizing and deployment of hybrid energy storage systems. These integrated energy systems incorporate wind and solar power, natural gas supply, and interactions with electric vehicles and the main power grid. In the optimization planning method developed, the objectives of cost-effective and low-carbon operation, the lifecycle cost of hybrid energy storage, power quality improvements, and renewable energy utilization are targeted and coordinated by using utility fusion theory. Our planning method addresses multiple energy forms—cooling, heating, electricity, natural gas, and renewable energies—which are integrated through a combined cooling, heating, and power system and a natural gas turbine. The hybrid energy storage system incorporates batteries and compressed-air energy storage systems to handle fast and slow variations in power demand, respectively. A sensitivity matrix between the output power of the energy sources and the voltage is modeled by using the power flow method in DistFlow, reflecting the improvements in power quality and the respective constraints. The method proposed is validated by simulating various typical scenarios on the modified IEEE 13-node distribution network topology. The novelty of this paper lies in its focus on the application of integrated energy systems within large buildings and its approach to hybrid energy storage system planning in multiple dimensions, including making co-location and capacity sizing decisions. Other innovative aspects include the coordination of hybrid energy storage combinations, simultaneous siting and sizing decisions, lifecycle cost calculations, and optimization for power quality enhancement. As part of these design considerations, microgrid-related technologies are integrated with cutting-edge nearly zero-energy building designs, representing a pioneering attempt within this field. Our results indicate that this multi-objective, multi-dimensional, utility fusion-based optimization method for hybrid energy storage significantly enhances the economic efficiency and quality of the operation of integrated energy systems in large-building microgrids in building-level energy distribution planning. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We first introduce an efficient and accurate simulation-based method applicable to generalized diffusion models to approximate the optimal derivatives-based portfolio strategy. We build upon a double optimization approach, i.e., expected utility maximization and risk exposure minimization, already proposed in the literature, demonstrating that strangle options are the best choices for market completion within equity options. They lead to lower investors' risk exposure for a wide range of strikes compared to the lesser flexibility of calls, puts, and strangles. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when short-term maturity equity options are not available. [ABSTRACT FROM AUTHOR]

Human decision-making (HDM) is a complex process. Various human factors play a significant role in this process. Human factors directly or indirectly affect the entire process of decision-making (DM). In this study, an attempt has been made to integrate some of the human factors like past experiences (pe), emotion (ef), times factors (tf), and uncertain (un) with the reinforcement learning (RL) method to develop model for HDM. For this Iowa gambling Task (IGT) has been used as a data collection tool, data of 57 subjects were collected. It is a well-known experience-based task that helps to identify the DM behaviour of participants. An AHP method has been also used to decide the criteria weight to different human factors in the HDM model. Four learning models are developed that are the combination of different utility functions, learning rules, and choice rules. The AHP method decides the preference of various factors incorporated in the developed models. From the results, it is observed that the model based on prospect utility, decay RL, and trial dependency (PU-DRI-TDC Model) performs better when the emotion factor was given the highest preference than others. In addition to this, the IGT learning of participants was also analysed. [ABSTRACT FROM AUTHOR]

In order to quantitatively calculate the critical depth and critical load of mines affected by rock burst, and to achieve effective prevention and control of rock burst in coal mines, this paper proposes a mechanical model for predicting the occurrence of rock burst in coal mine roadways based on catastrophe theory. Additionally, a theoretical calculation formula for initiating rock burst is derived. The first step was to establish a mechanical analysis model, which directly correlated with the in-situ stress, physical and mechanical characteristics of the coal-rock mass, and engineering structural parameters. Following this, a mechanical instability criterion was derived for the key load-bearing circle within the surrounding rock of the roadway. In the final step, the critical depth and load for rock burst initiation were verified for 25 distinct coal mines in China that were prone to rock burst hazards. The research results demonstrate that the discrepancy between the theoretically calculated critical depth and the actual measured statistical values was less than 35%. In addition, the difference between the theoretically determined critical depth and the value calculated by Pan Yishan was less than 32%. Notably, the ratio of the theoretically calculated critical load to the uniaxial compressive strength of the coal-rock mass ranged from 0.38 to 1.93. This aligns with empirical data on rock burst occurrences, as set out in the engineering classification standards for rock masses. These research outcomes substantiated the practical utility of the proposed theory, thereby laying a robust theoretical groundwork for the quantitative control of rock burst. [ABSTRACT FROM AUTHOR]

