Utility Maximization Framework Research Articles

An agent‐based model of consumer demand

AbstractThis paper studies an agent‐based model of consumer demand. Agents are heterogeneous with respect to their preferences and incomes. There are two basic ingredients in the model. The first ingredient is a metric that captures the degree of heterogeneity between agents. The second ingredient is a serial computer algorithm that is used in order to compute a terminal consumption bundle at which income is exhausted and overall utility is maximized. Agents are clustered into heterogeneous groups based on their preferences and incomes. We extract information about the evolution of consumer expenditure under different price regimes and the buildup of optimal demand for varying levels of income and preference parameter values. These features cannot be obtained in the classical framework of static utility maximization. Our agent‐based data‐driven methodology can be applied to any relevant data set and so provide a reliable model for forecasting demand given some agent characteristics.

The world’s productivity distribution and optimal knowledge absorption

An optimal growth model is devised and subject to dynamic analysis. The model is a standard intertemporal utility maximization framework, with a one-dimensional constraint characterizing the pace of productivity growth. In this setting, knowledge acquisition depends not only on the investment in the adoption of existing technologies, but also on the pre-determined shape of the world’s productivity distribution. The configuration of the distribution will determine the probability of successful imitation and, therefore, the extent in which the economy might approach the world’s technology frontier. The dynamic analysis points to the formation of a saddle-path stable equilibrium. The type of distribution is decisive in defining the exact location of the stable trajectory and of the equilibrium point.

Households’ energy conservation and efficiency awareness practices in the Cape Coast Metropolis of Ghana

AbstractThermal power generation is the main source of power in the energy mix of the country. Erratic rainfall patterns, the high cost of fossil fuel for thermal generation, and increase electricity demand have contributed to rampant power outages in the recent past. This paper examined energy conservation and efficiency awareness practices of households in the Cape Coast Metropolis of Ghana. It assessed the level and variability in energy conservation practices and the level of energy-savings awareness education among households. Three hundred and ninety-six households were randomly selected from nine communities using stratified sampling techniques. Five stakeholders from the electricity sector were purposively sampled for in-depth interviews. The data sets were modeled using the utility maximization framework to econometrically estimate socioeconomic factors influencing the energy conservation behaviour of households. The findings reveal that years spent in school by household heads, income levels, expenditure, age of households, and the number of times electricity power triples off daily were among the key factors influencing individual households’ choice of energy-efficient appliances. There was significant variability between existing social strata in terms of income and use of electrical appliances among households that warrants policy direction. Based on the findings, this study recommends a robust energy literacy program to improve households’ energy efficiency practices awareness, and to ensure energy cost savings, environmental protection, and climate change mitigation that will enhance the drive towards achieving sustainable development goals seven.

Assortment Optimization Under the Multinomial Logit Model with Utility-Based Rank Cutoffs

Augmenting the Modeling Power of the Multinomial Logit Model Using choice models to capture customer choice behavior has steadily become the common practice in revenue management. Discrete choice models allow one to capture the fact that customers substitute among the offered products, so if a particular product is not offered, then a portion of the customers interested in this product will substitute into a suitable available alternative. The multinomial logit model is one of the most commonly used choice models to capture customer choice in practice. This choice model is based on the utility maximization framework. A customer associates random utilities with the products, as well as the no-purchase option, in which case, the customer purchases the product with the largest utility, as long as its utility exceeds that of the no-purchase option. In “Assortment Optimization under the Multinomial Logit Model with Utility-Based Rank Cutoffs,“ Bai, Feldman, Topaloglu, and Wagner augment the modeling flexibility of the multinomial logit model, where a customer also leaves without a purchase if she cannot find one of her top few choices. They develop algorithms to find revenue-maximizing assortments.

