2,130 research outputs found
The cg-average tree value for games on cycle-free fuzzy communication structures
The main goal in a cooperative game is to obtain a fair allocation of the profit due
the cooperation of the involved agents. The most known of these allocations is the
Shapley value. This allocation considers that the communication among the players
is complete. The Myerson value is a modification of the Shapley value considering a
communication structure which determines the feasible bilateral relationships among
the agents. This allocation of the profit is not always a stable solution. Another payoff
allocation for games with a communication structure from the definition of the Shapley
value is the average tree value. This one is a stable solution for any game using
a cycle-free communication structure. Later fuzzy communication structures were
introduced. In a fuzzy communication structure, the membership of the agents and
the relationships among them are leveled. The Myerson value was extended in several
different ways depending on the behavior of the agents. In this paper, the average tree
value is extended to games with fuzzy communication structures taking one particular
version: the Choquet by graphs (cg). We present an application to the management of
an electrical network with an algorithmic implementation.Spanish Ministry of Education and Science MTM2017-83455-PAndalusian Government FQM23
Marginality and the position value
We present a new characterization of the position value, one of the most prominent allocation rules for communication situations (graph-games or games with restricted communication). This characterization includes the PL-marginality property, an extension for communications situations of the classic marginality for TU-games, as well as component efficiency and balanced link contributions for necessary players
The Naming Game in Social Networks: Community Formation and Consensus Engineering
We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat.
Mech.: Theory Exp. P06014] in empirical social networks. This stylized
agent-based model captures essential features of agreement dynamics in a
network of autonomous agents, corresponding to the development of shared
classification schemes in a network of artificial agents or opinion spreading
and social dynamics in social networks. Our study focuses on the impact that
communities in the underlying social graphs have on the outcome of the
agreement process. We find that networks with strong community structure hinder
the system from reaching global agreement; the evolution of the Naming Game in
these networks maintains clusters of coexisting opinions indefinitely. Further,
we investigate agent-based network strategies to facilitate convergence to
global consensus.Comment: The original publication is available at
http://www.springerlink.com/content/70370l311m1u0ng3
Fuzzy decision making system and the dynamics of business games
Effective and efficient strategic decision making is the backbone for the success of
a business organisation among its competitors in a particular industry. The results
of these decision making processes determine whether the business will continue to
survive or not. In this thesis, fuzzy logic (FL) concepts and game theory are being used
to model strategic decision making processes in business organisations. We generally
modelled competition by business organisations in industries as games where each
business organization is a player. A player formulates his own decisions by making
strategic moves based on uncertain information he has gained about the opponents.
This information relates to prevailing market demand, cost of production, marketing,
consolidation efforts and other business variables. This uncertain information is being
modelled using the concept of fuzzy logic.
In this thesis, simulation experiments were run and results obtained in six different
settings. The first experiment addresses the payoff of the fuzzy player in a typical
duopoly system. The second analyses payoff in an n-player game which was used
to model a perfect market competition with many players. It is an extension of the
two-player game of a duopoly market which we considered in the first experiment.
The third experiment used and analysed real data of companies in a case study. Here,
we chose the competition between Coca-cola and PepsiCo companies who are major
players in the beverage industry. Data were extracted from their published financial
statements to validate our experiment. In the fourth experiment, we modelled
competition in business networks with uncertain information and varying level of
connectivity. We varied the level of interconnections (connectivity) among business
units in the business networks and investigated how missing links affect the payoffs
of players on the networks.
We used the fifth experiment to model business competition as games on boards with
possible constraints or restrictions and varying level of connectivity on the boards.
We also investigated this for games with uncertain information. We varied the level of
interconnections (connectivity) among the nodes on the boards and investigated how
these a ect the payoffs of players that played on the boards. We principally used these
experiments to investigate how the level of availability of vital infrastructures (such
as road networks) in a particular location or region affects profitability of businesses
in that particular region.
The sixth experiment contains simulations in which we introduced the fuzzy game approach
to wage negotiation in managing employers and employees (unions) relationships.
The scheme proposes how employers and employees (unions) can successfully
manage the deadlocks that usually accompany wage negotiations.
In all cases, fuzzy rules are constructed that symbolise various rules and strategic
variables that firms take into consideration before taken decisions. The models also
include learning procedures that enable the agents to optimize these fuzzy rules and
their decision processes. This is the main contribution of the thesis: a set of fuzzy
models that include learning, and can be used to improve decision making in business
Playing Smart - Artificial Intelligence in Computer Games
Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games
Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005
Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)
"The connection between distortion risk measures and ordered weighted averaging operators"
Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and nite random variables is presented. This connection oers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed.Fuzzy systems; Degree of orness; Risk quantification; Discrete random variable JEL classification:C02,C60
Marginality and the position value
We present a new characterization of the position value, one of the most prominent allocation rules for communication situations (graph-games or games with restricted communication). This characterization includes the PL-marginality property, an extension for communications situations of the classic marginality for TU-games, as well as component efficiency and balanced link contributions for necessary players
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