集体智能通过依赖频率的学习来促进新出现的资源分区
Mina Ogino1,2, Damien R Farine1,2,3
1Department of Evolutionary Biology and Environmental Science, University of Zurich , Zurich Winterthurerstrasse 190, 8057, Switzerland.
群体生活的动物通过汇集信息,比单独生活的动物更好地分配资源,从而减少了竞争. 较大的群体表现出增强的资源分割,特别是有更多的选择,有利于人口层面的食策略.
科学领域:
- 行为生态学 行为生态学
- 人口动态 人口动态
- 集体情报是一种集体情报.
背景情况:
- 采食决策需要考虑息地质量和竞争对手的存在.
- 负频率依赖性学习有助于个人避免利用资源 (间接竞争),促进资源分割.
- 对个人来说,感知间接竞争线索可能是具有挑战性的.
研究的目的:
- 调查群体生活动物的集体决策是否与单身动物相比增强了资源分割.
- 为了确定群体规模对资源划分有效性的影响.
- 探索信息共享在减轻食竞争中的作用.
主要方法:
- 基于代理的建模模拟基于最近的成功的个人食行为.
- 模拟群体采食决策,使用多数规则机制.
- 在模拟的孤独群体与生活在群体中的群体中比较资源分区.
主要成果:
- 孤独的动物通过负频率依赖的学习表现出部分避免间接竞争.
- 群体生活的动物表现出比单独生活的个体更有效的资源分割.
- 较大的群体规模会导致更好的资源分割,特别是在资源多样化的环境中.
结论:
- 通过集体决策的集体智能显著改善了资源分区.
- 群体生活和更大的群体规模有利于管理食竞争并促进专业化.
- 研究结果提供了关于社交,群体规模和领土行为的演变的见解.
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