There has been progress on a variety of fronts concerning OEE in the past decade. To take stock of and document recent work, and to identify key milestones for the immediate future, a workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1) was held at the European Conference on Artificial Life (ECAL 2015) at the University of York, UK, in July 2015. 2 The workshop aimed to create a common framework for discussing and evaluating research on open-ended evolution, and to catalyze further progress. In particular, a follow-up workshop (OEE2) will take place at the ALIFE XV conference in Cancún, Mexico in July 2016. 3 The Cancún workshop will be followed by a special issue on open-ended evolution in the Artificial Life journal, including a comprehensive review paper on work on OEE.

From the first experiments with digital evolution in the 1950s to the increasingly sophisticated simulations of the present day, the concept of open-ended evolution (OEE) has been a central concern for artificial life (ALife) researchers [ 51 ]. Loosely defined, an open-ended evolutionary system is one that is capable of producing a continual stream of novel organisms 1 rather than settling on some quasi-stable state beyond which nothing fundamentally new occurs. Some definitions of OEE further require that the maximum complexity of organisms in the system increases over time, or that ecosystem complexity increases. Understanding open-ended evolution remains a holy grail in ALife—and yet there remains little agreement within the community on precise definitions and measures.

2 Summary of Short Presentations Section: Choose Top of page Abstract 1 Introduction 2 Summary of Short Presen... << 3 Themes from the Open Di... 4 Conclusion Acknowledgments Notes References CITING ARTICLES

In “Karl Popper, artificial life, and the curious tale of the hopeful behavioral monster,” Barry McMullin highlighted Karl Popper's philosophy of evolutionary epistemology and its relevance to ALife and OEE. Focusing particularly on Popper's work “Evolution and the Tree of Knowledge” (a chapter of his book Objective Knowledge: An Evolutionary Approach [37]) based upon a lecture delivered in 1961, McMullin outlined Popper's thought experiment on how mutations in an agent's “central propensity structure” (a hierarchical control system defining its set of skills and behaviors) could guide the future evolution of the agent, leading to apparently “goal-directed” evolution. This perspective suggests that the existence of hierarchical control organization and the continuing feasibility of inheritable change at the highest control levels (including emergence of higher, newly dominating levels) may be critical to the substantive openness of evolution of complex function.

The presentation by Wolfgang Banzhaf on “Open-Endedness and Novelty in Evolution” started with the observation that the notion of novelty in a system must be defined with respect to a particular model. Banzhaf identified three different types of novelty: (1) novelty within a model (variation), (2) novelty that changes the model (innovation), and (3) novelty that changes the meta-model5 (emergence). He then addressed the question of whether OEE requires unbounded novelty or unbounded complexity. Observing that the universe is limited and hence cannot afford an unbounded increase in levels of complexity, and also that all combinatorial possibilities at any one level are bounded, he argued that novelty can still be practically unbounded if the number of levels of complexity in the system is allowed to grow (as novelties grow exponentially with complexity). Hence, Banzhaf's position is that OEE does not require unbounded complexity, but that unbounded novelty is sufficient. He concluded with some comments on the competing roles of exponential growth and competition due to resource constraints in natural selection, and the analogous situation in hierarchical systems whereby climbing the levels of complexity introduces exponentially more possibilities, but exploration of these possibilities is restricted by resource constraints on the number of individuals that can populate higher levels. Banzhaf's talk was based upon a forthcoming paper [3].

After highlighting some of the many different concepts associated with the term OEE in the literature, in “Requirements for Open-Ended Evolution in Natural and Artificial Systems” Tim Taylor proposed a high-level classification of these issues in the form of five basic requirements for a system to exhibit OEE: (1) robustly reproductive individuals, (2) individuals capable of producing more complex offspring, (3) mutational pathways to other viable individuals, (4) a medium allowing the possible existence of a practically unlimited diversity of individuals and interactions, and (5) drive for continued evolution. For each requirement, Taylor explained why it was important, what theoretical issues it encompassed, and what practical issues were involved in implementing a system to meet the requirement. The talk was based upon a paper [50] presented at the EvoEvo Workshop6 at the same ECAL 2015 conference.

