A General Theory of Complex Living Systems: Exploring the Demand Side of Dynamics

complex living systems, unified theory, dynamics, general theory, demand-side, methodology

Constructing a General Theory of Life: The Dynamics of Human and Non-human Systems

The ultimate objective of theorists studying living systems is to construct a general theory of life that can explain and predict the dynamics of both human and nonhuman systems. Yet little progress has been made in this endeavour. Why? Because of the inappropriate methods adopted by complexity theorists. By assuming that the supply-side physics model – in which local interactions are said to give rise to the emergence of order and complexity – could be transferred either entirely (social physics) or partially (agent-based models, or ABMs) from the physical to the life sciences, we have distorted reality and, thereby, delayed the construction of a general dynamic theory of living systems. Is there a solution? Yes, but only if we abandon the deductive and analogical methods of complexity theorists and adopt the inductive method. With this approach it is possible to construct a realist and demand-side general dynamic theory, as in the case of the dynamic-strategy theory presented in this paper.complex living systems, unified theory, general theory of life, dynamics. Demand-side, methodology

Self-organisation or Selfcreation? From Social Physics to Realist Dynamics

The currently fashionable theory of self-organisation has its origins in statistical physics. Many believe that the underlying physics model, which is based on inanimate systems, can be employed to explain and predict the emergence of social structures, even of history itself. Some are even convinced that it will be possible to construct a social physics to displace the social sciences. The purpose of this article is to test those claims by reviewing some of the physical studies that have been made of human society, and its conclusion is that those claims cannot be substantiated. The underlying problem is that self-organisation is a one-dimensional theoretical concept that focuses exclusively upon supply-side interactions, from which order and complexity are said to ‘emerge’. But there is a better way. By systematic observation of living systems, both human and non-human, it has been possible to derive a general dynamic theory that embraces a more complex reality, involving a creative exchange between decision-making individuals and the changing needs of their society. I have called this interaction between the dynamic forces of demand and supply in living systems, the process of ‘strategic exchange’. And it is this strategic exchange that determines all other structural relationships in society, including the interaction between its constituent members. It is important in the social sciences, therefore, to move on from social physics to realist dynamics.agent-based modelling, complexity theory, dynamic-strategy theory, power laws, realist dynamics, self-organised criticality, Snooks-Panov algorithm, social physics, strategic demand

Self-organised criticality in the evolution of a thermodynamic model of rodent thermoregulatory huddling

A thermodynamic model of thermoregulatory huddling interactions between endotherms is developed. The model is presented as a Monte Carlo algorithm in which animals are iteratively exchanged between groups, with a probability of exchanging groups defined in terms of the temperature of the environment and the body temperatures of the animals. The temperature-dependent exchange of animals between groups is shown to reproduce a second-order critical phase transition, i.e., a smooth switch to huddling when the environment gets colder, as measured in recent experiments. A peak in the rate at which group sizes change, referred to as pup flow, is predicted at the critical temperature of the phase transition, consistent with a thermodynamic description of huddling, and with a description of the huddle as a self-organising system. The model was subjected to a simple evolutionary procedure, by iteratively substituting the physiologies of individuals that fail to balance the costs of thermoregulation (by huddling in groups) with the costs of thermogenesis (by contributing heat). The resulting tension between cooperative and competitive interactions was found to generate a phenomenon called self-organised criticality, as evidenced by the emergence of avalanches in fitness that propagate across many generations. The emergence of avalanches reveals how huddling can introduce correlations in fitness between individuals and thereby constrain evolutionary dynamics. Finally, a full agent-based model of huddling interactions is also shown to generate criticality when subjected to the same evolutionary pressures. The agent-based model is related to the Monte Carlo model in the way that a Vicsek model is related to an Ising model in statistical physics. Huddling therefore presents an opportunity to use thermodynamic theory to study an emergent adaptive animal behaviour. In more general terms, huddling is proposed as an ideal system for investigating the interaction between self-organisation and natural selection empirically

Self-organised criticality in the evolution of a thermodynamic model of rodent thermoregulatory huddling