Theories of self-interest and ingroup-favouritism dominate Western thought. They give Black reparations in the US little chance of electoral success while African Americans remain a minority. Could Black reparations become feasible despite this limitation? I propose 'relational identity theory', a combination of Simon's (1993) utility theory as well as Aron and Aron's (1986) self-expansion theory, as an alternative to theories of self-interest and ingroup-favouritism. The theory allows for the inclusion of an outgroup in the self-concept. An online reaction-time procedure (n = 1,341) suggests that among White non-Hispanic respondents, implicit Black identification is the strongest predictor of support for Black reparations. Pooled reaction-time data from three studies involving 1,594 White non-Hispanic respondents suggests that implicit Black identification is based on feelings of closeness, warmth, and empathic concern for African Americans resembling Black group attachment. Significant implicit Black identifiers among White non-Hispanic respondents fall between Black non-Hispanic and other White non-Hispanic respondents in their race-related political opinions. 'Relational identity theory' can help economists, political scientists, and psychologists explain how majority domination (e.g. slavery and Jim Crow in the United States) could be overcome despite the democratic principle of majority rule. The theory suggests that Black reparations might become politically feasible one day. [ABSTRACT FROM AUTHOR]

Purpose: Although growing Internet penetration in the hinterlands has attracted agribusinesses to promote digital platforms, farmers are sceptical about using them. The literature discusses agricultural platforms from the theoretical perspective of technological determinism, where the platforms are developed and promoted by firms in a top-down manner to be accepted by farmers. However, this approach results in poorly configured platforms with limited utility for farmers. It is evident from the existing literature that the mere creation of a platform business is not sufficient to guarantee adoption by users. Hence, this study explores how to make the agricultural platform more attractive for farmers. Design/methodology/approach: The present study is based on a discrete choice experiment performed on 126 Indian farmers using agricultural platforms. The data were analysed using the conditional logistic regression method. Findings: The study suggests that farmers expect government and cooperative entities to be also embedded with the platforms. Complementary features such as prompt service, competitive pricing and farm credit were identified as essential attributes. Further, the platforms should enable smallholders to trade farm produce by providing a mechanism for real-time online nudging and bargaining with buyers. Research limitations/implications: The study is based on the applications of random utility theory. The research has utility for Agtech managers, cooperative institutions and agricultural policymakers. Originality/value: This is one of the first studies focussing on agricultural platform design from the farmers' perspective. The study implies that incorporating preferred attributes can help practitioners configure platforms to benefit farmers with prospects concerning farm management decisions. [ABSTRACT FROM AUTHOR]

Problem statement: This paper addresses the challenge of effectively responding to the opioid epidemic stemming from prescription pills through a public health lens. It centers on the strategic distribution of resources across diverse interventions aimed at preventing and mitigating the consequences of opioid use disorder (OUD) and overdose occurrences. Methodology: This paper proposes a decision aid tool built on the expected utility theory that leverages a Susceptible-Infected-Removed compartmental model to simulate the dynamics of the epidemic in a population. This model then feeds into a Markov Decision Process (MDP) model to generate optimal policies upon the current state of the epidemic. The optimal policies allocate the intervention budget to primary preventive and mitigating interventions in each decision period by minimizing the cost of fatal overdoses relative to the population's number of individuals with OUD, considering the impact magnitude of each intervention, based on the current state of the epidemic. A 10-year simulation of the epidemic's progression is conducted to assess the dynamic efficacy of the proposed decision tool. Results: The findings reveal an average reduction of 29% in total costs compared to the scenario without interventions and a decrease of 12% in total costs on average compared to the scenario with a 50-50 allocation. The extensive sensitivity analysis of key parameters validates the decision aid tool. We observe that it is optimal to allocate a significant portion of the budget to prevention when the rate of opioid pill acquisition rises. Even with a heightened rate of fatal overdoses, it remains optimal to mostly invest in preventive interventions, as long as fatal overdose rates are lower than opioid access rates. Practical implications: This study provides practitioners with a tool to effectively address the opioid epidemic and enhance public health by deciding how to allocate their budget to various levels of intervention. [ABSTRACT FROM AUTHOR]

Our paper presents the first optimal analytical solution for an investor maximizing both consumption and terminal wealth within expected utility theory in the realm of GARCH models. Working in a general family of affine GARCH models, we derive an affine GARCH optimal wealth process, providing analytical representations for optimal allocation, consumption and value functions. In particular, the optimal consumption ratio avoids the undesirable scenario of investors consuming all wealth prior to maturity. Our numerical study highlights the importance of formally accounting for consumption as it disrupts the level of optimal risky allocations. It also shows a larger impact of stochastic conditional variance (heteroscedasticity) on risky allocations in comparison to the impact of non-Gaussianity. We find, in a numerical study based on S&P 500 index data over a 5-years horizon, that an investor following a Gaussian GARCH strategy can achieve 10% more total consumption and at the same time 8% more terminal wealth than another investor following a constant variance (homoscedastic) strategy. [ABSTRACT FROM AUTHOR]

