Modifications of the Hurwicz’s decision rule

The Hurwicz’s criterion is one of the classical decision rules applied in decision making under uncertainty as a tool enabling to find an optimal pure strategy both for interval and scenarios uncertainty. The interval uncertainty occurs when the decision maker knows the range of payoffs for each alternative and all values belonging to this interval are theoretically probable (the distribution of payoffs is continuous). The scenarios uncertainty takes place when the result of a decision depends on the state of nature that will finally occur and the number of possible states of nature is known and limited (the distribution of payoffs is discrete). In some specific cases the use of the Hurwicz’s criterion in the scenarios uncertainty may lead to quite illogical and unexpected results. Therefore, the author presents two new procedures combining the Hurwicz’s pessimism-optimism index with the Laplace’s approach and using an additional parameter allowing to set an appropriate width for the ranges of relatively good and bad payoffs related to a given decision. The author demonstrates both methods on the basis of an example concerning the choice of an investment project. The methods described may be used in each decision making process within which each alternative (decision, strategy) is characterized by only one criterion (or one synthetic measure).     

Decision making under uncertainty with fuzzy targets

This paper discusses the issue of how to use fuzzy targets in the target-based model for decision making under uncertainty. After introducing a target-based interpretation of the expected value on which it is shown that this model implicitly assumes a neutral behavior on attitude about the target, we examine the issue of using fuzzy targets considering different attitudes about the target selection of the decision maker. We also discuss the problem for situations on which the decision maker’s attitude about target may change according to different states of nature. Especially, it is shown that the target-based approach can provide an unified way for solving the problem of fuzzy decision making with uncertainty about the state of nature and imprecision about payoffs. Several numerical examples are given for illustration of the discussed issues.     

Dynamic decision making without expected utility: An operational approach

Non-expected utility theories, such as rank dependent utility (RDU) theory, have been proposed as alternative models to EU theory in decision making under risk. These models do not share the separability property of expected utility theory. This implies that, in a decision tree, if the reduction of compound lotteries assumption is made (so that preferences at each decision node reduce to RDU preferences among lotteries) and that preferences at different decision nodes are identical (same utility function and same weighting function), then the preferences are not dynamically consistent; in particular, the sophisticated strategy, i.e., the strategy generated by a standard rolling back of the decision tree, is likely to be dominated w.r.t. stochastic dominance. Dynamic consistency of choices remains feasible, and the decision maker can avoid dominated choices, by adopting a non-consequentialist behavior, with his choices in a subtree possibly depending on what happens in the rest of the tree. We propose a procedure which: (i) although adopting a non-consequentialist behavior, involves a form of rolling back of the decision tree; (ii) selects a non-dominated strategy that realizes a compromise between the decision maker’s discordant goals at the different decision nodes. Relative to the computations involved in the standard expected utility evaluation of a decision problem, the main computational increase is due to the identification of non-dominated strategies by linear programming. A simulation, using the rank dependent utility criterion, confirms the computational tractability of the model.     

Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis

Cross efficiency method is an extension of data envelopment analysis (DEA), and has been widely used for ranking performance of decision making units (DMUs). To eliminate the non-uniqueness of cross efficiency scores, the aggressive and benevolent strategies have been proposed as secondary goals to determine the unique cross efficiency score. The current paper aims to propose an alternative strategy which does not consider the preference of the decision maker in choosing aggressive or benevolent strategy. Instead, the paper considers all possible weight sets in weight space when computing the cross efficiency and each DMU is given an interval cross efficiency. By using the stochastic multicriteria acceptability analysis (SMAA-2) method, all DMUs in the interval cross efficiency matrix (CEM) could be fully ranked according to the acceptability indices. A numerical example about efficiency evaluation to seven academic departments in a university is illustrated.     

Multiattribute utility analysis for policy selection and financing for the preservation of the forest

In this paper, we examine effective policies for financing and activities for the preservation of the forest on Mount Ryuoh in the city of Higashi-Hiroshima by multiattribute utility analysis. In multiattribute utility analysis, we deal with decision making problems with multiple attributes and select the most effective solution among several alternatives by deriving preference of the decision maker. Although in our decision making problem, the decision maker is a representative of a hypothetical nonprofit organization established for the preservation of the forest, the decision maker gives serious consideration to intentions of several groups of people receiving the benefit from the mountain, and then from this viewpoint, our problem can be interpreted as a group decision making problem.     

A Radon-Nikodym approach to measure information

We consider a decision maker facing uncertainty which behaves as a subjective expected utility maximizer. The value of information is traditionally captured as a greater expected utility the decision maker can achieve by selecting a best strategy as information arrives. We deal with the limit process of being better informed, and introduce an information density function depending solely on the states that gives an exact least upper bound to being more informed. This information density function is given by a Radon-Nikodym-type theorem for set functions and is explicitly computed for the countable case.     

