Indirect social learning through collective performance favors decentralization
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Many models of learning in teams assume that team members can share solutions or learn concurrently. These assumptions break down in multi-disciplinary teams where team members tend to complete distinct, interrelated pieces of larger tasks. Such contexts make it difficult for individuals to separate the performance effects of their own actions from the actions of interacting neighbors. In this work, we show that individuals can also learn indirectly from network neighbors. In this context, individuals in dense networks tend to perform better when refining their work (through "exploiting" searches) by efficiently finding local optima. However when individuals innovate (through "exploring" searches), dense networks hurt performance by increasing uncertainty. We also find that decentralization improves team performance across a wide variety of tasks. Our results offer new design principles for multi-disciplinary teams where direct learning is not realistic or possible.
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