Maker's Preferences Research Articles

A Causal Measure of Freedom

Behind the veil of ignorance, a policy maker ranks combinations of game forms and information about how players interact within the game forms. The paper presents axioms on the preferences of the policy maker that are necessary and sufficient for the policy maker's preferences to be represented by the sum of an expected valuation and a freedom measure. The freedom measure is the mutual information between players' strategies and the players' outcomes of the game, capturing the degree to which players control their outcomes. The measure extends several measures from the opportunity set based freedom literature to situations where agents interact. This allows freedom to be measured in general economic models and thus derive policy recommendations based on the freedom instead of the welfare of agents. To illustrate the measure and axioms, applications to civil liberties and optimal taxation are provided.

A New Link Between Output-oriented BCC Model with Fuzzy Data in the Present of Undesirable Outputs and MOLP

In recent years, the relation between data envelopment analysis and multiple objective linear programming has received a great deal of attention from researchers. However, there are two difficulties in doing an objective evaluation of the performance of decision making units. The first one is how to treat undesirable factors jointly produced with the desirable factors and the second one is how to treat with imprecise data. In this paper, we establish an equivalence relation between multiple objective linear programming and the output-oriented Banker, Charnes, Cooper(BCC) model in the present of undesirable factors and fuzzy data such that the decision maker's preference can be taken into account in an interactive fashion for finding target unit.

An Integrated Control Model for Freeway Corridor Under Nonrecurrent Congestion

This paper presents an integrated model and its solution algorithm for freeway corridor control during incident management. With a parallel arterial as the detour route, the proposed model aims at producing the optimal diversion rates from the freeway mainline to relieve the congestion at the incident segment and concurrently adjust signal timings at the arterial intersections to best accommodate the detour traffic. Different from previous studies, the presented model and algorithm have the following two critical features: 1) modeling explicitly the evolution of detour traffic along the ramps and surface streets with a set of dynamic network flow formulations to capture the local bottlenecks caused by demand surge due to diversion operations and to properly set the responsive signal timing plans and 2) developing a multiobjective optimization framework to maximize the utilization of the available corridor capacity via detour operations but not to incur excessive congestion on the arterials and ramps. This study employs a genetic algorithm (GA)-based heuristic to efficiently yield the reliable solution, depending on the decision maker's preference. Extensive numerical tests on a segment along the I-95 corridor with its neighboring arterials have demonstrated the potential of the developed model for integrated freeway corridor control.

INTERACTIVE MULTICRITERIA DECISION AIDING UNDER RISK—METHODS AND APPLICATIONS / INTERAKTYVIŲ DAUGIAKRITERINIŲ SPRENDIMŲ PASISKIRSTYMAS RIZIKOS SĄLYGOMIS: METODAI IR SPRENDIMAI

In the paper a discrete multicriteria decision making problem under risk is considered. It is assumed that the set of alternatives consists of a finite number of elements that are explicitly described. The evaluations of alternatives with respect to criteria are represented by distribution functions. The decision maker tries to find a solution preferred to all other solutions. To solve the problem one has to analyze the decision maker's preferences. In the study interactive approach is used. Three interactive methods and its applications in operations management are presented. Santrauka Straipsnyje pateikiama diskreèiuju sprendimu priemimo problemos analize apimant rizikos veiksnius. Pasirinkimo alternatyvos suprantamos kaip kompleksas baigtiniu elementu ir alternatyvos, atsižvelgiant i kriterijus, yra išreikštos paskirstymo funkcijomis. Priimant sprendimus butina atrasti išeiti, priimtin tolesniems sprendimams. Straipsnyje siuloma, kad, norint išsprêsti vien problem, butina išanalizuoti sprendimu vertintojo lukesèius.

ELECTRE I Decision Model of Reliability Design Scheme for Computer Numerical Control Machine

The ELECTRE I is one of the most extensively used methods to solve multiple criteria decision making (MCDM) problems. In this paper, we propose a novel AHP- based ELECTRE I method of reliability design scheme decision for computer numerical control (CNC) machine. Based on the AHP method combined with ELECTRE I, the decision model is built to select the optimal design scheme. The AHP method is applied to determinate the weights of reliability design factors through the decision model. ELECTRE I method is then designed to rank reliability design scheme in order of decision maker's preference. To evaluate performance of the developed algorithm, an illustrative example of CNC machine is given. The computational results show that the proposed approach is reliable and performs well.

