4.1 The Extended `El Farol Bar' Model

4.1.2 The agents

Each agent learns its own set of strategies of what to do and say in a flexible and open-ended way. It can base its strategy of what to say upon what other agents did or said in previous weeks as well as on trends in past attendance statistics, random inputs and what it said or did itself in precious weeks. It can base its strategy of what to do on what the other agents just said as well as what it just said itself, what it did the previous week and a random decision. In either case it can build up mixtures of strategies using logical or numerical operators, without limit as to their complexity. It so happens that the learning mechanism is implemented by an evolutionary mechanism but this is entirely internal to each agent. I have no reason to suppose that the learning mechanism is critical to the results of this model (as long as it is open-ended, creative and context-sensitive).

An example strategy that an agent learned is shown in figure 4. This would cause the agent to say "[IPredictedLastWeek]" if the attendance predicted by the trend in attendances over the last 2 weeks was greater than 8/3 and false otherwise, but it would only actually go if either it said it would or if barGoer-3 said it would.

Figure 4. An example model

A second example (figure 5) shows the operation of a memetic process. In this example the agent says whatever the expression last uttered by barGoer-5 evaluates to, but goes if it went last time and a random coin-flip turns up heads. In this case this agent would propagate what ever barGoer-5 said.

Figure 5. A second example model

A full indication of the sort of building-blocks that the agents can use to make these models is illustrated in figure 11.

On Modelling in Memetics - Bruce Edmonds - 18 AUG 98

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