Guillaume Beslon started his talk “Is Biological Evolution Open-Ended?” by observing that the vast majority of literature on OEE comes from the ALife community and not from evolutionary biology. Historically, the mathematical models of evolutionary biologists have focused on stable states. Moreover, selection is thought to commonly act as a stabilizing force on genetic diversity. However, although the concept of OEE is largely lacking in the biological literature, the concept of novelty pervades it in many forms. Of all kinds of biological novelty, Beslon identified coevolution and major transitions as the two being most closely related to the concept of OEE. He proposed that the most important idea of open-endedness was the emergence of novelty leading to new levels of individuality (i.e., major transitions). However, he conjectured that biology cannot be open-ended with regard to major transitions, arguing that as higher levels of organization are inevitably populated by smaller population sizes, this leads to decreasing probability of fixation of beneficial mutations. A saving grace for computational systems, according to Beslon, was that this limitation could be overcome by tricks such as suitable fitness landscapes (although whether they were open-ended would still be an open question). A version of Beslon's argument can also be found in the forthcoming paper mentioned by Banzhaf [3].

In “Normalised Evolutionary Activity Statistics and the Need for Phenotypic Evidence,” Alastair Channon noted that there was widespread agreement that OEE involves the continued evolution of new adaptive traits. As this can be achieved trivially,7 he argued that OEE must also involve a sustained increase in some measure of accumulated adaptive success. However, he questioned the inclusion of increasing complexity as a hallmark of OEE, as this would preclude the possibility of addressing important questions such as whether or not OEE can be the cause of increasing maximal complexity (whether individual, group, or system complexity) or what conditions might be necessary or sufficient for this. Channon then described his Geb system [15], based upon Harvey's SAGA principles [19] with the addition of coevolutionary feedback arising via biotic selection rather than being specified by abiotic fitness functions, followed by a description of Bedau et al.'s work on evolutionary activity statistics [4, 14]. Two useful features of these measures, he suggested, were that they are widely applicable, and that the key metric (cumulative evolutionary activity, based on adaptive persistence) is “a measure of the continual adaptive success of the components in the system” [7], that is a measure of accumulated adaptive success. When applied to Geb, these measures classify the system as producing unbounded evolutionary dynamics (OEE). Despite that, it becomes increasingly difficult (over evolutionary time) to visually observe the behaviors that evolve. Channon identified three critical future milestones for the field: (1) more systems classified as OEE according to the evolutionary activity statistics, in order to refine definitions and tests for the hallmarks of OEE; (2) evidence of complex artifacts or behaviors arising from evolutionary changes (rather than from a very small number of mutations from a hard-coded ancestor); and (3) evidence of long evolutionary sequences of evolved artefacts or behaviors (a result that has not been conclusively observed in work to date).

In “Indefinite Scalability for Open-Ended Evolution,” David Ackley agreed with Beslon that major transitions are the most important aspect of OEE, and that if we accept that, then a finite system cannot be open-ended: Successive major transitions produce larger and slower individuals until ultimately producing a population size of one that lives forever. He argued that conventional component-based evolutionary activity measures of OEE are problematic because they require us to identify the components of interest beforehand—if we treat components as priors rather than observables, we will be unable to detect major transitions. To avoid this problem and treat evolutionary components as observables, models should be defined at the level of physics and chemistry, not at the level of biological components. But this raises the question, what kind of physics and chemistry is appropriate? Ackley's answer is that satisfactory models should, in principle, be indefinitely scalable. This rules out the whole class of deterministic, synchronous models (such as Game-of-Life-type systems), and suggests that OEE models should embrace nondeterminism. This approach could create a unifying research strand between different ALife projects that implemented different kinds of indefinitely scalable systems. Ackley concluded his talk by proposing a research challenge to develop a statistical OEE measure based upon identifying potential evolutionary components at a given scale by near-perfect spatial autocorrelation of elements, study of the phase space defined by the “life lines” of such components over time, and application of the same technique at different scales in the system. Ackley presented further details of his concept of indefinitely scalable architectures in a paper at the main ECAL 2015 conference [1].

In “Emergence of Emergence,” Norman Packard discussed current work with Nicholas Guttenberg and others on the evolution of coding in the transition from prebiotic systems to biotic evolutionary systems. The central question being tackled is: What dynamical processes lead generically to sequestration of information into units that have long-term stability, control fast time-scale dynamics, and can serve as evolvable elements? The goal is to understand this transition well enough to be able to engineer systems that will naturally implement information sequestration, evolvability, and robustness. Packard observed that evolutionary dynamics is very different from attractor dynamics: It behaves somewhat like an attractor on the short term, but over a longer term, instabilities lead to the generation of innovation. Their work augments the language of dynamical systems theory with concepts capable of describing such phenomena. In particular, the concept of dynamical canals is used in place of attractors. A mechanism that seems to produce this kind of system generically is one involving an alternation between unstable (or neutrally stable) dynamics and contracting, fixed-point dynamics. Alternation forces the system to produce information bottlenecks, which seem to imply the emergence of informationally stable components that become proto-code. In addition to Packard's own work on this origin-of-life transition, colleagues are working on applying these ideas to other transitions, including multicellularity, ecological niche formation, and the evolution of cognitive mechanisms.

