Utility Research Articles

Reintroducing Time, Money and Constraints: Viability to Bridge the Economic and Monetary Theories

Why use a viabilistic approach to model economy? Because it is urgent to re-assess the modeling of the business cycle that integrates dynamic money creation and that has at its core the notion of constraints. The purpose of this paper is thus to propose an evolutionary economic model using a mathematical framework derived from set-valued analysis, differential inclusions and viability theory. As this approach is dynamical, the model will not use two concepts on which neo-classical economics are built i.e., (1) the existence of static repeated equilibria, (2) the optimization by an economic agent of a utility function. Furthermore, we will not use any self-organization process or “invisible hand”, as in our viabilist framework the economic viability will be maintained by explicit choices of the economic agents and of the lender of last resort over the budgetary and monetary rules to apply. Indeed, even simple constraints on the second-derivative of the means of payments already induce four-phased business cycles. Coupling these constraints over the creation of means of payment with economic evolutions of prices and commodities through a budgetary constraint allows for a single formalism, tackling jointly the question of the viability of economic and monetary evolutions.

Modeling of the process of multicriteria distribution and execution of work packages in the design of technological systems

Modeling of the process of multicriteria distribution and execution of work packages in the design of technological systems

The Feasibility Study of In Situ Conversion of Oil Shale Based on Calcium-Oxide-Based Composite Materia Hydration Exothermic Reaction

Oil shale, as a vast potential resource, is considered an important alternative energy source, and its effective development and economic utilization are of significant importance in alleviating the contradiction between energy supply and demand. Presently, the in situ conversion technology for oil shale has gained significant global attention, with numerous extraction methods undergoing active research and development. One of these methods is the in situ conversion of oil shale based on the hydration reaction of calcium-oxide-based composite material (CaO-CM). This approach harnesses the heat produced by the reaction between CaO-CM and water as a heat source for the pyrolysis of oil shale. This paper conducted experiments to assess the feasibility of temperature associated with this method. The feasibility study mainly includes two aspects: First, it is necessary to investigate whether the temperature generated by the hydration reaction of CaO-CM can meet the temperature requirements for the pyrolysis of oil shale. Through pyrolysis experiments of Xinjiang oil shale, the minimum temperature required for oil shale pyrolysis was determined to be 330 °C. High-temperature and high-pressure reaction vessels were employed to explore the temperature generated by the hydration reaction of CaO-CM. The results show that with the increase in environment pressure, environment temperature, and reaction mass, the maximum temperature generated by the hydration reaction of CaO-CM also increases (reach 455.5 °C), meeting the temperature requirements for the pyrolysis of oil shale. Second, the study evaluates whether the hydration reaction of CaO-CM can induce pyrolysis hydrocarbons of the oil shale. Through the pyrolysis experiments of oil shale based on the hydration reaction of CaO-CM, the changes in the content of pyrolysis hydrocarbons (S2) in oil shale before and after pyrolysis are measured. The results show that under 10 MPa pressure, the content of pyrolysis hydrocarbons in the oil shale decreased from 40.96 mg/g to 0.08 mg/g after pyrolysis. This confirms the feasibility of the temperature conditions for the in situ conversion of oil shale based on the hydration reaction of CaO-CM.

Single and Multiobjective Shutdown Optimization of a Multistage Continuous Crystallizer.

This study presents the first model-based optimal shutdown procedure of a multistage continuous crystallization process which aims at the maximization of on-spec production and minimization of the shutdown time. The cooling antisolvent crystallization of Aspirin (acetylsalicylic acid) in a three-stage continuous crystallizer was used as a case study. To address the optimal shutdown problem, several single optimization scenarios were considered to assess the impact of the degrees of freedom, discretization schemes, and optimization settings such as the constraints. The proposed optimal shutdown procedures showed that significant amounts of on-spec crystals can be produced both at fixed and variable shutdown times. Most importantly, the optimal shutdown procedures can match the steady-state productivity, based on the shutdown to steady-state productivity ratio (STSPR) which can easily reach 100%. Moreover, the residual shutdown material, considered as waste, can be dramatically reduced by >80% compared to the current standard shutdown procedures. Given the conflicting nature of the maximization of on-spec production and minimization of the shutdown time, multiobjective optimization of the shutdown operation was also addressed to identify the set of Pareto optimal solutions. Finally, a multicriteria decision-aiding method, based on multiattribute utility theory, was proposed to rank the Pareto optimal solutions to support the decision-making and help identify a suitable and feasible single optimal shutdown solution.

Optimal work extraction from quantum batteries based on the expected utility hypothesis.