Modelling a Decision Maker's Preferences: Testing for Relevance and Application Development

The need for developing models in the tourism and hospitality industry which identify and make use of a decision maker's preferences has been demonstrated extensively in the works of Brown et al (2002) and Israeli and Brown (2004), as well as others (Ingold and Yeoman, 1997). In this study, the authors examine historical data of Israeli hotels in order to demonstrate empirically the need for these models and to determine which model best fits the empirical data. Three preference-based pricing models are tested, built with indifference curves to analyse the two-attribute case of room rate versus occupancy. These models differ from standard ones in that the decision maker's actual choices for specific budget scenarios are built directly into them. These constrained choice points are built into a table and are used to create an entire pricing schedule based on the preference ratio thus determined. The first model uses an unconstrained desired point and assumes that managers find rate and occupancy to be perfectly substitutable at all budget constraints. The second model assumes linear fractional substitutability and alters the ratio for specific constrained choice points (Israeli and Brown, 2004). The third model assumes perfect complementarity at each constrained choice point and assumes a consistent ratio for each constrained choice segment (Brown, 2004). The heuristic nature of the models makes statistical analysis of their validity difficult, but is thus attempted in any case. The motivation behind this test is to determine which model can best be used to evaluate behaviour and performance in a future study.

Minimum Deviation Models for Multiple Attribute Decision Making in Intuitionistic Fuzzy Setting

With respect to intuitionistic fuzzy multiple attribute decision making problems with preference information on alternatives and incomplete weight information, a method based the minimum deviation is proposed. Firstly, some operational laws of intuitionistic fuzzy numbers, score function and accuracy function of intuitionistic fuzzy numbers are introduced. Then, to reflect the decision maker's preference information, an optimization model based on the minimum deviation method, by which the attribute weights can be determined, is established. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the intuitionistic fuzzy weighted averaging (IFWA) operator to aggregate the intuitionistic fuzzy information corresponding to each alternative, and then rank the alternatives and select the most desirable one(s)...

A TZBM-Based Algorithm for Flow Shop Scheduling Problem Considering the Decision Maker's Preference

Nowadays steel manufacturing pays a critical attention to on-time delivery and inventory reduction. In our study, we propose a heuristic algorithm to solve the hybrid flow shop scheduling problem (HFSP) considering reducing total tardiness and inventory time. In order to achieve a feasible solution in limited computational time, the algorithm is based on Three-zone Buffer Management (TZBM) approach. TZBM approach combines the Drum Buffer Rope (DBR) method proposed in theory of constraints (TOC), with a contract net protocol in multi-agent system (MAS). The effectiveness of the proposed algorithm is shown by simulation results comparing with the results obtained by a Tabu search (TS) algorithm. In the production field, a shipment buffer is set between the due date and the shipping time in order to avoid possible late shipment. The size of shipment buffer is always decided according to the experience of a system operator who has responsibility to draw up a production schedule. Wherein, an operator needs to take a decision considering trade-off between on-time delivery and inventory reduction. Therefore, a new interactive decision support system is proposed which is based on the two-stage TZBM approach. The operator needs to make his/her judgment whether an optimal solution shown by the system is preferable or not, then to change the trade-off between the two conflicting objectives. The system needs to compute another optimal solution according to judgment given by the operator. Under the result of numerical experiments, to integrate the experience of operators with the computational intelligence of systems proves to be an effective scheduling method.

The r-Dominance: A New Dominance Relation for Interactive Evolutionary Multicriteria Decision Making

Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in finding a representative set of Pareto optimal solutions in the past decade and beyond. Several techniques have been proposed in the specialized literature to ensure good convergence and diversity of the obtained solutions. However, in real world applications, the decision maker is not interested in the overall Pareto optimal front since the final decision is a unique solution. Recently, there has been an increased emphasis in addressing the decision-making task in searching for the most preferred alternatives. In this paper, we introduce a new variant of the Pareto dominance relation, called r-dominance, which has the ability to create a strict partial order among Pareto-equivalent solutions. This fact makes such a relation able to guide the search toward the interesting parts of the Pareto optimal region based on the decision maker's preferences expressed as a set of aspiration levels. After integrating the new dominance relation in the NSGA-II methodology, the efficacy and the usefulness of the modified procedure are assessed through two to ten-objective test problems a priori and interactively. Moreover, the proposed approach provides competitive and better results when compared to other recently proposed preference-based EMO approaches.