The central claim of Nathaniel Virgo in “Open-Ended Fitness Landscapes” was that open-endedness is a property of fitness landscapes, and not of the process of evolution itself. He characterized OEE in terms of increasing phenotypic complexity, and argued that ecological factors (e.g., changing environments, coevolution, and niche construction) might not be necessary for OEE, or at least that this is a hypothesis worth taking seriously. To evaluate this claim, he suggested we should focus on understanding how to create more “lifelike” fitness landscapes of high-dimensionality, containing many qualitatively different “solutions,” where fitter solutions also generally tend to be more complex, and where those solutions can be reached through a sequence of small changes. By comparison with the biological-physical case, Virgo argued that this kind of fitness landscape required the existence of many degrees of freedom (DoFs), which he characterized as the “capacity [of a system] to be changed in some nontrivial way.” Complex systems with many DoFs, he suggested, enable the existence of many qualitatively different solutions and the capacity to move between those solutions. Virgo then hypothesized that many nontrivial landscapes have small regions of evolvability, and that evolutionary systems might evolve towards such regions through a process of the evolution of evolvability. His tentative conclusion was that the requirements for OEE in computational systems might just involve larger search spaces, more nontrivial fitness functions, larger populations, weaker selection pressure, and more computer time.

In “Empirical Measurements of Door-Opening Evolution of Technology,” Mark Bedau described recent work with colleagues on studying open-ended evolution within the context of cultural rather than biological evolution. Specifically, the work investigates the evolution of human technology. Technological evolution differs from biological evolution in many ways, including the presence of hyper-parental reproduction, intentionally directed progress, and indirect (human-mediated) reproduction. Furthermore, one can identify populations of technology adopters, technology designers, technological innovations, and technological products as four distinct, but interrelated components. Technological evolution is therefore different from biological evolution in nontrivial ways, but, Bedau argued, we should not restrict ourselves to studying OEE just in biological systems. He identified the concept of reach—intuitively, the idea of an invention that has descendants that are very different to itself—as an important aspect of technological OEE. In recent preliminary work, the evolution of technological innovations was studied by using text-mining techniques on historical patent records to extract relevant traits in each record. Dimensionality reduction and clustering techniques were used to study the reach of different traits in particular genealogies of technologies. These new results build on earlier work on operationalizing the study of technological evolution [43, 11, 13], and they open the door to the empirical study of many questions about open-ended evolution in nature.

In “The OEE Measure—Will It Blend?” Simon Hickinbotham questioned whether existing evolutionary activity methods reduced the complexity of a system too much to produce a simple measure, and whether they really highlight the relevant features relating to a system's open-endedness. He argued that (improved) evolutionary activity measures were useful for making sense of the huge amounts of data produced by computational evolutionary systems, and, more importantly, they allow us to rigorously compare different systems and thereby demonstrate when improvements in ALife systems have been achieved. Hickinbotham then introduced his new quantitative non-neutral (QNN) evolutionary activity measure. He highlighted some of its attractive features as being that (1) it produces a single numerical value, (2) it is based solely on population data (like other evolutionary measures), and (3) it can be applied to systems with intrinsic or extrinsic fitness. The application of the QNN measure to the Tierra and Stringmol systems was described, with discussion of how it was used to guide improvements in the design of each system: Further details can be found in papers presented during the main conference [23, 22] and in a subsequently published article [20]. Hickinbotham concluded by suggesting that we need more measures to address different aspects of OEE, and that once we had developed an adequate suite of such measures, there was the potential to create a meta-evolver for OEE.8

