User:CognitiveMMA/sandbox/Human-centric functional modeling
In human-centric functional modeling systems are represented as having a set of functions within each domain of their behavior, where these functions transition the system from one functional state to another. These states are “functional states” because they are defined in terms of the observed functions of the system rather than in terms of any theoretical mechanisms through which those functions are presumed to operate.
The set of all possible functional states in a given domain and the functions through which the system transitions between those functional states within that domain is represented as a graph containing a network of functional states in which each functional state represents a node in the network, and in which each edge connecting two nodes in the network represents a function through which the system might transition between functional states. These functional states are separated by a distance that indicates their similarity in terms of number of shared connections to any other given functional state. All the states accessible through these functions form a "functional state space", which the system observed as acting in that domain moves through. All the states accessible through those operations represented by the “functional state space” then reflect all possible behaviors of each system. This functional state space can potentially be virtual and abstract, such as in the case of the cognitive system which can be represented as moving through a space of concepts (a “conceptual space”) [1], or it can be physical, such as the sensory-motor space the body can be represented as moving through. If this functional state space represents all possible behaviors of the system then all methods of thinking about the system (i.e. all approaches to systems thinking) must describe behaviors that are confined to that space, at least wherever the functional state space model represent actual functionality that exists in the system (can be executed within the capacity of the system).
In the case of human cognition, the cognitive system is represented as using reasoning processes to navigate a hypothetical space of concepts or “conceptual space” that has been hypothesized to represent the functional state space of cognition. This conceptual space is hypothesized to provide a complete representation of the meaning of each concept in terms of the reasoning processes that connect it to other concepts, and is hypothesized to provide a complete representation of the meaning of each reasoning process in terms of the concepts it connects. Assuming that this conceptual space is a complete representation of meaning, then it is a complete semantic representation. If it is a complete semantic representation this conceptual space must be capable of representing every possible concept and every possible reasoning process.
All human reasoning consists of type 1 or type 2 reasoning. Type 1 (intuitive) reasoning is represented in conceptual space by a direct path from one concept to another and is useful for solving uncomputable problems through processes such as recognizing patterns of solutions that have worked in the past (pattern recognition). Type 2 (rational methodical) reasoning is represented in conceptual space by a logical stepwise path through intermediate concepts, and is useful for solving computable problems through some algorithm or other sequence of logic. By analogy, all functional state spaces for all other systems also are predicted to facilitate the use of type 1 processes to solve uncomputable problems, and type 2 processes to facilitate computable ones.
Any system can potentially be represented in terms of a network [2]. Representing systems as such graphs permits network analysis of the system in terms of its processes, their inputs, and their outputs, also called input–output analysis [3]. Because of its basic assumptions about component interconnections, this network analysis can and has been applied to many fields in which a system can be idealized as a network of interacting parts [4]. Furthermore, properties of any system that can be described in terms of motion through a space, can be described mathematically in terms of that space. Since functional state space is a mathematical space, properties of systems can be defined in terms of this space. One property of an open (unbounded) functional state space is hypothesized to be that each vertex representing a functional state is defined by a geometry in that space. Another property is hypothesized to be that there are four basic types of interactions in the network, and that all transitions can be expressed as a composition of those four interactions. Since this is also true for physical matter (in which case the four interactions represent the four fundamental forces), functional state space can also potentially be used to represent any region in the entire physical universe, or any real or imaginary virtual representation of it such as a metaverse [5].
Systems modeling
When applied to understanding human systems such as cognition or consciousness, a human-centric approach allows systems to be understood through first person observation (observations that can be validated within the individual’s observation of their own awareness), as opposed to observations requiring third party validation (i.e. measurements made by external tools or assessing validity in terms of consistency with some theory defined by some third party). In understanding human systems like cognition in which many of the functions cannot be externally measured, a first person approach is essential [6].
In defining abstract mathematical spaces to represent perception (sensory-motor, emotional, cognitive, and conscious awareness), human-centric functional modeling enables simple mathematical expressions for properties of perceptual systems to be deduced where not possible before. As an example, when applied to cognition HCFM is hypothesized to enable properties of reasoning such as "complexity", or general problem-solving ability (intelligence) to be computed. HCFM based modeling of systems organizing groups into a single collective cognition (a General Collective Intelligence or GCI [7]) suggests that under certain conditions a phase shift might occur in the collective conceptual space of this system, leading to an exponential increase in group intelligence.
