Estimation Of Utility Functions Research Articles

Optimization of day-ahead pricing electricity markets based on a simplified methodology for stochastic utility function estimation

In the context of smart markets’ popularization, day-ahead pricing of electricity has been deployed more commonly as an attempt to improve the grid’s operation and maximize the socioeconomic welfare. However, estimating the utility function is necessary to maximize the socioeconomic welfare, which is not a trivial task. The utility function models the quality of life added by electricity consumption and depends on several factors, such as the consumer’s class (residential, commercial, industrial, etc.), income, climate conditions, and individual preferences. This paper presents a simplified methodology to estimate the utility function in the context of day-ahead pricing. The main benefit of the proposed method is that its application is straightforward since the modeling is mainly based on simple matrix equations. The demand elasticities (self- and cross-elasticities), which are general concepts in economics, are addressed as input parameters. Due to the inherent uncertainties related to human behavior, a stochastic utility function is proposed to enhance the model’s accuracy/robustness. The proposed model particularly thrives when the effects of cross-elasticities are significant but not massive (although procedures can be applied to mitigate the error if higher cross-elasticities are verified). By applying the proposed model to a hypothetical smart microgrid with 100 consumers and 24 daily periods (hourly prices), results demonstrate that it successfully provides the rates that maximize the socioeconomic welfare created by the market. In the case study, the conventional quadratic utility function implies an error of 73%, whereas the proposed model an error of 10%, highlighting the usefulness of the proposed model.

Optimization of Day-Ahead Pricing Electricity Markets Based on a Simplified Methodology for Stochastic Utility Function Estimation

Optimization of Day-Ahead Pricing Electricity Markets Based on a Simplified Methodology for Stochastic Utility Function Estimation

Social Welfare Effects of Progressive Income Taxation under Increasing Inequality

During the 2008 economic crisis, labour market inactivity, unemployment and work informality in Serbia rose substantially, triggering a salient increase in Gini-measured inequality (by 4.3 pp), while income tax progressivity remained very low. Using the micro-simulation and utility function estimation techniques on 2007 and 2012 household survey data for Serbia, we compare the social welfare effects of a hypothetical shift from flat to progressive taxation, before and after the crisis. We find that a shift from flat to progressive tax and the consequent behavioural response lead to a reduction in inequality, a rise in total labour supply and an increase in the overall social welfare in both years. Although the decrease in inequality is higher in 2012, the overall welfare effects are slightly larger in 2007, due to the stronger labour supply response and the stronger disutility of work found in the latter year. This may suggest that a rise in inequality does not per se create a stronger case for progressive taxation, as the welfare effects are considerably driven by the structure of income-leisure preferences.

Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations

Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multitime and multi-lateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the counting method, which counts the number of values offered, considering the time elapsed when they were offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with existing utility function estimation methods without boosting.

Discounting behavior: A reconsideration

We re-evaluate the theory, experimental design and econometrics behind claims that individuals exhibit non-constant discounting behavior. Theory points to the importance of controlling for the non-linearity of the utility function of individuals, since the discount rate is defined over time-dated utility flows and not flows of money. It also points to a menagerie of functional forms to characterize different types of non-constant discounting behavior. The implied experimental design calls for individuals to undertake several tasks to allow us to identify these models, and to several treatments such as multiple horizons and the effect of allowing for a front end delay on earlier payments. The implied econometrics calls for structural estimation of th`e theoretical models, allowing for joint estimation of utility functions and discounting functions. Using data collected from a representative sample of 413 adult Danes in 2009, we draw surprising conclusions. Assuming an exponential discounting model we estimate discount rates to be 9% on average. We find no evidence to support quasi-hyperbolic discounting or “fixed cost” discounting, and only modest evidence to support other specifications of non-constant discounting. Furthermore, the evidence for non-constant discounting, while statistically significant, is not economically significant in terms of the size of the estimated discount rates. We undertake extensive robustness checks on these findings, including a detailed review of the previous, comparable literature.

An application of general maximum entropy to utility

Methodologies related to information theory have been increasingly used in studies in economics and management. In this paper, we use generalised maximum entropy as an alternative to ordinary least squares in the estimation of utility functions. Generalised maximum entropy has some advantages: it does not need such restrictive assumptions and could be used with both well and ill-posed problems, for example, when we have small samples, which is the case when estimating utility functions. Using linear, logarithmic and power utility functions, we estimate those functions and confidence intervals and perform hypothesis tests. Results point to the greater accuracy of generalised maximum entropy, showing its efficiency in estimation.

Estimating utility functions using generalized maximum entropy

This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods.

Living Healthy and Living Long: Valuing the Nonpecuniary Loss from Disability and Death

This analysis uses three valuation approaches—risk–risk tradeoff, paired risk–dollar comparison, and utility function estimation—to estimate the nonpecuniary cost associated with disability in late life. In addition, we obtain an estimate of the value of life using a paired risk–dollar comparison. The data were obtained from interviews with 548 persons using an iterative computerized questionnaire. Respondents reported a median value of life of $12 million. They were willing-to-pay .7–1.4 million to avoid disability in late life or approximately $47–$95 thousand for each year of disability over age 62. The results were robust to the valuation technique employed.

Estimates of optimal public capital stocks in Japan using a public investment discount rate framework

The purpose of this paper is to empirically assess the optimality of the level of public capital in Japan. We use a methodological approach based on Burgess's (1988) procedure for calculating the public discount rate. This approach involves estimating a production function, but does not necessarily require utility function estimation. The results indicate that, although the Japanese economy experienced a public capital deficiency over the period 1960–1982, public capital moved toward optimal levels throughout the period.

Structuring a Multiproduct Sales Quota-Bonus Plan for a Heterogeneous Sales Force: A Practical Model-Based Approach

This paper presents an agency theoretic model-based approach that assists sales managers in determining the profit-maximizing structure of a common multiproduct sales quota-bonus plan for a geographically specialized heterogeneous sales force operating in a repetitive buying environment. This approach involves estimating each salesperson's utility function for income and effort and using these models to predict individual sales achievements and the associated aggregate profit for the firm under a specified plan. The utility function estimation is based on the salesperson's own preference rank-ordering of alternative sales quota-bonus plans. Once these functions have been estimated, they are incorporated in a mathematical programming model that the sales manager can use to determine the best plan. The authors demonstrate the approach in the context of a case involving the design of a two-product sales quota-bonus plan for a set of salespeople at a pharmaceutical products firm.

An empirical evaluation of two assessment methods for utility measurement for life years

Measurements of utility functions over life years provide useful information for decision making in the health care field. However, biases in the assessment procedures of utility functions is a well-known and documented phenomenon. In this paper we investigate possible biases in the assessment of utility functions when two different methods (direct and indirect assessment) are used. More specifically, we examine the estimation of utility functions over different lengths of life. The main findings, obtained from an empirical investigation in which the two assessment techniques were applied to a sample of students, are: (a) the use of the different methods does not lead to significant differences in the utility evaluation from a social point of view (health program evaluation); (b) the use of the different methods does lead to significant differences in the utility evaluation from an individual point of view (clinical decision making); (c) in both methods risk aversion was found to be common for shorter periods of time while risk prone behavior, when it exists, was found mainly for longer periods of time.

Utility Functions for Test Performance

This paper discusses a utility function estimation procedure developed to provide curriculum planning information to elementary school principals. Both theoretical and empirical studies were performed to evaluate the procedure. The results of the use of this approach to obtain data from a national sample of principals are presented.

APPLICATIONS OF A RISK AVERSION CONCEPT

ABSTRACTIn decision theory the concept denoted variously as “risk aversion increment” or “risk premium” has not been fully exploited, although it is neither new nor complex. In this paper we will show how the concept of the risk aversion increment can be used for developing an alternative to the explicit use of the utility function. For most people the use of a risk aversion increment provides a better conceptual reference than does the use of a utility function.To illustrate the usefulness of the concept as a basis for gaining insight into problem statements and their analysis, the following applications are developed:1) general results for the exponential utility function. 2) estimation of utility functions. 3) general results for various combinations of utility functions and probability distributions. 4) use in sequential decisions. 5) application in the theory of incentives.