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets. [ABSTRACT FROM AUTHOR]

Traveling has become an essential need for people to fulfill their psychological needs. Generally, tourists want to visit a new destination for several days. To get route guidance (visiting schedule), tourists usually use the services of a travel agent, but this service cannot be tailored to the tourist's wishes. In previous research, many have concluded that one-day and multi-day tourist routes are analogous to the Traveling Salesman Problem (TSP). However, this study has yet to emphasize daily optimization for multi-day routes because daily routes are only cut based on time constraints. One possible approach to optimize tourist routes per day is the analogy of solving Vehicle Routing Problem (VRP). Therefore, in this research, we propose a new model that combines the Whale Optimization Algorithm (WOA) with a Variable Neighborhood Search (VNS) strategy known as WOA-VNS to recommend multi-day tourist routes, which is analogous to the VRP to overcome deficiencies with the TSP analogy. The number of vehicles corresponds to the number of days tourists visit, thus ensuring optimal daily routes. The system considers user preferences for popularity, ratings, and time using the concept of Multi-Attribute Utility Theory (MAUT). The MAUT value are used as WOA-VNS fitness values. Five metrics (fitness value, number of Point of Interest (POI)s, trip duration, cost, and rating attributes) were tested on five random POIs. Results show the VRP analog is more suitable for multi-day routes, with WOA-VNS-VRP outperforming WOA-VNS-TSP and conventional algorithms such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Bat Algorithm (BA), achieving value average fitness of 0.8570, on the tourist location dataset in Yogyakarta. [ABSTRACT FROM AUTHOR]

In response to cases that involve tiny probabilities of huge payoffs, some argue that we ought to discount small probabilities down to zero. However, this paper shows that doing so violates Independence and Continuity, and as a result of these violations, those who discount small probabilities can be exploited by money pumps. Various possible ways of avoiding exploitation will be discussed. This paper concludes that the money pump for Independence undermines the plausibility of discounting small probabilities. Much of the discussion on Independence generalizes to other views that also violate Independence. [ABSTRACT FROM AUTHOR]

Countenancing unbounded utility in ethics gives rise to deep puzzles in formal decision theory. Here, these puzzles are taken as an invitation to assess the most fundamental principles relating probability and value, with the aim of demonstrating that unbounded utility in ethics is compatible with a desirable decision theory. The resulting theory frames further discussion of Expected Utility Theory and of principles concerning symmetries of utility. [ABSTRACT FROM AUTHOR]

We formulate a centrally planned portfolio selection problem with the investor and the manager having S-shaped utilities under a recently popular first-loss contract. We solve for the closed-form optimal portfolio, which shows that a first-loss contract can sometimes behave like an option contract. We propose an asymptotic approach to investigate the portfolio. This approach can be adopted to illustrate economic insights, including the fact that the portfolio under a convex contract becomes more conservative when the market state is better. Furthermore, we discover a means of Pareto improvement by simultaneously considering the investor's utility and increasing the manager's incentive rate. This is achieved by establishing the collection of Pareto points of a single contract, proving that it is a strictly decreasing and strictly concave frontier, and comparing the Pareto frontiers of different contracts. These results may be helpful for the illustration of risk choices and the design of Pareto-optimal contracts. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We show how prospect theory uncovers critical decision-making insights in the design of sequential experiences by formulating a general framework and applying it to three experience design settings. First, we study the problem of releasing a piece of good news versus bad news, where a firm may incrementally reveal the news over a preemptive period. We characterize the optimal release strategy for both types of news and show that when the ultimate news is good (resp., bad) and the audience is sufficiently gain-seeking, it is optimal first to release information of a negative (resp., positive) sentiment. Second, we consider the problem of organizing an event such as a concert with performances of known valuations, where an event organizer needs to arrange the sequence of all performances. We show that for both loss-averse and gain-seeking audiences, interior peaks can be optimal, where pleasant and aversive performances are arranged to alternate throughout the event. Lastly, we investigate the problem of simultaneous versus sequential release of a series, such as songs or TV episodes, where a content provider does not know a priori the audience's exact valuation of each item. We show that if the audience's sensitivity to losses is sufficiently small (resp., large), the optimal strategy is to release all items in the series sequentially (resp., simultaneously). Across all of the settings, we show that the audience's sensitivity to losses relative to a reference point is a critical factor that governs how to design and manage the audience's evolving experience dynamics. [ABSTRACT FROM AUTHOR]

