Principle Of Utility Maximization Research Articles

Finite-Mixture Continuous Logit Model: Formulation and Application for Shopping Start Time Choice of Non-Commuters

As a continuous generalization of the multinomial logit (MNL) model, the continuous logit (CL) model can be used for continuous response variables (e.g., departure time and activity duration). However, the existing CL model requires the calculation of numerical integrals to obtain the choice probabilities; it thus takes a long time to estimate the model parameters, particularly when the sample size is large. In this paper, we formulate the finite-mixture CL (FMCL) model as a new continuous choice model by combining the finite-mixture method and the CL model, in which the continuous distributional function of the finite mixture is embedded in the CL model. As a result, the individual choice probability can be obtained directly by computing the probability density of the continuous distribution function; this avoids calculation of the integral but still obeys the random utility maximization (RUM) principle. Simulation experiments are conducted to demonstrate the capability of the model. In an empirical study, the proposed model is applied for non-commuters’ shopping activity start time using the expectation-maximization (EM) algorithm based on Shanghai Household Travel Survey data. The results show that the FMCL model developed in this paper can greatly reduce the model estimation time (10,048 observations requiring only 3 min) of the CL model, and the model also has a more intuitive interpretation of model coefficients, directly reflecting variable effects on time-of-day choice. These two advantages can greatly enhance the practical value of the proposed modeling method.

Coordination of dual-channel supply chain considering differential pricing and loss-aversion based on quality control

<p style='text-indent:20px;'>This paper investigates the coordination of dual-channel supply chain under quality control with a loss-averse manufacturer and a loss-averse retailer. Facing various uncertain factors, supply chain members tend to show loss aversion, which makes their actual decision deviate from the optimal decision without considering loss aversion. Therefore, the loss aversion effect function is applied to characterize the loss aversion of members. Besides, under quality control, utility model is constructed under centralized decision and decentralized decision, and the optimal decisions are solved according to the principle of utility maximization. Further, by analyzing and comparing the optimal strategies of two typical decision structures, the wholesale price and the quality cost-sharing contract is designed to coordinate the dual-channel supply chain, and the contract is proved to be valid. Finally, the impacts of the parameters change on the optimal quality level and order price are presented through the sensitivity analysis. It is found that quality control strategy and loss aversion degree of supply chain members affect the setting of coordination contract parameters and utility of supply chain. Moreover, the coordination of dual-channel supply chain is conducive to improving the level of product quality and reducing the price difference between channels.</p>

Against Futility Judgments for Patients with Prolonged Disorders of Consciousness

Against Futility Judgments for Patients with Prolonged Disorders of Consciousness

Toward a post‐neoliberal social citizenship?

Toward a post‐neoliberal social citizenship?

A Utility Maximization Model of Pedestrian and Driver Interactions

Many models account for the traffic flow of road users, but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on an existing model of human sensorimotor control, we develop an agent-based modeling framework applying the principles of utility maximization, motor primitives, and intermittent action decisions to account for the details of interactive behaviors among road users. The framework connects the three principles to the decision theory and is applied to determine whether such an approach can reproduce the following four phenomena. Firstly, when two pedestrians travel on crossing paths, their interaction is sensitive to initial kinematic asymmetries, and secondly, based on the asymmetries, the two pedestrians rapidly resolve collision conflict by adapting their behaviors. Thirdly, when a pedestrian crosses a road while facing an approaching car, the pedestrian adapts his or her crossing behavior according to the time-to-arrival of the car, and fourthly, either the pedestrian or the driver of the car may yield to the other to resolve their conflict. We show that these phenomena emerge naturally from our modeling framework. We believe the proposed behavior model and phenomenon-centered approach of analysis offer promising tools to examine road user interactions. We conclude with a discussion on how the model can be generalized to safety-critical situations and to include other variables affecting road-user interactions.

Study on the choice of transportation mode in Zheng-Jiao Transportation Corridor

In order to study travelers’ choice of different travel modes, quantitative analysis of travelers’ travel behavior preferences can effectively choose the most effective way to travel. Based on the influencing factors of travel mode, the safety index, economic index, speediness index and convenience index of the choice of travel mode are proposed. According to the principle of utility maximization, the multiple Logit model is used to calculate the sharing rate of each travel mode in the transportation channel. Taking the transportation passage from Zhengzhou to Jiaozuo as an example, this paper makes an empirical analysis to study the traveler’s choice of intercity bus and intercity railway.

