摘要翻译：
社会计算，无论是由一群智能体执行的搜索，还是对市场的集体预测，通常为复杂问题提供非常好的解决方案。在许多例子中，试图在本地解决问题的个人可以聚合他们的信息，并共同努力，以获得一个更好的全局解决方案。这表明，可能存在超越特定应用的信息聚合和协调的一般原则。在这里，我们证明了这个问题的一般结构可以用信息论的观点来描述，并导出了导致最优多智能体搜索的数学条件。具体来说，我们用自治代理寻找随机源空间位置的局部搜索算法来说明这个问题。我们探索了搜索问题的类型，根据源的统计特性和每个agent上测量的性质来定义，对于这些问题，多个搜索者之间的协调产生了比拥有相同数量的独立搜索者所获得的优势。我们证明了有效的协调对应于协同，无效的协调对应于信息论定义的独立性。我们根据协同作用的潜力对源的明确类型进行了分类。我们表明，发射不相关信号的源没有提供协同协调的机会，而发射以某种方式相关信号的源确实允许搜索者之间的强大协同。这些一般性的考虑对于在现实世界中为特定的搜索问题设计最优算法是至关重要的。
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英文标题：
《When is social computation better than the sum of its parts?》
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作者：
Vadas Gintautas, Aric Hagberg, Luis M. A. Bettencourt
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  Social computation, whether in the form of searches performed by swarms of agents or collective predictions of markets, often supplies remarkably good solutions to complex problems. In many examples, individuals trying to solve a problem locally can aggregate their information and work together to arrive at a superior global solution. This suggests that there may be general principles of information aggregation and coordination that can transcend particular applications. Here we show that the general structure of this problem can be cast in terms of information theory and derive mathematical conditions that lead to optimal multi-agent searches. Specifically, we illustrate the problem in terms of local search algorithms for autonomous agents looking for the spatial location of a stochastic source. We explore the types of search problems, defined in terms of the statistical properties of the source and the nature of measurements at each agent, for which coordination among multiple searchers yields an advantage beyond that gained by having the same number of independent searchers. We show that effective coordination corresponds to synergy and that ineffective coordination corresponds to independence as defined using information theory. We classify explicit types of sources in terms of their potential for synergy. We show that sources that emit uncorrelated signals provide no opportunity for synergetic coordination while sources that emit signals that are correlated in some way, do allow for strong synergy between searchers. These general considerations are crucial for designing optimal algorithms for particular search problems in real world settings.
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PDF链接：
https://arxiv.org/pdf/1103.4854