Let me suggest that for anthropic reasoning, you are not directly calculating expected utility but actually trying to determine priors instead. And this traces back to Occam’s razor and hence complexity measures (complexity prior). Further, it is not probabilities that you are trying to directly manipulate, but degrees of similarity. (i.e which reference class does a given observer fall into? – what is the degree of similarity between given algorithms?). So rather than utility and probability, you are actually trying to manipulate something more basic , i.e., complexity and similarity measures
Suggested analogy:
Complexity (is like) Utility
Similarity (is like ) Probability
Let me suggest that rather than trying to ‘maximize utility’ directly, you should first attempt to ‘minimize complexity’ using a new generalized new form of rationality based on the above analogy (The putative method would be an entirely new type of rationality which subsumes ordinary Bayesian reasoning as a special case). The ‘expected complexity’ (analogous to ‘expected utility’) would be based on a ‘complexity function’ (analogous to ‘utility function’) that combines similarity measures (similiarities between algorithms) with the complexities of given outcomes. The utilities and probabilities would be derived from these calculations (ordinary Bayesian rationality would be derivative rather than fundamental).
Let me suggest that for anthropic reasoning, you are not directly calculating expected utility but actually trying to determine priors instead. And this traces back to Occam’s razor and hence complexity measures (complexity prior). Further, it is not probabilities that you are trying to directly manipulate, but degrees of similarity. (i.e which reference class does a given observer fall into? – what is the degree of similarity between given algorithms?). So rather than utility and probability, you are actually trying to manipulate something more basic , i.e., complexity and similarity measures
Suggested analogy:
Complexity (is like) Utility Similarity (is like ) Probability
Let me suggest that rather than trying to ‘maximize utility’ directly, you should first attempt to ‘minimize complexity’ using a new generalized new form of rationality based on the above analogy (The putative method would be an entirely new type of rationality which subsumes ordinary Bayesian reasoning as a special case). The ‘expected complexity’ (analogous to ‘expected utility’) would be based on a ‘complexity function’ (analogous to ‘utility function’) that combines similarity measures (similiarities between algorithms) with the complexities of given outcomes. The utilities and probabilities would be derived from these calculations (ordinary Bayesian rationality would be derivative rather than fundamental).
M J Geddes (Black Swan Siren!)