Types Of Utility Functions Research Articles

The Dokdo/Takeshima Dispute between Korea and Japan: Understanding the Whole Picture*

In the past, most studies on territorial disputes have been in the discipline of history and international law. In this paper, we expand the debate by adopting a utility‐theory approach to the issue of Dokdo/Takeshima. We introduce two different types of utility functions over the disputed islet, and show how moving from one to the other can alter the outcome of the dispute.We conclude that: (i) utilizing historical and/or legal argument alone will not resolve the Dokdo/Takeshima dispute; (ii) the different mixture of two different types of utilities changes the outcome of the debate; and (iii) the current configuration of preferences/utilities among Koreans and Japanese over the issue of Dokdo/Takeshima will never allow mutually satisfactory negotiated solutions to the dispute. Overall our diagnosis and predictions are pessimistic.

Utility-Based Power Allocation for Multiclass Wireless Systems

We study power-allocation problems in wireless systems through a network utility maximization framework. In this framework, the type of utility function represents the characteristics of each service. Hence, to deal with multiclass services in the system, it is important to accommodate various types of utility functions. In this paper, we consider four types of utility functions that can represent most of the services in wireless networks. We first develop a simple algorithm that solves a general optimization problem with those four types of utility function. We show that, even though the proposed algorithm might not provide the optimal solution, it provides an asymptotically optimal solution, which could be a good approximation to the optimal solution. The proposed algorithm can be applied to various resource-allocation problems in wireless systems. Among them, in this paper, we show that the proposed algorithm can be applied to both uplink and downlink power-allocation problems.

Optimal Human Investment Allocation: A Note on Government Spending and Labor-Leisure Choice

This paper presents an analysis of government spending and optimal human investment allocation rate in the two sector model of an endogenous growth (education and good sectors), by comparing solutions between social planner maximization problem and decentralized competitive equilibrium, and by considering two types of utility functions: the Constant Inter-temporal Elasticity of Substitution (CIES) and the Constant Elasticity of Substitution (CES) with labor-leisure choice. The results show that the optimal rate of human investment allocation depends on the elasticity of inter-temporal substitution in which it describes insignificant changes when we use CIES or CES. Moreover, the optimal rate of human investment allocation both in social planner as well as decentralized competitive equilibrium are equivalent which suggest that Pareto optimum is prevailed

The Value of Information and Intensity of Preference

Previous research has documented the lack of clear relationships between the value of information and characteristics of the decision problem. This paper shows that the intensity of the decision maker's preference toward the prior choice alternatives can be regarded as the primary determinant of information value. The value of an information source, measured in utility units, is maximal when the decision maker is indifferent vis-à-vis the prior alternatives, and it is lower as the preference for one alternative over the others gets stronger. This holds under quite general conditions, which are made explicit, and for any decision maker's utility function, wealth, and background risks. The maximum buying price of information follows the same pattern for linear utility and, with a restriction, exponential utility, and this may hold approximately for other types of utility functions. The result provides a general, concise, and intuitive explanation for seemingly ill-behaved variations in the value of information. It indicates that nonmonotonic, quasi-concave relationships should generally be expected between value of information and parameters of the decision problem.

Socially Rational Models for Autonomous Agents

Autonomous multi-agent systems that are to coordinate must be designed according to models that accom- modate such complex social behavior as compromise, negotiation, and altruism. In contrast to individually rational models, where each agent seeks to maximize its own welfare without regard for others, socially rational agents have interests beyond themselves. Such models require a new type of utility function—a social utility—to ensure three de- sirable properties: (a) conditional preferences—agents may adjust their preferences to account for the preferences of others; (b) endogeny—group preferences are determined internally by interactions between individual agents; (c) fram- ing invariance—reformulations of the social model using exactly the same information should not alter the conclusions; and (d) social coherence—no individual's welfare is categorically subjugated to th e welfare of the group. Social utilities in turn require a compatible solution concept—optimal failure-avoidance. Satisficing game theory embodies both social rationality and optimal failure-avoidance and provides a formal mathematical framework in which to balance group and individual interests in mixed-motive societies. The satis ficing approach is applied to two scenarios: the Ultimatum ga me and a random graph search problem. The Ultimatum game is one for which game-theoretic analysis does not correspond well to empirical data regarding human behavior; it is thus an important test case for a new theory. The graph search scenario is an idealization of an important resource alloca tion problem in which the ability to compromise and negotiate can greatly facilitate the search for a solution.