Psychological observations are by now well integrated into economics, especially in the theory of finance, as can also be seen in the Nobel Prize awarded to Thaler. On the contrary, Simon's attempt to reforge economic theory on the paradigm of bounded rationality failed. Starting from the birth of the neoclassical paradigm, we'll describe the attempt to give it psychological foundations with a direct measurement of utility, then the axiomatic turn of the paradigm and its first anomalies. We'll then sum up the debate on rationality, taking place in the group of economists led by Simon, which brought to the rational expectations hypothesis. Finally, we'll discuss the development of behavioral economics and its progressive acceptance in economic theory. This historical reconstruction allows us to understand the actual hard core of the neoclassical paradigm and the growing need of the paradigm for practical flexibility that determines how to choose arguments, methods and evidence that can be useful to its development, including psychological ones. [ABSTRACT FROM AUTHOR]

This paper establishes a previously unexplored connection between expected utility theory and potential games. Starting with a decision problem with a complete preference relation over lotteries on a finite set of alternatives, we construct a two-person symmetric game using a payoff function that represents the preference relation, and show that if the preference relation satisfies the von Neumann-Morgenstern expected utility axioms then the constructed game is a potential game. Conversely, starting with a two-player symmetric game, we (uniquely) construct a (complete) preference relation over the lotteries using the first player's payoffs in the game, and show that if the game is a potential game then the resulting preference relation satisfies the expected utility axioms. [ABSTRACT FROM AUTHOR]

Scanning the indices of popular culture textbooks reveals an asymmetry: numerous entries on masculinities but not a single femininities entry. What does this asymmetry say about gender theory and, more specifically, femininity? While feminist theorists have produced important scholarship illuminating femininity as a patriarchal tool wielded through popular culture, these analyses often overlook how various axes of identity intersect with femininity, leaving a sizeable gap in both the conceptualization of femininity and the analysis of popular culture. This article examines how femme theory can help to both highlight and remedy gender theory's tendency to privilege masculinity and overlook femininity. After providing an overview of femme theory and its core theoretical concepts, this article highlights femme theory's utility in enriching our interpretations of representation. [ABSTRACT FROM AUTHOR]

The utility foundation of the Cobb-Douglas demand function is known for the one and two attributes cases. It is unknown in the case of three or more attributes, a regular need for modellers. In this article, we contribute by founding from a programme of (quasi-linear) utility maximization a Cobb-Douglas demand function of an arbitrary large number of attributes. We also contribute by providing simple formulas for consumer surplus and social welfare. By generalizing previous results, our research offers a normative foundation to a larger class of demand functions. [ABSTRACT FROM AUTHOR]

In this study, a tripartite decision-making parking pricing model was developed based on Game Theory to comprehensively reflect the impact of parking pricing on private car travelers, parking lot operators, and traffic managers. Utility theory is introduced to analyze the behavioral characteristics of the tripartite participants in parking pricing. A parking behavior model for private car travelers, an operating profit model for parking lot operators, and a social negative utility model for traffic managers are established. This article presents an analysis of the mutual influence between them based on a game theory perspective and introduces parking saturation and road saturation as new factors influencing parking pricing to address the interactive relationship among the tripartite participants. A parking pricing model based on tripartite games is established, and a solution algorithm is designed. The results indicate that when the parking fee rates for the two public parking lots in the scenario are 8.5 CNY/h and 9 CNY/h, respectively, the parking demand is 300, and the sum of the total travel costs of private car travelers and the total operating profits are CNY 20,589 and 2187.9, respectively. The parking saturation of the public parking lot and the difference between the expected value is minimized to 0.25, and the road saturation and the difference between the expected value are minimized to 1.48, which is the parking pricing plan that minimizes the conflicts of interest among the tripartite stakeholders in the tripartite game. The parking pricing model of a public parking lot provides a reference for formulating parking fee strategies that comprehensively reflect the needs of the three parties involved in the public parking lot. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the relationship between behavioral biases, and demand decisions for life insurance products in Uganda. Design/methodology/approach: Data were collected from 351 life insurance policyholders in Uganda. The authors used a cross-sectional survey by applying a structured questionnaire. Descriptive analysis was conducted and hypothesized relationships between the constructs were evaluated through the use of structural equation modeling. Findings: Results indicate that, behavioral biases are significant predictors of life insurance demand among Ugandan policyholders. Also, the two behavioral bias variables (heuristic bias and prospect bias) are significant predictors of demand decisions for life insurance products. Practical implications: These results are helpful for both insurers and regulators. For insurers, it is now evident that demand decisions for life insurance products are not fully rational. It is imperative for insurers to simplify life insurance product information (heuristics), integrate product education and widen dissemination of product information (prospect bias) to allow policyholders to come up with optimal demand decisions. While for insurance policymakers, the study provides an understanding of behavioral biases. With such insights, policymakers can identify exploitative and deceptive information that target policyholders to better guide life insurance documentation and product designs. Originality/value: This study is the first to offer insights into behavioral biases' influence on demand decisions for life insurance products in a developing country like Uganda. By integrating prospects and expected utility theory, this study examines rationality and irrationality in demand decisions for life insurance products. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0201 [ABSTRACT FROM AUTHOR]

