Welcome to Computational Social Sciences, a trimonthly exploration of interesting phenomena at the intersection of social sciences, computational sciences, and complex systems, in human and non-human living systems, from cells to societies. Authored by Anamaria Berea, Associate Professor of Computational and Data Sciences, this series will bring forth topics about processes, case studies, and fundamental laws that transcend traditional social systems boundaries.
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In complex systems, particularly when we study scalability, we address phenomena from “cells to societies”. The cell is an “ecology” of information (DNA) and chemical substances (also DNA) that self-organize in a biological entity. The social world is an “ecology” of information and biological organisms that self-organize in ecosystems. Social behavior is a subset of information specific to human ecosystems and human society is a subset of biological ecosystems. But information and ecosystems coevolve and so do social behavior and human societies. Therefore, social and ecological phenomena are subsystems within subsystems within the overall ecosystem. In short, we use, perhaps not quite fully encompassing, the term “social ecologies” to constrain our understanding of these two co-evolving phenomena into one entity.
Near-decomposability is one of the least studied characteristics of complex systems. Scientists try to understand how it relates to scaling, how communication between subsystems relates to morphogenesis and near-decomposability, and how near-decomposability and subsystems communication may be responsible for generative processes in social phenomena.
Whether financial or economic or ecological, social phenomena are near-decomposable subsystems of the larger human-environment ecosystem, and understanding this near-decomposability can help us to understand how human societies and the environment may coexist in a healthy and sustainable ecosystem.
What I hope to find in the theory and application of near-decomposability in the context of complex social systems and ecology are answers to the following interconnected key questions: 1. is near-decomposability responsible for generative processes in complex social phenomena in general? 2. if so, are social phenomena and ecological phenomena near-decomposable subsystems of human life? 3. and if so, can we build generative policies and institutions as a new, sustainable way to achieve health and prosperity in the human-environment ecosystems? Perhaps even avert climate change?
Near-decomposability, an understudied characteristic of complex social phenomena
Near-decomposability of complex systems (Simon, 1996) is one of the least studied aspects of complex phenomena, particularly in complex social phenomena. But in social sciences, the theoretical study of near-decomposable systems lacks stable foundations, beyond the brilliant work of Herbert Simon. The sciences of organizations and organizations research are often dealing with network aspects or hierarchical aspects of entrepreneurial near-decomposability (Simon, 2002; Sarasvathy, 2003; Williamson, 1983) but there is clearly a whole body of research or depth missing in understanding social near-decomposability (Cioffi-Revilla, 2003).
Simon places near-decomposability between short-run non-interaction and independence in subsystems, and long-run interaction and dependence on the same subsystems (Simon, 1996).
Theoretically, near-decomposability is also linked to the issue of collective behavior emerging from pairwise interactions and to the idea of growing societies bottom-up, from cells (NAKFI, 2014). This topic has been widely discussed at the National Academies of Science Keck Futures Initiative Collective Behavior workshop in November 2014. Some subsequent research on the application of complex systems to the economics of information in the pairwise communication of cells and genes leads not only to the detection of communication and emergence but also triggers very interesting questions about the differences in the near-decomposability of physical vs. biological systems (for example, a pair or a network of atoms decomposes differently than a pair or network of cells; or a pair or network of people decomposes differently than a pair or network of institutions).
This is also intrinsically linked to the idea of morphogenesis (Turing, 1952) and there is a very blurry line between “purely biological” and “social” behavior (are plants or cells as social as humans? Are two cells communicating as two dolphins?). Thus, problems of near-decomposability transcend subsystems through some form of communication emergence.
If we apply the Hayekian theory of mental maps (Hayek, 1976) and Searle’s ontological subjectivity (Searle, 1998) in agent-based models (which are a specific class of computer simulations suitable for complex systems), that show how simple rules of interaction that are non-linear, adaptive and selective can lead to macro emergent patterns, we can already see some very interesting patterns of communication even with simulating just two conspecific organisms (Berea, 2018).
What if near-decomposability is a mirroring and/or reverse process of scaling, based on communication emergence between subsystems? For example, the process of mergers and acquisitions in finance and financial organizations is remarkably similar to the process of cells growing into organs, and organs growing into organisms. Similar examples of scalability can be seen in cities, firms, etc. Is near-decomposability responsible for such growth processes?
The cell is an “ecology” of information and substances (Pattee, 2015). The social world is an “ecology” of information and biological organisms that self-organize in ecosystems. For example, finance is a subset of information specific to human ecosystems and human society is a subset of biological ecosystems. But information and ecosystems coevolve (Simon, 2002) and so do finance and human societies. Hayek was a Nobel Prize-awarded economist who recognized the power of knowledge transmitted through finance and the importance of money as a means of transmitting information efficiently in an economic system (Hayek, 1945).
Computer scientists and data scientists design various types of informational subsystems (that mimic biological, social, and financial systems) in ways that are modular, refactoring, and both hierarchical and horizontal. These systems simulate and replicate to some degree the morphogenetic processes of life. In addition to the designed processes, there are also emergent generative processes in large data architectures and computer systems. These systems are currently supporting the communication highways that keep larger complex systems like finance, economies, cities, and other large social scale phenomena healthy and sustainable.
But what makes an economy healthy and what roles do financial institutions play in nourishing vitality? A wide variety of “systems” or “complexity” approaches – such as network theory, complexity theory, living systems theory, and agent-based modeling to name a few – are developing more accurate understandings of economics and finance using network phenomena, such as tipping points, positive feedback, resilience, brittleness, emergence, and contagion, which are easily seen in financial markets (Fath et al, 2019).
