Groups of diverse problem solvers can outperform groups of high-ability problem solvers¶
Hong, L., & Page, S. E. (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences, 101(46), 16385-16389.
Cited by¶
3 citations across 2 artifacts.
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Primes¶
- Diversity
- The two are compatible and often mutually reinforcing, as Hong and Page (2004) formally model when showing that diverse problem-solvers outperform high-ability solvers under the right cooperative conditions.
Supported in partVerified against the work's full text
Hong and Page formally show a random diverse group can beat a group of the best performers under stated conditions, but frame it as a diversity-ability trade-off, not as mutual reinforcement or cooperative conditions.
“The main result of this paper provides conditions under which, in the limit, a random group of intelligent problem solvers will outperform a group of the best problem solvers.”
- The two are compatible and often mutually reinforcing, as Hong and Page (2004) formally model when showing that diverse problem-solvers outperform high-ability solvers under the right cooperative conditions.
- Wisdom of the Crowds
- It frames a market price, a vote tally, an ensemble prediction, and a neural readout as instances of one estimator, so an insight proven in one (the diversity-prediction decomposition) transfers as a theorem to all.
This sourceFormal model and theorem showing that under conditions of complex problem-solving, cognitively diverse groups outperform homogeneous groups of high-ability individuals through cooperative integration of distinct heuristics.
- The machine-learning result that decorrelated weak learners average into a strong one is recognizably the same insight as the economist's claim that a market price reveals dispersed knowledge and the political theorist's jury theorem, a unity Hong and Page (2004) made formal in showing that diversity can trump individual ability in collective problem-solving.
This sourceEstablishes the cross-substrate unity of the aggregation insight: the same diversity-over-ability logic that governs human problem-solving groups governs ensemble predictors and other pooled estimators.
Supported in partVerified against the work's full text
Hong and Page formally show diversity can beat individual ability in group problem-solving, but not that this unifies ML ensembles, market prices and the jury theorem.
“It is in this sense that we might say diversity trumps ability.”
- It frames a market price, a vote tally, an ensemble prediction, and a neural readout as instances of one estimator, so an insight proven in one (the diversity-prediction decomposition) transfers as a theorem to all.
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