Kuhnian Lessons for the Study of Open-Ended Evolution

Kuhnian philosophy of science implies that progress in the study of open-ended evolution (OEE) would be accelerated if the OEE science community were to agree on some examples of striking success in OEE science. This article recounts the important role of scientific paradigms and scientific exemplars in creating the productivity of what Kuhn, in The Structure of Scientific Revolutions, calls "normal" science, and it describes how the study of OEE today would benefit from exhibiting more of the hallmarks of normal science. The article concludes by describing five proposed projects that would help create a consensus in the OEE community on some good examples of the scientific study of OEE.

Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution I Special Issue

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the first of two special issues on current research on OEE and on the more general concept of open-endedness. Most of the papers presented in these special issues are elaborations of work presented at the Third Workshop on Open-Ended Evolution, held in Tokyo as part of the 2018 Conference on Artificial Life.

Open-Ended Technological Innovation

We detect ongoing innovation in empirical data about human technological innovations. Ongoing technological innovation is a form of open-ended evolution, but it occurs in a nonbiological, cultural population that consists of actual technological innovations that exist in the real world. The change over time of this population of innovations seems to be quite open-ended. We take patented inventions as a proxy for technological innovations and mine public patent records for evidence of the ongoing emergence of technological innovations, and we compare two ways to detect it. One way detects the first instances of predefined patent pigeonholes, specifically the technology classes listed in the United States Patent Classification (USPC). The second way embeds patents in a high-dimensional semantic space and detects the emergence of new patent clusters. After analyzing hundreds of years of patent records, both methods detect the emergence of new kinds of technologies, but clusters are much better at detecting innovations that are unanticipated and undetected by USPC pigeonholes. Our clustering methods generalize to detect unanticipated innovations in other evolving populations that generate ongoing streams of digital data.

An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

Measuring the evolution of the drivers of technological innovation in the patent record

We argue that technology changes over time by an evolutionary process that is similar in important respects to biological evolution. The process is adaptive in the sense that technologies are selected because of their specific adaptive value and not at random, but this adaptive evolutionary process differs from the Darwinian process of random variation followed by natural selection. We find evidence for the adaptive evolution of technology in the US patent record, specifically, the public bibliographic information of all utility patents issued in the United States from 1976 through 2010. Patents record certain innovations in the evolution of technology. The 1976-2010 patent record is huge, containing almost four million patents. We use a patent's incoming citations to measure its impact on subsequent patented innovations. Weighting innovative impact by the dissimilarity between parent and child technologies reveals that many of the most fecund inventions are door-opening technologies that spawn innovations in widely diverse categories.

An Aristotelian account of minimal chemical life

This paper addresses the open philosophical and scientific problem of explaining and defining life. This problem is controversial, and there is nothing approaching a consensus about what life is. This raises a philosophical meta-question: Why is life so controversial and so difficult to define? This paper proposes that we can attribute a significant part of the controversy over life to use of a Cartesian approach to explaining life, which seeks necessary and sufficient conditions for being an individual living organism, out of the context of other organisms and the abiotic environment. The Cartesian approach contrasts with an Aristotelian approach to explaining life, which considers life only in the whole context in which it actually exists, looks at the characteristic phenomena involving actual life, and seeks the deepest and most unified explanation for those phenomena. The phenomena of life might be difficult to delimit precisely, but it certainly includes life's characteristic hallmarks, borderline cases, and puzzles. The Program-Metabolism-Container (PMC) model construes minimal chemical life as a functionally integrated triad of chemical systems, which are identified as the Program, Metabolism, and Container. Rasmussen diagrams precisely depict the functional definition of minimal chemical life. The PMC model illustrates the Aristotelian approach to life, because it explains eight of life's hallmarks, one of life's borderline cases (the virus), and two of life's puzzles.

