Collective intelligence through aggregation

Abstract:Suppose a committee, expert panel, or other group is making judgments on some issues, where these may be not just yes/no-questions, such as whether a defendant is guilty, but also variables with many possible values, such as macroeconomic or meteorological variables or travel directions. Furthermore, there may be interconnections between different issues, as in the case of economic or climate variables. How can the group arrive at "intelligent" collective judgments, based on the group members' individual judgments? We investigate three challenges raised by this judgment-aggregation problem. First, reasonable methods of aggregation (such as defining the collective judgment for each issue as the average or median judgment) can produce inconsistent collective judgments. Secondly, many methods of aggregation are manipulable by strategic voting. Finally, not all methods of aggregation are conducive to tracking the truth on the issues in question. We prove new impossibility or possibility theorems on all three challenges, identifying what it takes to produce collective judgments in a consistent, non-manipulable, and truth-tracking manner and thereby to achieve collective intelligence through aggregation. Overall, the median method, though imperfect, performs reasonably well. We also note the relevance of our analysis for non-human group decisions.

Artificial Intelligence (cs.AI)

arXiv:2610.06652 [cs.AI]

(or arXiv:2610.06652v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.06652 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1098/rstb.2024.0454 Focus to learn more DOI(s) linking to related resources

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