User:CognitiveMMA/sandbox/Human-centric functional modeling
In human-centric functional modeling living systems such as the human organism or systems composed of inanimate matter are modeled as having a set of human-observable behaviors (functions or processes). The behavior of any system within a given domain of behavior is represented as transitioning 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 processes through which the system transitions between those functional states within that domain is represented by a graph in which each functional state represents a node, and in which each edge connecting two nodes represents a process through which the system might transition between functional states. When all the states accessible through these functions (single input per execution) or processes (multiple inputs per execution) occur within a given domain of behavior, these states 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, or all behaviors of each subsystem within its own domain. 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 define behaviors that are confined to that space, at least wherever those models represent actual functionality that exist (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]. Consider any network of functional states to be a graph containing a network of nodes, with each node representing a functional state, and with the edges or connections between functional states representing the behavior through which the system can transition from one functional state to another. This graph of the network of states accessible through these behaviors is a representation of the externally observable behavior of the system as reflected in the “functional state space”. In the case of the cognitive system properties such as general problem-solving ability are represented in external (consciously observable) terms as the ability of the cognitive system to potentially navigate its entire functional state space using reasoning processes. A set of internal (not consciously observable) functions is also hypothesized as being required to achieve this navigation of conceptual space with general problem-solving ability. These internal functions of the cognitive system are believed to implement general problem-solving ability in the conceptual domain through the same pattern of dynamical stability in fitness space that represents general problem-solving ability in every other functional state space of every other system. In addition, 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]
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 [5].
In defining abstract mathematical spaces to represent perception, 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 [6]) 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 only in the case that individuals can be reliably engaged in complex cooperation to accomplish large transdisciplinary projects. Transdisciplinary projects require breaking down of team members' understanding of systems into functional components to decouple them from the need to understand other fields [7][8][9] and to remove the need for engaging in a multitude of fields outside of one’s traditional training; a knowledge and skill acquisition task that frequently acts as a barrier to converging on a single collective understanding of complex systems [10]. In conventional functional modeling engaging individuals in large complex projects can be accomplished in a top-down way. In a decentralized approach orchestrated by a group decision-system like general collective intelligence, this engagement is predicted to be possible when each individual’s problem of understanding their own tasks can be decoupled from understanding the entire complex cooperation, so they as specialists can reliably solve the problem of deciding to engage. This suggests the need for systems thinking approaches to methodically divide any large projects 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, where the representations of those spaces can be used to describe other systems, from social systems, to software or hardware,[3] or even to the entire physical universe, and where 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 this biomimicry enables it to be seen that in the human organism nature has already solved the same general problem 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 [11].
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.
- ^ 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).
- ^ 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
- ^ 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