Driven by utility or regret? Investigating the influence of decision-rule heterogeneity on public preferences for desertification control policies

In the matter of investigating public preferences and stated value for desertification control policies, the choice experiment (CE) could provide valuable insights. However, traditional CE studies modeled the data based on the random utility maximization (RUM) framework, which ignores the significant impact of decision rules on individual choice behavior, such as the random regret minimization (RRM). To examine the influence of decision-rule heterogeneity on public preferences for desertification control scenarios, this study introduced a generalized random regret minimization (G-RRM) model to analyze public choices and developed a hybrid latent class model incorporating the RUM, RRM, and G-RRM choice paradigms to clarify the formation mechanism of decision-rule heterogeneity. The findings indicate that all RRM models demonstrate a better fit with the data than the RUM model, and 68.77% of respondents express significant preference for the RRM choice paradigm. Regret can serve as an important driving factor in public choices for desertification control policies. The hybrid latent class model containing the G-RRM class with different attribute-specific values achieves the best fit, revealing that decision-rule heterogeneity significantly influences individuals' choice behavior, and such heterogeneity is not only among different respondents, but also in their trade-offs across various attributes. The results also reveal the relationship between respondents’ heterogeneous preferences for choice paradigms and their personality traits. Specifically, respondents with less education, residence years, and perceived usefulness of desertification control tend to prefer the RUM choice paradigm; female and respondents with more income, attention, and perceived usefulness of desertification control tend to prefer the RRM choice paradigm; as well as respondents with higher satisfaction to the current desertification situation show a greater decision-rule heterogeneity across different attributes. These results may provide interesting policy consequences, as they offer a precise comprehension of public choices for desertification control policies, through a combination of diverse decision rules.

The impacts of superstition on risk preferences and beliefs: Evidence from the Chinese zodiac year

Previous studies have extensively confirmed that superstition profoundly influences a wide range of economically consequential decisions. However, the underlying mechanisms largely remain unexplored. Specifically, superstitions can affect either people's endogenous risk preferences or their subjective beliefs about uncertain events. Clarifying which of these two mechanisms is at work holds both policy and practical relevance. Notably, a change in risk preferences does not deviate from the conventional utility maximization framework, while a distortion in beliefs may lead to a welfare loss in decision-making. In this paper, we distinguish these two mechanisms using novel experimental methods, taking the Chinese zodiac year as an example. We find that the zodiac year correlates with both an increase in risk aversion and excessive pessimism in decision-making. Furthermore, we illustrate the potential impacts of zodiac year superstition on real-world businesses through two case studies.

Fishery participation and location choice model: the West Coast salmon troll commercial fishery

Random utility models have been widely used to model spatial choice within fisheries, but less attention has been paid to modeling participation and movement between fisheries. Fishers may switch fisheries in response to time closures or changes in profitability potentially creating management implications for those fisheries, as well as the fishery with the closure. We used a random utility maximization framework to model participation, fishery choice, and location choice for a large fleet of West Coast salmon trollers, many of which also participate in other fisheries. We used the model to demonstrate substitution effects across fisheries due to spatial policies implemented in the salmon fishery. Our work suggests that spatial management of a single fishery needs to take into consideration fishers’ full choice set to predict behavioral responses to spatial policies. Our analysis also provides insights into how fishers construct multifishery harvest strategies that enable them to more fully use capital or adjust to closures or changes in relative profitability.

Exploring doctors’ trade-offs between management, research and clinical training in the medical curriculum: a protocol for a discrete choice experiment in Southern Africa

IntroductionMedical curricula should prepare doctors for roles that extend beyond that of a clinician. But the formal inclusion of both management and research training still appear to be neglected. It...

Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different QoS requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and Service Function Chaining (SFC) routing problem. However, those prior approaches mainly assume that the network state is static and known, disregarding dynamic network variations. To bridge that gap, we leverage Markov Decision Process (MDP) to model the dynamic network state transitions. To jointly minimize the delay and cost of NFV providers and maximize the revenue, we first devise a customized Deep Reinforcement Learning (DRL) algorithm for the VNF placement problem. The algorithm uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). We then propose attention mechanism-based DRL algorithm for the SFC routing problem, which is to find the path to deliver traffic for the VNFs placed on different nodes. The simulation results show that our proposed algorithms outperform the state-of-the-art algorithms in terms of network utility, delay, cost, and acceptance ratio.

Willingness to pay for COVID-19 mitigation measures in public transport and paratransit in low-income countries.