This exponential increase in group intelligence is predicted to enable individuals to accomplish transdisciplinary projects of a scale and complexity not currently achievable so that some wicked problems that have not yet proved to be reliably definable or solvable might be reliably solved, particularly those that are potentially existential threats such as poverty or climate change [8]. Success in transdisciplinary projects require breaking down the team's understanding of systems into functional components in order to decouple each team member from the need to understand other fields [9][10][11], thereby removing the need for any individual to engage in a multitude of fields outside of one’s training; a knowledge and skill acquisition task that frequently acts as a barrier to converging on a single collective understanding of complex problems or solutions [12]. Below some hypothetical limit of complexity, traditional functional modeling can be used to manage large complex projects in a top-down way. Beyond this limit of complexity, a decentralized approach orchestrated by a group decision-system like a hypothetical General Collective Intelligence platform that leverages Human-Centric Functional Modeling to represent problems in terms of the functional state space of the system concerned, is predicted to be required. When each individual’s problem of understanding their own tasks can be expressed in this universal way, they can be decoupled from understanding the entire complex cooperation, so they as specialists can reliably solve the smaller problems that apply to them. This suggests the need for systems thinking approaches to methodically divide any large problems into a great many smaller problems that might be coordinated.
Applications
The applications of human-centric functional modeling are broad. When applied to systems thinking, HCFM provides a universal approach to modeling systems which facilitates a kind of biomimicry in which the human organism is represented in terms of abstract mathematical spaces that can be used to define simple expressions to represent properties like “complexity” for human systems like cognition. Since those spaces can be used to describe other systems, from social systems, to software or hardware,[3] or even to the entire physical universe, the underlying equivalence of the representations allows the same mathematical expressions to potentially define the same properties for these very different systems. The broader importance of HCFM is that it allows any group to be represented as a system governed by a network of functional states so that achieving any collective outcome can be modeled as network problems like those which exist in human systems like cognition. This biomimicry enables it to be seen that in the human organism nature has already solved the same generalized network problems that must be solved to address problems in a wide range of other systems, including existential challenges from poverty to climate change, and that nature has demonstrated those solutions to have worked for hundreds of millions of years [8].
References
- ^ Williams, A. E. (2021). Human-Centric Functional Modeling and the Unification of Systems Thinking Approaches: A Short Communication. Journal of Systems Thinking, 1(1), 5. https://doi.org/10.54120/jost.v1i1.1369 (Original work published August 20, 2021)
- ^ Newman MEJ (2010) Networks: an introduction. Oxford University Press, New York
- ^ a b Leontief, W.W., 1966. Input–Output Economics. Oxford University Press, New York, NY.
- ^ Solé, Ricard, and Sergi Valverde. "Evolving complexity: how tinkering shapes cells, software and ecological networks." Philosophical Transactions of the Royal Society B 375.1796 (2020): 20190325.
- ^ WİLLİAMS, Andy. "Human-Centric Functional Modeling and the Metaverse." Journal of Metaverse 2.1 (2022): 23-28.
- ^ Varela, Francisco J., and Jonathan Shear. "First-person methodologies: What, why, how." Journal of Consciousness studies 6.2-3 (1999): 1-14.
- ^ Williams A.E. (2021), Defining a Continuum from Individual, to Swarm, to Collective Intelligence, to General Collective Intelligence, International Journal of Collaborative Intelligence, in print (2021).
- ^ a b Williams, A.E. (2021c) Are wicked problems a lack of general collective intelligence?. AI & Soc (2021). https://doi.org/10.1007/s00146-021-01297-8
- ^ Wan, Jiang, Arquimedes Canedo, and Mohammad Abdullah Al Faruque. "Functional model-based design methodology for automotive cyber-physical systems." IEEE Systems Journal 11.4 (2015): 2028–2039.
- ^ Eisenbart, B., Gericke, K., & Blessing, L. (2013). An analysis of functional modeling approaches across disciplines. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 27(3), 281-289. doi:10.1017/S0890060413000280
- ^ Kurfman, MA, Stone, RB, Rajan, JR, & Wood, KL. "Functional Modeling Experimental Studies." Proceedings of the ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 4: 13th International Conference on Design Theory and Methodology. Pittsburgh, Pennsylvania, USA. September 9–12, 2001. pp. 267-279. ASME. https://doi.org/10.1115/DETC2001/DTM-21709
- ^ Petersen, A.M., Ahmed, M.E. & Pavlidis, I. Grand challenges and emergent modes of convergence science. Humanit Soc Sci Commun 8, 194 (2021). https://doi.org/10.1057/s41599-021-00869-9