This paper considers a class of growth models with idiosyncratic human capital risk and private information about individual effort choices (moral hazard). Households are infinitely-lived and have preferences that allow for a time-additive expected utility representation with a one-period utility function that is additive over consumption and effort as well as logarithmic over consumption. Human capital investment is risky due to idiosyncratic shocks that follow a Markov process with transition probabilities that depend on effort choices. The production process is represented by an aggregate production function that uses physical capital and human capital as input factors. We show that constrained optimal allocations are simple in the sense that individual effort levels and individual consumption growth rates are history-independent. Further, constrained optimal allocations are the solutions to a recursive social planner problem that is simple in the sense that exogenous shocks are the only state variables. We also show that constrained optimal allocations can be decentralized as competitive equilibrium allocations of a market economy with a simple tax- and transfer scheme. Finally, it is always optimal to subsidize human capital investment in the market economy. [ABSTRACT FROM AUTHOR]

This paper expands on the previous research on the relationship between workaholic characteristics and individual emotional exhaustion, and studies the influence of workaholic characteristics on emotional exhaustion and its internal mechanism from the new perspectives of utility theory and conservation of resources theory. Based on a questionnaire survey of 3892 rural teachers in China, this paper first constructs a model of the influence of workaholic characteristics on emotional exhaustion, and finds that the two have the stable quadratic relationship. Workaholic characteristics will reduce emotional exhaustion, but when it exceeds the certain level, workaholic characteristics will no longer reduce emotional exhaustion or even aggravate emotional exhaustion, and moderate workaholic characteristics will minimize emotional exhaustion. The increase in teaching age slows down the threshold of the "U-shaped" curve between workaholic characteristics and emotional exhaustion. By constructing a moderation mediation model, it is found that work-family conflict and work efficacy partially mediate the relationship between workaholic characteristics and rural teachers' emotional exhaustion, and work-family conflict and work-efficacy promote and inhibit the effects of workaholic characteristics on rural teachers' emotional exhaustion, respectively. Moreover, teaching age negatively moderated the indirect effect of rural teachers' workaholic characteristics on emotional exhaustion through work-family conflict, and novice teachers in rural areas were more susceptible to the emotional exhaustion caused by work-family conflict. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) present a paradigm shift in addressing conventional parking challenges. Unlike human‐driven vehicles, AVs can strategically park or cruise until summoned by users. Utilizing utility theory, the parking decision‐making processes of AVs users are explored, taking into account constraints related to both cost and time. An agent‐based simulation approach is adopted to construct an AV parking model, reflecting the complex dynamics of the parking decision process in the real world, where each user's choice has a ripple effect on traffic conditions, consequently affecting the feasible options for other users. The simulation experiments indicate that 11.50% of AVs gravitate towards parking lots near their destinations, while over 50% of AVs avoid public parking amenities altogether. This trend towards minimizing individual parking costs prompts AVs to undertake extended empty cruising, resulting in a significant increase of 48.18% in total vehicle mileage. Moreover, the pricing structure across various parking facilities and management dictates the parking preferences of AVs, establishing a nuanced trade‐off between parking expenses and proximity for these vehicles. [ABSTRACT FROM AUTHOR]

Accepted by: Konstantinos Nikolopoulos This research studies the pricing strategy of a supply chain with a manufacturer and a retailer. We analyze the customers' purchase behaviour in advance selling with deposit by using utility theory and obtain the pricing decision of the supply chain by using the Stackelberg model. We find that when consumers are not sensitive to the distribution lead time, the retailer's advance selling strategy could Pareto improve the whole supply chain. Better informed consumers and moderate advance selling distribution lead time information could expand the retailer's pre-sale possibilities by enlarging the feasible range of advance selling. [ABSTRACT FROM AUTHOR]