Integrating discrete choice models with MATSim scoring

Agent-based transport simulations rely on realistic representation of agents’ movements in the transport system, but also on the simulation of choice making processes that resemble reality well. The MATSim framework uses the scoring-based co-evolutionary algorithm to achieve this, based on the principle of utility maximization. Discrete choice models are another common tool in transport planning which rely on the same principle. Through a toy example, the paper at hand shows that the direct use of discrete choice parameters in MATSim is not a technically sound approach, but that a consistent integration can be achieved by generating deterministic pseudo-errors based on cryptographic hash functions. Further caveats with integrating choice parameters in MATSim scoring are discussed.

Simulation of tourists’ spatiotemporal behaviour and result validation with social media data

ABSTRACT This study explores the pattern and formation mechanism of tourists’ spatiotemporal behaviour by modelling, which is crucial for tourism transportation planning and management. The tourism utility maximization principle and tourism demand spillover effect are introduced to explain personal spatiotemporal behaviour. Based on the mathematical description of agent behaviour and simulation environment, an Agent Based Tourist Travel Simulation Model (ABTTSM) is systematically established to include an evaluation of the impact of a new high-speed rail operation in a region of high tourist attraction. Novel spatiotemporal data from social media is employed to test the simulation results. It is found that the transfer probability matrices of the simulation results and social media data are highly correlated and, as a consequence, the tourism circle division is almost unanimous. This means the ABTSM can effectively simulate tourists’ spatiotemporal behaviour and be applied in the planning and management of tourism and transportation.

Stackelberg Game Model of Railway Freight Pricing Based on Option Theory

In recent years, although rail transport has contributed significantly to the productivity of the Chinese economy, it has also been faced with the fierce competition and challenge from other modes of transportation, and therefore, freight-pricing issue has received more attention by researchers. In this paper, the rail freight option (RFO) based on option theory is proposed to study the optimal pricing decision of the railway transportation enterprise and contract customers’ optimal purchase decisions. To obtain an effective RFO contract, the railway freight contract transaction process is first analyzed. Then, the theoretical framework for the RFO contract trading is put forward in the railway freight market. Next, a two-stage Stackelberg game theoretic approach is presented based on the principle of utility maximization to achieve the optimal decision of RFO contract. Subsequently, the reverse reasoning method in dynamic programming is used to solve the optimal combination decision of the contract customer. Finally, the optimal pricing decision of RFO is discussed using Kuhn–Tucker conditions and Lagrangian function. The result shows that the railway transportation enterprise should pay more attention to the option strike price w1 in terms of maximizing system utility and achieving Pareto optimal.

By force of habit: Self-trapping in a dynamical utility landscape.

Historically, rational choice theory has focused on the utility maximization principle to describe how individuals make choices. In reality, there is a computational cost related to exploring the universe of available choices and it is often not clear whether we are truly maximizing an underlying utility function. In particular, memory effects and habit formation may dominate over utility maximization. We propose a stylized model with a history-dependent utility function, where the utility associated to each choice is increased when that choice has been made in the past, with a certain decaying memory kernel. We show that self-reinforcing effects can cause the agent to get stuck with a choice by sheer force of habit. We discuss the special nature of the transition between free exploration of the space of choice and self-trapping. We find, in particular, that the trapping time distribution is precisely a Zipf law at the transition, and that the self-trapped phase exhibits super-aging behavior.

RETRACTED ARTICLE: Blockchain-based intelligent contract for factoring business in supply chains

Factoring business, an important aspect in the supply chain finance field, has significant potential in adopting blockchain-based intelligent contract technology. Based on the existing theories of factoring business in supply chains, this paper conducts a coupling analysis between blockchain technology and supply chain factoring business. Specifically, we propose the application scenarios of blockchain-based intelligent contract technology in the supply chain factoring business from three aspects: the division and transfer of creditors’ rights certificate, the factoring financing of upstream suppliers, and the due payment of core enterprises, and elaborate their implementation processes. Furthermore, from a game theoretical perspective, we analyze the mechanism of the key technology implementation of the intelligent contract to verify whether the nodes on the blockchains will follow the relevant protocols to automatically execute them. Finally, we conduct a three-way game analysis of the supply chain factor financing process and obtain an equilibrium solution based on the principle of utility maximization, which highlights the optimization effect of the intelligent contract technology on the decision-making behavior of individual entities in the supply chain.

