Rethinking evolution, entropy and economics: A triadic conceptual framework for the maximum entropy principle as applied to the growth of knowledge

Recently, the maximum entropy principle has been applied to explain the evolution of complex non-equilibrium systems, such as the Earth system. I argue that it can also be fruitfully deployed to reconsider the classical treatment of entropy in economics by Georgescu-Roegen, if the growth of knowledge is seen as a physical process. Relying on central categories of Peirce's theory of signs, I follow the lines of a naturalistic evolutionary epistemology. In this framework, the three principles of Maximum Entropy (Jaynes), Maximum Power (Lotka) and Maximum Entropy Production can be arranged in a way such that evolution can be conceived as a process that manifests the physical tendency to maximize information generation and information capacity. This implies that the growth of knowledge is the dual of the process of entropy production. This theory matches with recent empirical research showing that economic growth can be tracked by measures of the throughput of useful work, mediated by the thermodynamic efficiency of the conversion of exergy into useful work. --Peirce,Georgescu-Roegen,maximum entropy,maximum power,natural selection,semeiosis,physical inference devices,economic growth,useful work

Revisiting the Gaia hypothesis: Maximum Entropy, Kauffman's 'Fourth Law' and physiosemeiosis

Recently, Kleidon suggested a restatement of the Gaia hypothesis based on Maximum Entropy approaches to the Earth system. Refuting conceptions of Gaia as a homeostatic system, Gaia is seen as a non-equilibrium thermodynamic system which continuously moves away from equilibrium, driven by maximum entropy production which materializes in hierarchically coupled mechanisms of energetic flows via dissipation and physical work. I propose to relate this view with Kauffman's 'Fourth Law of Thermodynamics', which I interprete as a proposition about the accumulation of information in evolutionary processes. Then, beyond its use in the Kleidon model, the concept of physical work is expanded to including work directed at the capacity to work: I offer a twofold specification of Kauffman's concept of an 'autonomous agent', one as a 'self-referential heat engine', and the other in terms of physiosemeiosis, which is a naturalized application of Peirce's theory of signs emerging from recent biosemiotic research. I argue that the conjunction of these three theoretical sources, Maximum Entropy, Kauffman's Fourth Law, and physiosemeiosis, allows to show that the Kleidon restatement of the Gaia hypothesis is equivalent to the proposition that the biosphere is a system of generating, processing and storing information, thus directly treating information as a physical phenomenon. I substantiate this argument by proposing a more detailed analysis of the notion of hierarchy in the Kleidon model. In this view, there is a fundamental ontological continuity between the biological processes and the human economy, as both are seen as information processing and entropy producing systems. As with other previous transitions in evolution, the human economy leverages the mechanisms by which Gaia moves further away from equilibrium. This implies that information and natural resources or energy are not substitutes, i.e. the knowledge economy continues to build on the same physical principles as the biosphere, with energy and information being two aspects of the same underlying physical process. --Gaia,non-equilibrium systems,Fourth Law,work,Peirce,triadism,hierarchy,economic growth

Inductive Game Theory and the Dynamics of Animal Conflict

Conflict destabilizes social interactions and impedes cooperation at multiple scales of biological organization. Of fundamental interest are the causes of turbulent periods of conflict. We analyze conflict dynamics in an monkey society model system. We develop a technique, Inductive Game Theory, to extract directly from time-series data the decision-making strategies used by individuals and groups. This technique uses Monte Carlo simulation to test alternative causal models of conflict dynamics. We find individuals base their decision to fight on memory of social factors, not on short timescale ecological resource competition. Furthermore, the social assessments on which these decisions are based are triadic (self in relation to another pair of individuals), not pairwise. We show that this triadic decision making causes long conflict cascades and that there is a high population cost of the large fights associated with these cascades. These results suggest that individual agency has been over-emphasized in the social evolution of complex aggregates, and that pair-wise formalisms are inadequate. An appreciation of the empirical foundations of the collective dynamics of conflict is a crucial step towards its effective management

Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems

Recently, the focus of complex networks research has shifted from the analysis of isolated properties of a system toward a more realistic modeling of multiple phenomena - multilayer networks. Motivated by the prosperity of multilayer approach in social, transport or trade systems, we propose the introduction of multilayer networks for language. The multilayer network of language is a unified framework for modeling linguistic subsystems and their structural properties enabling the exploration of their mutual interactions. Various aspects of natural language systems can be represented as complex networks, whose vertices depict linguistic units, while links model their relations. The multilayer network of language is defined by three aspects: the network construction principle, the linguistic subsystem and the language of interest. More precisely, we construct a word-level (syntax, co-occurrence and its shuffled counterpart) and a subword level (syllables and graphemes) network layers, from five variations of original text (in the modeled language). The obtained results suggest that there are substantial differences between the networks structures of different language subsystems, which are hidden during the exploration of an isolated layer. The word-level layers share structural properties regardless of the language (e.g. Croatian or English), while the syllabic subword level expresses more language dependent structural properties. The preserved weighted overlap quantifies the similarity of word-level layers in weighted and directed networks. Moreover, the analysis of motifs reveals a close topological structure of the syntactic and syllabic layers for both languages. The findings corroborate that the multilayer network framework is a powerful, consistent and systematic approach to model several linguistic subsystems simultaneously and hence to provide a more unified view on language

The Knowledge Base Evolution in Biotechnology: A Social Network Analysis.

This paper applies the methodological tools typical of social network analysis (SNA) within an evolutionary framework, to investigate the knowledge base dynamics of the biotechnology sector. Knowledge is here considered a collective good represented as a co-relational and a retrieval-interpretative structure. The internal structure of knowledge is described as a network the nodes of which are small units within traces of knowledge, such as patent documents, connected by links determined by their joint utilisation. We used measures referring to the network, like density, and to its nodes, like degree, closeness and betweenness centrality, to provide a synthetic description of the structure of the knowledge base and of its evolution over time. Eventually, we compared such measures with more established properties of the knowledge base calculated on the basis of co-occurrences of technological classes within patent documents. Empirical results show the existence of interesting and meaningful relationships across the different measures, providing support for the use of SNA to study the evolution of the knowledge bases of industrial sectors and their lifecycles.Knowledge Base, Social Network Analysis, Variety, Coherence, Industry lifecycles; exploration/exploitation

Time and M-theory

We review our recent proposal for a background independent formulation of a holographic theory of quantum gravity. The present review incorporates the necessary background material on geometry of canonical quantum theory, holography and spacetime thermodynamics, Matrix theory, as well as our specific proposal for a dynamical theory of geometric quantum mechanics, as applied to Matrix theory. At the heart of this review is a new analysis of the conceptual problem of time and the closely related and phenomenologically relevant problem of vacuum energy in quantum gravity. We also present a discussion of some observational implications of this new viewpoint on the problem of vacuum energy.Comment: 86 pages, 5 figures, LaTeX, typos fixed, references added, and Sec. 6.2 revised; invited review for Int. J. Mod. Phys.