@InProceedings{RS88a,
  author = 	 { Ronald L. Rivest and Robert H. Sloan },
  title = 	 { A New Model for Inductive Inference },
  pages =        { 13--27 },                  
  url =          { http://www.tark.org/proceedings/tark_mar7_88/p13-rivest.pdf },                  
  booktitle =    { Proceedings Second Annual Conference on Theoretical Aspects of Rationality and Knowledge },
  date =         { 1988 },                  
  publisher =    { Morgan Kaufmann },
  editor = 	 { Moshe Y. Vardi },
  OPTyear = 	 { 1988 },
  OPTmonth = 	 { March },
  eventdate =    { 1988-03-07/1988-03-09 },                  
  eventtitle =   { TARK II },                  
  venue =        { Asilomar, Pacific Grove, California },                  
  organization = { TARK },
  abstract =     {
         We introduce a new model for inductive inference, by combining a Bayesian
         approach for representing the current state of knowledge with a simple model
         for the computational cost of making predictions from theories.  We investigate
         the optimization problem: how should a scientist divide his time between doing
         experiments and deducing predictions for promising theories.  We propose an
         answer to this question, as a function of the relative costs of making predictions
         versus performing experiments.  We believe our model captures many of the
         qualitative characteristics of ``real'' science.
         \par
         We believe that this model makes two important contributions.  First, it allows
         us to study how a scientist might go about acquiring knowledge in a 
         world where (as in real life) there are costs associated with both performing
         experiments and with computing the predictions of various theories.
         \par
         This model also lays the groundwork for a rigorous treatment of a machine-implementable
         notion of ``subjective probability''.  Subjective probability is at the heart
         of probability theory [5].  Previous treatments have not been able to
         handle the difficulty that subjective probabilities can change as the result of
         ``pure thinking''; our model captures this (and other effects) in a realistic
         manner.  In addition, we begin to provide an answer to the question of how to
         trade-off ``thinking'' versus ``doing''---a question that is fundamental for
         computers that must exist in the world and learn from their experience.                  
  },
}