A note on Youden's Jand its cost ratio

BackgroundThe Youden index, the sum of sensitivity and specificity minus one, is an index used for setting optimal thresholds on medical tests.DiscussionWhen using this index, one implicitly uses decision theory with a ratio of misclassification costs which is equal to one minus the prevalence proportion of the disease. It is doubtful whether this cost ratio truly represents the decision maker's preferences. Moreover, in populations with a different prevalence, a selected threshold is optimal with reference to a different cost ratio.SummaryThe Youden index is not a truly optimal decision rule for setting thresholds because its cost ratio varies with prevalence. Researchers should look into their cost ratio and employ it in a decision theoretic framework to obtain genuinely optimal thresholds.

A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

BackgroundDecision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA.MethodsFirst, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker.ResultsWe developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease.ConclusionsWe present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc).

OPTIMIZATION OF THE TEST INTERVALS OF A NUCLEAR SAFETY SYSTEM BY GENETIC ALGORITHMS, SOLUTION CLUSTERING AND FUZZY PREFERENCE ASSIGNMENT

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into families. On the basis of the decision maker's preferences, each is then synthetically represented by a head of the family solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

Irrational decision-making in an amoeboid organism: transitivity and context-dependent preferences

Most models of animal foraging and consumer choice assume that individuals make choices based on the absolute value of items and are therefore 'economically rational'. However, frequent violations of rationality by animals, including humans, suggest that animals use comparative valuation rules. Are comparative valuation strategies a consequence of the way brains process information, or are they an intrinsic feature of biological decision-making? Here, we examine the principles of rationality in an organism with radically different information-processing mechanisms: the brainless, unicellular, slime mould Physarum polycephalum. We offered P. polycephalum amoebas a choice between food options that varied in food quality and light exposure (P. polycephalum is photophobic). The use of an absolute valuation rule will lead to two properties: transitivity and independence of irrelevant alternatives (IIA). Transitivity is satisfied if preferences have a consistent, linear ordering, while IIA states that a decision maker's preference for an item should not change if the choice set is expanded. A violation of either of these principles suggests the use of comparative rather than absolute valuation rules. Physarum polycephalum satisfied transitivity by having linear preference rankings. However, P. polycephalum's preference for a focal alternative increased when a third, inferior quality option was added to the choice set, thus violating IIA and suggesting the use of a comparative valuation process. The discovery of comparative valuation rules in a unicellular organism suggests that comparative valuation rules are ubiquitous, if not universal, among biological decision makers.

Preferences for Consumption Streams: Scale Invariance, Correlation Aversion, and Delay Aversion Under Mortality Risk

Lifetime financial decisions often require a decision analyst to elicit a decision maker's preferences for consumption streams. In assessing such preferences, the analyst might look for a set of reasonable conditions to check when selecting a utility form. We provide such a set of conditions and show that they lead to the multiplicative-expo-power (MEP) utility form. Some of our conditions involve trade-offs under certainty and others relate to choices under uncertainty. In the deterministic setting, we invoke increasingness, continuous differentiability, mutual preferential independence, and preferential scale invariance. In the uncertainty setting, we invoke componentwise risk aversion, a utility independence condition, and correlation aversion. We apply the MEP utility in a life-cycle consumption-planning problem under mortality risk. In this context, we find that the correlation-averse MEP utility is more realistic than, and as tractable as, the popular correlation-neutral additive-power and additive-exponential utilities. We show that the correlation-averse decision maker prefers to consume more in life and leave less behind in death. In addition, we demonstrate that the correlation-averse decision maker is more averse to delaying consumption in the face of mortality risk.

Ranking Fuzzy Numbers Based on Ideal Solution

In this paper, we consider the factor of the decision maker's risk preference, and define the left and right deviation degree respectively. Besides, we propose a new formula of the fuzzy degree. Then we get the multiattribute matrix of fuzzy numbers. We rank fuzzy numbers with the help of an ideal solution. Some numerical examples are displayed to illustrate the validity and advantages of the proposed ranking method.

