The El Farol Bar Problem for next generation systems

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20 CHAPTER 2. PREVIOUS APPROACHES TO THE EL FAROL probability. Nobody makes profit, nobody is happy, but everybody is content and they seem to have no intention of changing their strategy, all these are properties of Nash equilibrium points. But in terms of attendance ‘full information algorithm’ is not absolutely fair, since again there is a classification of agents in two groups, although this time casual bargoers have a much wider distribution. frequency 0 10 20 30 40 frequency 0 5 10 15 20 frequency 0 10 20 30 partial information 0.0 0.2 0.4 0.6 0.8 1.0 probability of attendance full information 0.2 0.3 0.4 0.5 0.6 0.7 0.8 probability of attendance partial information with signs 0.0 0.2 0.4 0.6 0.8 1.0 probability of attendance frequency 0 10 20 30 40 50 frequency 0 1 2 3 4 5 6 7 frequency 0 5 10 15 20 25 30 partial information 0.0 0.2 0.4 payoff probability 0.6 0.8 full information −0.06 −0.04 −0.02 0.00 0.02 0.04 0.06 payoff probability partial information with signs 0.0 0.1 0.2 0.3 0.4 0.5 0.6 payoff probability Figure 2.7: fairness and efficiency plots for original algorithms. It is obvious that these results are open to different interpretations, and vary according to the objectives set each time. In general partial information is the most appropriate algorithm if the objective is to maximise the average payoff for each agent. It might be the most unfair of all, but even full information is not fair enough to justify the minimal profits. Considering system’s efficiency, it can be seen in the cumulative plot of the running std for the original algorithms (Figure: 2.8), that ‘partial information’ has the least standard deviation and a negative slope. On the contrary ‘full
2.2. APPROACHES TO EFBP 21 information’ has a relatively high std, with no signs of improvement. standard deviation 0 1 2 3 4 5 6 original algorithms partial full signed 0 500 1000 1500 iterations 2000 2500 3000 Figure 2.8: standard deviation off attendance for original algorithms.
Page 1: The El Farol Bar Problem for next g
Page 4 and 5: List of Figures 1.1. Bar attendance
Page 6 and 7: Acknowledgements I would like to de
Page 8 and 9: 2 CHAPTER 1. CHAOS, COMPLEXITY AND
Page 16 and 17: 10 CHAPTER 2. PREVIOUS APPROACHES T
Page 30 and 31: 24 CHAPTER 3. ANALYSIS AND EXTENSIO
Page 44 and 45: 38 CHAPTER 4. CASE STUDY: MULTIPLE
Page 50 and 51: 44 CHAPTER 5. CONCLUSIONS Another n
Page 52 and 53: 46 CHAPTER 5. CONCLUSIONS
Page 54 and 55: 48 APPENDIX A. C CODE FOR THE EL FA
Page 66: 60 BIBLIOGRAPHY [14] Challet D. and

The El Farol Bar Problem for next generation systems

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