Posts Tagged ‘Ted Szuba’

Collective Intelligence Quotient

March 11, 2010 Leave a comment

from Wikipedia: Collective Intelligence # Mathematical techniques:

One measure sometimes applied, especially by more artificial intelligence focused theorists, is a “collective intelligence quotient” (or “cooperation quotient”)—which presumably can be measured like the “individual” intelligence quotient (IQ)—thus making it possible to determine the marginal extra intelligence added by each new individual participating in the collective, thus using metrics to avoid the hazards of group think and stupidity.

In 2001, Tadeusz (Ted) 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 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 multithread 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.