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As modern engineered systems become ever more connected and interdependent there is an increasing need to evaluate their complexity. However, evaluating system complexity is challenging due to the complicated conceptual landscape of competing definitions of the term complexity itself, and the range of perspectives that can be taken on what constitutes the System of Interest. This paper attempts to overcome these hurdles through introducing a Complexity Register with which to build and record a shared understanding of system complexity for key stakeholders. In order to overcome current challenges in evaluating the complexity of an engineering system, the Complexity Register encourages personnel to adopt a broad range of perspectives on the potential issues, impacts and mitigations to manage system complexity. The formulation of the Complexity Register is informed by design principles derived from a case study analysis of a system complexity evaluation tool. The Complexity Register should enable more effective shared understanding by encouraging collaboration that makes explicit the multiple viewpoints taken when evaluating system complexity and promoting continued re-evaluation throughout a system or project lifecycle.

Complexity has been argued to limit operational efficiency, hinder decision-making and induce disruption in supply chain networks. The main aim of this paper is to investigate the architectural trade-off between complexity and modularity in modular assembly supply chain networks. Towards this, an information-entropic complexity model is developed and applied to the domain of assembly supply chains and logistics. This approach characterises complexity as a combination of the intrinsic complexity of the system modules/interfaces and the influence of the topological composition of the network. The model is then used within an optimisation framework, where the optimal granularity level for assembly supply chain design solutions for a given assembly product can be automatically verified by considering the trade-off between complexity and network modularity. It is concluded that the proposed methodology could help to minimise the complexity of supply chain assembly configurations while maximising their modularity and thereby help to increase both the reliability and performance of supply chain networks.

Evaluating the complexity of an engineered system is challenging for any organization, even more so when operating in a System-of-Systems (SoS) context. Here, we analyse one particular decision support tool as an illustratory case study. This tool has been used for several years by Thales Group to evaluate system complexity across a variety of industrial engineering projects. The case study is informed by analysis of semi-structured interviews with systems engineering experts within Thales Group. This analysis reveals a number of positive and negative aspects of (i) the tool itself, and (ii) the way in which the tool is embedded operationally within the wider organization. While the first set of issues may be solved by making improvements to the tool itself, informed by further comparative analysis and growing literature on complexity evaluation, the second ``embedding challenge'' is distinct, seemingly receiving less attention in the literature.In this paper we focus on addressing this embedding challenge, by introducing a complexity evaluation framework, designed according to a set of principles derived from the case study analysis; namely that any effective complexity evaluation activity should feature collaborative effort towards building an evaluation informed by a shared understanding of contextually relevant complexity factors, iterative (re-)evaluation over the course of a project, and progressive refinement of the complexity evaluation tools and processes themselves through linking project evaluations to project outcomes via a wider organizational learning cycle. The paper concludes by considering next steps including the challenge of assuring that such a framework is being implemented effectively.

Organizations are increasingly faced with the challenge of architecting complex systems that must operate within a System of Systems (SoS) context. While network science has offered usefully clear insights into product and system architectures, we seek to extend these approaches to evaluate enterprise system architectures. Here, we explore the application of graph-theoretic methods to the analysis of two real-world enterprise architectures (a military communications system and a search and rescue system) and to assess the relative importance of different architecture components. For both architectures, different topological measures of component significance identify differing network vertices as important. From this we identify several significant challenges a system architect needs to be cognisant of when employing graph-theoretic approaches to evaluate architectures; finding suitable abstractions of heterogeneous architectural elements and distinguishing between network-structural properties and system-functional properties. These challenges are summarized as five guiding principles for utilizing network science concepts for enterprise architecture evaluation.

Traveling in extreme adverse weather involves a high risk of travel delay and traffic accidents. There is a need to assess the impact of extreme weather on transport infrastructure and to find suitable mitigation strategies to alleviate the associated undesirable outcomes. Previous work in vulnerability studies applied either a constant failure probability or an assumed probabilistic distribution. Such assumptions ignored many factors causing the occurrence of road failure, especially that infrastructure components tend to fail interdependently. Based on empirical data of road failures and rainfall intensity during a typhoon, this study develops a statistical model, incorporating spatial correlations among the segments of road infrastructure, and uses it to evaluate the impact of the typhoon on travel time reliability. Mixed-effects logistic regression as well as rare-events logistic regression are applied to understand the factors involved in road failures and the spatial correlations of the failed segments. The analysis suggested that, in addition to the rainfall intensity, the road geometry, including elevation, land slope, and distance from the nearest river, were important factors in the failure. In addition, there is a significant correlation of failures within watersheds. This model gives an insight into the characteristics of road failures and their associated travel risks, which is useful for authorities to find proper mitigations to reduce the adverse effects in future disasters.