Automated discovery of novel drug formulations using predictive iterated high throughput experimentation

BACKGROUND

We consider the problem of optimizing a liposomal drug formulation: a complex chemical system with many components (e.g., elements of a lipid library) that interact nonlinearly and synergistically in ways that cannot be predicted from first principles.

METHODOLOGY/PRINCIPAL FINDINGS

The optimization criterion in our experiments was the percent encapsulation of a target drug, Amphotericin B, detected experimentally via spectrophotometric assay. Optimization of such a complex system requires strategies that efficiently discover solutions in extremely large volumes of potential experimental space. We have designed and implemented a new strategy of evolutionary design of experiments (Evo-DoE), that efficiently explores high-dimensional spaces by coupling the power of computer and statistical modeling with experimentally measured responses in an iterative loop.

CONCLUSIONS

We demonstrate how iterative looping of modeling and experimentation can quickly produce new discoveries with significantly better experimental response, and how such looping can discover the chemical landscape underlying complex chemical systems.

Living technology: exploiting life's principles in technology

The concept of living technology-that is, technology that is based on the powerful core features of life-is explained and illustrated with examples from artificial life software, reconfigurable and evolvable hardware, autonomously self-reproducing robots, chemical protocells, and hybrid electronic-chemical systems. We define primary (secondary) living technology according as key material components and core systems are not (are) derived from living organisms. Primary living technology is currently emerging, distinctive, and potentially powerful, motivating this review. We trace living technology's connections with artificial life (soft, hard, and wet), synthetic biology (top-down and bottom-up), and the convergence of nano-, bio-, information, and cognitive (NBIC) technologies. We end with a brief look at the social and ethical questions generated by the prospect of living technology.

Evolutionary self-organization in complex fluids

This paper explores the ability of molecular evolution to take control of collective physical phases, making the first decisive step from independent replicators towards cell-like collective structures. We develop a physical model of replicating combinatorial molecules in a ternary fluid of hydrocarbons, amphiphiles and water. Such systems are being studied experimentally in various laboratories to approach the synthesis of artificial cells, and are also relevant to the origin of cellular life. The model represents amphiphiles by spins on a lattice (with Ising coupling in the simplest case), coupled to replicating molecules that may diffuse on the lattice and react with each other. The presence of the replicating molecules locally modulates the phases of the complex fluid, and the physical replication process and/or mobility of the replicating molecules is influenced by the local amphiphilic configuration through an energetic coupling. Consequently, the replicators can potentially modify their environment to enhance their own replication. Through this coupling, the system can associate hereditary properties, and the potential for autonomous evolution, to self-assembling mesoscale structures in the complex fluid. This opens a route to analyse the evolution of artificial cells. The models are studied using Monte Carlo simulation, and demonstrate the evolution of phase control. We achieve a unified combinatorial framework for the description of isotropic families of spin-lattice models of complex phases, opening up the physical study of their evolution.

Topology and evolution of technology innovation networks

The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the U.S. Patent and Trademark Office. We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such an attachment kernel is shared by scientific citation networks, thus indicating a universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.

Exploring the dynamics of adaptation with evolutionary activity plots

Evolutionary activity statistics and their visualization are introduced, and their motivation is explained. Examples of their use are described, and their strengths and limitations are discussed. References to more extensive or general accounts of these techniques are provided.

Artificial life: organization, adaptation and complexity from the bottom up

Artificial life attempts to understand the essential general properties of living systems by synthesizing life-like behavior in software, hardware and biochemicals. As many of the essential abstract properties of living systems (e.g. autonomous adaptive and intelligent behavior) are also studied by cognitive science, artificial life and cognitive science have an essential overlap. This review highlights the state of the art in artificial life with respect to dynamical hierarchies, molecular self-organization, evolutionary robotics, the evolution of complexity and language, and other practical applications. It also speculates about future connections between artificial life and cognitive science.

