T.B. Klos (Tomas)
http://repub.eur.nl/ppl/36115/
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http://repub.eur.nl/
RePub, Erasmus University RepositorySolving Weighted Voting Game Design Problems Optimally: Representations, Synthesis, and Enumeration
http://repub.eur.nl/pub/32170/
Tue, 01 May 2012 00:00:01 GMT<div>B. de Keijzer</div><div>T.B. Klos</div><div>Y. Zhang</div>
We study the inverse power index problem for weighted voting games: the problem of finding a weighted voting game in which the power of the players is as close as possible to a certain target distribution. Our goal is to find algorithms that solve this problem exactly. Thereto, we study various subclasses of simple games, and their associated representation methods. We survey algorithms and impossibility results for the synthesis problem, i.e., converting a representation of a simple game into another representation. We contribute to the synthesis problem by showing that it is impossible to compute in polynomial time the list of ceiling coalitions of a game from its list of roof coalitions, and vice versa. Then, we proceed by studying the problem of enumerating the set of weighted voting games. We present first a naive algorithm for this, running in doubly exponential time. Using our knowledge of the synthesis problem, we then improve on this naive algorithm, and we obtain an enumeration algorithm that runs in quadratic exponential time. Moreover, we show that this algorithm runs in output-polynomial time, making it the best possible enumeration algorithm up to a polynomial factor. Finally, we propose an exact anytime algorithm for the inverse power index problem that runs in exponential time. By the genericity of our approach, our algorithm can be used to find a weighted voting game that optimizes any exponential time computable function. We implement our algorithm for the case of the normalized Banzhaf index, and we perform experiments in order to study performance and error convergence.Multiagent task allocation in social networks
http://repub.eur.nl/pub/26276/
Mon, 28 Feb 2011 00:00:01 GMT<div>M.M. Weerdt</div><div>Y. Zhang</div><div>T.B. Klos</div>
This paper proposes a new variant of the task allocation problem, where the agents are connected in a social network and tasks arrive at the agents distributed over the network. We show that the complexity of this problem remains NP-complete. Moreover, it is not approximable within some factor. In contrast to this, we develop an efficient greedy algorithm for this problem. Our algorithm is completely distributed, and it assumes that agents have only local knowledge about tasks and resources. We conduct a broad set of experiments to evaluate the performance and scalability of the proposed algorithm in terms of solution quality and computation time. Three different types of networks, namely small-world, random and scale-free networks, are used to represent various social relationships among agents in realistic applications. The results demonstrate that our algorithm works well and also that it scales well to large-scale applications. In addition we consider the same problem in a setting where the agents holding the resources are self-interested. For this, we show how the optimal algorithm can be used to incentivize these agents to be truthful. However, the efficient greedy algorithm cannot be used in a truthful mechanism, therefore an alternative, cluster-based algorithm is proposed and evaluated.Agent-based computational transaction cost economics
http://repub.eur.nl/pub/71036/
Thu, 01 Mar 2001 00:00:01 GMT<div>T.B. Klos</div><div>B. Nooteboom</div>
This article explores the use of 'agent-based computational economics' (ACE) for modelling the development of transactions between firms. Transaction cost economics neglects learning and the development of trust, ignores the complexity of multiple agents, and assumes rather than investigates the efficiency of outcomes. Efficiency here refers to minimum cost or maximum profit. We model how co-operation and trust emerge and shift adaptively as relations evolve in a context of multiple, interacting agents. This may open up a new area of application for the ACE methodology. A simulation model is developed in which agents make and break transaction relations on the basis of preferences, based on trust and potential profit. Profit is a function of product differentiation, specificity of assets, economy of scale and learning by doing in ongoing relations. Agents adapt their trust in a partner as a function of his loyalty, exhibited by his continuation of a relation. They also adapt the weight they attach to trust on the basis of realized profit. The model enables an assessment of the efficiency of outcomes relative to the optimum, as a function of trust and market conditions. We conduct a few experiments to illustrate this application of ACE.