A Unied Characterization of Belief-Revision Rules
نویسندگان
چکیده
This paper characterizes several belief-revision rules in a uni ed framework: Bayesian revision upon learning some event, Je¤rey revision upon learning new probabilities of some events, Adams revision upon learning some new conditional probabilities, and dual-Je¤reyrevision upon learning a new conditional probability function. Despite their di¤erences, these revision rules can be characterized in terms of the same two axioms: responsiveness, which requires that revised beliefs incorporate what has been learnt, and conservativeness, which requires that beliefs on which the learnt input is silentdo not change. So, the four revision rules apply the same principles, albeit to di¤erent learnt inputs. To illustrate that there is room for non-Bayesian belief revision in economic theory, we also sketch a simple decision-theoretic application. Keywords: Subjective probability, Bayess rule, Je¤reys rule, axiomatic foundations, ne-grained versus coarse-grained beliefs, unawareness
منابع مشابه
A Model of Minimal Probabilistic Belief Revision∗
In the literature there are at least two models for probabilistic belief revision: Bayesian updating and imaging (Lewis (1973, 1976), Gärdenfors (1988)). In this paper we focus on imaging rules that can be described by the following procedure: (1) Identify every state with some real valued vector of characteristics, and accordingly identify every probabilistic belief with an expected vector of ...
متن کاملBelief revision generalized: A joint characterization of Bayesís and Je§reyís rules
We present a general framework for representing belief-revision rules and use it to characterize Bayesís rule as a classical example and Je§reyís rule as a non-classical one. In Je§reyís rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayesís rule, but a new assignment of probabilities to some events. Despite their di§erences, Bayesís and J...
متن کاملA Joint Characterization of Belief Revision Rules1
This paper characterizes di¤erent belief revision rules in a uni ed framework: Bayesian revision upon learning some event, Je¤rey revision upon learning new probabilities of some events, Adams revision upon learning some new conditional probabilities, and dual-Je¤reyrevision upon learning an entire new conditional probability function. Though seemingly di¤erent, these revision rules follow fr...
متن کاملBelief revision generalized: A joint characterization of Bayess and Je¤reys rules
We present a general framework for representing belief-revision rules and use it to characterize Bayess rule as a classical example and Je¤reys rule as a non-classical one. In Je¤reys rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayess rule, but a new assignment of probabilities to some events. Despite their di¤erences, Bayess and J...
متن کاملBelief revision generalized: A joint characterization of Bayes' and Jeffrey's rules
We present a general framework for representing belief-revision rules and use it to characterize Bayess rule as a classical example and Je¤reys rule as a non-classical one. In Je¤reys rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayess rule, but a new assignment of probabilities to some events. Despite their di¤erences, Bayess and J...
متن کامل