Searching for psychologically stable solutions of multiple criteria decision problems

In interactive decision making, a choice behavior of the decision maker may differ depending on proximity of a current solution to satisfactory values of objectives. An interactive approach proposed in this paper allows the decision maker to use different search principles depending on his/her perception of the achieved values of objectives and trade-offs. While an analysis of the values of objectives may guide the initial search for a final solution, it can be replaced by trade-off evaluations at some later stages. Such an approach allows the decision maker to change search principles, and to identify a psychologically stable solution to the multiple criteria decision problem.     

P2q hierarchical decomposition algorithm for quantile optimization: application to irrigation strategies design

Decision theory dealing with uncertainty is usually considering criteria such as expected, minimum or maximum values. In economic areas, the quantile criterion is commonly used and provides significant advantages. This paper gives interest to the quantile optimization in decision making for designing irrigation strategies. We developed P2q, a hierarchical decomposition algorithm which belongs to the branching methods family. It consists in repeating the creation, evaluation and selection of smaller promising regions. Opposite to common approaches, the main criterion of interest is the α-quantile where α is related to the decision maker risk acceptance. Results of an eight parameters optimization problem are presented. Quantile optimization provided optimal irrigation strategies that differed from thus reached with expected value optimization, responding more accurately to the decision maker preferences.     

Approaches to multistage one-shot decision making

In this research, multistage one-shot decision making under uncertainty is studied. In such a decision problem, a decision maker has one and only one chance to make a decision at each stage with possibilistic information. Based on the one-shot decision theory, approaches to multistage one-shot decision making are proposed. In the proposed approach, a decision maker chooses one state amongst all the states according to his/her attitude about satisfaction and possibility at each stage. The payoff at each stage is associated with the focus points at the succeeding stages. Based on the selected states (focus points), the sequence of optimal decisions is determined by dynamic programming. The proposed method is a fundamental alternative for multistage decision making under uncertainty because it is scenario-based instead of lottery-based as in the other existing methods. The one-shot optimal stopping problem is analyzed where a decision maker has only one chance to determine stopping or continuing at each stage. The theoretical results have been obtained.     

INSDECM—an interactive procedure for stochastic multicriteria decision problems

A new interactive technique for a discrete stochastic multiattribute decision making problem is proposed in this paper. It is assumed that performance probability distribution for each action on each attribute is known. Two concepts are combined in the procedure: stochastic dominance and interactive approach. The first one is employed for generating efficient actions and constructing rankings of actions with respect to attributes. The second concept is used when the communication between the DM and the model is conducted. It is assumed that decision maker’s restrictions are defined by specifying minimal or maximal values of scalar criteria measuring either expected outcome or variability of outcomes. As such restrictions are, in general, not consistent with stochastic dominance rules, we suggest verifying this consistency and asking the decision maker to redefine inconsistent restrictions.     

Resource allocation based on cost efficiency

In this paper, we consider a resource allocation (RA) problem and develop an approach based on cost (overall) efficiency. The aim is to allocate some inputs among decision making units (DMUs) in such way that their cost efficiencies improve or stay unchanged after RA. We formulate a multi-objective linear programming problem using two different strategies. First, we propose an RA model which keeps the cost efficiencies of units unchanged. This is done assuming fixed technical and allocative efficiencies. The approach is based on the assumption that the decision maker (DM) may not have big changes in the structure of DMUs within a short term. The second strategy does not impose any restrictions on technical and allocative efficiencies. It guarantees that none of the cost efficiencies of DMUs get worse after RA, and the improvement for units is possible if it is feasible and beneficial. Two numerical examples and an empirical illustration are also provided.     

Decision Making with Linear Partial Information (L.P.I.)

This paper deals with decision making in a set of circumstances intermediate between the classical extremes of pure risk and pure uncertainty. It extends the Cannon-Kmietowicz-Pearman methods of finding maximum and minimum expected values of strategies, when probabilities of occurrence of future states of nature can be ranked, to the more general case investigated by Kofler and Menges, in which a set of linear constraints is imposed on the probabilities. A general solution to the problem is obtained, and its relationship to earlier results is discussed. A new criterion for strategy choice, in the condition mentioned above, is also proposed. It recommends selection of a strategy with the highest minimum expected utility (max E_min). The criterion arises naturally from the extension of the von Neumann-Morgenstern utility theory to the case of incomplete knowledge about the probabilities. These developments greatly enhance the attractiveness of the earlier results for applied work and lay foundations for further theoretical advances.     

