Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case

In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences

An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behaviour classification is twofold: (1) ‘individual manipulation’ where each expert manipulates his/her own behaviour to achieve higher importance degree (weight); and (2) ‘group manipulation’ where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of fixed feedback parameter. To counteract ‘individual manipulation’, a behavioural weights assignment method modelling sequential attitude ranging from ‘dictatorship’ to ‘democracy’ is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent ‘group manipulation’, an optimal feedback model with objective function the individual adjustments cost and constraints related to the threshold of group consensus is investigated. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model

Choice Rules with Size Constraints for Multiple Criteria Decision Making

In outranking methods for Multiple Criteria Decision Making (MCDM), pair-wise comparisons of alternatives are often summarized through a fuzzy preference relation. In this paper, the binary preference relation is extended to pairs of subsets of alternatives in order to define on this basis a scoring function over subsets. A choice rule based on maximizing score under size constraint is studied, which turns to formulate as solving a sequence of classical location problems. For comparison with the kernel approach, the interior stability property of the selected subset is discussed and analyzed.Combinatorial optimization; Fuzzy preferences; Integer Programming; Location; Multiple Criteria Decision Aid

The dynamics of consensus in group decision making: investigating the pairwise interactions between fuzzy preferences.

In this paper we present an overview of the soft consensus model in group decision making and we investigate the dynamical patterns generated by the fundamental pairwise preference interactions on which the model is based. The dynamical mechanism of the soft consensus model is driven by the minimization of a cost function combining a collective measure of dissensus with an individual mechanism of opinion changing aversion. The dissensus measure plays a key role in the model and induces a network of pairwise interactions between the individual preferences. The structure of fuzzy relations is present at both the individual and the collective levels of description of the soft consensus model: pairwise preference intensities between alternatives at the individual level, and pairwise interaction coefficients between decision makers at the collective level. The collective measure of dissensus is based on non linear scaling functions of the linguistic quantifier type and expresses the degree to which most of the decision makers disagree with respect to their preferences regarding the most relevant alternatives. The graded notion of consensus underlying the dissensus measure is central to the dynamical unfolding of the model. The original formulation of the soft consensus model in terms of standard numerical preferences has been recently extended in order to allow decision makers to express their preferences by means of triangular fuzzy numbers. An appropriate notion of distance between triangular fuzzy numbers has been chosen for the construction of the collective dissensus measure. In the extended formulation of the soft consensus model the extra degrees of freedom associated with the triangular fuzzy preferences, combined with non linear nature of the pairwise preference interactions, generate various interesting and suggestive dynamical patterns. In the present paper we investigate these dynamical patterns which are illustrated by means of a number of computer simulations.

Multicriteria decision making for enhanced perception-based multimedia communication

This paper proposes an approach that integrates technical concerns with user perceptual considerations for intelligent decision making in the construction of tailor-made multimedia communication protocols. Thus, the proposed approach, based on multicriteria decision making (MDM), incorporates not only classical networking considerations, but, indeed, user preferences as well. Furthermore, in keeping with the task-dependent nature consistently identified in multimedia scenarios, the suggested communication protocols also take into account the type of multimedia application that they are transporting. Lastly, this approach also opens the possibility for such protocols to dynamically adapt based on a changing operating environment and user's preferences

Fuzziness and Funds Allocation in Portfolio Optimization

Each individual investor is different, with different financial goals, different levels of risk tolerance and different personal preferences. From the point of view of investment management, these characteristics are often defined as objectives and constraints. Objectives can be the type of return being sought, while constraints include factors such as time horizon, how liquid the investor is, any personal tax situation and how risk is handled. It's really a balancing act between risk and return with each investor having unique requirements, as well as a unique financial outlook - essentially a constrained utility maximization objective. To analyze how well a customer fits into a particular investor class, one investment house has even designed a structured questionnaire with about two-dozen questions that each has to be answered with values from 1 to 5. The questions range from personal background (age, marital state, number of children, job type, education type, etc.) to what the customer expects from an investment (capital protection, tax shelter, liquid assets, etc.). A fuzzy logic system has been designed for the evaluation of the answers to the above questions. We have investigated the notion of fuzziness with respect to funds allocation.Comment: 21 page