Work extraction in quantum finite systems is an important issue in quantum thermodynamics. The optimal work extracted is called ergotropy, and it is achieved by maximizing the average work extracted over all the unitary cycles. However, an agent that is non-neutral to risk is affected by fluctuations and should extract work by following the expected utility hypothesis. Thus, we investigate the optimal work extraction performed by a risk non-neutral agent by maximizing the average utility function over all the unitary cycles. We mainly focus on initial states that are incoherent with respect to the energy basis, achieving a probability distribution of work. In this case we show how the optimal work extraction will be performed with an incoherent unitary transformation, namely a permutation of the energy basis, which depends on the risk aversion of the agent. We give several examples, in particular also the work extraction from an ensemble of quantum batteries is examined. Furthermore, we also investigate how work extraction is affected by the presence of initial quantum coherence in the energy basis by considering a quasiprobability distribution of work.

Family Resilience and Children's Subjective Well-Being: A Two-Wave Study.

According to the Theory of Change, the resilience of the family unit plays a crucial role in shaping the developmental trajectory of children. Families exhibiting higher levels of family resilience are typically characterized by transparent and effective communication, optimistic outlooks on adversity, adept problem-solving skills, strong spiritual beliefs, and effective management of social and financial resources. While existing research has indicated that parental and familial characteristics can predict diverse outcomes for children, investigations concerning the association between family resilience and children's subjective well-being remains limited. Therefore, this study aims to examine whether different dimensions of family resilience can predict changes in children's subjective well-being, tested one year later. The sample includes 762 child-mother-father triads (intact families). Children aged 9-13 years (48% boys, age = 11.04, SD = 1.16) assessed their life satisfaction, positive and negative affect in two study waves, while mothers and fathers assessed family resilience in the first wave. A dyadic data common fate model was employed to create latent variables representing family resilience. Three latent variables were: family problem-solving, family spirituality, and utilization of social and economic resources. Findings from the structural equation model indicated a positive association between higher levels of family problem-solving and increased children's life satisfaction, alongside a negative relationship between higher family spirituality and negative affect. Parental assessments of social and economic resources utilization were not uniquely related to children's life satisfaction, positive, or negative affect.

Theories of Uncertainty Communication: An Interdisciplinary Literature Review

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.

A Bayesian quasi-likelihood design for identifying the minimum effective dose and maximum utility dose in dose-ranging studies.

Most existing dose-ranging study designs focus on assessing the dose-efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit-risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility dose to optimize the benefit-risk tradeoff. The binary toxicity endpoint is modeled using a beta-binomial model. The efficacy endpoint is modeled using the quasi-likelihood approach to accommodate various types of data (e.g. binary, ordinal or continuous) without imposing any parametric assumptions on the dose-response curve. Our design utilizes a utility function as a measure of benefit-risk tradeoff and adaptively assign patients to doses based on the doses' likelihood of being the minimum effective dose and maximum utility dose. The design takes a group-sequential approach. At each interim, the doses that are deemed overly toxic or futile are dropped. At the end of the trial, we use posterior probability criteria to assess the strength of the dose-response relationship for establishing the proof-of-concept. If the proof-of-concept is established, we identify the minimum effective dose and maximum utility dose. Our simulation study shows that compared with some existing designs, the Bayesian quasi-likelihood dose-ranging design is robust and yields competitive performance in establishing proof-of-concept and selecting the minimum effective dose. Moreover, it includes an additional feature for further maximum utility dose selection.

Risky choice and diminishing sensitivity in MaaS context: A nonlinear logit analysis of traveler behavior

In the rapidly transforming landscape of urban transportation, Mobility-as-a-Service (MaaS) has emerged as a potentially transformative solution to adapt to fluctuating travel demand. However, despite arguments in the literature suggesting that uncertainty in travel plans may influence travelers’ preferences for MaaS, the extent and mechanisms of this impact remain relatively underexplored. This study aims to bridge this gap by evaluating travelers’ preferences toward MaaS subscriptions, mode choices, and their reactions to uncertainty within a risky-choice modeling framework. We incorporate a linear-specified benchmark model and two nonlinear models (one based on Expected Utility Theory with Constant Absolute Risk Aversion adjustments, and the other on Prospect Theory), to estimate travelers’ risk perceptions and their valuation of gains and losses associated with travel time and costs caused by occasional activities during commute trips. The data utilized in our study originates from a specifically designed stated preference survey carried out in Hong Kong. Our findings present insightful observations into MaaS bundle configurations, socio-demographic patterns, and notably, behaviors under uncertain circumstances. In the face of uncertainty, we observe a noticeable increase in reluctance to adopt MaaS, and a heightened preference for taxis. The models also underscore significant risk aversion and diminishing sensitivity concerning travel time and cost, with these tendencies proving more pronounced for travel time compared to cost. These insights are valuable for policymakers and MaaS operators, offering an enhanced understanding of users’ risk attitudes and the intricate interplay between travelers’ preferences for MaaS and the uncertainties inherent in daily travel.

