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The 'Perfect Storm' metaphor describes a combination of events that causes a surprising or dramatic impact. It lends an evolutionary perspective to how social-ecological interactions change. Thus, we argue that an improved understanding of how social-ecological systems have evolved up to the present is necessary for the modelling, understanding and anticipation of current and future social-ecological systems. Here we consider the implications of an evolutionary perspective for designing research approaches. One desirable approach is the creation of multi-decadal records produced by integrating palaeoenvironmental, instrument and documentary sources at multiple spatial scales. We also consider the potential for improved analytical and modelling approaches by developing system dynamical, cellular and agent-based models, observing complex behaviour in social-ecological systems against which to test systems dynamical theory, and drawing better lessons from history. Alongside these is the need to find more appropriate ways to communicate complex systems, risk and uncertainty to the public and to policy-makers.

Many natural and technological systems are complex, with organizational structures that exhibit characteristic patterns but defy concise description. One effective approach to analyzing such systems is in terms of repeated topological motifs. Here, we extend the motif concept to characterize the dynamic behavior of complex systems by introducing developmental motifs, which capture patterns of system growth. As a proof of concept, we use developmental motifs to analyze the developmental cell lineage of the nematode Caenorhabditis elegans, revealing a new perspective on its complex structure. We use a family of computational models to explore how biases arising from the dynamics of the developmental gene network, as well as spatial and temporal constraints acting on development, contribute to this complex organization. © 2010 Wiley Periodicals, Inc. Complexity 16: 48-57, 2011 © 2011 Wiley Periodicals, Inc.

It has been proposed that a strong relationship exists between the population size and density of Pleistocene hominins and their competence in making stone tools. Here we focus on the first ‘Out of Africa’ dispersal, 1.8 Ma ago, and the idea that it might have featured lower population density and the fragmentation of hominin groups in areas furthest away from the point of origin. As a result, these distant populations in Central and East Asia and Europe would not be able to sustain sophisticated technological knowledge and reverted to a pattern of simpler stone-knapping techniques. This process could have led to the establishment of the ‘Movius Line’ and other long-lasting continental-scale patterns in the spatial distribution of Lower Palaeolithic stone technology. Here we report on a simulation developed to evaluate if, and under what conditions, the early ‘Out of Africa’ dispersal could lead to such a demographic pattern. The model comprises a dynamic environmental reconstruction of Old World vegetation in the timeframe 2.5–0.25 Ma coupled with a standard biological model of population growth and dispersal. The spatial distribution of population density is recorded over the course of the simulation. We demonstrate that, under a wide sweep of both environmental and behavioural parameter values, and across a range of scenarios that vary the role of disease and the availability of alternative crossing points between Africa, Europe and Asia, the demographic consequence of dispersal is not a gradual attenuation of the population size away from the point of origin but a pattern of ecologically driven local variation in population density. The methodology presented opens a new route to understand the phenomenon of the Movius Line and other large-scale spatio-temporal patterns in the archaeological record and provides a new insight into the debate on the relationship between demographics and cultural complexity. This study also highlights the potential of simulation studies for testing complex conceptual models and the importance of building reference frameworks based on known proxies in order to achieve more rigorous model development in Palaeolithic archaeology and beyond.

Culture is a central component in the study of numerous disciplines in social science and biology. Nevertheless, a consensus on what it is and how we can represent it in a meaningful and useful way has been hard to reach, especially due to the multifaceted aspects of its nature. In this work we dissect culture into its most basic components and propose horizontal information transfer as the most crucial aspect of it. We discuss the two fundamental processes that are required for culture to emerge in an evolutionary context, namely: increased mutation rates and survival selection. To show how each of these components affect the emergence of culture, a genetic algorithm was explored for a range of conditions. Here, we formalize when and how a population is said to move from biological to cultural evolution and why such a transition radically changes its evolutionary dynamics. Our results suggest that horizontal transfer of information in cultural systems requires the evolution of survival enhancing traits rather reproduction enhancing ones. We consider this requirement to be key for the evolution of rich cultural systems, like the one present in humans.

This paper investigates the dynamics of decentralised nest construction in the ant species Leptothorax tuberointerruptus, exploring the contribution of, and interaction between, a pheromone building template and a physical building template (the bodies of the ants themselves). We present a continuous-space model of ant behaviour capable of generating ant-like nest structures, the integrity and shapes of which are non-trivially determined by choice of parameters and the building template(s) employed. We go on to demonstrate that the same behavioural algorithm is capable of generating a somewhat wider range of architectural forms, and discuss its limitations and potential extensions.

Immune systems provide a unique window on the evolution of individuality. Existing models of immune systems fail to consider them as situated within a biochemical context. We present a model that uses an NK landscape as an underlying metabolic substrate, represents organisms as having both internal and external structure, and provides a basis for studying the coevolution of pathogens and host immune responses. Early results from the model are discussed; we show that interaction between organisms drives a population to optima distinct from those found when adapting against an abiotic background.

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.

Inspired by the virulence of natural parasites, a novel approach is developed to tackle disengagement, a detrimental phenomenon coevolutionary systems sometimes experience [1]. After demonstrating beneficial results in a simple model, minimum comparison sorting networks are coevolved, with results suggesting that moderating parasite virulence can help in practical problem domains.

How can artificial life (AL) advance scientific understanding? Is AL best seen as a new\ud discipline, or as a collection of novel computational methods that can be applied to old\ud problems? And given that the products of AL research range from abstract existence\ud proofs to working robots to detailed simulation models, are there standards of quality\ud or usefulness that can be applied across the whole field? On September 16th, 1999 in\ud Lausanne, Switzerland, a debate on these questions was held as part of the Fifth European\ud Conference on Artificial Life. As the organizers, we wanted to foster a constructive\ud discussion regarding the scientific status, and future, of AL. We were well aware that\ud some of these issues had been raised before (e.g., Miller [2]) but we felt that earlier treatments\ud had perhaps not reached a wide enough audience. The format for the debate\ud consisted of contributions from invited panelists followed by an open discussion. The\ud panelists were Chris Langton, Mark Bedau, Simon Kirby, and Inman Harvey—Hiroaki\ud Kitano was scheduled to participate but regrettably could not attend the conference.

Whilst the rate at which mutations occur in artificial evolutionary systems has received considerable attention, there has been little analysis of the mutation operators themselves. Here attention is drawn to the possibility that inherent biases within such operators might arte-factually affect the direction of evolutionary change. Biases associated with several mutation operators are detailed and attempts to alleviate them are discussed. Natural evolution is then shown to be subject to analogous mutation "biases". These tendencies are explicable in terms of (i) selection pressure for low mutation rates, and (ii) selection pressure to avoid parenting non-viable offspring. It is concluded that attempts to eradicate mutation biases from artificial evolutionary systems may lead to evolutionary dynamics that are more unnatural, rather than less. Only through increased awareness of the character of mutation biases, and analyses of our models' sensitivity to them, can we guard against artefactual results.

Parents raising multiple offspring must decide how to divide resources between them. Much empirical data on the parenting behaviour of particular species has been collected. Birds, in particular, have been shown to follow a number of provisioning rules. However, the adaptive significance of this variation in decision-making strategies has been largely unexplored. Here we present a simulation model of the western bluebird, Sialia mexicana, with which we explore the utility of various simple feeding heuristics. The simulated parents face the task of simultaneously raising several offspring who are of differing ages and thus have differing resource needs. We show that the success of simple rules of thumb varies with environmental parameters in a manner which (i) predicts experimental results in the biology literature, and (ii) can be explained using a notion of parental egalitarianism.