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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Decision-Making Using Random Boolean Networks In

The El Farol Bar Problem

Authors:

Daniel Epstein, Ana L. C. Bazzan

Published in:

 

(2011).ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho. European Council for Modeling and Simulation. doi:10.7148/2011 

 

ISBN: 978-0-9564944-2-9

 

25th European Conference on Modelling and Simulation,

Jubilee Conference

Krakow, June 7-10, 2011

 

Citation format:

Epstein, D., & Bazzan, A. L. V. (2011). Decision-Making Using Random Boolean Networks In The El Farol Bar Problem. ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho (pp. 84-90). European Council for Modeling and Simulation. doi:10.7148/2011-0084-0090

DOI:

http://dx.doi.org/10.7148/2011-0084-0090

Abstract:

In our daily lives we often face binary decisions where we seek to take either the minority or the majority side. One of these binary decision scenarios is the El Farol Bar Problem, which has been used to study how agents achieve coordination. Previous works have shown that agents may reach appropriate levels of coordina- tion, mostly by looking at their own individual strate- gies that consider the complete history of the bar atten- dance. No structure of the network of players has been explicitly considered. Here we use the formalism of ran- dom boolean networks to help agents to make decisions considering a network of other decision-makers. This is especially useful because random boolean networks al- low the mapping of actions of K other agents (hence not based on complete history) to the decision-making of each single individual. Therefore a contribution of this work is the fact that we consider agents as participants of a social network. In the original proposition for this problem, strategies would change within time and even- tually would lead agents to, collectively, decide on a ef- ficient attendance, at each time step. Hence there was no explicit modeling of such a social network. Our results using random boolean networks show a similar pattern of convergence to an efficient attendance, provided agents do experimentation with the number of boolean func- tions, have a good update strategy, and a certain number of neighbors is considered.

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