The article focuses on consumers with heterogeneous preferences for product carbon footprints under two carbon labeling systems: carbon reduction labels and carbon footprint labels. Using mathematical models, the study explores the optimal decision‐making behavior of enterprises under the carbon labeling system based on consumer utility theory at the enterprise level. It also investigates consumer surplus, environmental improvement, and social welfare under different production modes of the carbon reduction label and carbon footprint label systems at the societal level. The research findings are as follows: (1) At the level of economic benefits for enterprises, choosing to produce products with carbon reduction labels is more favorable when the investment costs for emission reduction are high, while choosing to produce products with carbon footprint labels is more favorable when the investment costs for emission reduction are low. The optimal prices of products under the two carbon labeling systems show opposite trends compared to the investment costs for emission reduction. (2) At the level of environmental benefits for enterprises, under the carbon reduction labeling system, whether through full production or a mixed production mode, enterprises are only willing to reduce the carbon footprint of products to the standard value. However, under the carbon footprint labeling system, especially in the mixed production mode, enterprises show a stronger willingness to overachieve in emission reduction target. (3) At the level of social benefits, compared to the situation under the carbon reduction labeling system, when the investment costs for emission reduction are low, the carbon footprint labeling system is more favorable in terms of consumer surplus and social welfare. However, this advantage gradually diminishes as the investment costs for emission reduction increase. (4) Excessive investment costs for emission reduction are detrimental to the implementation of both carbon reduction and carbon footprint labeling systems. The research conclusions suggest that companies should develop appropriate production strategies based on actual circumstances. Additionally, it is necessary for both the government and companies to reduce investment costs in emissions reductions by improving the system and optimizing emission reduction methods in order to better implement carbon labeling policies. [ABSTRACT FROM AUTHOR]

The Horn of Africa Drylands are increasingly experiencing severe droughts, which impose a threat on traditional livelihood strategies. Understanding adaptation behavior in rural communities is key to helping reduce the impact of these droughts. We investigate adaptation behavior by assessing four established economic and social psychological theories on decision making under risk: expected utility theory (EUT), rank dependent utility theory (RDU), protection motivation theory (PMT), and theory of planned behavior (PMT). To measure adaptation behavior and the theory constructs, we conducted a household survey in Kenya (N = 502). Regression analysis shows that the economic theories (EUT and RDU) have the best fit for our data. Risk and time preferences are found to play an important role in adaptation decisions. An analysis of differences in decision making for distinct types of adaptation measures shows that risk averse (agro‐)pastoralists are more likely to implement adaptation measures that are adjustments to their current livelihood practices, and less willing to invest in adaptation measures that require a shift to other livelihood activities. Moreover, we find significant effects for elements of the social psychological theories (PMT and TPB). A person's belief in their own ability to implement an adaptation measure (perceived self‐efficacy) and adaptation by family and friends are important factors in explaining adaptation decisions. Finally, we find that the type of adaptation measures that people implement is influenced by, among others, gender, education level, access to financial resources, and access to government support or aid. Our analysis gives insights into the drivers of individual adaptation decisions, which can enhance policies promoting adaptation of dryland communities. [ABSTRACT FROM AUTHOR]

The nematode intestine is the primary site for nutrient uptake and storage as well as the synthesis of biomolecules; lysosome-related organelles known as gut granules are important for many of these functions. Aspects of intestine biology are not well understood, including the export of the nutrients it imports and the molecules it synthesizes, as well as the complete functions and protein content of the gut granules. Here, we report a mass spectrometry (MS)-based proteomic analysis of the intestine of the Caenorhabditis elegans and of its gut granules. Overall, we identified approximately 5,000 proteins each in the intestine and the gonad and showed that most of these proteins can be detected in samples extracted from a single worm, suggesting the feasibility of individual-level genetic analysis using proteomes. Comparing proteomes and published transcriptomes of the intestine and the gonad, we identified proteins that appear to be synthesized in the intestine and then transferred to the gonad. To identify gut granule proteins, we compared the proteome of individual intestines deficient in gut granules to the wild type. The identified gut granule proteome includes proteins known to be exclusively localized to the granules and additional putative gut granule proteins. We selected two of these putative gut granule proteins for validation via immunohistochemistry, and our successful confirmation of both suggests that our strategy was effective in identifying the gut granule proteome. Our results demonstrate the practicability of single-tissue MS--based proteomic analysis in small organisms and in its future utility. [ABSTRACT FROM AUTHOR]