Automated tariff design for energy supply–demand matching based on Bayesian optimization: Technical framework and policy implications

With the emergence of renewable energy sources, designing tariffs becomes crucial to match unstable energy supply with varying energy demand. However, traditional tariff design frameworks depend on a certain type of tariff and the demand response of customers, which makes designing tariffs rigid and hard to automate. To overcome the challenges, in this paper, we propose an automated tariff design framework for energy supply–demand matching based on Bayesian optimization. The proposed framework allows us to design an optimized tariff for any given objective of energy matching automatically. This automation can provide flexibility in designing tariffs for energy management. Moreover, we propose a novel matching evaluation metric focusing on the mismatch of actual energy supply–demand patterns related to the stability and reliability of energy management systems. Adopting it along with traditional economic utility, actual energy pattern matching and economic values can be deliberately balanced in the automated tariff design. Through simulations with real datasets, we demonstrate that the proposed framework can effectively design tariffs in the real world such as time-of-use and demand charge tariffs, considering energy patterns and economic costs separately. Finally, we provide potential policy implications resulting from the automated tariff design.

International time trends and differences in topical actinic keratosis therapy utilization

Actinic Keratoses (AK) are precancerous lesions that can lead to Squamous Cell Carcinoma. International differences in the utilization of topical medications to treat AK are not well described. To describe international differences in topical AK medication utilization, including associations of countries' economic status with AK medication utilization. We used IQVIA MIDAS pharmaceutical sales data for 65 countries (42 high-income, 24 middle-income) from April 2011 to December 2021. We calculated each country's quarterly utilization of medications in grams per 1000 population. We used univariable linear regression to assess the association between country economic status and AK medication utilization. High-income countries used 15.37 more grams per 1000 population of 5-fluorouracil (95% CI: 9.68, 21.05), 4.64 more grams per 1000 population of imiquimod (95% CI: 3.45, 5.83), and 0.32 more grams per 1000 population of ingenol mebutate (95% CI: 0.05, 0.60). Missing medication utilization data for some countries. High-income countries use more topical AK therapies than middle-income countries.

Partially observable discrete-time discounted Markov games with general utility

In this paper, we investigate partially observable zero sum games where the state process is a discrete time Markov chain. We consider a general utility function in the optimization criterion. We show the existence of value for both finite and infinite horizon games and also establish the existence of optimal polices. The main step involves converting the partially observable game into a completely observable game which also keeps track of the total discounted accumulated reward/cost.

HCEL: Hybrid Clustering Approach for Extending WBAN Lifetime

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.

Potential impacts of the Common Agricultural Policy's Income Stabilisation Tool on farmers' incomes and crop diversity: A French case study

AbstractThis paper analyses the potential impacts of a hypothetical implementation of the Income Stabilisation Tool (IST) in France for the field crops sector. The IST is a risk management tool available within the 2014–2020 Common Agricultural Policy (CAP) to support farmers facing a severe drop in their incomes. This analysis was conducted using a farm‐level model relying on expected utility theory and based on positive mathematical programming with risk. The model was applied to a sample of 1375 field crop farms in France derived from Farm Accountancy Data Network (FADN) data. Simulation results show that the uptake rate of the tool is relatively low, less than 37% in all scenarios. It is strongly dependent on CAP public support and on how much premium farmers have to pay. Highest uptake rates are observed in farms specialising in Other Field Crops, such as potatoes, pulses and sugar beet, and farms located in regions highly exposed to climatic risks. Previous experience with insurance favours the acceptance of the IST. Model results also show that the IST improves adopters' income and reduces income inequality. However, its impacts on crop diversity, measured by the Shannon index, are negative.

Distributed Congestion Control Based on Utility Function

This paper introduces the Distributed Utility Function Algorithm (D-AFU) as a notable progression in managing and optimizing network traffic within distributed settings. Based on the utility function principle, D-AFU dynamically adjusts data rate in response to ever-changing network demands, with optimal performance and a higher user experience. Contrary to the centralized model, D-AFU employs a distributed, scalable, and resilient against failures and system overloads mechanism. Its efficiency is validated using the NS-3 simulator. Three main metrics were used: the data rate allocation, utility per session, and fairness (quantified by the Gini coefficient). D-AFU displays exceptional performance and low latency, particularly vital for real-time applications with high Quality of Service (QoS) requirements.