Generation planning including distributed generators under uncertainty of demand growth

AbstractThis paper addresses the problem of generation planning in the competitive power market with uncertainty of demand growth. The distributed generator (DG) is given attention against a large‐scale generator to correspond to uncertain demand growth. Optimization consists of minimizing the average cost and hedging risk over the scenario trees of demand growth. At first, based on the idea of a Real Option, Dynamic Programming using the utility function is applied to generation planning. Utility functions can model investor's risk‐return profile. The decisions in the first stage indicate that they are influenced by the type of utility functions and demand growth scenarios, and data of generators. Next, a Monte Carlo simulation is applied to a Brownian motion model of demand growth. This model can increase the possible number of demand levels. With this simulation, the case that the distributed generator has an advantage over the large‐scale generator is quantitatively discussed. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 146(3): 17–25, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10276

Generation Planning including Distributed Generator under Uncertainty of Demand Growth.

This paper addresses the problem of generation planning in the competitive power market with uncertainty of demand growth. The distributed generator (DG) is paid attention against a large-scale generator to correspond uncertain demand growth. Optimization consists in minimizing the average cost and hedging risk over the scenario trees of demand growth. At first, based on the idea of Real Option, Dynamic Programming using the utility function is applied to generation planning. Utility functions can model investor's risk-return profile. The decisions in the first stage indicate that they are influenced by the type of utility functions and demand growth scenarios, and data of generators. Next, Monte Carlo Simulation is applied to Brown motion model of demand growth. This model can increase the possible number of demand level. With this simulation, the case that the distributed generator has advantage against the large-scale generator is quantitatively discussed.

High Stakes and Acceptance Behavior in Ultimatum Bargaining

This paper presents the results of a within-subject experiment testing whether an increase in the monetary stakes by a factor of 50 – which had never been done before – influences individual behavior in a simple ultimatum bargaining game. Contrary to current wisdom, we found that lowest acceptable offers stated by the responder are proportionally lower in the high-stake condition than in the low-stake condition. This result may be interpreted in terms of the type of utility functions which characterize the subjects. However, in line with prior results, we find that an important increase of the monetary stakes in the ultimatum game has no effect on the offers made by the proposer. Yet, the present research suggests that the reasons underlying these offers are quite different when the stakes are high.

Optimal partitioning of groups in selecting the best choice

This article deals with the group interview problem, in which each group contains several alternatives and each group of alternatives is presented and evaluated sequentially over time. We derive the optimal selection strategy for the group interview problem with a general utility function. Among the various types of utility function, we focus on the best choice problem, in which our utility is one if we successfully select the best choice and zero otherwise. We derive a simple selection rule called the optimal partitioning strategy in which the decision-maker divides the entire groups into two disjoint sets and, after evaluating the choices in the first set, chooses the relatively best choice available for the first time in the second set. Because the selected choice is not necessarily the absolutely best choice, we also consider the probability distribution of the actual rank of the choice selected under the partitioning strategy. Scope and purpose In many managerial decision situations such as buying a car, selling a house, or searching for a job, several alternatives are presented sequentially and an accept-or-reject decision is made immediately after evaluating each alternative. The classical secretary problem and its extensions have been successfully applied to such a sequential search and selection problem. This article deals with a more generalized version of the secretary problem, called the group interview problem, in which several groups of alternatives are presented and evaluated sequentially over time. Based on a stochastic dynamic programming approach, we propose the optimal selection strategy for the group interview problem with various types of the decision-maker's utility function. There are many potential applications of the group interview problem, including consumer search and purchase process, job search problem, sequential assignment of batch jobs, and so on.