The Entscheidungsnavi is an open-source decision support system based on multi-attribute utility theory, that offers various methods for dealing with uncertainties. To model decisions with uncertainties, decision-makers can use two categories: Forecast and Parameter Uncertainties. Forecast Uncertainty is modeled with (combined) influence factors using discrete, user-defined probability distributions or predefined 'worst-median-best' distributions. Parameter Uncertainty allows imprecision for utilities, objective weights, and probability distributions. To analyze these uncertainties, the Entscheidungsnavi offers several methods and tools, like a robustness check, based on (Monte Carlo) simulations and a sensitivity analysis. The objective weight analysis provides insights into the effects of different objective weight combinations. Indicator impacts, tornado diagrams, and risk profiles visualize the impact of uncertainties in a decision under risk. Risk profiles also enable a check for stochastic and simulation dominance. This article presents the complete range of methods for dealing with uncertainties in the Entscheidungsnavi using a hypothetical case study. [ABSTRACT FROM AUTHOR]

Purpose: This paper (Part 2 of 2) aims to expand the previously developed performance assessment model (PAM) into a decision support system (DSS) to produce optimized decisions on rehabilitation of facilities. Design/methodology/approach: The research includes a comprehensive PAM that integrates analytical hierarchy process-multi attribute utility theory methods for the physical, operational, and environmental aspects of the mosque, as described in Part 1 of the study. An optimized intervention tool is added to the PAM to complete the DSS development. The tool determines the optimum maintenance decisions that are in line with the budget and condition criticality. The deterioration measures are created using a deterministic straight-line extrapolation technique, and the intervention actions used in the optimization are specified through literature review and in-depth interviews with 11 experts. The optimization model was simplified into a user-friendly visual basic application and applied to an existing facility case study depicting its useful functionality. Findings: The optimization model recommendation of interventions recovered the overall condition rating index (CRI) of the facility while maintaining budget constraints. The CRI in the first approach (minimum budget to achieve acceptable overall performance) was improved from 6.5 to 7.76, costing 275,850 SAR. While the second approach (Maximum recovery with a selected budget constraint, 300,000 SAR in this case) CRI was improved from 6.5 to 8.3, costing 299,850 SAR. Originality/value: The presented DSS in this paper offers a tool that enables the rehabilitation and overall management of any facility by its managers. The DSS can also be further customized to fit the facility manager's specific strategies and achieve desired objectives. [ABSTRACT FROM AUTHOR]

This systematic literature review presents an interdisciplinary overview of theories tested in experiments on the effects of communicating uncertainty. Using a machine learning-aided pipeline, we selected and manually coded 413 experimental studies. We discuss core assumptions and predictions of the main theories of uncertainty communication. Most normative theorizing (e.g., Bayesianism, Expected Utility Theory) is rooted in Probability Theory, which is only suitable for addressing shallow and medium uncertainty. This explains the underrepresentation of experimental research into deep uncertainty communication. To foster a more comprehensive understanding of uncertainty communication effects, we identify research questions and theories deserving greater attention. [ABSTRACT FROM AUTHOR]

We investigate how economic uncertainty, specifically stock market uncertainty, correlates to individuals' life satisfaction. Using expected price volatility (VIX) as our anticipatory indicator and life satisfaction as our measure of utility, our hypothesis is built on the Anticipatory Utility framework, which suggests that people also derive utility from their beliefs. After accounting for associations with the unemployment rate and stock ownership, we find a positive relationship between the VIX and low self-reported life satisfaction. This analysis captures the contemporaneous effects of future beliefs and indicate that economic sentiment about the future plays an important role in individuals' feelings about the present. [ABSTRACT FROM AUTHOR]

This article provides an overview of utility functions and expected utility theory in economic decision-making. It discusses the history of expected utility theory, including the St. Petersburg paradox and the development of prospect theory as an alternative. The article also explores the concept of utility saturation and its impact on risk aversion. It introduces the use of Markov chains to analyze decision-making and concludes by acknowledging the limitations of models in understanding expected utility theory. [Extracted from the article]

Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivatives of expected utility for various utility functions, significantly broadening the potential for analytical and computational applications. We apply these theoretical advancements to a case study involving the optimal production strategies of competitive firms, demonstrating the practical implications of our findings in economic decision making. [ABSTRACT FROM AUTHOR]

This article is a review of Stephen Engelmann's book, "Economic Rationality: What is Political Economy?" The author explores the tensions between economics and political economy and argues that economic rationality has both political and normative significance. Engelmann examines the historical trajectory of economic reasoning and how it has lost its political and ethical embedding. The review highlights the role of choice and rationality in economics, the explicit normativity in political economy, and the value-laden nature of instrumental economic rationality. The author concludes by discussing the potential for collective action and the need for conceptions of rationality beyond efficiency in economics. [Extracted from the article]