Examination of Cyber-Physical System Applications in Manufacturing Enterprises with the Unified Technology Acceptance and Utilization Theory

In this study, the perception, acceptance and usage status of Cyber Physical Systems (CPSs) which seem likely to become a more dominant tool in the industrial sector in the coming years, in manufacturing industry enterprises, and the advantages it provides to small and medium-sized enterprises (SMEs) in the manufacturing sector are examined in line with the requirements of the sector. In this regard, the study will offer comprehensive insights to researchers, scientists, and decision-makers within industrial sectors, as well as institutions and organizations tasked with making strategic decisions. In addition, as with any new technology, the importance of determining the attitudes of staff and managers, who are the users of the technology, towards CPS was evaluated with an acceptance model developed by considering industrial conditions. By applying a questionnaire with open-ended questions including the opinions of SMEs, it is aimed to examine the factors in the Unified Technology Acceptance and Utilization Theory (UTAUT) model whose effects on usage are advocated. analysis demonstrate that the major hesitations of the enterprises are concentrated on the high costs of the technology, the lack of human resource competence to use the systems and the lack of solution partners in case of possible failures, but they still have positive opinion about the use. The study's findings shed light on the factors influencing the feasibility of CPS technology in the industrial sector. These results can serve as a guide for conceptualization, policy support, planning, and the development of new management strategies that will be essential in this field in the forthcoming years.

An optimal investment consumption model for retirees with no health insurance

Retirees meet a number of problems as they are growing older which needs persistent attention. Hence, without a doubt, the outcomes of the financial markets influence the choices that people make when nearing retirement. In our model, the stock price dynamics follow Geometric Brownian motion (GBM) and our goal was to optimize the expected discounted utility of consumption and terminal wealth whilst considering health expenses. The investment return process comprises risk free asset and risky assets, and the health expenses. We choose power utility functions where comprehensive solutions for Hyperbolic Absolute Risk Aversion (HARA) utility functions are obtained and optimal investment, consumption and health expenditure strategies are derived by applying dynamic programming and variable change technique on the Hamilton-Jacobi-Bellman (HJB) equations. In our numerical results it showed various effects of some economic and market parameters on the optimal investment, consumption and health expense strategies. The inflation price market risk governs the amount invested in stock, bond and also how much to be put in health to sustain a given period of the retiree's lifetime. As the health welfare rate R increases, the proportion of wealth invested in the stock increases. We also investigated the effects of the high correlation coefficients and low correlation coefficients on consumption and income rate respectively. As the constant variance discounting coefficient increases, seasoned enterprise annuity retirees decrease their allocation to the risky assets. Finally, a numerical example is presented to depict the effects of financial parameters on the optimal investment strategy with health expenditure.

Conflict analysis of multiple decision-makers ecological environment governance of water resources based on graph model with heterogeneous preference in China's South-to-North water transfer project

Ecological environment governance of water resources is the foundation and key measures to ensure good water quality in China's South-to-North Water Transfer Project. However, as a limited rational economic person, considering their economic benefit, different involvers in China's South-to-North Water Transfer Project show different types of preference relations and make different action decisions as well as play conflicts with each other, which have a negative effect on ecological environmental governance. To overcome above-described shortcomings, heterogeneous preferences are integrated into graph model for conflict resolution (GMCR) to resolve ecological environment governance conflict of water resources. Specifically, first, four different structures of preference expression, including preference orderings, utility functions, multiplicative preferences, and additive preferences, are used to represent decision-maker's (DM's) preference information over states. Then, a novel preference ranking method is designed to calculate the ordinal preferences over all states by using matrix representation. Based on this, a set of heterogeneous stability definitions is proposed in GMCR to describe human diversified choice and behavior in detail, and it is applied to the ecological environment governance conflict of water resources in China's South-to-North Water Transfer Project. The heterogeneous stability analysis result showed that incentive policies used by water resources agencies can effectively encourage local governments and enterprises to actively participate in the ecological environment governance, and promote the ecological environment governance conflict to evolve into a state of equilibrium.

A model of sustainable development in economic, social, and environmental aspects: the role of social capital in China

This paper takes China as an example to quantitatively study the weight relationship between economic growth, social welfare, and environmental quality, and explores the role of social capital in promoting sustainable development. This paper constructs the Hamilton optimization model, the ‘sustainable’ element is introduced on the basis of the traditional utility function, and given China’s economic characteristics of numerical simulation analysis was carried out on the model parameters, to reveal the connotation of Chinese sustainable development from the mathematical, to explore the influence mechanism of social capital in promoting sustainable development. Research has found that sustainable development seeks coordinated economic, social, and environmental development. Setting the weight ratio of economic growth, social welfare, and environmental quality at 1:1.5:1.5 can better explain China’s concept of sustainable development. When social capital accelerates its accumulation, it can effectively control the development and utilization of natural resources, and the ecosystem environment can maintain the stable state of the ecosystem through its own regeneration and restoration ability. Social capital has become a new driving force for sustainable development, achieving a steady and rising trend of China’s economic development level. The research conclusions are helpful to promote the sustainable development, and to enrich and expand the relevant research on social capital.

MONETARY UTILITY FUNCTIONS ON Cb(X) SPACES

We will characterize robust monetary utility functions defined on the space of real valued (bounded) continuous functions on a Polish space.