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Collective intelligence through aggregation
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AI
DOI：10.1098/rstb.2024.0454.png)
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Abstract
En 中文
Suppose a committee, expert panel or other group is making judgements 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 judgements, based on the group members' individual judgements? We investigate three challenges raised by this judgement-aggregation problem. First, reasonable methods of aggregation (such as defining the collective judgement for each issue as the average or median judgement) can produce inconsistent collective judgements. Second, 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 judgements 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.This article is part of the theme issue 'The evolution of collective intelligence'.
Keywords:
collective intelligence
aggregation
judgements
collective cognition
collective agency
Condordet's jury theorem
social choice
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