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Many models of learning in teams assume that team members can share solutions or learn concurrently. However, these assumptions break down in multidisciplinary teams where team members often 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 overcome this challenge by learning from network neighbors through mediating artifacts (like collective performance assessments). When neighbors’ actions influence collective outcomes, teams with different networks perform relatively similarly to one another. However, varying a team’s network can affect performance on tasks that weight individuals’ contributions by network properties. Consequently, when individuals innovate (through “exploring” searches), dense networks hurt performance slightly by increasing uncertainty. In contrast, dense networks moderately help performance when individuals refine their work (through “exploiting” searches) by efficiently finding local optima. We also find that decentralization improves team performance across a battery of 34 tasks. Our results offer design principles for multidisciplinary teams within which other forms of learning prove more difficult.
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This content will become publicly available on February 28, 2026
The behavioral mechanisms governing collective motion in swarming locusts
Collective motion, which is ubiquitous in nature, has traditionally been explained by “self-propelled particle” models from theoretical physics. Here we show, through field, lab, and virtual reality experimentation, that classical models of collective behavior cannot account for how collective motion emerges in marching desert locusts, whose swarms affect the livelihood of millions. In contrast to assumptions made by these models, locusts do not explicitly align with neighbors. While individuals respond to moving-dot stimuli through the optomotor response, this innate behavior does not mediate social response to neighbors. Instead, locust marching behavior, across scales, can be explained by a minimal cognitive framework, which incorporates individuals’ neural representation of bearings to neighbors and internal consensus dynamics for making directional choices. Our findings challenge long-held beliefs about how order can emerge from disorder in animal collectives.
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- Award ID(s):
- 2021795
- PAR ID:
- 10598524
- Publisher / Repository:
- AAAS
- Date Published:
- Journal Name:
- Science
- Volume:
- 387
- Issue:
- 6737
- ISSN:
- 0036-8075
- Page Range / eLocation ID:
- 995 to 1000
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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