12,180 research outputs found
New Graphical Model for Computing Optimistic Decisions in Possibility Theory Framework
This paper first proposes a new graphical model for decision making under uncertainty based on min-based possibilistic networks. A decision problem under uncertainty is described by means of two distinct min-based possibilistic networks: the first one expresses agent's knowledge while the second one encodes agent's preferences representing a qualitative utility. We then propose an efficient algorithm for computing optimistic optimal decisions using our new model for representing possibilistic decision making under uncertainty. We show that the computation of optimal decisions comes down to compute a normalization degree of the junction tree associated with the graph resulting from the fusion of agent's beliefs and preferences. This paper also proposes an alternative way for computing optimal optimistic decisions. The idea is to transform the two possibilistic networks into two equivalent possibilistic logic knowledge bases, one representing agent's knowledge and the other represents agent's preferences. We show that computing an optimal optimistic decision comes down to compute the inconsistency degree of the union of the two possibilistic bases augmented with a given decision
Possibilistic decision theory: from theoretical foundations to influence diagrams methodology
Le domaine de prise de décision est un domaine multidisciplinaire en relation avec plusieurs disciplines telles que l'économie, la recherche opérationnelle, etc. La théorie de l'utilité espérée a été proposée pour modéliser et résoudre les problèmes de décision. Ces théories ont été mises en cause par plusieurs paradoxes (Allais, Ellsberg) qui ont montré les limites de son applicabilité. Par ailleurs, le cadre probabiliste utilisé dans ces théories s'avère non approprié dans certaines situations particulières (ignorance totale, incertitude qualitative). Pour pallier ces limites, plusieurs travaux ont été élaborés concernant l'utilisation des intégrales de Choquet et de Sugeno comme critères de décision d'une part et l'utilisation d'une théorie d'incertitude autre que la théorie des probabilités pour la modélisation de l'incertitude d'une autre part. Notre idée principale est de profiter de ces deux directions de recherche afin de développer, dans le cadre de la décision séquentielle, des modèles de décision qui se basent sur les intégrales de Choquet comme critères de décision et sur la théorie des possibilités pour la représentation de l'incertitude. Notre objectif est de développer des modèles graphiques décisionnels, qui représentent des modèles compacts et simples pour la prise de décision dans un contexte possibiliste. Nous nous intéressons en particulier aux arbres de décision et aux diagrammes d'influence possibilistes et à leurs algorithmes d'évaluation.The field of decision making is a multidisciplinary field in relation with several disciplines such as economics, operations research, etc. Theory of expected utility has been proposed to model and solve decision problems. These theories have been questioned by several paradoxes (Allais, Ellsberg) who have shown the limits of its applicability. Moreover, the probabilistic framework used in these theories is not appropriate in particular situations (total ignorance, qualitative uncertainty). To overcome these limitations, several studies have been developed basing on the use of Choquet and Sugeno integrals as decision criteria and a non classical theory to model uncertainty.
Our main idea is to use these two lines of research to develop, within the framework of sequential decision making, decision models based on Choquet integrals as decision criteria and possibility theory to represent uncertainty. Our goal is to develop graphical decision models that represent compact models for decision making when uncertainty is represented using possibility theory. We are particularly interested by possibilistic decision trees and influence diagrams and their evaluation algorithms
Platform Competition as Network Contestability
Recent research in industrial organisation has investigated the essential
place that middlemen have in the networks that make up our global economy. In
this paper we attempt to understand how such middlemen compete with each other
through a game theoretic analysis using novel techniques from decision-making
under ambiguity. We model a purposely abstract and reduced model of one
middleman who pro- vides a two-sided platform, mediating surplus-creating
interactions between two users. The middleman evaluates uncertain outcomes
under positional ambiguity, taking into account the possibility of the
emergence of an alternative middleman offering intermediary services to the two
users. Surprisingly, we find many situations in which the middleman will
purposely extract maximal gains from her position. Only if there is relatively
low probability of devastating loss of business under competition, the
middleman will adopt a more competitive attitude and extract less from her
position.Comment: 23 pages, 3 figure
Local and Global Trust Based on the Concept of Promises
We use the notion of a promise to define local trust between agents
possessing autonomous decision-making. An agent is trustworthy if it is
expected that it will keep a promise. This definition satisfies most
commonplace meanings of trust. Reputation is then an estimation of this
expectation value that is passed on from agent to agent.
Our definition distinguishes types of trust, for different behaviours, and
decouples the concept of agent reliability from the behaviour on which the
judgement is based. We show, however, that trust is fundamentally heuristic, as
it provides insufficient information for agents to make a rational judgement. A
global trustworthiness, or community trust can be defined by a proportional,
self-consistent voting process, as a weighted eigenvector-centrality function
of the promise theoretical graph
Towards a Step Semantics for Story-Driven Modelling
Graph Transformation (GraTra) provides a formal, declarative means of
specifying model transformation. In practice, GraTra rule applications are
often programmed via an additional language with which the order of rule
applications can be suitably controlled.
Story-Driven Modelling (SDM) is a dialect of programmed GraTra, originally
developed as part of the Fujaba CASE tool suite. Using an intuitive,
UML-inspired visual syntax, SDM provides usual imperative control flow
constructs such as sequences, conditionals and loops that are fairly simple,
but whose interaction with individual GraTra rules is nonetheless non-trivial.
In this paper, we present the first results of our ongoing work towards
providing a formal step semantics for SDM, which focuses on the execution of an
SDM specification.Comment: In Proceedings GaM 2016, arXiv:1612.0105
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