A posterior preference articulation approach to dual-response-surface optimization

In dual-response-surface optimization, the mean and standard deviation responses are often in conflict. To obtain a satisfactory compromise, a Decision Maker (DM)'s preference information on the trade-offs between the responses should be incorporated into the problem. In most existing works, the DM expresses a subjective judgment on the responses through a preference parameter before the problem-solving process, after which a single solution is obtained. This study proposes a posterior preference articulation approach to dual-response-surface optimization. The posterior preference articulation approach initially finds a set of non-dominated solutions without the DM's preference information, and then allows the DM to select the best solution among the non-dominated solutions. The proposed method enables a satisfactory compromise solution to be achieved with minimum cognitive effort and gives the DM the opportunity to explore and better understand the trade-offs between the two responses.

A Generalisation of the Mean‐Variance Analysis

Abstract In this paper we consider a decision maker whose utility function has a kink at the reference point with different functions below and above this reference point. We also suppose that the decision maker generally distorts the objective probabilities. First we show that the expected utility function of this decision maker can be approximated by a function of mean and partial moments of distribution. This ‘mean‐partial moments’ utility generalises not only mean‐variance utility of Tobin and Markowitz, but also mean‐semivariance utility of Markowitz. Then, in the spirit of Arrow and Pratt, we derive an expression for a risk premium when risk is small. Our analysis shows that a decision maker in this framework exhibits three types of aversions: aversion to loss, aversion to uncertainty in gains, and aversion to uncertainty in losses. Finally we present a solution to the optimal capital allocation problem and derive an expression for a portfolio performance measure which generalises the Sharpe and Sortino ratios. We demonstrate that in this framework the decision maker's skewness preferences have first‐order impact on risk measurement even when the risk is small.

FAIR CONSISTENCY EVALUATION FOR RECIPROCAL RELATIONS AND IN GROUP DECISION MAKING

In decision-making processes, it often occurs that the decision maker is asked to pairwise compare alternatives. His/her judgements over a set of pairs of alternatives can be collected into a matrix and some relevant properties, for instance, consistency, can be estimated. Consistency is a desirable property which implies that all the pairwise comparisons respect a principle of transitivity. So far, many indices have been proposed to estimate consistency. Nevertheless, in this paper we argue that most of these indices do not fairly evaluate this property. Then, we introduce a new consistency evaluation method and we propose to use it in group decision making problems in order to fairly weigh the decision maker's preferences according to their consistency. In our analysis, we consider two families of pairwise comparison matrices: additively reciprocal pairwise comparison matrices and multiplicatively reciprocal pairwise comparison matrices.

Development of a supplier selection model using fuzzy logic

PurposeThis paper aims to provide a tool for decision makers to help them with selection of the appropriate supplier.Design/methodology/approachCompanies often depend on their suppliers to meet customers' demands. Thus, the key to the success of these companies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteria and then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision maker's preferences on the importance of these criteria as inputs.FindingsHuman assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguistic expressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in linguistic terms. Decision maker's preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to these criteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzy mathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allow the ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, and operations management.Practical implicationsThe procedure proposed here can help companies to identify the best supplier.Originality/valueThe paper describes a decision model that incorporates decision maker's subjective assessments and applies fuzzy arithmetic operators to manipulate and quantify these assessments.

Lower Price Limits for Flat-Fee Service Contracts Under Risk

We analyze price limits as decision aids for identifying favorable and unfavorable contracts from the perspective of a selling firm. The corresponding decision problem is shaped by uncertainty of the outcomes and incomplete information about the decision maker's preferences. The proposed price limits are based on the theory of almost stochastic dominance. We make this theory more applicable by contributing a version of almost second-degree stochastic dominance that (1) refers to the same aggregate information about marginal utilities as almost first-degree stochastic dominance, (2) does without information about higher order derivatives of the utility function, and (3) is implied by almost first-degree stochastic dominance (hierarchy property). Numerical examples show that the incomplete preference information may lead to substantial improvements of the price limits relative to the situation where no preference information is available.