Maker's Attitude Research Articles

A survey analysis of service quality for domestic airlines

This paper presents an effective approach for evaluating service quality of domestic passenger airlines by customer surveys. To reflect the inherent subjectiveness and imprecision of the customers' perceptions to the quality levels provided by airlines with respect to multiple service attributes, crisp survey results are represented and processed as fuzzy sets. A fuzzy multicriteria analysis (MA) model is used to formulate the evaluation problem. The model is solved by an effective algorithm which incorporates the decision maker's attitude or preference for customers' assessments on criteria weights and performance ratings. An empirical study of domestic airlines on a highly competitive route in Taiwan is conducted to demonstrate the effectiveness of the approach. The evaluation outcome provides airlines with their internal and external competitive advantages, relative to competitors in terms of customer-perceived quality levels of service.

The Shape of Utility Functions and Organizational Behavior

Based on measurements among 332 owner-managers, we investigate how the global shape of the utility function (i.e., S-shaped versus concave or convex over the total range of outcomes) relates to choice behavior. We find that the global shape of the utility function differs across decision makers (about one-third of the owner-managers exhibit an S-shaped utility function) and that the global shape is linked to organizational behavior (i.e., the production system employed), a result that does not change when using different methods to identify the decision maker's global shape of the utility function. The decision maker's risk attitude (risk averse or risk seeking) does not affect the choice of the production system. Whereas the degree of risk aversion, based on the local shape of the utility function, may be important in explaining owner-managers' trading behavior (Pennings and Smidts 2000), more structural organizational behavior appears to be linked to the global shape of the utility function.

A Most Probable Point-Based Method for Efficient Uncertainty Analysis

Abstract Uncertainty is inevitable at every stage of life cycle development of a product. To make use of probabilistic information and to make reliable decisions by incorporating decision maker's risk attitude, methods for propagating the effect of uncertainty are therefore needed. When designing complex systems, the efficiency of methods for uncertainty propagation becomes critical. In this paper, a most probable point (MPP)-based uncertainty analysis (MPPUA) method is proposed. The concept of the MPP is utilized to generate the cumulative distribution function (CDF) of a system output by evaluating probability estimates at a serial of limit states across a range of output performance. To improve the efficiency of locating the MPP, a novel MPP search algorithm is presented that employs a new search algorithm and a search strategy. A mathematical example and an engine design problem are used to verify the effectiveness of the proposed method. The proposed MPPUA method will serve as a useful tool for propa...

A Most Probable Point-Based Method for Efficient Uncertainty Analysis

Abstract Uncertainty is inevitable at every stage of life cycle development of a product. To make use of probabilistic information and to make reliable decisions by incorporating decision maker's risk attitude, methods for propagating the effect of uncertainty are therefore needed. When designing complex systems, the efficiency of methods for uncertainty propagation becomes critical. In this paper, a most probable point (MPP)-based uncertainty analysis (MPPUA) method is proposed. The concept of the MPP is utilized to generate the cumulative distribution function (CDF) of a system output by evaluating probability estimates at a serial of limit states across a range of output performance. To improve the efficiency of locating the MPP, a novel MPP search algorithm is presented that employs a new search algorithm and a search strategy. A mathematical example and an engine design problem are used to verify the effectiveness of the proposed method. The proposed MPPUA method will serve as a useful tool for propagating the effect of uncertainty when making decisions under uncertainty.

Use of fuzzy numbers in project risk (criticality) assessment

We put forward a fuzzy way of measuring the criticality of project activities and of the whole project. In the proposed approach both the decision maker attitude and the project network structure are taken into account. The criticality measure obtained may serve as a measure of risk or of the supervision effort needed and can help in making the decision whether to accept or to reject the project. Its numerical implementation is as difficult as that of the classical CPM method.

A GENERAL APPROACH TO DECISION MAKING WITH INTERVAL PROBABILITIES

We propose a general method for decision making in situations in which our information about the relevant probabilities is available in the form of intervals. A key component of this approach is our algorithm which is used to resolve the imprecise probabilities. This algorithm makes considerable use of the decision maker's attitude as expressed in a scale with optimistic and pessimistic as its poles.

Fuzzy modeling for intelligent decision making under uncertainty

We consider here the problem of decision making under uncertainty. We suggest an approach for the construction of decision functions which allow for the inclusion of probabilistic information as well as for the inclusion of information about the decision maker's attitude and preferences. Use is made of the fuzzy modeling technology to construct these functions from specifications provided by the decision maker.

