User:CognitiveMMA/sandbox/GCI4WebSci2022/generalproblemsolving

Increasing the General Problem-Solving Ability of Groups

From the perspective of Human-Centric Functional Modeling, general problem-solving ability is defined as the ability to use any reasoning required to potentially navigate from any one initial concept to any one target concept. The magnitude of general problem-solving ability in individuals, as measured by the individual intelligence factor g is represented as the volume of conceptual space which the individual cognition can search through per unit time, multiplied by the density of concepts in the volume which that individual cognition must search through. Similarly, the magnitude of general problem-solving ability in groups, as measured by the general collective intelligence factor c is represented as the volume of the collective conceptual space which the collective cognition can search through per unit time, multiplied by the density of concepts in the volume which that collective cognition must search through.

Capacity for generalization and semantic representation capacity are thought to be important factors in significantly increasing the general problem-solving ability of individuals and groups. Since individuals might already be presumed to have semantic representation capability (any internal representation of concepts and reasoning is by virtue of being human a human representation of meaning and therefore a semantic one), introducing semantic representation capability is thought to be an important factor in significantly increasing the general problem-solving ability of groups.

Generalization is important in creating new reasoning paths so that new problems might be solved. Otherwise the group cognition might be confined to repeating the exact same reasoning where it was unsuccessful in solving a problem, and therefore continually making the same mistakes. Semantic representation is important in being able to exchange understanding rather than just information, so that understanding might be exchanged at vastly greater speed and scale in order to execute collective reasoning processes more quickly as well.

While a complete representation of functional state space does not yet exist, and therefore a complete semantic representation does not yet exist, these benefits of increased capacity for generalization and of having semantic representation capability might be partially obtained through approximating some subset of concepts and the reasoning processes connecting them using an ontology. As an example, patterns of problem-solving defined with such ontologies are predicted to radically increase narrow problem-solving ability across a broad range of areas relevant to each of the STEM (science, technology, engineering, and math) disciplines, as well as being predicted to radically increase narrow problem-solving ability across a wide range of problems related to sustainable development. Furthermore, representing technology in terms of ontologies that allow these and other problem-solving patterns to be applied is predicted to significantly accelerate the speed and scale of development of web 3.0, blockchain, metaverse, and other technologies, where a complete representation of those technologies in terms of functional state spaces is predicted to exponentially increase that speed and scale, accelerating development to the point at which it’s predicted that non-GCI based development might no longer reliably be able to compete.