Some issues in mathematical modelling of multiple criteria decision-making problems

Models for analyzing and solving multiple criteria decision-making (MCDM) problems are difficult to evaluate and compare, because they are intended for diverse orderings of a set of feasible alternatives. These models are based on a variety of assumptions about the decision maker's preferences and use different types of preference information. In this paper, a conceptual framework is developed for evaluating and comparing discrete alternative MCDM models available for a given decision situation. The procedure employed in the framework guides the user through an analysis of the decision situation making it possible for a decision maker or analyst to select the most appropriate MCDM model from among several alternative feasible models.     

The optimal sequential information acquisition structure: A rational utility-maximizing perspective

We consider a rational utility maximizer decision maker (DM) who must gather two pieces of information from a set of multidimensional products before making a choice. We analyze the resulting sequential information acquisition process where the DM tries to find the best possible product subject to his information acquisition constraint. In addition, we introduce publicly observable signals that allow the DM to update his expected utility functions following a standard Bayesian learning rule. Even though it seems intuitively plausible to assume that the transmission of positive and credible information may lead DMs to accept any product signaled more eagerly, this paper illustrates how transmitting credible positive information is not sufficient to decrease the rejection probability faced by the information sender on its set of products. A significant difference in product rejection probabilities arises depending on the characteristic on which signals are issued, as will be illustrated numerically for both risk-neutral and risk-averse DMs.     

Interactive multiple objective programming subject to flexible constraints

An interactive decision support system is introduced which aids in solving multiple objective programming problems subject to strict and flexible constraints. Integral part is an extension of a well-known fuzzy sets approach evaluating possible solutions by their degrees of membership to objectives and constraints. This approach is linked to classical multiple objective programming models. If the decision maker cannot determine membership functions a priori the system suggests functions dependent on the given information and interactive modifications are allowed.     

有限理性条件下考虑决策者动态期望的多属性决策方法

有限理性条件下针对带有决策者期望的多属性决策问题,提出一种基于累积前景理论的决策分析方法。在本文中,首先考虑了决策者的有限理性的心理行为特征,以决策者在不同时期对各属性的特定期望作为参照点,然后将具有正态分布信息形式的决策矩阵转化为相对于各参照点的益损决策矩阵,在此基础上,考虑决策者对待收益和损失的不同理性态度,依据累积前景理论计算各时期中每个方案的前景值,并计算关于整个时期的综合前景值,然后依据综合前景值的大小对所有方案进行排序。最后,通过一个算例说明了该方法的可行性和有效性。     

Fuzzy expected utility

The aim of this paper is to state the foundations of fuzzy decision theory under uncertainty. Attention is focused on the analysis of a fuzzy expected utility.Given a fuzzy decision problem, which does not rest on a Borel field but on a weak Borel field, and according to the information available, we shall use successively the probability and the possibility of a fuzzy subset.When the imprecise preference structure of the decision maker verifies four axioms, we can determine a function of fuzzy expected utility. This latter is a numerical translation of the imprecise preferences of the decision maker, in an uncertain world.     

Optimal initial capital induced by the optimized certainty equivalent

This paper proposes the notion of optimal initial capital (OIC) induced by the optimized certainty equivalent (OCE), as discussed in Ben-Tal and Teboulle (1986) and Ben-Tal and Teboulle (2007). It also investigates the properties of the OIC with various types of utility functions. It is shown that the OIC can be a monetary utility function (negative value of risk measure) for future payoffs with the decision maker’s concrete criteria in the background.     

On the Role of the Group Composition for Achieving Optimality

We show the inability of any pure strategy imitation rule for leading a decision maker towards optimality for given and fixed population behaviour. The intuition is that a pure strategy state space is too small to deal with a large variety of environments. This result helps to understand the optimality result obtained by Schlag (1998), where the population behaviour is let to evolve over time. The intuition is that the group composition provides an additional state space in which information about the environment can be accumulated.     

Generalizing variance to allow the inclusion of decision attitude in decision making under uncertainty

The problem of decision making under uncertainty is considered. It is noted that an alternative is described in terms of an uncertainty profile. We observe that a major difficulty in the decision process is the comparison of these uncertainty profiles. We discuss the need for introducing some features of an uncertainty profile to help simplify this comparison. We note that the quantification of these simplifying features involves some subjective considerations about the decision makers preferences. We introduce the idea of the decision maker’s attitudinal character to help in the formulation of these considerations. We then investigate two important features associated with an uncertainty profile. The first, the representative value, is a generalization of expected value commonly used under probabilistic uncertainty. The second, called the measure of deviation, provides a generalization of the concept of variance. We show how these new measures allows us to consider uncertainty profiles other then just the probabilistic one. They also allow us introduce other decision maker attitudes then the one implicitly assumed with the expected value and variance.