Multiple risks in multi-objective forest planning: integration and importance

Multiple risks were integrated in multi-objective forest planning with the scenario technique and with an application of multi-attribute utility theory. The sources of risks were divided into three categories: risks related to inventory data, risks arising from unknown future states of nature, and risks associated with the decision maker's preferences. Stochastic models and other procedures for producing scenarios of inventory data, growth rate, mortality and natural regeneration of trees, success of tree planting, timber prices, cost level, and importance of management objectives are described. Some of the models for the scenarios were based on empirical data and obtained from earlier investigations, while others were specified by parameters given by the decision maker. The decision maker's attitude toward risk was integrated in planning via the distribution of a utility index. The utility index was computed from an additive utility function. Cumulative points of the distribution were weighed in a manner reflecting decision maker's risk preferences. The weighed utility index was used as the ultimate criterion when searching for the optimal plan. Optimization was done using a heuristic algorithm. The case study conducted indicated that both risks and attitude toward risks affect the choice of plan; optimal plans produced by a deterministic system were different from the stochastic optima, and a risk-neutral decision maker had optima different from those of risk-averse and risk-seeking decision makers. Depending on the decision maker's attitude toward risk, utility losses ranging from 0.1% to 4.5% were to be expected when the deterministic optimum was selected. Implementing a risk-neutral decision maker's optimal plan caused utility losses less than 3.5% for risk-averse and risk-seeking decision makers.

A method for stochastic multiobjective optimization of stand management

The paper describes a way of simultaneously optimizing several stand management objectives differing in measurement units and character. The simulation-optimization system accommodates, besides multiple objectives, the time and risk preferences of the decision maker. Multiple objectives are integrated in optimization through an additive utility function. Time preferences are dealt with by discounting, and the discounting rate is specified separately for each objective. Objectives related to products are described by the sum of the discounted values while objectives related to the status of the forest are described by the integral of the discounted value of a state variable. Different units are made commensurable via subutility functions that scale all objective variables between zero and one. Risks associated with the future growth level and timber prices, and with the preferences of the decision maker, are taken into account by stochastic optimization based on the scenario technique. With this technique, each management alternative produces a distribution of utility indices. This distribution is used to compute a utility index corrected by risk preferences; different parts of the distribution are weighted in a manner that reflects the decision maker's attitude toward risk. A case simulation-optimization system is described, based on the above techniques, to deal with multiple objectives, time preferences, risks and risk preferences. Calculations using this system indicated that security and recreational amenities lengthen the optimal rotation of mixtures of Scots pine and Norway spruce, compared to a situation in which the maximal soil expectation value is the only objective. In a multiobjective case, stochastic growth, timber price and preferences increased the rotation length by 15 years (17%) from the deterministic optimum. Attitude toward risk also affected the optimal stand management.

A research agenda for muti-attribute utility analysis in human resource management

Most applications of utility analysis in Human Resource Management have focussed upon only one outcome of a selection system-the value of job performance in dollars. Multi-attribute utility (MAU) analysis allows decision makers to incorporate multiple outcomes into their analytic decisions. MAU also increases the participation of decision makers in the utility analysis process by asking them what factors to consider, how to measure the factors, and what functions should be used to combine them. This manuscript outlines the steps necessary to conduct MAU analysis and delineates research issues that arise from using a MAU approach. Based on these issues, we suggest systematic research questions and directional hypotheses which will improve our understanding and implementation of MAU procedures in organizations. The topics, derived from utility analytic and groups research literatures, include contrasting single attribute utility analysis with MAU, examining the effect of MAU on decision maker attitudes, the ability of MAU to explicate managerial policies, and improving existing MAU approaches using results from the group research literature.

Optimal greenhouse gas emissions under various assessments of climate change ambiguity

The probabilities associated with global warming damage are likely to be continuously revised in the light of new information. Such revisions of probability are the defining characteristic of ambiguity, as opposed to risk. This paper examines how climate change ambiguity may affect optimal greenhouse gas emission strategies, via the decision maker's attitude towards anticipated changes of damage probabilities. Two conceptualizations of ambiguity are distinguished, according to the emphasis placed on the ambiguity of priors or on the ambiguity of news, respectively. It is shown that the way in which ambiguity is viewed and the attitude taken towards it have a substantial influence on the optimal emission trajectory.