A GENERAL EQUILIBRIUM THEORY OF ASSET PRICES, WITH APPLICATIONS TO THE THEORY OF THE TERM STRUCTURE OF INTEREST RATES

THE FIRST PART of this thesis presents a detailed general-equilibrium solution to the portfolio-allocation problem, based on Stone's synthesis of the portfolio models of Tobin, Sharpe, Lintner, and Mossin. The thesis shows that Stone's contribution revolves around introducing utility functions of the Lancaster type, i.e., utility functions whose arguments are a set of characteristics derived from the set of goods demanded by the consumer. Stone applies this approach to portfolio allocation by an investor limiting the characteristics to risk and return in his two-parameter functional representation or TPFR. It has been argued that the consumer and investor have different types of utility functions, the former's having quantities of consumption goods as its arguments and the latter's depending on parameters such as risk and return derived from the portfolio held. By formally establishing the correspondence between Stone's work and Lancaster's we show that the investor's demand for securities and the consumer's demand for consumption goods both result from maximizing utility as a function of characteristics and have the same mathematical structure. Placing Stone's synthesis of portfolio models in the Lancaster framework suggests interesting extensions of the TPFR. Our approach to these extensions draws on a contribution by Bierwag and Grove who argued that as long as portfolios are limited to financial assets, for every asset there must be an offsetting liability in the portfolio of another individual, so that the total amount of each asset outstanding sums to zero. This approach, as opposed to that of Tobin, Sharpe, Lintner, Mossin, and Stone, produces several interesting results. The extended TPFR is solved for explicit equations of security demand bringing the model a step closer to a form suitable for empirical applications. Demand for a security depends on the investor's marginal rate of substitution of risk for return and on two parameters of his subjective probability distribution on future security prices: the mean (expected future price) and some measure of dispersion of possible outcomes (confidence in expectations), not necessarily variance. We go on to investigate the implications of assuming that all investors hold identical subjective probability distributions on future prices (homogeneous expectations or perfect agreement) and show that it leads to zero demand for all risky assets in this type of model, as Bierwag and Grove were the first to demonstrate. The model reproduces their result in a more general context; we do not assume any specific utility function. We conclude that if homogeneous expectations prevail, there must be some real assets in the model, i.e., assets which are not offset by liabilities, if anyone is to hold risky assets in equilibrium. The second part of the thesis develops the extended TPFR into a model explaining the term structure of interest rates, recasting the list of variables from one-period securities into multi-period bonds and introducing real assets (consumer durables) into the investor's portfolio. We use this model to study the micro-economic basis

Demand Functions Derived from the SMAC and Modified SMAC Type of Utility-Functions

Demand Functions Derived from the SMAC and Modified SMAC Type of Utility-Functions

The determination and scaling of a bivariate utility function

A considerable body of evidence exists which indicates a lack of a monotonic relationship between real rewards and the utility for the corresponding outcomes in an experimental game environment. This phenomenon we have called utility function confounding. It often appears that individuals are influenced in their choice behavior not only by their expected rewards, but also by the relative amount of their reward to that of the other party or parties to the game. The authors develop a bivariate utility function designed to accommodate both utility for own reward and for own reward relative to that of the other party. This model is then shown to be general enough to encompass several distinct types of utility function confounding which have appeared in experimental situations. While difficulties remain in the measures required of the model, the authors' main hope is that this neglected aspect of subjects' utilities will receive the attention that it deserves, to the benefit of more meaningful experimentation of interpersonal interactions.

On the Problem of Degrees of Freedom in the Analysis of Consumer Behavior

HE existence of a gap between theoretical models and empirical analysis in economics has been generally recognized. In the analysis of consumer demand, this gap is such that the classical model does not yield workable relationships for measurement purposes because of a lack of degrees of freedom. This deficiency stems from an insufficient specificity in the hypotheses that structure the concepts of the model. In particular, the type of utility function and the optimization process that are assumed to hold in the classical model are of such a level of generality that, except for consistency, they do not restrict appreciably consumer choices and can only provide an ex post rationalization for most observed behaviors. By contrast, and further contributing to the deficit in degrees of freedom, the specification of context within which the model is expected to hold is so specialized that it precludes the possibility of observing repetitions of the same choice process. In the present paper, we propose a way of dealing, at least partially, with the problem of degrees of freedom in the measurement of consumer demand and consumption, while remaining in the framework of a model of utility maximization.

A Bayesian Approach to Some Best Population Problems

Abstract : Certain best population problems adopting a Bayesian approach were investigated. One main advantage of such an approach is that satisfactory decision procedures can be obtained in the presence of nuisance parameters. The use of Bayes' theorem allows one to analyze best population problems from many points of view. A general description of best population problems is outlined. The criterion for 'bestness' is regarded as a 'utility' function of the statistician. The decision procedure then adopted is based upon the principle of maximizing posterior expected utility. The application of this procedure when sampling from normal populations and exponential populations was discussed. The criterion defining the best population is taken to be the coverage of the population considered in a certain given interval. The procedures are shown to be consistent. The decision analysis by considering the pos terior distribution of the criterion is presented. This extension enables other decision procedures to be proposed which may be more appropriate in certain situations than that resulting from the principle of maximizing posterior expected utility.