A molecular approach to complex adaptive systems

Complex Adaptive Systems (CAS) are dynamical networks of interacting agents which as a whole determine the behavior, adaptivity and cognitive ability of the system. CAS are ubiquitous and occur in a variety of natural and artificial systems (e.g., cells, societies, stock markets). To study CAS, Holland proposed to employ an agent-based system in which Learning Classifier Systems (LCS) were used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations are reflexive in CAS, e.g., in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based Artificial Chemistry based on Holland’s broadcast language. In the MCS.b, no explicit fitness function or rule discovery mechanism is specified, moreover no distinction is made between messages and rules. In the context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS: Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks must replicate themselves prior to cell division. In this paper we present a series of experiments focusing on the self-replication ability of these CAS. Results indicate counter intuitive outcomes as opposed to those inferred from the literature. This work highlights the current deficit of a theoretical framework for the study of Artificial Chemistries

The Algorithmic Origins of Life

Although it has been notoriously difficult to pin down precisely what it is that makes life so distinctive and remarkable, there is general agreement that its informational aspect is one key property, perhaps the key property. The unique informational narrative of living systems suggests that life may be characterized by context-dependent causal influences, and in particular, that top-down (or downward) causation -- where higher-levels influence and constrain the dynamics of lower-levels in organizational hierarchies -- may be a major contributor to the hierarchal structure of living systems. Here we propose that the origin of life may correspond to a physical transition associated with a shift in causal structure, where information gains direct, and context-dependent causal efficacy over the matter it is instantiated in. Such a transition may be akin to more traditional physical transitions (e.g. thermodynamic phase transitions), with the crucial distinction that determining which phase (non-life or life) a given system is in requires dynamical information and therefore can only be inferred by identifying causal architecture. We discuss some potential novel research directions based on this hypothesis, including potential measures of such a transition that may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.Comment: 13 pages, 1 tabl

How the Dimension of Space Affects the Products of Pre-Biotic Evolution: The Spatial Population Dynamics of Structural Complexity and The Emergence of Membranes

We show that autocatalytic networks of epsilon-machines and their population dynamics differ substantially between spatial (geographically distributed) and nonspatial (panmixia) populations. Generally, regions of spacetime-invariant autocatalytic networks---or domains---emerge in geographically distributed populations. These are separated by functional membranes of complementary epsilon-machines that actively translate between the domains and are responsible for their growth and stability. We analyze both spatial and nonspatial populations, determining the algebraic properties of the autocatalytic networks that allow for space to affect the dynamics and so generate autocatalytic domains and membranes. In addition, we analyze populations of intermediate spatial architecture, delineating the thresholds at which spatial memory (information storage) begins to determine the character of the emergent auto-catalytic organization.Comment: 9 pages, 7 figures, 2 tables; http://cse.ucdavis.edu/~cmg/compmech/pubs/ss.ht

Artificial life meets computational creativity?

I review the history of work in Artificial Life on the problem of the open-ended evolutionary growth of complexity in computational worlds. This is then put into the context of evolutionary epistemology and human creativity

Heredity, Complexity, and Surprise: Embedded Self-Replication and Evolution in CA

Abstract. This paper reviews the history of embedded, evolvable selfreplicating structures implemented as cellular automata systems. We relate recent advances in this field to the concept of the evolutionary growth of complexity, a term introduced by McMullin to describe the central idea contained in von Neumann's self-reproducing automata theory. We show that conditions for such growth are in principle satisfied by universal constructors, yet that in practice much simpler replicators may satisfy scaled-down -yet equally relevant -versions thereof. Examples of such evolvable self-replicators are described and discussed, and future challenges identified

Ideas are not replicators but minds are

An idea is not a replicator because it does not consist of coded self-assembly instructions. It may retain structure as it passes from one individual to another, but does not replicate it. The cultural replicator is not an idea but an associatively-structured network of them that together form an internal model of the world, or worldview. A worldview is a primitive, uncoded replicator, like the autocatalytic sets of polymers widely believed to be the earliest form of life. Primitive replicators generate self-similar structure, but because the process happens in a piecemeal manner, through bottom-up interactions rather than a top-down code, they replicate with low fidelity, and acquired characteristics are inherited. Just as polymers catalyze reactions that generate other polymers, the retrieval of an item from memory can in turn trigger other items, thus cross-linking memories, ideas, and concepts into an integrated conceptual structure. Worldviews evolve idea by idea, largely through social exchange. An idea participates in the evolution of culture by revealing certain aspects of the worldview that generated it, thereby affecting the worldviews of those exposed to it. If an idea influences seemingly unrelated fields this does not mean that separate cultural lineages are contaminating one another, because it is worldviews, not ideas, that are the basic unit of cultural evolution

Top-Down Causation and the Rise of Information in the Emergence of Life

abstract: Biological systems represent a unique class of physical systems in how they process and manage information. This suggests that changes in the flow and distribution of information played a prominent role in the origin of life. Here I review and expand on an emerging conceptual framework suggesting that the origin of life may be identified as a transition in causal structure and information flow, and detail some of the implications for understanding the early stages chemical evolution