Pricing Decisions Under Demand Uncertainty: A Bayesian Mixture Model Approach

The advent of optical scanning devices and decreases in the cost of computing power have made it possible to assemble databases with sales and marketing mix information in an accurate and timely manner. These databases enable the estimation of demand functions and pricing/promotion decisions in “real” time. Commercial suppliers of marketing research like A. C. Nielsen and IRI are embedding estimated demand functions in promotion planning and pricing tools for brand managers and retailers. This explosion in the estimation and use of demand functions makes it timely and appropriate to re-examine several fundamental issues. In particular, demand functions are latent theoretical constructs whose exact parametric form is unknown. Estimates of price elasticities, profit maximizing prices, inter-brand competition and other policy implications are conditional on the parametric form assumed in estimation. In practice, many forms may be found that are not only theoretically plausible but also consistent with the data. The different forms could suggest different profit maximizing prices leaving it unclear as to what is the appropriate pricing action. Specification tests may lack the power to resolve this uncertainty, particularly for non-nested comparisons. Also, the structure of these tests does not permit seamless integration of estimation, specification analysis and optimal pricing into a unified framework. As an alternative to the existing approaches, I propose a Bayesian mixture model (BMM) that draws on Bayesian estimation, inference, and decision theory, thereby providing a unified framework. The BMM approach consists of input, estimation, diagnostic and optimal pricing modules. In the input module, alternate parametric models of demand are specified along with priors. Utility structures representing the decision maker's attitude towards risk can be explicitly specified. In the estimation module, the inputs are combined with data to compute parameter estimates and posterior probabilities for the models. The diagnostic module involves testing the statistical assumptions underlying the models. In the optimal pricing module the estimates and posterior probabilities are combined with the utility structure to arrive at optimal pricing decisions. Formalizing demand uncertainty in this manner has many important payoffs. While the classical approaches emphasize choosing a demand specification, the BMM approach emphasizes constructing an objective function that represents a mixture of the specifications. Hence, pricing decisions can be arrived at even when there is no consensus among the different parametric specifications. The pricing decisions will reflect parametric demand uncertainty, and hence be more robust than those based on a single demand model. The BMM approach was empirically evaluated using store level scanner data. The decision context was the determination of equilibrium wholesale prices in a noncooperative game between several leading national brands. Retail demand was parametrized as semilog and doublelog with diffuse priors for the models and the parameters. Wholesale demand functions were derived by incorporating the retailers' pricing behavior in the retail demand function. Utility functions reflecting risk averse and risk neutral decision makers were specified. The diagnostic module confirms that face validity measures, residual analysis, classical tests or holdout predictions were unable to resolve the uncertainty about the parametric form and by implication the uncertainty with regard to pricing decisions. In contrast, the posterior probabilities were more conclusive and favored the specification that predicted better in a holdout analysis. However, across the brands, they lacked a systematic pattern of updating towards any one specification. Also, none of the priors updated to zero or one, and there was considerable residual uncertainty about the parametric specification. Despite the residual uncertainty, the BMM approach was able to determine the equilibrium wholesale prices. As expected, specifications influence the BMM pricing solutions in accordance with their posterior probabilities which act as weights. In addition, differing attitudes towards risk lead to considerable divergence in the pricing actions of the risk averse and the risk neutral decision maker. Finally, results from a Monte Carlo experiment suggest that the BMM approach performs well in terms of recovering potential improvements in profits.

Statistical approximations for recourse constrained stochastic programs

The two stage stochastic program with recourse is known to have numerous applications in financial planning, energy modeling, telecommunications systems etc. Notwithstanding its applicability, the two stage stochastic program is limited in its ability to incorporate a decision maker's attitudes towards risk. In this paper we present an extension via the inclusion of a recourse constraint. This results in a convex integrated chance constraint (ICC), which inherits the convexity properties of two stage programs. However, it also inherits some of the difficulties associated with the evaluation of recourse functions. This motivates our study of conditions that may be applicable to algorithms using statistical approximations of such ICC. We present a set of sufficient conditions that these approximations may satisfy in order to assure convergence. Our conditions are satisfied by a wide range of statistical approximations, and we demonstrate that these approximations can be generated within standard algorithmic procedures.

Proper efficiency and cardinal utilities in multicriteria decision making

The concept of proper efficiency has been characterized in several ways in the Multicriteria Decision Making (MCDM) literature. Geoffrion has optimized a positively weighted sum of the multicriteria value functions, and Choo and Atkins have used an extended form of the generalized Tchebycheff norm. This paper investigates proper efficiency in MCDM when it is possible to represent the decision maker's risk attitudes and preferences by cardinal (especially additive cardinal) utilities. Proper efficiency is characterized by optimizing associated utility-related scalar optimization problems. More detailed relationships among proper efficiency, measure of risk aversion and allocation strategies are discussed in an asset allocation context.