Steen Rasmussen in “Minimal Life and Open-Ended Evolution” conjectured that high-dimensional systems with rich object complexity and/or diversity enable the emergence of higher-order functionalities, and that these are necessary for OEE. However, simply adding complexity and diversity is not a sufficient condition. He stresses the existence of two different ways to increase complexity in a physical system: through the aggregation of things from the environment, and through the evolution of new encoded entities. His view is based upon his work over many years with protocells—minimal self-reproducing molecular machines comprising a metabolism, genes, and a container in a given environment. Protocells utilize self-organization and self-assembly processes to maintain their organization, and are driven by a metabolism feeding on free energy and resources from the environment. A full chemical protocell system has not yet been synthesized in the lab, but simulation results show that published protocell designs apparently lack the ability to evolve in an open-ended manner. For example, in the case of Rasmussen's own protocell design, simulations show that the system's evolution is limited to the optimization of its metabolic rate. Both experimental and simulation results show that a richer environment is necessary to expand the system's evolutionary potential. Real chemical systems demonstrate the emergence of higher-order functionality at multiple hierarchical levels, and Rasmussen described simulation results in which similar higher-order functionality had emerged [38]. This was achieved by adding to the complexity of the lowest-level elements in the system. A similar approach might therefore be viable in the protocell systems. However, Rasmussen pointed out that just adding complexity to the system in an unprincipled way was likely to lead to “black tar” rather than any interesting higher-order behavior—the addition of complexity must be done with care. This leads to an as yet unanswered question: Are there principles to guide us in adding complexity at the right places in the system, or are we essentially left to experiment by trial and error?

Emily Dolson started her presentation “Understanding Complexity Barriers in Evolving Systems” with an informal definition of open-endedness as the ability of a system to “keep doing interesting things.” Dolson discussed how we might more accurately define both “keep doing” and “interesting things.” She suggested that there is fairly general agreement that “keep doing” means unbounded rather than asymptotic behavior of a measure. With regard to what measures to use, that is, what constitutes “interesting things,” she argued that it might be productive to flip the question around, and ask what kinds of barriers might prevent a system from exhibiting open-endedness. Dolson proceeded to describe four barriers that she and her colleagues had come up with: (1) change potential—how much we expect the population composition to change during an interval; (2) novelty potential—how many entirely new strategies we expect to arise during an interval; (3) complexity potential—how much we expect the greatest individual complexity to increase during an interval; and (4) ecosystem potential—how much we expect “meaningful” diversity to increase during an interval. This breaks down the concept of OEE into separate aspects, each of which suggests more clearly focused lines of research for advancing our understanding of open-endedness. Dolson went on to discuss the relationships between these factors: To have novelty potential, a system requires change potential, and to have complexity potential or ecosystem potential, a system requires novelty potential. She acknowledged that other barriers might also exist, and, in particular, she and her colleagues are currently considering including a barrier of major transition potential in their picture.9

In “A New Design Principle for Open-Ended Evolution,” Takashi Ikegami discussed evolution in the context of web-based systems. Specifically, he reported work with colleagues on studying the dynamics of a social network site10 where users can upload photos and other users can attach tags to the photos to describe their content. By using a variety of mathematical techniques to analyze the use and evolution of tags in the system over a period of three years, Ikegami argued that the increasing vocabulary of tags observed over time generated a self-maintaining system in which certain types of tags stimulate users to create new combinations, and which prompted users to upload new photos to be annotated by those tags. Furthermore, a phase-transition-like event was observed in the system's activity, involving a sudden increase both in typical social network size and in tagging activity of each user. Based upon these observations, Ikegami and colleagues hypothesize that OEE in such systems can be driven by the users' collective activities. The work discussed in this talk is described in more detail in a late-breaking paper presented at the main ECAL 2015 conference [33].

Tom Froese began his talk, “Groundlessness Avoids Openness Reduction in Hierarchies of Emergence,” with the observation that problems of OEE are of interest to origin-of-life researchers (even if not being addressed by biologists more broadly, as claimed by Beslon). In particular, he highlighted recent work by Peter Strazewski on evolution in chemical systems: Strazewski argues that OEE is more likely if we move away from well-defined systems to messy systems with many possible variants (in chemical composition, property, reactivity, shape, size, etc.) [46]. Froese attempted to formalize this intuition by characterizing OEE in terms of a system's emergence of new degrees of freedom (DoFs). He argued that if emergence is defined as collective dynamics resulting from nonlinear coupling between two or more components, then the number of DoFs of the emergent phenomena cannot, in principle, be greater than the sum of the numbers of DoFs of its components. Froese argued that this suggests that as we climb to higher hierarchical levels of complexity in a system, we inevitably witness a decrease in the number of DoFs of the system at those levels. This might not be a problem in systems that have sufficient complexity (many DoFs) at the bottom level. However, an alternative approach to avoiding this limitation would be to assume there is no bottom level—that the system is groundless (a line of thought inspired by Michel Bitbol [8]). Froese suggested that an explanation of OEE in the real world might therefore require us to conceptualize reality as a groundless system.