In order to combat the spread of COVID-19, various measures were taken in most countries to make public transit and paratransit safer. These additional measures, which include restrictions on number of passengers, provision of hand sanitisers and face coverings, and more frequent cleaning, add to the costs of operations or reduce profitability. The resulting financial pressure on the transport operators raises an important question on who pays for these additional measures. In most countries, this has been covered by one-time government bailouts to operators or strategies to increase fare, the latter of which directly affects the users. However, even without these interventions, there could be a demand and as such willingness to pay (WTP) for some of these intervention measures from the consumers concerned about safety. Knowing such WTP will not only help operators set their fare, but also help the governments decide the appropriate bailout needed. This paper addresses the issue by estimating the user's willingness to pay for selected COVID-19 mitigation measures in public transport and paratransit (motorcycle taxis) using survey data collected from two cities in low-income countries as case studies - Kampala, Uganda and Dhaka, Bangladesh. For public transport, these measures are - (1) social distancing (passenger loading at half capacity), and (2) mandatory hand sanitisation and increased cleaning of surfaces, while for paratransit, they are - (1) provision of a transparent shield between the rider and the passenger, and (2) provision of cleaned helmets at the start of each trip. The study analyses stated preference data using the utility maximisation framework and finds that the implementation or provision of COVID-19 mitigation measures improves the attractiveness of the associated public transport or paratransit alternatives, and transport users make trade-offs between safety and cost when making travel decisions. We find positive willingness to pay for all four mitigation measures, suggesting potential existence of a market for these measures. We also find that the typical mode choice factors such as costs, travel time and convenience became less important during the pandemic and the safety measures became more important considerations.

Optimal investment strategies for asset-liability management with affine diffusion factor processes and HARA preferences

<p style='text-indent:20px;'>This paper investigates an optimal asset-liability management problem within the expected utility maximization framework. The general hyperbolic absolute risk aversion (HARA) utility is adopted to describe the risk preference of the asset-liability manager. The financial market comprises a risk-free asset and a risky asset. The market price of risk depends on an affine diffusion factor process, which includes, but is not limited to, the constant elasticity of variance (CEV), Stein-Stein, Schöbel and Zhu, Heston, 3/2, 4/2 models, and some non-Markovian models, as exceptional examples. The accumulative liability process is featured by a generalized drifted Brownian motion with possibly unbounded and non-Markovian drift and diffusion coefficients. Due to the sophisticated structure of HARA utility and the non-Markovian framework of the incomplete financial market, a backward stochastic differential equation (BSDE) approach is adopted. By solving a recursively coupled BSDE system, closed-form expressions for both the optimal investment strategy and optimal value function are derived. Moreover, explicit solutions to some particular cases of our model are provided. Finally, numerical examples are presented to illustrate the effect of model parameters on the optimal investment strategies in several particular cases.</p>

The Global Consumption of Truths and Big Lies in the Epoch of Digital Information Technologies: A Theoretical Analysis

This paper asserts that political leaders around the world and the emergence of digital information technologies have contributed remarkably to the amplifications and normalizations of telling big lies, thus the noticeable increase in the global consumption of big lies at varying degrees. To underscore this assertion, this paper uses the theoretical framework of utility maximization to show that human beings, in a world in which “we are all liars,” consume truths and big lies subject to their information constraints. Furthermore, we use the concept of welfare economics to underscore the assertion that truths generate positive externalities, which can lead to outcomes that are social welfare-enhancing while big lies generate negative externalities, which can result in outcomes that are social welfare-retarding. The United States provides an illustrative example of a country where the consumption of big lies may be outpacing the consumption of truths because big lies have become the political philosophy of one of the political parties whose objective is to use big lies as the weapons of democracy destruction.