By Force of Habit: Self-Trapping in a Dynamical Utility Landscape

Historically, rational choice theory has focused on the utility maximization principle to describe how individuals make choices. In reality, there is a computational cost related to exploring the universe of available choices and it is often not clear whether we are truly maximizing an underlying utility function. In particular, memory effects and habit formation may dominate over utility maximization. We propose a stylized model with a history-dependent utility function where the utility associated to each choice is increased when that choice has been made in the past, with a certain decaying memory kernel. We show that self-reinforcing effects can cause the agent to get stuck with a choice by sheer force of habit. We discuss the special nature of the transition between free exploration of the space of choice and self-trapping. We find in particular that the trapping time distribution is precisely a Zipf law at the transition, and that the self-trapped phase exhibits super-aging behavior.

Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?

With the continuous expansion of the network scale and increasing of passengers, metro emergencies such as operational equipment failure are happening more frequently. Due to the narrow space and crowds of people, metro emergencies always have more of an impact than road traffic emergencies. In order to adopt appropriate measures to ensure passenger safety and avoid risks, we need to get a better understanding of passengers’ travel choice behaviors under emergencies. Most of the existing research studies related to travel choice behaviors took the random utility maximization (RUM) principle for granted, but failed to realize the potential of different decision-making processes and changes to the decision-making environment. In this research, we aim to analyze metro passengers’ travel choice behaviors under metro network emergency contexts. Based on the data collected from a survey about travel choices under metro emergencies in the Guangzhou Metro, we compared the performances of models that follow the RUM and random regret minimization (RRM) principles, and established a hybrid RUM-RRM model as well as a nested logit model following RRM (NL-RRM) to estimate the effects of various factors on passengers’ travel choice behaviors. Comparisons illustrate that the hybrid model and NL-RRM model can improve model fit, and the combination of RUM and RRM outperforms either of them respectively.

An Inattention Model for Traveler Behavior with e-Coupons

In this study, we consider traveler coupon redemption behavior from the perspective of an urban mobility service. Assuming traveler behavior is in accordance with the principle of utility maximization, we first formulate a baseline dynamical model for traveler's expected future trip sequence under the framework of Markov decision processes and from which we derive approximations of the optimal coupon redemption policy. However, we find that this baseline model cannot explain perfectly observed coupon redemption behavior of traveler for a car-sharing service. To resolve this deviation from utility-maximizing behavior, we suggest a hypothesis that travelers may not be aware of all coupons available to them. Based on this hypothesis, we formulate an inattention model on unawareness, which is complementary to the existing models of inattention, and incorporate it into the baseline model. Estimation results show that the proposed model better explains the coupon redemption dataset than the baseline model. We also conduct a simulation experiment to quantify the negative impact of unawareness on coupons' promotional effects. These results can be used by mobility service operators to design effective coupon distribution schemes in practice.

Maximization of utility and portfolio selection models

Modern portfolio theory deals with the combination of assets into a portfolio.It has diversification and maximization of expected utility as foundational principles. Its purpose is to find the portfolio which best meet the objectives of the investor. Markowitz and Athayde e Flores have characterized the portfolios as solutions of constrained optimization problems.However, the relationship between the proposed problems and the utility maximization principle is not clear.Taking into account the results of Scott and Horvath, we prove that such problems correspond to the maximization of the of expected utility of the investor underlying each of the models.

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basis of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.

