The El Farol Bar Problem for next generation systems

1.1. OVERVIEW 3
Emergence occurs when agents that operate in the same environment start
to interact which each other. The number of the interactions increases when
the number of agents increases, this leads to the appearance of new types of
behaviour. This process can result to an increase of the complexity and since it
is an internal property of the system and not managed by an outside source, it
is a ‘self-organised’ process.
The subject of this dissertation is the study and expansion of a famous complex
adaptive system known as El Farol Bar Problem which was introduced by
the economist W. B. Arthur in 1994[1]. El Farol is a bar in Santa Fe in New Mexico
which plays each Thursday Irish music. People enjoy visiting it and hearing
some quality music but eventually it becomes overcrowded, so people stop enjoying
themselves. Each customer decides independently whether to attend or not,
based on a set of predictors. This scenario provides a simplified mathematical
model of a class of congestion and coordination problems that arise in modern
Information and Communications Technology (ICT) systems.
One application of great interest is networks of cognitive radios, where agents
compete with each other for the same resource (RF spectrum). Cognitive radios
are autonomous agents that have the ability to sense the external environment,
learn from history and make intelligent decisions in order to optimise their performance
and adjust better to the environment[9]. Another application is internet
when a large number of people try to visit the same web page or access the same
ftp server simultaneously and independently.
In this first chapter, the original Arthur’s EFBP is defined and analysed. In
the end of the chapter, some basic Game Theory concepts are explained and
defined.
In chapter two, various different approaches to the El Farol Bar problem are
reviewed. First it is viewed as a minority game and various techniques from
statistical mechanics are implemented. Also strategies are redefined using a binary
approach as an attempt to reduce complexity. The next approach tries to
overcome the restrictive strategies using an evolutionary learning algorithm and
viewing the problem as a Markov stochastic process. The last approach suggests
a very simple adaptive algorithm which is based on the maximisation of the
probability of attendance for each agent. There are no specific strategies to guide
agents during their decision process, only their intention to attend the bar.
In chapter three, the last algorithm is analysed in depth. In this original work,
the stochastic adaptive learning algorithm is extended and several derivatives of
it are examined as an attempt to deal with the unfairness or low efficiency issues
that occurred in some cases with the original algorithm. Considerable effort was
put in order to define the stationary state of one variation. Also fairness and
efficiency are defined and measured both from the bar management’s and agent’s
point of view.
In chapter four, it is examined whether three bars in the same town would
affect the agents ways of entertainment. They attempt to enter the bars in a