Sponsored Data: On the Effect of ISP Competition on Pricing Dynamics and Content Provider Market Structures

We analyze the effect of sponsored data when Internet service providers (ISPs) compete for subscribers and content providers (CPs) compete for a share of the bandwidth usage by customers. Our model is of a full information, leader-follower game. ISPs lead and set sponsorship prices. CPs then make the binary decision of sponsoring or not sponsoring their content on the ISPs. Lastly, based on both of these, users make a two-part decision-choose the ISP to subscribe to, and amount of data to consume from each CPs through the chosen ISP. User consumption is determined by a utility maximization framework, sponsorship decision is determined by a non-cooperative game between CPs, and ISPs set their prices to maximize their profit in response to prices set by competing ISP. We analyze the dynamics of the prices set by ISPs, the sponsorship decisions of CPs, the market structure therein, and surpluses of the ISPs, CPs, users. This is the first analysis of the effect sponsored data in the presence of ISP competition. We show that inter-ISP competition does not inhibit ISPs from extracting a significant fraction of CP surplus, leaving CPs no better off (and sometimes worse off) as compared to the scenario where data sponsoring is disallowed. Moreover, ISPs often have an incentive to significantly skew the CP marketplace in favor of the most profitable CP.

A choice experiment approach to evaluate maize farmers’ decision-making processes in Lao PDR

Sustainable intensification seeks to increase outputs from existing farmland in ways that have a lower environmental impact. An extensive literature has examined the determinants of farmers' adoption of the different agro-ecological cropping systems needed to achieve these goals. However, the farmers' preferences for the attributes of these systems and the decision processes for choosing between available systems is still poorly understood. To fill this gap, this paper proposes a methodology that relies on a discrete choice experiment to analyse farmers’ preferences for cropping systems and estimate the heterogeneity of decision processes among farmers. We modelled three major types of decision processes potentially used by farmers to evaluate the systems that are not consistent with the standard utility maximization framework. These findings offer insights into the behavioural patterns of respondents and should help crop system promoters and developers to better understand how their proposed systems are likely to be evaluated by different types of farmers.

Optimal DC pension investment with square-root factor processes under stochastic income and inflation risks

ABSTRACT This paper studies optimally defined contribution (DC) pension investment problems under the expected utility maximization framework with stochastic income and inflation risks. The member has access to a financial market consisting of a risk-free asset (money account), an inflation-indexed bond, and a stock. The market price of volatility risk is assumed to depend on an affine-form, Markovian, square-root factor process, while the return rate and the volatility of the stock are possibly given by general non-Markovian, unbounded stochastic processes. This financial framework recovers the Black–Scholes model, constant elasticity of variance (CEV) model, Heston model, 3/2 model, 4/2 model, and some non-Markovian models as exceptional cases. To tackle the potentially non-Markovian structures, we adopt a backward stochastic differential equation (BSDE) approach. By solving the associated BSDEs explicitly, closed-form expressions for the optimal investment strategies and optimal value functions are obtained for the power, logarithmic, and exponential utility functions. Moreover, explicit solutions to some special cases of our portfolio model are provided. Finally, numerical examples are provided to illustrate the effects of model parameters on the optimal investment strategies under the 4/2 model.

Digital exposure, age, and entrepreneurship

This study focuses on age and digital exposure as factors driving individuals to be (1) employees or entrepreneurs, (2) full-time or part-time, or (3) opportunity or necessity entrepreneurs. It extends occupational choice models, relying on a utility maximization framework, to entrepreneur types incorporating age and digital exposure effects. Using 132 months of Current Population Survey data and multilevel modelling with individuals’ fixed effects and metropolitan area random effects, the study finds that (1) workers with low- and high- digital exposure are more likely to become entrepreneurs than peers with medium digital exposure, mirroring digitization’s “push” and “pull” mechanisms on entrepreneurship; (2) age strengthens digitization’s “pull” mechanism to be entrepreneurs (versus employees) and opportunity (versus necessity) entrepreneurs; (3) digital exposure has a weak marginal potential to increase workers’ odds to be part-time (versus full-time) entrepreneurs. The study also notes the importance of location and concludes with discussion and implications.

