Draft:General Collective Intelligence

General Collective intelligence (GCI) platforms are hypothetical software platforms that have general collective intelligence as defined by Woolley et al., that is, general problem-solving ability that emerges from the cooperation between many individuals and/or intelligent agents, where that general problem-solving ability is enabled by technology. The scope of General Collective Intelligence platforms is distinct from collective intelligence (CI) platforms which are similar in terms of being described as "group intelligence (GI) that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making". However, GCI platforms differ in that instead of being limited to consensus decision making or other narrow strategies, as many collective intelligence (CI) solutions might be, having general problem-solving ability a GCI platform can potentially adopt whatever strategy in general that is optimal.

The concept of a strong collective intelligence (strong CI) having general problem-solving ability in analogy with the concept of a strong Artificial Intelligence (strong AI) having general problem-solving ability, as opposed to narrow AI, is a concept that arises in the systems sciences, as well as in behavioral science, and conventional collective intelligence.

The term "General Collective Intelligence" in the context of the broader study of collective intelligence appears in sociobiology, political science and in context of mass peer review and crowdsourcing applications, where it describes the innate general problem-solving ability of groups, or their collective IQ. Collective IQ is a measure of the general collective intelligence or c factor, although it is often used interchangeably with the term collective intelligence. Collective intelligence has also been attributed to swarms, flocks, and other groups of animals.

The term Collective General Intelligence or General Collective Intelligence platform appears in sustainable development, systems science, and behavioral science, where it describes the general problem-solving ability of groups that is enabled by technology platforms.

History
The term "strong collective intelligence" originated in 2012 with French mathematician Daniel Andler. The term "weak collective intelligence" to distinguish CI with narrow problem-solving ability rather than general problem-solving ability originated in 2015 with Alexander Laszlo, the 57th president of the International Society for the Systems Sciences (ISSS) in a lecture given during the 59th Meeting of the ISSS in Berlin. The term "Collective General Intelligence" platform originated in 2018 with behavioral scientist Johannes Castner.

Key Concepts
Laszlo discussed the concept of "strong collective intelligence," as it pertains to platforms, signifying an advancement beyond traditional limitations of collective intelligence (CI) systems. Steven Johnson explored how current CI systems often rely heavily on consensus-based strategies, which may not always yield the most effective outcomes, particularly in scenarios where the most accurate answers are known only to a minority. GCI platforms aim to overcome these limitations by introducing the ability to adaptively switch strategies depending on the context of the problem at hand.

According to Watson and Levin, in nature, collective intelligence is observed at multiple scales, from ant colonies to human societies, and is characterized by its adaptability and efficiency in problem-solving. Strong CI platforms as discussed by Laszlo seek to emulate this aspect of nature's intelligence, where the choice of problem-solving strategy is dynamically optimized. Scott Page describes the need for such flexibility so as to potentially employ a consensus approach in situations where it is most effective, such as high signal-to-noise environments, while in other scenarios, it might switch to strategies that elevate insights from individual experts or specialized algorithms.

This ability to adaptively select the optimal strategy for a given problem is a key distinguishing feature of what constitutes "strong" collective intelligence in GCI platforms. Johannes Castner suggests this technology enabled general collective intelligence represents a significant shift from the static nature of traditional CI systems, moving towards a more fluid and context-sensitive approach to collective problem-solving.