Evolution of evolvability via adaptation of mutation rates

We examine a simple form of the evolution of evolvability-the evolution of mutation rates-in a simple model system. The system is composed of many agents moving, reproducing, and dying in a two-dimensional resource-limited world. We first examine various macroscopic quantities (three types of genetic diversity, a measure of population fitness, and a measure of evolutionary activity) as a function of fixed mutation rates. The results suggest that (i) mutation rate is a control parameter that governs a transition between two qualitatively different phases of evolution, an ordered phase characterized by punctuated equilibria of diversity, and a disordered phase of characterized by noisy fluctuations around an equilibrium diversity, and (ii) the ability of evolution to create adaptive structure is maximized when the mutation rate is just below the transition between these two phases of evolution. We hypothesize that this transition occurs when the demands for evolutionary memory and evolutionary novelty are typically balanced. We next allow the mutation rate itself to evolve, and we observe that evolving mutation rates adapt to values at this transition. Furthermore, the mutation rates adapt up (or down) as the evolutionary demands for novelty (or memory) increase, thus supporting the balance hypothesis.

Collective intelligence of the artificial life community on its own successes, failures, and future

We describe a novel Internet-based method for building consensus and clarifying conflicts in large stakeholder groups facing complex issues, and we use the method to survey and map the scientific and organizational perspectives of the artificial life community during the Seventh International Conference on Artificial Life (summer 2000). The issues addressed in this survey included artificial life's main successes, main failures, main open scientific questions, and main strategies for the future, as well as the benefits and pitfalls of creating a professional society for artificial life. By illuminating the artificial life community's collective perspective on these issues, this survey illustrates the value of such methods of harnessing the collective intelligence of large stakeholder groups.

Is Echo a complex adaptive system?

We evaluate whether John Holland's Echo model exemplifies his theory of complex adaptive systems. After reviewing Holland's theory of complex adaptive systems and describing his Escho model, we describe and explain the characteristic evolutionary behavior observed in a series of Echo model runs. We conclude that Echo lacks the diversity of hierarchically organized aggregates that typify complex adaptive systems, and we explore possible explanations for this failure.

Open problems in artificial life

This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated.

Visualizing evolutionary activity of genotypes

We introduce a method for visualizing evolutionary activity of genotypes. Following a proposal of Bedau and Packard [11], we define a genotype's evolutionary activity in terms of the history of its concentration in the evolving population. To visualize this evolutionary activity we graph the distribution of evolutionary activity in the population of genotypes as a function of time. Adaptively significant genotypes trace a salient line or "wave" in these graphs. The quality of these waves indicates a variety of neutral variation, and random genetic drift. We apply this method in an evolutionary model of self-replicating assembly language programs competing for room in a two-dimensional space. Comparison with fitness graphs and with a nonadaptive analogue of this model shows how this method highlights adaptively significant events.

Four puzzles about life

To surmount the notorious difficulties of defining life, we should evaluate theories of life not by whether they provide necessary and sufficient conditions for our current preconceptions about life but by how well they explain living phenomena and how satisfactorily they resolve puzzles about life. On these grounds, the theory of life as supple adaptation gets support from the natural and compelling way it resolves the following four puzzles: (a) How are different forms of life at different levels of the vital hierarchy related? (b) Is there a continuum between life and nonlife? (c) Does life essentially concern a living entity's material composition or its form? (d) Are life and mind intrinsically connected?

Emergent models of supple dynamics in life and mind

The dynamical patterns in mental phenomena have a characteristic suppleness--a looseness or softness that persistently resists precise formulation--which apparently underlies the frame problem of artificial intelligence. This suppleness also undermines contemporary philosophical functionalist attempts to define mental capacities. Living systems display an analogous from of supple dynamics. However, the supple dynamics of living systems have been captured in recent artificial life models, due to the emergent architecture of those models. This suggests that analogous emergent models might be able to explain supple dynamics of mental phenomena. These emergent models of the supple mind, if successful, would refashion the nature of contemporary functionalism in the philosophy of mind.