Thomas F. Varley, Joshua Bongard

There has recently been an explosion of interest in how “higher-order” structures emerge in complex systems. This “emergent” organization has been found in a variety of natural and artificial systems, although at present the field lacks a unified understanding of what the consequences of higher-order synergies and redundancies are for systems. Typical research treat the presence (or absence) of synergistic information as a dependent variable and report changes in the level of synergy in response to some change in the system. Here, we attempt to flip the script: rather than treating higher-order information as a dependent variable, we use evolutionary optimization to evolve boolean networks with significant higher-order redundancies, synergies, or statistical complexity. We then analyse these evolved populations of networks using established tools for characterizing discrete dynamics: the number of attractors, average transient length, and Derrida coefficient. We also assess the capacity of the systems to integrate information. We find that high-synergy systems are unstable and chaotic, but with a high capacity to integrate information. In contrast, evolved redundant systems are extremely stable, but have negligible capacity to integrate information. Finally, the complex systems that balance integration and segregation (known as Tononi-Sporns-Edelman complexity) show features of both chaosticity and stability, with a greater capacity to integrate information than the redundant systems while being more stable than the random and synergistic systems. We conclude that there may be a fundamental trade-off between the robustness of a systems dynamics and its capacity to integrate information (which inherently requires flexibility and sensitivity), and that certain kinds of complexity naturally balance this trade-off.

Read the full article at: arxiv.org

Ahmad, M.A.; Baryannis, G.; Hill, R

Systems 2024, 12(2), 45

Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures.

Read the full article at: www.mdpi.com

Dan Rockmore

Mathematical models power our civilization—but they have limits.

Read the full article at: www.newyorker.com

Stuart Kauffman, Andrea Roli

We propose a novel definition of life in terms of which its emergence in the universe is expected, and its ever-creative open-ended evolution is entailed by no law. Living organisms are Kantian Wholes that achieve Catalytic Closure, Constraint Closure, and Spatial Closure. We here unite for the first time two established mathematical theories, namely Collectively Autocatalytic Sets and the Theory of the Adjacent Possible. The former establishes that a first-order phase transition to molecular reproduction is expected in the chemical evolution of the universe where the diversity and complexity of molecules increases; the latter posits that, under loose hypotheses, if the system starts with a small number of beginning molecules, each of which can combine with copies of itself or other molecules to make new molecules, over time the number of kinds of molecules increases slowly but then explodes upward hyperbolically. Together these theories imply that life is expected as a phase transition in the evolving universe. The familiar distinction between software and hardware loses its meaning in living cells. We propose new ways to study the phylogeny of metabolisms, new astronomical ways to search for life on exoplanets, new experiments to seek the emergence of the most rudimentary life, and the hint of a coherent testable pathway to prokaryotes with template replication and coding.

Read the full article at: arxiv.org