Effective leadership and decision-making in animal groups on the move

群体决策 质量(理念) 群(周期表) 决策质量 心理学 社会心理学 计算机科学 知识管理 团队效能 认识论 哲学 有机化学 化学
作者
Iain D. Couzin,Jens Krause,Nigel R. Franks,Simon A. Levin
出处
期刊:Nature [Springer Nature]
卷期号:433 (7025): 513-516 被引量:2674
标识
DOI:10.1038/nature03236
摘要

Moving groups of animals, including fish, ungulates, birds and honeybee swarms seem able to take complex decisions in the absence of signalling mechanisms, and when group members cannot establish who has or has not got information. A numerical simulation shows how such groups can make accurate consensus decisions, and that the larger the group, the smaller the proportion of informed individuals needed to guide the group. A very small proportion of informed individuals is sufficient for near maximal accuracy. This has implications for our understanding of the evolution of information transfer in groups, and also suggests a new design protocol for the guidance of grouping robots. Cover photo, by Phillip Colla Natural History Photography ( http://www.OceanLight.com ), shows schooling jack mackerel. For animals that forage or travel in groups, making movement decisions often depends on social interactions among group members1,2. However, in many cases, few individuals have pertinent information, such as knowledge about the location of a food source3,4, or of a migration route5,6,7,8,9. Using a simple model we show how information can be transferred within groups both without signalling and when group members do not know which individuals, if any, have information. We reveal that the larger the group the smaller the proportion of informed individuals needed to guide the group, and that only a very small proportion of informed individuals is required to achieve great accuracy. We also demonstrate how groups can make consensus decisions, even though informed individuals do not know whether they are in a majority or minority, how the quality of their information compares with that of others, or even whether there are any other informed individuals. Our model provides new insights into the mechanisms of effective leadership and decision-making in biological systems.
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