No One Size Fits All. Women Commercial Farm Employment and Fertility in Ethiopia: A Study of Saudi Star and MERTI Agricultural Development Farms☆

AbstractModern agricultural farms offer huge potential in creating employment opportunities, which, in turn, are expected to reduce fertility. The Ethiopian government has recently leased out millions of hectares of farmland to investors under highly concessionary terms. However, little is known regarding women's farm employment effect on their fertility. Using survey data, this paper analyzed this link in the context of Saudi Star and Merti Agricultural Development farms. Results showed lower odds of fertility intention among employed women compared to non‐employed women where employment is measured more broadly. This result at a glance appears to be consistent with conventional theory which considers children as resulting from parental choice relative to other essential household goods constrained by resource shortages in the household's utility maximization framework. Where employment is measured rather narrowly (standard way) as in most socioeconomic research, however, the odds of fertility intention is higher. The two results were expected to be reversed since fertility was expected to be lower where employment is measured more narrowly than broadly where a range of employments are included. Additionally, effects associated with the recent establishment of Saudi Star farm compared to the more matured farms in Merti are likely to reduce fertility intention.

A Theoretical Note on Tax Evasion: The Case of the American FairTax Plan

Issues that occur with any tax system involve the incentives and disincentives of individuals to evade the tax. This theoretical note investigates how risk preferences could affect tax evasion under the proposed FairTax. We develop a constrained utility-maximization framework to examine how individuals with different risk preferences could respond to a broad-based sales tax based on the costs associated with tax evasion.

Assessment of Metastatic Colorectal Cancer Patients’ Preferences for Biologic Treatments in Germany Using a Discrete Choice Experiment

BackgroundCurrent treatment regimens for metastatic colorectal cancer (mCRC) include biologics such as epidermal growth factor receptor and vascular endothelial growth factor inhibitors, which have specific side-effect profiles. There is a lack of information on mCRC patient preference in Germany regarding biologics in combination with chemotherapy. This study aims to identify German mCRC patients’ preference for these treatments Patients and MethodsThis was a multicenter cross-sectional discrete choice experiment (DCE). Data were collected using electronic case report forms and structured phone interviews. DCE attributes were related to efficacy, side effects, frequency of administration, and distance to treating physicians’ practice. Patients’ characteristics and choices were analyzed descriptively. Choice data was modeled using a random utility maximization framework. ResultsAll attributes, except distance to treating physicians’ practice, had a significant impact on patients’ decision. The most important driver of patients’ treatment decision was overall survival, followed by safety-related attributes and frequency of administration. Overall survival was the main driver in all subgroups analyzed. Risk of severe skin toxicities was more important to women, than men. In patients with prior experience of side effects, the risk of side effects accounted for 45% of a patient's decision, compared to 35% in those without prior experience. ConclusionOverall survival remains the most important driver in mCRC patients’ preferences for biologic treatment in combination with chemotherapy. Attributes related to safety were less important to patients when considering their treatment decision. These results indicate that understanding patient preferences may lead to increased treatment compliance.

An agent-based model of elephant crop consumption walks using combinatorial optimization

Conceptual contextWildlife crop consumption is a worldwide problem. This paper builds on the theoretical framework of profit and utility maximization from economics as established in the theory of optimal foraging, bringing this perspective to the issue of wildlife crop consumption by testing whether elephants forage for crops in an optimal way. Methodological approachUsing combinatorial optimization in an agent-based model, in which elephants’ objective is to find a valid walk that maximizes their energy balance. We used empirical data from GPS collars on African savanna elephants to train and test the model. Main results and conclusionsWhen we focused solely on which terrain blocks the elephants of GNP visit and spend time in, our ABM got 56 percent of these blocks correct. Our ABM performed roughly 25 percent better than two alternative models, including the random walk model. In both subsamples of data that we looked at, the ABM performed better in terms of fitting the data on real walks than the alternative models. The ABM's performance improved, and the alternative models’ performance worsened, when we only looked at data on real walks that involve crop consumption. This suggests that there is more randomness involved when elephants are engaged in foraging activity that do not include crop consumption. At the same time, elephant walks involving crop consumption seem to more closely follow optimizing principles.Findings from this ABM approach support ecological understanding of elephant crop foraging, highlighting the optimal movements involved in crop foraging events as well as the importance of trespassing costs and landscape configuration. It may give conservationists and policy-makers a starting point to use in formulating policies to minimize the harms and costs that result from elephant crop consumption.