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Conflicts of interest improve collective computation of adaptive social structures.
Science Advances ( IF 13.6 ) Pub Date : 2018-Jan-01 , DOI: 10.1126/sciadv.1603311 Eleanor R. Brush 1, 2 , David C. Krakauer 2 , Jessica C. Flack 2
Science Advances ( IF 13.6 ) Pub Date : 2018-Jan-01 , DOI: 10.1126/sciadv.1603311 Eleanor R. Brush 1, 2 , David C. Krakauer 2 , Jessica C. Flack 2
Affiliation
In many biological systems, the functional behavior of a group is collectively computed by the system's individual components. An example is the brain's ability to make decisions via the activity of billions of neurons. A long-standing puzzle is how the components' decisions combine to produce beneficial group-level outputs, despite conflicts of interest and imperfect information. We derive a theoretical model of collective computation from mechanistic first principles, using results from previous work on the computation of power structure in a primate model system. Collective computation has two phases: an information accumulation phase, in which (in this study) pairs of individuals gather information about their fighting abilities and make decisions about their dominance relationships, and an information aggregation phase, in which these decisions are combined to produce a collective computation. To model information accumulation, we extend a stochastic decision-making model-the leaky integrator model used to study neural decision-making-to a multiagent game-theoretic framework. We then test alternative algorithms for aggregating information-in this study, decisions about dominance resulting from the stochastic model-and measure the mutual information between the resultant power structure and the "true" fighting abilities. We find that conflicts of interest can improve accuracy to the benefit of all agents. We also find that the computation can be tuned to produce different power structures by changing the cost of waiting for a decision. The successful application of a similar stochastic decision-making model in neural and social contexts suggests general principles of collective computation across substrates and scales.
中文翻译:
利益冲突改善了适应性社会结构的集体计算。
在许多生物系统中,一组的功能行为是由系统的各个组件共同计算的。一个例子就是大脑通过数十亿个神经元的活动做出决策的能力。一个长期存在的难题是,尽管存在利益冲突和信息不完善,组件的决策如何组合以产生有益的组级输出。我们使用灵长类动物模型系统中功率结构计算的先前工作结果,从机械第一性原理推导了集体计算的理论模型。集体计算有两个阶段:信息积累阶段,在此阶段(在本研究中),成对的个人收集有关其战斗能力的信息并做出关于其优势关系的决策;以及一个信息汇总阶段,在这些决策中,将这些决策结合起来以产生一个集体计算。为了对信息积累建模,我们将随机决策模型(用于研究神经决策的泄漏积分器模型)扩展到多主体博弈理论框架。然后,我们测试用于汇总信息的替代算法-在本研究中,根据随机模型得出的关于支配地位的决定-并测量由此产生的权力结构与“真实”战斗力之间的相互信息。我们发现利益冲突可以提高准确性,从而使所有代理人受益。我们还发现,可以通过改变等待决策的成本来调整计算以产生不同的功率结构。
更新日期:2018-01-18
中文翻译:
利益冲突改善了适应性社会结构的集体计算。
在许多生物系统中,一组的功能行为是由系统的各个组件共同计算的。一个例子就是大脑通过数十亿个神经元的活动做出决策的能力。一个长期存在的难题是,尽管存在利益冲突和信息不完善,组件的决策如何组合以产生有益的组级输出。我们使用灵长类动物模型系统中功率结构计算的先前工作结果,从机械第一性原理推导了集体计算的理论模型。集体计算有两个阶段:信息积累阶段,在此阶段(在本研究中),成对的个人收集有关其战斗能力的信息并做出关于其优势关系的决策;以及一个信息汇总阶段,在这些决策中,将这些决策结合起来以产生一个集体计算。为了对信息积累建模,我们将随机决策模型(用于研究神经决策的泄漏积分器模型)扩展到多主体博弈理论框架。然后,我们测试用于汇总信息的替代算法-在本研究中,根据随机模型得出的关于支配地位的决定-并测量由此产生的权力结构与“真实”战斗力之间的相互信息。我们发现利益冲突可以提高准确性,从而使所有代理人受益。我们还发现,可以通过改变等待决策的成本来调整计算以产生不同的功率结构。