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→Alternative mathematical techniques
值得一提的是，确认结果中涉及的研究人员之间，以及与参与有关安妮塔·伍利Anita Woolley最初第一项研究的作者之间也存在广泛的重叠。
值得一提的是，确认结果中涉及的研究人员之间，以及与参与有关安妮塔·伍利Anita Woolley最初第一项研究的作者之间也存在广泛的重叠。
== Alternative mathematical techniques ==
== Alternative mathematical techniques 其他数学替代技术 ==
=== Computational collective intelligence ===
=== Computational collective intelligence 计算集体智能 ===
[[File:Computational collective intelligence.jpg|thumb|Computational Collective Intelligence, by Tadeusz Szuba]]
[[文件:计算集体智能.jpg|缩略图|右|计算集体智能]]
In 2001, Tadeusz (Tad) Szuba from the [[Akademia Górniczo-Hutnicza|AGH University]] in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure.
In 2001, Tadeusz (Tad) Szuba from the [[Akademia Górniczo-Hutnicza|AGH University]] in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure.<ref name="szuba">Szuba T., ''Computational Collective Intelligence'', 420 pages, Wiley NY, 2001</ref>
In 2001, Tadeusz (Tad) Szuba from the AGH University in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure.
In 2001, Tadeusz (Tad) Szuba from the AGH University in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure.
2001年，来自波兰AGH科技大学的Tadeusz（Tad）Szuba提出了一种具有集体智能现象的正式模型。模型假定是一个无意识，随机，并行和分布式的计算程序，其社会结构以数学逻辑方式运行。
In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic.<ref name="szuba" /> They are quasi-randomly displacing due to their interaction with their environments with their intended displacements.<ref name="szuba" /> Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence.<ref name="szuba" /> Thus, a non-[[Alan Turing|Turing]] model of computation is used. This theory allows simple formal definition of collective intelligence as the property of [[social structure]] and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as "the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure".<ref name="szuba" /> While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation.<ref name="szuba" /> Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies.<ref name="szuba"/>
In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic. They are quasi-randomly displacing due to their interaction with their environments with their intended displacements. Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence. Thus, a non-[[Alan Turing|Turing]] model of computation is used. This theory allows simple formal definition of collective intelligence as the property of [[social structure]] and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as "the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure". While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation. Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies.
In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic. They are quasi-randomly displacing due to their interaction with their environments with their intended displacements. Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence. Thus, a non-Turing model of computation is used. This theory allows simple formal definition of collective intelligence as the property of social structure and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as "the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure". While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation. Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies.
In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic. They are quasi-randomly displacing due to their interaction with their environments with their intended displacements. Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence. Thus, a non-Turing model of computation is used. This theory allows simple formal definition of collective intelligence as the property of social structure and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as "the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure". While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation. Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies.
在此模型中，将真实环境中的生物和信息两部分进行建模，表示为带有数学逻辑表达式的抽象信息分子。由于它们与环境之间的相互作用，以及它们自身带有的目标位移属性，会准随机地进行挪动。随后，它们会在抽象的计算空间中交互，进而创建多线程推导处理程序，其过程则被视为集体智能。因此，非图灵计算模型被采用。该理论将集体智能简单定义为社会结构的属性，而且似乎对于各类生物（从细菌菌落到人类社会结构）均适用。集体智能被视为是一种特定的计算过程，它为几种社会现象提供了直接的解释。对于这种集体智能模型，科学家们提出了IQS（即IQ社会）的正式定义，并将其定义为“在时间和N元素推理域（反映社会结构推理活动）上的概率函数”。IQS在计算上似乎很难，但是根据如上所述的计算过程对社会结构进行建模的话，可以得到近似的结果。通过最大化IQS，公司可以优化其潜在的应用，另外医学上，也可以对细菌菌落的集体智能进行建模，来分析耐药性。
=== Collective intelligence quotient 集体智商 ===
One measure sometimes applied, especially by more artificial intelligence focused theorists, is a "collective intelligence quotient" (or "cooperation quotient") – which can be normalized from the "individual" [[intelligence quotient]] (IQ) – thus making it possible to determine the marginal intelligence added by each new individual participating in the [[collective action]], thus using [[Metric (mathematics)|metrics]] to avoid the hazards of [[group think]] and [[stupidity]].
One measure sometimes applied, especially by more artificial intelligence focused theorists, is a "collective intelligence quotient" (or "cooperation quotient") – which can be normalized from the "individual" intelligence quotient (IQ) thus making it possible to determine the marginal intelligence added by each new individual participating in the collective action, thus using metrics to avoid the hazards of group think and stupidity.
有时候我们会采用另一种度量方式表达，称为“集体智商Collective intelligence quotient” （或“合作商Cooperation quotient”），它特别受到以人工智能为研究重点的理论家的青睐。它可以由“个体”智商归一化处理后得到。因此可以进一步确定参加集体行动的新增组员所带来的额外边际智商，还可以使用度量标准来避免由群体愚蠢思维带来的危险。
== Applications ==
== Applications ==