Collective behavior of El Farol attendees

Arthur’s paradigm of the El Farol bar for modeling bounded rationality and inductive behavior is undertaken. The memory horizon available to the agents and the selection criteria they utilize for the prediction algorithm are the two essential variables identified to represent the heterogeneity of agent strategies. The latter is enriched by including various rewarding schemes during decision making. Though the external input of comfort level is not explicitly coded in the algorithm pool, it contributes to each agent’s decision process. Playing with the essential variables, one can maneuver the overall outcome between the comfort level and the endogenously identified limiting state. The distribution of algorithm clusters significantly varies for shorter agent memories. This in turn affects the long-term aggregated dynamics of attendances. We observe that a transition occurs in the attendance distribution at the critical memory horizon where the correlations of the attendance deviations take longer time to decay to zero. A larger part of the crowd becomes more comfortable while the rest of the bar-goers still feel the congestion for long memories. Agents’ confidence on their algorithms and the delayed feedback of attendance data increase the overall collectivity of the system behavior

Collective behavior of El Farol attendees

Arthur’s paradigm of the El Farol bar for modeling bounded rationality and inductive behavior is undertaken. The memory horizon available to the agents and the selection criteria they utilize for the prediction algorithm are the two essential variables identified to represent the heterogeneity of agent strategies. The latter is enriched by including various rewarding schemes during decision making. Though the external input of comfort level is not explicitly coded in the algorithm pool, it contributes to each agent’s decision process. Playing with the essential variables, one can maneuver the overall outcome between the comfort level and the endogenously identified limiting state. The distribution of algorithm clusters significantly varies for shorter agent memories. This in turn affects the long-term aggregated dynamics of attendances. We observe that a transition occurs in the attendance distribution at the critical memory horizon where the correlations of the attendance deviations take longer time to decay to zero. A larger part of the crowd becomes more comfortable while the rest of the bar-goers still feel the congestion for long memories. Agents’ confidence on their algorithms and the delayed feedback of attendance data increase the overall collectivity of the system behavior

Agent-Based Modeling of the El Farol Bar Problem

In this paper, we study the self-coordination problem as demonstrated by the well-known El Farol problem (Arthur, 1994), which has later become what is known as the minority game in the econophysics community. While the El Farol problem or the minority game has been studied for almost two decades, existing studies are mostly only concerned with efficiency. The equality issue, however, has been largely neglected. In this paper, we build an agent-based model to study both efficiency and equality and ask whether a decentralized society can ever possibly self-coordinate a result with the highest efficiency while also maintaining the highest degree of equality. Our agent-based model shows the possibility of achieving this social optimum. The two key determinants to make this happen are social preferences and social networks. Hence, not only doe institutions (networks) matter, but individual characteristics (preferences) also matter. The latter are open to human-subject experiments for further examination.El Farol Bar problem, Social Preferences, Social Networks, Self-Organization, Emergence of Coordination.

COLLECTIVE BEHAVIOR OF EL FAROL ATTENDEES

Arthur's paradigm of the El Farol bar for modeling bounded rationality and inductive behavior is employed. Manipulating the memory horizon available to the agents and the selection criteria they utilize for prediction algorithms, one can maneuver the mean attendance away from the externally provided threshold. We observe that a transition occurs in the attendance distribution at a critical memory, beyond which a larger part of the crowd becomes more comfortable. Agents' confidence in their algorithms and the delayed feedback of attendance data increase the overall collectivity of the system behavior. It is possible to manipulate the time evolution of the attendance either externally, by providing past data with delay, or internally, if agents postpone algorithm modification upon failure.Bounded rationality, inductive behavior, delayed feedback, agent rigidity, critical memory, attendance correlation