Understanding the fundamentals of generative processes by tackling the underexplored concept of near-decomposability might be a useful fundamental avenue for research on the health and sustainability of social and ecological ecosystems as a whole.
Complex Social Systems and Near-Decomposability
Here are some examples of complex systems processes that can be thoroughly studied in concordance with near-decomposability and their effect on social and ecological ecosystems:
Emergence – this is either a poorly understood or too broadly defined concept. For example, the emergence of financial subsystems can be understood as both an intermediary institution for trade and growth, but also as a product and an institution in itself with goals other than just intermediating the growth of societies. Tackling the history of money and finance not from the economic perspective, but from the complex systems and near-decomposability perspective, and the ecology of the money environment from early societies until the present can shed more light on generative financial processes and their health.
Scaling and Sustainability – understanding how subsystems coevolve seen through a regenerative lens that emphasizes the overlaps and differences of scaling versus near-decomposability and generative processes (i.e., short-run modularity in subsystems) versus sustainability (i.e., long-run modularity in subsystems).
Selection and Adaptation – in the context of “financial ecologies”, what are the adaptation and selection mechanisms, and how do these fundamental evolutionary processes relate to near-decomposability? In which scenarios have there been “maladaptation” of the financial subsystem to the human and ecological subsystems and was this also related to existing or non-existing near-decomposability?
Co-evolution – what is the overall markup of near-decomposability and modularity in the short-run (how subsystems interact or cannot interact), in the long-run span of human societies and how can we generate sustainable processes out of this understanding?
In general, model-based or data-based research is useful in its applications but limited in its epistemological scope. Without a paradigmatic or theoretical backbone, data-based research cannot stand the test of long-term macro phenomena. It can only carve niches. Most of the interdisciplinary approaches today first look at the data and models and then at the theory, if there is one. The methodology should complement the conceptual approach and not drive it.
Much work in complex systems has been based on bottom-up approaches. These are good approaches, yet incomplete. There should be an integration of bottom-up and top-down approaches because macro-phenomena also co-evolve with micro-phenomena and are inherently non-deterministic (Schelling, 2006). This is why the in-depth understanding of near-decomposability and generative processes from both directions can help us uncover bottlenecks to how humans and ecologies interact and how to design healthier and more sustainable policies. Very interesting work in this regard has been done on flow networks anchored in a network of co-evolving complex systems (Fath et al, 2019; Goerner et al, 2015).
Data science could also help here. While there is no current database that traces the co-evolution of social, financial, or economic behavior with the ecological health and the health of ecosystems at large, a comprehensive database of timelines for subsystems’ evolution would be extremely valuable. If anything, it can help climate scientists pinpoint even better the social causes of climate change, and also the lags and maladaptations between societies and climate. But this is just one example.
I believe the fundamental concepts of near-decomposability and regeneration can provide a theoretical background for empirically sound and socially-compelling ways to apply research in designing policies afterward, and integrate all of the above in a unified vision of the health and sustainability of social and economic phenomena. Regeneration refers to the self-feeding, self-renewing processes that natural systems use to nourish their capacity to thrive for long periods of time and their ability to adapt to unexpected, sometimes threatening circumstances. No system can sustain itself over the long term if it is not designed to continuously regenerate.
For example, regenerative economics is a small branch of economics that uses the universal laws of systemic health and self-renewal to show how we can develop durably vibrant socio-economic systems as well. It uses the empirical study of flow networks to make this idea precise.
Perhaps we can show, in the context of the near-decomposability of complex systems, how theoretically discovered regenerative processes can be backed by the empirical science of flow networks to create a holistic, sustainable, rigorous, and actionable theory of systemic human-environment health.
Anamaria Berea
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References
NAKFI. (2014). http://keckfutures.org/conferences/collective-behaviors/index.html
Berea, A. (2018). Emergence of communication in socio-biological networks. Springer International Publishing.
Fath, B. D., Fiscus, D. A., Goerner, S. J., Berea, A., & Ulanowicz, R. E. (2019). Measuring regenerative economics: 10 principles and measures undergirding systemic economic health. Global Transitions, 1, 15-27.
Goerner, S., Fiscus, D., & Fath, B. D. (2015). Using energy network science (ENS) to connect resilience with the larger story of systemic health and development. Emergence: Complexity and Organization, 17(3), 1-21.
Hayek, F. A. (1945). The use of knowledge in society. The American economic review, 519-530.
Hayek, F. A. (1976). The sensory order. University of Chicago Press.
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Searle, J. R. (1998). Mind, language and society: Philosophy in the real world (p. 144). New York: Basic books.
Schelling, T. C. (2006). Micromotives and macrobehavior. WW Norton & Company.
Simon, H. (1996). The Sciences of the Artificial. MIT Press
Simon, H. A. (2002). Near decomposability and the speed of evolution. Industrial and corporate change, 11(3), 587-599.
Sarasvathy, S. D. (2003). Entrepreneurship as a science of the artificial. Journal of Economic Psychology, 24(2), 203-220.
Stiglitz, J. E. (1999). Knowledge as a global public good. Global public goods, 1(9), 308-326.
Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 237(641), 37-72.
Ostrom, E. (1996). Crossing the great divide: coproduction, synergy, and development. World development, 24(6), 1073-1087.
Williamson, O. E. (1983). Organization form, residual claimants, and corporate control. The Journal of Law & Economics, 26(2), 351-366.
| Citation: Anamaria Berea, Near-Decomposability as A Regenerative Process in Complex Social Phenomena, Network Law Review, Fall 2022. |