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Open Access Publications from the University of California

Computation-Limited Bayesian Updating


Effectively updating one’s beliefs requires sufficient empirical evidence (i.e., data) and the computational capacity to process it. Yet both data and computational resources are limited for human minds. Here, we study the problem of belief updating under limited data and limited computation. Using information theory to characterize constraints on computation, we find that the solution to the resulting optimization problem links the data and computational limitations together: when computational resources are tight, agents may not be able to integrate new empirical evidence. The resource-rational belief updating rule we identify offers a novel interpretation of conservative Bayesian updating.

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