A thermodynamic model of thermoregulatory huddling interactions between endotherms is developed. The model is presented as a Monte Carlo algorithm in which animals are iteratively exchanged between groups, with a probability of exchanging groups defined in terms of the temperature of the environment and the body temperatures of the animals. The temperature-dependent exchange of animals between groups is shown to reproduce a second-order critical phase transition, i.e., a smooth switch to huddling when the environment gets colder, as measured in recent experiments. A peak in the rate at which group sizes change, referred to as pup flow, is predicted at the critical temperature of the phase transition, consistent with a thermodynamic description of huddling, and with a description of the huddle as a self-organising system. The model was subjected to a simple evolutionary procedure, by iteratively substituting the physiologies of individuals that fail to balance the costs of thermoregulation (by huddling in groups) with the costs of thermogenesis (by contributing heat). The resulting tension between cooperative and competitive interactions was found to generate a phenomenon called self-organised criticality, as evidenced by the emergence of avalanches in fitness that propagate across many generations. The emergence of avalanches reveals how huddling can introduce correlations in fitness between individuals and thereby constrain evolutionary dynamics. Finally, a full agent-based model of huddling interactions is also shown to generate criticality when subjected to the same evolutionary pressures. The agent-based model is related to the Monte Carlo model in the way that a Vicsek model is related to an Ising model in statistical physics. Huddling therefore presents an opportunity to use thermodynamic theory to study an emergent adaptive animal behaviour. In more general terms, huddling is proposed as an ideal system for investigating the interaction between self-organisation and natural selection empirically

Self-organisation or Selfcreation? From Social Physics to Realist Dynamics

The currently fashionable theory of self-organisation has its origins in statistical physics. Many believe that the underlying physics model, which is based on inanimate systems, can be employed to explain and predict the emergence of social structures, even of history itself. Some are even convinced that it will be possible to construct a social physics to displace the social sciences. The purpose of this article is to test those claims by reviewing some of the physical studies that have been made of human society, and its conclusion is that those claims cannot be substantiated. The underlying problem is that self-organisation is a one-dimensional theoretical concept that focuses exclusively upon supply-side interactions, from which order and complexity are said to ‘emerge’. But there is a better way. By systematic observation of living systems, both human and non-human, it has been possible to derive a general dynamic theory that embraces a more complex reality, involving a creative exchange between decision-making individuals and the changing needs of their society. I have called this interaction between the dynamic forces of demand and supply in living systems, the process of ‘strategic exchange’. And it is this strategic exchange that determines all other structural relationships in society, including the interaction between its constituent members. It is important in the social sciences, therefore, to move on from social physics to realist dynamics

Modelling coevolution in multispecies communities

We introduce the Webworld model, which links together the ecological modelling of food web structure with the evolutionary modelling of speciation and extinction events. The model describes dynamics of ecological communities on an evolutionary timescale. Species are defined as sets of characteristic features, and these features are used to determine interaction scores between species. A simple rule is used to transfer resources from the external environment through the food web to each of the species, and to determine mean population sizes. A time step in the model represents a speciation event. A new species is added with features similar to those of one of the existing species and a new food web structure is then calculated. The new species may (i) add stably to the web, (ii) become extinct immediately because it is poorly adapted, or (iii) cause one or more other species to become extinct due to competition for resources. We measure various properties of the model webs and compare these with data on real food webs. These properties include the proportions of basal, intermediate and top species, the number of links per species and the number of trophic levels. We also study the evolutionary dynamics of the model ecosystem by following the fluctuations in the total number of species in the web. Extinction avalanches occur when novel organisms arise which are significantly better adapted than existing ones. We discuss these results in relation to the observed extinction events in the fossil record, and to the theory of self-organized criticality.Comment: 21 pages, 3 Postscript figures, uses psfig.sty Affiliations correcte

Digital ecosystems

We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which are considered to be robust, self-organising and scalable architectures that can automatically solve complex, dynamic problems. So, this work is concerned with the creation, investigation, and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological ecosystems. First, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. We then investigated its self-organising aspects, starting with an extension to the definition of Physical Complexity to include the evolving agent populations of our Digital Ecosystem. Next, we established stability of evolving agent populations over time, by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics. Further, we evaluated the diversity of the software agents within evolving agent populations, relative to the environment provided by the user base. To conclude, we considered alternative augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating effect of a clustering catalyst on the evolutionary dynamics of our Digital Ecosystem, through the direct acceleration of the evolutionary processes. We also studied the optimising effect of targeted migration on the ecological dynamics of our Digital Ecosystem, through the indirect and emergent optimisation of the agent migration patterns. Overall, we have advanced the understanding of creating Digital Ecosystems, the self-organisation that occurs within them, and the optimisation of their Ecosystem-Oriented Architecture