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Dynamically rational judgment aggregation

Franz Dietrich and Christian List
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Christian List: LMU - Ludwig Maximilian University [Munich] = Ludwig Maximilians Universität München

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Abstract: Judgment-aggregation theory has always focused on the attainment of rational collective judgments. But so far, rationality has been understood in static terms: as "coherence" of judgments at a given time, understood as consistency, completeness, and/or deductive closure. By contrast, this paper discusses whether collective judgments can be dynamically rational, so that they change rationally in response to new information. Formally, a judgment aggregation rule is dynamically rational with respect to a given revision operator if, whenever all individuals revise their judgments in light of some information (a learnt proposition), then the new aggregate judgments are the old ones revised in light of this information, i.e., aggregation and revision commute. We prove a general impossibility theorem: if the propositions on the agenda are sufficiently interconnected, no judgment aggregation rule with standard properties is dynamically rational with respect to any revision operator satisfying some mild conditions (familiar from belief revision theory). Our theorem is the dynamic-rationality analogue of some well-known impossibility theorems for static rationality. We also explore how dynamic rationality might be achieved by relaxing some of the conditions on the aggregation rule and/or the revision operator.

Keywords: judgment aggregation; belief revision; static vs. dynamic rationality; premise-based rule (search for similar items in EconPapers)
Date: 2021-02
New Economics Papers: this item is included in nep-mic
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03140090v1
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Published in 2021

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Journal Article: Dynamically rational judgment aggregation (2024) Downloads
Working Paper: Dynamically rational judgment aggregation (2021) Downloads
Working Paper: Dynamically rational judgment aggregation (2021) Downloads
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