2005年01月23日 21:20
The El Farol Bar
有間店名叫"El Farol"的Bar，都會放些好聽又舒服的音樂，於是大家都很喜歡到這家店裡去。
可是要享受輕鬆的時光，Bar裡就不能有太多客人，如果今天來的人很多，就覺得很吵，沒法好好享受音樂；
如果今天來的人不多，那真是太好了，可以擁有一段放鬆的夜晚。
我猜去的人會很多 ，別人也猜會有很多人，所以人很少，因為大家都猜會有很多人；
我猜去的人會很多，但別人猜說去的人不多，說不定人很少，可惜了，我沒有去；
我猜去的人不多，別人猜說會有很多人，所以我去的時候人不多，太好了！可以好好享受；
我猜去的人不多，別人也猜說去的人不多，結果大家都跑去，整個Bar裡鬧哄哄的，人多到爆了…
不知道今天去Bar的人有多少，我到底要不要去呢？
以下是節錄的文章，有興趣的人可以看看哦！很有趣的一個問題。
如果我節錄的部份有不詳盡的部份，請多見諒。相關的細節就要麻煩您們自己去找資料了。
In 1994, Brian Arthur introduced the 'El Farol Bar' problem as a paradigm of complex economic systems. In this model a population of agents have to decide whether to go to the bar each thursday night. All agents like to go to the bar unless it is too crowded (i.e. when more that 60% of the agents go). So in order to epitomise its own utility each agent has to try and predict what everybody else will do. The problem is set up so that any model of the problem that is shared by most of the agents is self-defeating. For if most agents predict that the bar will not be too crowded then they will all go and it will be too crowded, and vice versa.
Brian Arthur modelled this by randomly giving each agent a fixed menu of potentially suitable models to predict the number who will go given past data (e.g. the same as two weeks ago, the average of the last 3 week, or 90 minus the number who went last time). Each week each agent evaluates these models against the past data and chooses the one that was the best predictor on this data and then uses this to predict the number who will go this time. It will go if this prediction is less than 60 and not if it is more than 60.
可是要享受輕鬆的時光，Bar裡就不能有太多客人，如果今天來的人很多，就覺得很吵，沒法好好享受音樂；
如果今天來的人不多，那真是太好了，可以擁有一段放鬆的夜晚。
我猜去的人會很多 ，別人也猜會有很多人，所以人很少，因為大家都猜會有很多人；
我猜去的人會很多，但別人猜說去的人不多，說不定人很少，可惜了，我沒有去；
我猜去的人不多，別人猜說會有很多人，所以我去的時候人不多，太好了！可以好好享受；
我猜去的人不多，別人也猜說去的人不多，結果大家都跑去，整個Bar裡鬧哄哄的，人多到爆了…
不知道今天去Bar的人有多少，我到底要不要去呢？
以下是節錄的文章，有興趣的人可以看看哦！很有趣的一個問題。
如果我節錄的部份有不詳盡的部份，請多見諒。相關的細節就要麻煩您們自己去找資料了。
In 1994, Brian Arthur introduced the 'El Farol Bar' problem as a paradigm of complex economic systems. In this model a population of agents have to decide whether to go to the bar each thursday night. All agents like to go to the bar unless it is too crowded (i.e. when more that 60% of the agents go). So in order to epitomise its own utility each agent has to try and predict what everybody else will do. The problem is set up so that any model of the problem that is shared by most of the agents is self-defeating. For if most agents predict that the bar will not be too crowded then they will all go and it will be too crowded, and vice versa.
Brian Arthur modelled this by randomly giving each agent a fixed menu of potentially suitable models to predict the number who will go given past data (e.g. the same as two weeks ago, the average of the last 3 week, or 90 minus the number who went last time). Each week each agent evaluates these models against the past data and chooses the one that was the best predictor on this data and then uses this to predict the number who will go this time. It will go if this prediction is less than 60 and not if it is more than 60.
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