IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

5. Common Code: A Further Report of Reliable, Invariant Occasions

Sole, Ricard and Sergi Valverde. Macroevolution in Silico: Scales, Constraints and Universals. Santa Fe Institute Working Papers. 12-11-019, November, 2012. Barcelona systems biologists continue their project to gain quantified insights into life’s iterative, sequential advance into somatic and behavioral complexities.

Large-scale evolution involves several layers of complexity spanning multiple scales, from genes and organisms to whole ecosystems. In this paper we review several models involving the macroevolution of artificial organisms, communities or ecosystems, highlighting their importance and potential role in expanding the modern synthesis. Afterwards, we summarize the key results obtained from our model of artificially evolved ecosystems where individuals are defined as embodied entities within a physical, simulated world where they can evolve different traits and exploit multiple resources. Starting from an initial state where single cells with identical genotypes are present, the system evolves towards complex communities where the feedbacks between population expansion, evolved cell adhesion and the structure of the environment leads to a major innovation resulting from the emergence of ecosystem engineering. The tempo and mode of this process illustrates the relevance in considering a physical embedding as part of the model description, and the feedbacks between different scales within the evolutionary hierarchy. (Abstract)

The existence of universal trends in large scale evolution might seem a rather bold idea. In the end, the paths followed by evolutionary trajectories are tangled and seem unique. Even so, convergent dynamics might be widespread. Such convergence is in itself a major component of evolution. Convergence is also a mark of universality and the common laws pervading the physics of adhesion or diffusion are likely to constrain potential pattern forming mechanisms. Disparate systems often display very common traits (particularly in their large scale patterns) associated with universal properties of the underlying dynamics.

Song, Chaoming, et al. Self-Similarity of Complex Networks. Nature. 433/392, 2005. The power-law scaling which distinguishes small world networks is found to exhibit a self-repeating fractal geometry. This is achieved by a renormalization procedure that ‘coarse-grains’ the system into a nest of nodes and modules. This novel work is introduced by Steven Strogatz in the same issue (365) with the title Romanesque Networks since this property is ideally displayed by broccoli of this name.

Here we show that real complex networks, such as the world-wide web, social, protein-protein interaction networks and cellular networks are invariant or self-similar under a length-scale transformation. (392) These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics. (392)

Sowinski, Damian, et al. Information-theoretic description of a feedback-control Kuramoto model. arXiv:2404.02221. University of Rochester physicists including Adam Frank propose an innovative synthesis of combinatorial methods with states of more or less orderly oscillations as novel way to root living systems in physical principles. See also Semantic Information in a model of Resource Gathering Agents by this group along with Marcelo Gleiser and Artemy Kolchinsky in PRX Life (1/023003, 2024).

Semantic Information Theory (SIT) offers a new approach to evaluating the information architecture of complex systems. In this study we describe its application to dynamical problems by four steps: (1) separate the system into agent-environment sub-systems; (2) choose appropriate coarse graining and quantifying correlations; (3) identify a measure of viability; and (4) implement a protocol to measure the semantic content. We study the neural dynamics of epileptic seizures whereby an agent tries to control a base environment in a desynchronized state through Kuramoto phase synchronization. The oscillation structure for agent and environment allows us to apply a computational perspective, where the agent-environment dynamics can be become a communication channel. (Excerpt)

St-Onge, Jonathan, et al. Socio-Semantic Networks as Mutualistic Networks. Nature Scientific Reports. 12/1889, 2022. We cite this entry by University of Quebec scholars as an instance of how these independent topologies are exemplified even by textual and behavioral stages. These common manifestations are then seen as similar to a far-removed symbiotic occasion of insect pollination patterns.

Several studies have shown that discourse and social relationships are intertwined and co-evolve. However, we lack theoretical models to explain this phenomenon. Here, we propose to model socio-semantic networks as an interaction between an ecological community process and a document-based process. We consider the link between semantic and social ties as analogous to interactions found in pollination networks whereby agents visit hidden topics in a similar way to how insects visit specific plants. Our results show that a socio-semantic interaction matrix respects a mutualism so to better understand the dynamic of human socio-semantic interactions. (Abstract excerpt)

Stanley, Eugene. Universality and Scale Invariance: Organizing Principles that Transcend Disciplines. www.societyforchaostheory.org/conf2003/abstracts.html. A keynote paper presented at the annual Society for Chaos Theory and Psychology Conference. The Boston University systems physicist describes how the same power-law behavior is being found in widely diverse realms from statistical physics to the “econophysics” of financial markets.

Stephens, Greg, et al. Searching for Simplicity: Approaches to the Analysis of Neurons and Behavior. arXiv:1012.3896. Posted in December 2010. By virtue of such many advances as this site tries to report, we altogether seem at the edge of some revelatory evidence, closer to history’s great secret answer. Geneticist Stephens and physicist William Bialek, Princeton University, and neurobiologist Leslie Osborne, University of Chicago, enter still another glimpse of constant analogs across widely diverse phenomena, ever suggestive of an implicate, iterative, genome-like source.

