Toward a computational model of collective intelligence and its IQ measure

This paper presents an attempt to build a universal computational theory of Collective Intelligence (Cl). which will serve social structures of beings including humans, ants. bacteria and other species that collectively solve problems king their families. social structures, or biological kind. The basic toll for formalization. analysis, and modeling is a quasi-chaotic model of computations, the Random PROLOG Processor (RPP). In the RPP. Clause-Molecules (CM) of facts, ales, goals, or higher-level logical structures enclosed by membranes move quasi-randomly in s@uctu& Computational-PROLOG-Space (C’S). When CM rendezvous in CS, an inference process can occur if and only if the logical conditions are h~lfXed. It is proposed in this theory that Collective Intelligence can be measured as follows: I) the mapping is done of a given social structure of beings into the RPP; 2) the beings and their behavior are tram&ed intO PROLOG expressions, carried by CMs; 3) the goal(s) of the social structure is(are) lranslatal into N-Element infere~es (.VEI); 4) the eficiency of the NEI is evaluated and given as the !ntelligence Quotient of a &ciai Structure (I@) projad onto NEI. Since fQS is computed as a probability function over time. them are various possibilities, eg.: to order social structures according to their IQS. to optimize social structures with IQS as a quality measure, or even to compare single beings with social structures. The use of probability allows estimation of IQS either by simulation, or on the basis of analytical calculations. I.BASICCONCEPTSOFMODELING COLLEC~IVE~NTELLIGENCE It is a paradox that the evaluation of the Collective Intelligence of social structures can be easier than the et aluation of the IQ of a single being, Individual intelligence has only been evaluated on the basis of external results of behavior during a problemsolving process in real life or during IQ tests. Neuropsychological processes accompanying problemsolving in the brain are still very far fkom being observable [2]. As a result, it is necessary to create abstract models of brain activity based on neuropsychological hypotheses (e.g. Luria [2j). or to use computer+riented models like Artificial Intelligence. Permission to make digital or hard copies of all or part of this wuk for pexsonal oc classroom use is gnnted without fee provided that copies UC not made or distributed for poffi or commercirl advantage and that copies bear thir notice and the tkll citation on the lint page. To copy otheawise. to republish, to post on servera or to redistribute to lists. requires prior specific permission and/or a fee. SAC 99. San Antonio, Texas 01998 ACM l-58113486-4/99/0001 $5.00 In contrast. many more elements of Coiledively Intelligent activity can be observ& tneasw& and evaluated in a social structure. We can observe displacements and resultant actiats of being, u we-0 as exchange of infonnatcm between beiryg (cg human language. the ant’s pheromone communication system. the dance of ho to direct toward a source of honey, the crossover of genes between bacteria resulting in spreading specific resistance to ar&oti~ CtC. Individual intelligence and behavior is scaled &MI as a factor to accidental, local, and pmMiiiic pwresses. Our fimdamentai assunt~ion is that $Zoktivq * lntelli ence can . with the helo of absbact chaoac models of ann~UM& . and statistical evaluation of the dobal beham of bcn ‘na in structured environments (111, [IO], [13). Underlying the design of the Random PROLOG Processor model of computations and justifjing its use ior Collective Intelligence Rxmaiiiat and modeling are these basic Mars: