Dynamics of inductive inference in a unified framework

Itzhak Gilboa*, Larry Samuelson, David Schmeidler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We present a model of inductive inference that includes, as special cases, Bayesian reasoning, case-based reasoning, and rule-based reasoning. This unified framework allows us to examine how the various modes of inductive inference can be combined and how their relative weights change endogenously. For example, we establish conditions under which an agent who does not know the structure of the data generating process will decrease, over the course of her reasoning, the weight of credence put on Bayesian vs. non-Bayesian reasoning. We illustrate circumstances under which probabilistic models are used until an unexpected outcome occurs, whereupon the agent resorts to more basic reasoning techniques, such as case-based and rule-based reasoning, until enough data are gathered to formulate a new probabilistic model.

Original languageEnglish
Pages (from-to)1399-1432
Number of pages34
JournalJournal of Economic Theory
Issue number4
StatePublished - Jul 2013


  • Analogies
  • Case-based reasoning
  • Induction
  • Learning
  • Rule-based reasoning
  • Theories


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