Utility and Happiness in a Prosperous Society

Some examples of human behavior which seem paradoxical or irrational in view of the utility maximization principle can be explained as rational if we distinguish between two types of utility. The first type is the conventional utility – cardinal or ordinal – which the rational economic actors are expected to maximize. The second type is connected to actions which fulfill some psychological needs and might appear irrational by cost-effective calculations. Two instances of the second type are philanthropy and altruism on the one hand and excessive consumption, on the other. Although they stem from totally different drives, both are economically irrational but can be linked to the utility of the second type. The two-utilities model is rooted in Humanistic Psychology tenets, such as Maslow's hierarchy of needs as well as in recent results in Happiness Economics. It can be assumed that, on the average, the richer a person is, the less he cares about maximizing the first type of utility, while the importance of the second type in his life increases. As the society becomes more prosperous, both positive and negative trends of the second type of utility become more widespread: more resources are wasted on unnecessary products and services, and more money is spent on philanthropy. The two-utilities model can explain some seemingly odd features of the prosperous society, and may have practical applications as well. This article is scheduled to be a chapter in a book on the economics of the prosperous society. My claim is that there is a gap between economic theory and economic reality in the western world, since economics was traditionally established to deal with conditions of scarcity. As many of our current problems are associated with abundance rather than with scarcity, new tools are needed to tackle the modern dilemmas. For a definition of a prosperous society, please see the previous chapter (Unemployment and Job Creation in a Prosperous Economy). http://ssrn.com/abstract=2827204

Modeling the Relationship between Expected Gain and Expected Value

Rational choice theory holds that the alternative with largest expected utility in the choice set should always be chosen. However, it is often observed that an alternative with the largest expected utility is not always chosen while the choice task itself being avoided. Such a choice phenomenon cannot be explained by the traditional expected utility maximization principle. The current study posits shows that such a phenomenon can be attributed to the gap between the expected perceived gain (or loss) and the expected perceived value. This study mathematically analyses the relationship between the expectation of an alternative’s gains or losses over the reference point and its expected value, when the perceived gains or losses follow continuous probability distributions. The proposed expected value (EV) function can explain the effects of loss aversion and uncertainty on the evaluation of an alternative based on the prospect theory value function. The proposed function reveals why the expected gain of an alternative should exceed some positive threshold in order for the alternative to be chosen. The model also explains why none of the two equally or similarly attractive options is chosen when they are presented together, but either of them is chosen when presented alone. The EV function and EG-EV curve can extract and visualize the core tenets of the prospect theory more clearly than the value function itself.

The American Dream, An Unsustainable Nightmare

America is arguably the greatest nation on our planet at this time. Its achivements in all fields of arts and sciences act as a shining beacon of the accomplishments of the human race. Fueled to such epic proportions of greatness, two of the stimuli have been the amazing natural wealth found on this land and its ability to attract talented indivuduals from across the globe. Digging deeper into the foundations of the American Dream, we find that a key ingredient has been the unencumbered pursuit of happiness as defined by the utility maximization principle of economics, the acceptance of seeking ever greater amounts of material goods and the axiom of the invisible hand. We conceptually discuss the asymptotic path of where this could lead to, perhaps, to an Unsustainable Nightmare. We ask the question if the social planner or the utility maximizer were the Budha (or any other wiseman) what this alternative view of the world coud be and whether it is practical enough to shape our future. The argument that the Budha is nothing close to the model citizen can be laid to rest by realizing that all education is but to establish better morals and by looking back at the enormous strides that we have taken as a society in overcoming hatred, crime and other forms oppression and misbehaviour.

A reference-dependent user equilibrium model for activity-travel scheduling

Existing user equilibrium models of activity-travel scheduling generally fall short in representing travelers’ decision-making processes. The majority have either implicitly or explicitly assumed that travelers follow the principle of utility maximization. This assumption ignores the fact that individuals may be loss–averse when making activity-travel decisions. Allowing for the situation that travelers possess accurate information of the urban-transportation system due to modern technologies, studies on reference-dependent decision-making under near-perfect information are receiving increasing attention. In view of traveler heterogeneity, individuals can be divided into multiple classes according to their reference points. In this paper, we propose a reference-dependent multi-class user equilibrium model for activity-travel scheduling, which can be reformulated as a variational inequality problem. Moreover, comparative analyses are conducted on the equilibrium states between utility-maximization (no reference) and reference-dependency of exogenous and endogenous references. A numerical example regarding combined departure-time and mode choice for commuting is conducted to illustrate the proposed model. The simulated results indicate that reference points and loss aversion attitudes have significant effects on the choice of departure time and mode.