Towards a Domain Specific Modeling Framework for General Collective Intelligence Platforms

2022

Preprint

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The newly emerging science of Human-Centric Functional Modeling or HCFM models systems in terms of “functional state spaces” that are hypothesized to have the capacity to provide a complete representation of the behavior of any system modeled this way. When used for modeling human cognition, AI, or any other intelligent system, these functional state spaces potentially provide a complete representation of the meaning of any concepts as the functional states of cognition, as well as a complete representation of the meaning of reasoning processes used by such systems to navigate between concepts, thereby providing what is believed to be the first complete semantic model of information. When used for modeling other systems, these functional state spaces potentially provide a complete representation of the functional states those systems might occupy as well as the processes through which the systems might transition between those functional states. In addition, HCFM provides a functional definition for general problem-solving ability as well as the magnitude of that ability, and introduces the possibility of artificial systems which might exponentially increase that ability when information about the system is defined in terms of functional state spaces. This introduces two possibilities, the first is to represent information in terms of the functional state space of cognition and to increase capacity to solve problems regarding a second system where that information might or might not represent valid behaviors of that second system, or to represent information directly in terms of the functional state space of the second system, so all information represents valid behaviors of that system. This paper explores the implications of both.

Section: Discussionmentioning

confidence: 99%

Selecting Between Semantic Modeling of Intelligence and Semantic Modeling of Systems

2022

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“…Any platform or other software is in a sense the automation of human logic, so if all logic is represented as being some path through conceptual space, then all platforms implement the automation of some set of paths through conceptual space. This suggests that through abstracting all collective reasoning processes so they can be represented as paths through this conceptual space, this conceptual space can in turn be used as the basis for domain specific modeling that can potentially be used to implement any logic modeled that way for any platform [7]. In addition, through abstracting all networks of cooperation so they can be represented as paths through this cooperation state space, this cooperation state space can in turn potentially be used for scaling the execution of any computing process through means such as parallel or distributed computing [8].…”

Section: Abstracting the Logic And Network Of Cooperation Of Computat...mentioning

confidence: 99%

General Collective Intelligence as a Platform for Computational Social Systems

2022

Preprint

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The emerging science of General Collective Intelligence or GCI defines the requirements for a hypothetical platform able to self-organize individuals in a self-sustaining way into massive networks of cooperation with the capacity to execute collective reasoning in a way that might exponentially increase the general problem-solving ability of the group. Some of these requirements of a GCI include the property of “dynamic decentralization”, without which any modeling, simulation, analysis or understanding of social systems from a quantitative and/or computational perspective are hypothesized to be centralized in a way that prevents them from being free of being constrained to be aligned with the individual interests of any powerful decision-makers involved in any of these processes. This paper explores why, as a consequence, building computational social systems on top of GCI is critical in ensuring that any class of problem can be solved where that class relates to the misalignment between the interests of influential individual decision-makers, and the outcomes that would be beneficial for the group.

“…Collectively intelligent cooperation based on GCI involves increasing the number of problems HCFM is being applied to, and using HCFM and GCI to increase the group's ability to reuse solutions across those different problems, until a critical mass of solutions is reached at which massive increase in impact on some collective problem is achieved to demonstrate the effectiveness of the approach. A key part of leveraging HCFM for reuse is model driven engineering [23], [24].…”

Section: Directions Forward: Changing Stem Education To Facilitate Ge...mentioning

confidence: 99%

“…Using Human-Centric Functional Modeling as a methodology for understanding societal impact or for understanding learning systems is relevant to a number of problems in a wide variety of disciplines [23], [24], [20]. In order to effectively target these problems, General Collective Intelligence is believed to be required to create the capacity for groups to execute collective reasoning processes that are reliably able to converge on a single coherent conclusion, and therefore to achieve the significantly increased outcomes of STEM activities that might accompany that conclusion.…”

Section: Research Limitationsmentioning

confidence: 99%

Increasing the Societal Impact of Science, Technology, Engineering, and Math with General Collective Intelligence

2022

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This paper explores how a technique called Human-Centric Functional Modeling (HCFM) might be used to radically increase the societal impact of STEM through a kind of biomimicry. HCFM represents systems in terms of functional state spaces, which enables it to be seen that nature has already solved problems that can be represented in these abstract spaces as being relevant to a wide range of science and engineering disciplines. Namely the same general problem of achieving adaptive general problem-solving ability in any domain can be represented as one that had to be solved by nature's equivalent to engineering and research processes in order for organisms to solve the problem of adapting to any aspect of their environments. This solution can be represented as having been used by nature to address complex problems of adaptation, the equivalent of “wicked problems” in human societies from poverty to climate change. Where these complex problems pose existential challenges, nature has not only solved them but has demonstrated the solutions to have worked for hundreds of millions of years. This paper describes how specific patterns in the use of HCFM and General Collective Intelligence (GCI) might be used to approach each of a range of engineering disciplines as well as a range of sciences and potentially mathematics, in order to create the potential to exponentially increase the societal impact of research and engineering activities.