What fascinates us about animal behavior is its richness and complexity, but understanding behavior and its neural basis requires a simpler description. An alternative is to ask whether we can search through the dynamics of natural behaviors to and explicit evidence that these behaviors are simpler than they might have been. We review two mathematical approaches to simplification, dimensionality reduction and the maximum entropy method, and we draw on examples from different levels of biological organization, from the crawling behavior of C. elegans to the control of smooth pursuit eye movements in primates, and from the coding of natural scenes by networks of neurons in the retina to the rules of English spelling. In each case, we argue that the explicit search for simplicity uncovers new and unexpected features of the biological system, and that the evidence for simplification gives us a language with which to phrase new questions for the next generation of experiments. The fact that similar mathematical structures succeed in taming the complexity of very different biological systems hints that there is something more general to be discovered. (Abstract)

Strassmann, Joan, et al. In the Light of Evolution V: Cooperation and Conflict. Proceedings of the National Academy of Sciences. Supplement 2, 2011. With co-authors David Queller, John Avise, and Francisco Ayala, an Introduction to this edition in the title series in search and support of an evolutionary vision, namely in the light of Theodosius Dobzhansky. We note in this section to record its witness of a universal principle, in this case a creative tension or union between entity and group, autonomy and assembly, that spans life’s emergence. Papers cover selfish and cooperative genes, bacterial symbiosis, kinship in social insects, animal troops, hominid clans, and onto human adaptations via collective learning.

Swarup, Samarth and Les Gasser. Unifying Evolutionary and Network Dynamics. Physical Review E. 75/066114, 2007. University of Illinois computer scientists propose that the two realms noted in the article share common features and that a cross-fertilization between them would be beneficial.

Szabo, Gyorgy and Gabor Fath. Evolutionary Games on Graphs. Physics Reports. 446/4-6, 2007. An extensive tutorial on game theory as seen from a statistical physics viewpoint, which could be viewed as another encounter with, in translation, nature’s non-equilibrium, agent-based, self-organizing emergence.

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games. (97)

Talaga, Szymon and Andrzej Nowak. Structural Measure of Similarity and Complementarity in Complex Networks. Nature Scientific Reports. 12/16580, 2022. We note this current entry by University of Warsaw and Florida Atlantic University complexity scholars (search AN), while densely theoretic, as an example to natural tendencies to commonly avail these creative reciprocities. See also Budel, Gabriel, et al. Topological Properties and Organizing Principles of Semantic Networks by Gabriel Budel, et al (2023) herein.

The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3 cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarities and synergies. Here we show that the first principle is linked to the abundance of quadrangles (4-cycles) and dense bipartite subgraphs. Using multiple social and biological networks, we demonstrate that our similarity coefficients capture structural properties related to domain-specific phenomena. (Excerpt)

In summary, we showed that both similarity and complementarity are important organizational principles shaping the structure of social and biological networks and can be linked to interpretable, domain-specific phenomena. (14)

Tao, Terence. E pluribus unum: From Complexity, Universality. Daedalus. 141/3, 2012. In a special issue on Science in the 21st Century, the UCLA polymathematician draws on the latest statistical physics to at last confirm this extant Nature is in fact grounded in and distinguished by an intelligible, recurrent, programmatic quality. Whatever the material realm or substrate the same phenomenal organization appears due to the constant interactions of many communicative entities. As a result, it becomes strongly evident that such an independent formative source or agency must be involved. And how could one not notice, quite fittingly, here anew is the very essence of our archetypal Taoist wisdom.

In this brief survey, I discuss some examples of the fascinating phenomenon of universality in complex systems, in which universal macroscopic laws of nature emerge from a variety of different microscopic dynamics. This phenomenon is widely observed empirically, but the rigorous mathematical foundation for universality is not yet satisfactory in all cases. (Abstract, 23)

A remarkable phenomenon often occurs once the number of components becomes large enough: that is, the aggregate properties of the complex system can mysteriously become predictable again, governed by simple laws of nature. Even more surprising, these macroscopic laws for the overall system are often largely independent of their microscopic counterparts that govern the individual components of that system. One could replace the microscopic components by completely different types of objects and obtain the same governing law at the macroscopic level. When this occurs, we say that the macroscopic law is universal. The universality phenomenon has been observed both empirically and mathematically in many different contexts, several of which I discuss below. In some cases, the phenomenon is well understood, but in many situations, the underlying source of universality is mysterious and remains an active area of mathematical research. (24)

The law of large numbers is one of the simplest and best understood of the universal laws in mathematics and nature, but it is by no means the only one. Over the decades, many such universal laws have been found to govern the behavior of wide classes of complex systems, regardless of the components of a system or how they interact with each other. (25) That the macroscopic behavior of a large, complex system can be almost totally independent of its microscopic structure is the essence of universality. (25)

Tauber, Uwe. Critical Dynamics: A Field Theory Approach to Equilibrium and Non-Equilibrium Scaling Behavior. Cambridge: Cambridge University Press, 2014. A Virginia Tech physicist contributes a technical text that well quantifies the latest and deepest theoretical basis for nature’s self-similar “scale invariance” from physical to planetary realms.

Introducing a unified framework for describing and understanding complex interacting systems common in physics, chemistry, biology, ecology, and the social sciences, this comprehensive overview of dynamic critical phenomena covers the description of systems at thermal equilibrium, quantum systems, and non-equilibrium systems. Powerful mathematical techniques for dealing with complex dynamic systems are carefully introduced, including field-theoretic tools and the perturbative dynamical renormalization group approach, rapidly building up a mathematical toolbox of relevant skills. Heuristic and qualitative arguments outlining the essential theory behind each type of system are introduced at the start of each chapter, alongside real-world numerical and experimental data, firmly linking new mathematical techniques to their practical applications.

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