By John Eatwell, Murray Milgate, Peter Newman (eds.)
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Extra resources for Utility and Probability
Example text
A failure of the model is attributed to a hitherto overlooked benefit or cost. From a statistical viewpoint, this stopping rule has obvious biases. I was taught as a graduate student that data mining was a major crime; mortality has changed here as elsewhere in society, but I am not persuaded that all these changes are for the better. The lesson is that the rationality hypothesis is by itself weak. To make it useful, the researcher is tempted into some strong assumptions. In particular, the homogeneity assumption seems to me to be especially dangerous.
1953. Le role des va leurs boursieres dans la repartition la meilleure des risques. In Econometrie, Paris: Centre National de la Recherche Scientifique. J. 1979. The property rights doctrine and demand revelation under incomplete information. In Economics and Human Welfare, ed. J. Boskin, New York: Academic Press. J. 1976. Agreeing to disagree. Annals of Statistics 4, 1236-9. Cass, D. and Shell, K. 1983. Do sunspots matter? Journal of Political Economy 91,193-227. Chamberlin, E. 1950. The Theory of Monopolistic Competition.
Oddly enough, it is not clear that limited knowledge means a smaller quantity of information than complete knowledge, and optimization under limited knowledge is certainly computationally more difficult. If individuals have private information, the others form some kind of conjecture about it. These conjectures must be common knowledge for there to be a rationality-based hypothesis. This seems to have as much informational content and to be as unlikely as knowing the private information. Further, the optimization problem for each individual based on conjectures (in a rational world, these are probability distributions) on the private information of others is clearly a more difficult and therefore computationally more demanding problem than optimization when there is no private information.