Harry Markowitz was the father of portfolio selection. His emphasis of the need to integrate utility theory and its corresponding risk-aversion coefficients, Bayesian and robust statistics for covariance and expected return forecasting, transactions costs, and data mining corrections testing educated many investors, asset managers, plan sponsors, and consultants to more objectively measure and assess portfolio performance. [ABSTRACT FROM AUTHOR]

The risk of lending money collected from savers is that it leaves banks liable to default with depositors if events (and hence repayment demands) become 'abnormal'. Even though international and national regulation has been introduced to ensure that a certain level of capital is retained by banks, such regulation can be subverted. The current system of international regulation based on the Basel III agreements does not stipulate a standardised approach for inspection frequency or penalty magnitude. This leaves the potential for regulatory arbitrage. The scientific value of an analysis to optimise regulatory efficiency and reduce such arbitrage is therefore considerable. This work therefore assesses the results of the empirical testing of a model based on the Von Neumann–Morgenstern utility function and consequently proposes that this model be used as a basis for standardising capital adequacy limit infraction penalties on an international level to prevent regulatory arbitrage. A survey is undertaken in order to test the responses of participants on the level of penalty which would deter them from regulatory transgression under different theorised levels of profit and probability of discovery. Based on the responses of two distinct subject groups ('bankers' and 'non-bankers') in different scenarios of hypothetical capital adequacy violation, the Von Neumann–Morgenstern utility function is reviewed against empirical results and revealed to show a semi-strong correlation. Lastly, the analysis reveals the striking similarities of the two groups' responses, posing regulatory implications for the industry. [ABSTRACT FROM AUTHOR]

This research paper explores the awareness, perceptions, and beliefs of English as a Foreign Language (EFL) students at the South East European University regarding Howard Gardner's Multiple Intelligences (MI) theory. Focusing on the dynamic landscape of contemporary education, the study aims to understand the extent to which students acknowledge and identify with different intelligences and the potential implications for language learning strategies and overall educational experiences. The research employs an exploratory mixed-methods approach, including a case study design, surveys, and qualitative analysis, to gather insights from bachelor's and master's-level students. Findings indicate a moderate level of familiarity with MI theory among the participants, with significant agreement on the utility of the theory in understanding learning abilities. The study underscores the importance of recognizing linguistic intelligence and suggests implications for pedagogical practices, curriculum development, and the creation of inclusive learning environments. Recommendations include fostering collaboration among educators to share best practices and seeking institutional support for initiatives related to MI theory integration in teaching. The research contributes to the ongoing discourse on effective language education and its alignment with theories of cognitive diversity, providing actionable insights for educators and institutions. [ABSTRACT FROM AUTHOR]

"Paying it forward" is a behavior in which people help someone else because they were helped in the past. Although experimental evidence exists that indicates that real human beings often "pay-it-forward" even in the face of free-rider risks, the theoretical basis for the evolution of this behavior remains unclear. In this paper, we propose a game-theoretical model that explains how pay-it-forward behavior can evolve despite the temptation to free-ride. By assuming that human beings exhibit cognitive distortions, as predicted by prospect theory, and that free-riding is punished with a tiny probability, we demonstrate that pay-it-forward, alongside unconditional altruistic behavior, can evolve and effectively deter free-riding behavior. [ABSTRACT FROM AUTHOR]

This paper demonstrates the benefits, from an expected utility perspective, of including a derivative into the universe of tradeable assets under the affine GARCH model proposed by Heston and Nandi (Rev Financ Stud 13(3):585–625, 2000. https://doi.org/10.1093/rfs/13.3.585). For this purpose, we first introduce a Power Option into the market, derive its value and moment generating function thanks to the affine GARCH structure. We then expand on the results presented by Escobar-Anel et al. (Oper Res Perspect 9:100216, 2022) by solving for the optimal investment allocations into the stock, a cash account and the option. We show that investors who are able to include a derivative indeed outperform those who only invest into the stock and the bank account. In this spirit, investors who fail to include, even a low level of exposure to the derivative, could see up to 7% annual wealth-equivalent losses. This confirms findings in continuous-time models dating to Liu and Pan (J Financ Econ 69(3):401–430, 2003). An empirical analysis on the S &P500 confirms the superiority in terms of Sharpe ratio, and maximum drawdown of portfolios with options, in-sample and out-of-sample. [ABSTRACT FROM AUTHOR]