An approach to ordinal decision making

We describe the problem of decision making under ignorance and discuss the need of introducing a decision attitude or disposition in solving this problem. We show the OWA operators provide a general framework for evaluating the alternatives when the payoffs are numeric values. We then turn to the problem of decision making when the payoffs are linguistic values drawn from an ordinal scale. A solution to this problem is then suggested using an ordinal version of the OWA aggregation method. We emphasize the role of the OWA weighting vector as a means for introducing the decision maker's attitude and investigate the representation of various prototypical decision attitudes. Two measures are provided for classifying the OWA weighting vectors used in the aggregations. The first of these measures is the degree of optimism implied by the vector. The second measures the degree of dispersion associated with the vector. In this work the interpretation of the decision making attitude, via the OWA weighting vector, as a kind of probability distribution is highlighted. In particular the ith component in the vector is interpreted as the probability that the ith best outcome will occur. This view helps unify the problems of decision making under ignorance and decision making under uncertainty and allows us to see the evaluation of an alternative, in both cases, as the formulation of an expected value. Furthermore this interpretation leads to a viewing of the measure of dispersion as a kind of entropy. In the ordinal environment this leads to the development of a new measure of entropy for ordinal probabilities.

Efficiency measurement of production operations under uncertainty

This paper focuses on the non-parametric efficiency measurement of production operations under uncertainties. The authors extend the existing data envelopment analysis method to deal with inputs and outputs subject to random disturbances. They propose a new approach to performance evaluation using certainty equivalence of utility functions, which can accommodate decision maker's risk attitude. They also formulate appropriate models to implement these approaches.

A decision analysis view of the effect of robot repeatability on profit

The purpose of this paper is to view recent developments in non-von Neumann and Morgenstern utility functions (vNM) via a stationary statistical-economic model. Such a model is suggested in the literature for evaluating robot precision, assuming that repeatability follows a Rayleigh distribution. Various vNM and non-vNM utility forms are discussed and illustrated for the robot evaluation and selection problem. The approach suggested here may be applicable for companies operating under profit centers where market systematic risk data are not available and decisions are based on the decision maker's (DM) attitude toward risk. Although the numerical illustrations demonstrate a consistency among the expected profit measure for most utility forms that were investigated, the analysis provides an extended framework for dealing with risky production problems. Furthermore, it illustrates the sensitivity of robot operational decisions such as speed and of both the output rate per time unit and the product quality to the DM's attitude toward risk. It allows the DM to analyze and explain decisions as affected by the selection of the utility form, as well as the technological and economic parameters and measures describing the problem, particularly the technical risk involved in the determination of a robot operational problem.

Observing different orders of risk aversion

A decision maker's attitude towards risk is said to be of orderi, i=1, 2, if for every given riskē with expected value zero, the risk premium the decision maker is willing to pay to avoid the risktē goes witht to zero at the same order asti. This article presents an experiment testing the order of decision makers' attitudes toward risk. Its major result is that both attitudes exist, each in significant proportions. Moreover, two classes of first-order behavior are defined. The rank-dependent model (Quiggin, 1982) belongs to one, the disappointment aversion model (Gul, 1991) to the other. We show that only the first of these two classes appears among our subjects.

The Role of Cardinal Utilities in Multiple Objective Programming

SYNOPTIC ABSTRACTOrdinal utilities are often used in investigating different types of efficient solutions in the multiple objective programming literature. The use of ordinal utilities implies that the decision making process does not involve risky alternatives, and very few of the important decisions in the real world can be successfully made without carefully considering the risk phenomenon. This paper examines the multiple objective programming problem by taking a decision maker's preferences and risk attitudes into account. The characterization of a particular type of efficiency, proper efficiency, is made via optimizing the associated utility-related scalar problems.

Risk seeking with diminishing marginal utility in a non-expected utility model

The present work takes place in the framework of a non-expected utility model under risk: the RDEU theory (Rank Dependent Expected Utility, first initiated by Quiggin under the denomination of Anticipated Utility), where the decision maker's behavior is characterized by two functionsu andf. Our first result gives a condition under which the functionu characterizes the decision maker's attitude towards wealth. Then, defining a decision maker as risk averter (respectively risk seeker) when he always prefers to any random variable its expected value (weak definition of risk aversion), the second result states that a decision maker who has an increasing marginal utility of wealth (a convex functionu) can be risk averse, if his functionf is“sufficiently below” his functionu, hence if he is sufficiently“pessimistic.” Obviously, he can also be risk seeking with a diminishing marginal utility of wealth. This result is noteworthy because with a stronger definition of risk aversion/risk seeking, based on mean-preserving spreads, Chew, Karni, and Safra have shown that the only way to be risk averse (in their sense) in RDEU theory is to have, simultaneously, a concave functionu and a convex functionf.