9,523 research outputs found
Industrial Symbiotic Networks as Coordinated Games
We present an approach for implementing a specific form of collaborative
industrial practices-called Industrial Symbiotic Networks (ISNs)-as MC-Net
cooperative games and address the so called ISN implementation problem. This
is, the characteristics of ISNs may lead to inapplicability of fair and stable
benefit allocation methods even if the collaboration is a collectively desired
one. Inspired by realistic ISN scenarios and the literature on normative
multi-agent systems, we consider regulations and normative socioeconomic
policies as two elements that in combination with ISN games resolve the
situation and result in the concept of coordinated ISNs.Comment: 3 pages, Proc. of the 17th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2018
Homo Socionicus: a Case Study of Simulation Models of Norms
This paper describes a survey of normative agent-based social simulation models. These models are examined from the perspective of the foundations of social theory. Agent-based modelling contributes to the research program of methodological individualism. Norms are a central concept in the role theoretic concept of action in the tradition of Durkheim and Parsons. This paper investigates to what extend normative agent-based models are able to capture the role theoretic concept of norms. Three methodological core problems are identified: the question of norm transmission, normative transformation of agents and what kind of analysis the models contribute. It can be shown that initially the models appeared only to address some of these problems rather than all of them simultaneously. More recent developments, however, show progress in that direction. However, the degree of resolution of intra agent processes remains too low for a comprehensive understanding of normative behaviour regulation.Norms, Normative Agent-Based Social Simulation, Role Theory, Methodological Individualism
Efficient computation of the Shapley value for game-theoretic network centrality
The Shapley value—probably the most important normative payoff division scheme in coalitional games—has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. Fo
The Norm Implementation Problem in Normative Multi-Agent Systems
Abstract. The norm implementation problem consists in how to see to it that the agents in a system comply with the norms specified for that system by the system designer. It is part of the more general problem of how to synthesize or create norms for multi-agent systems, by, for example, highlighting the choice between regimentation and enforcement, or the punishment associated with a norm violation. In this paper we discuss how various ways to implement norms in a multi-agent system can be distinguished in a formal game-theoretic framework. In particular, we show how different types of norm implementation can all be uniformly specified and verified as types of transformations of extensive games. We introduce the notion of retarded preconditions to implement norms, and we illustrate the framework and the various ways to implement norms in the blocks world environment
The role of information in multi-agent learning
This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design.multi-agent learning; auction markets; design economics; agent-based computational economics
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