This paper explores the differential impact of multi-agent system heterogeneity in the context of an idealised herding task. In simulation, a team of simple herders must move a flock towards a target location in a continuous 2d space. Flock heterogeneity is controlled by dividing the flock into a number of non-overlapping social groups that influence sheep movement. Results demonstrate that increasing system heterogeneity (i.e., the number of different social groups) reduces herding performance when social groups are self-attracting, but conversely, the same increase in system heterogeneity can increase herding performance when groups are other-attracting. Implications for designing heterogeneous multi-agent systems are considered.

Despite a wealth of system architecture frameworks and methodologies available, approaches to evaluate the robustness and resiliency of architectures for complex systems or systems of systems are few in number. As a result, system architects may turn to graph-theoretic methods to assess architecture robustness and vulnerability to cascading failure. Here, we explore the application of such methods to the analysis of two real-world system architectures (a military communications system and a search and rescue system). Both architectures are found to be relatively robust to random vertex removal but more vulnerable to targeted vertex removal. Hardening strategies for limiting the extent of cascading failure are demonstrated to have varying degrees of effectiveness. However, in taking a network perspective on architecture robustness and susceptibility to cascade failure, we find several significant challenges that impede the straightforward use of graph-theoretic methods. Most fundamentally, the conceptualization of failure dynamics across heterogeneous architectural entities requires considerable further investigation.

Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the Information-Cost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm’s ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the information-centred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

Social learning, defined as the imitation of behaviors performed by others, is recognized as a distinctive characteristic in humans and several other animal species. Previous work has claimed that the evolutionary fixation of social learning requires decision-making cognitive abilities that result in transmission bias (e.g., discriminatory imitation) and/or guided variation (e.g., adaptive modification of behaviors through individual learning). Here, we present and analyze a simple agent-based model that demonstrates that the transition from instinctive actuators (i.e., non-learning agents whose behavior is hardcoded in their genes) to social learners (i.e., agents that imitate behaviors) can occur without invoking such decision-making abilities. The model shows that the social learning of a trait may evolve and fix in a population if there are many possible behavioral variants of the trait, if it is subject to strong selection pressure for survival (as distinct from reproduction), and if imitation errors occur at a higher rate than genetic mutation. These results demonstrate that the (sometimes implicit) assumption in prior work that decision-making abilities are required is incorrect, thus allowing a more parsimonious explanation for the evolution of social learning that applies to a wider range of organisms. Furthermore, we identify genotype-phenotype disengagement as a signal for the imminent fixation of social learners, and explain the way in which this disengagement leads to the emergence of a basic form of cultural evolution (i.e., a non-genetic evolutionary system).

System complexity, and its evaluation, poses several challenges to any organization hoping to engineer systems operating in a System-of-Systems (SoS) context. Here, we analyse one particular industrial complexity evaluation decision support tool that has been in use for several years across a variety of engineering projects, with the aim of better understanding and overcoming a particular subset of these challenges. While improvements to the tool itself (such as making SoS considerations explicit, or employing structured communications techniques to improve elicitation) are a legitimate line of enquiry, the focus of the current paper is the set of issues relating to the wider organizational context within which any such tool needs to be embedded.Here we characterise this context in terms of a complexity evaluation framework, and, based on the case study analysis, argue for a set of key framework features; collaborative effort towards building a shared understanding of contextually relevant complexity factors, iterative complexity (re-)evaluation over the course of a project, and progressive refinement of complexity evaluation tools and processes through linking these to project outcomes in the form of a wider organizational learning cycle. The paper concludes by considering next steps including the challenge of assuring that such a framework is being implemented effectively, and, relatedly, the feasibility of collecting sufficient empirical evidence to conduct cost-benefit analyses of the impact of complexity evaluation.

Instead of committing to the first source of reward that it discovers, an agent engaged in "preferential foraging" continues to choose between different reward sources in order to maximise its foraging efficiency. In this paper, the effect of preferential source selection on the performance of robot swarms with different recruitment strategies is studied. The swarms are tasked with foraging from multiple sources in dynamic environments where worksite locations change periodically and thus need to be re-discovered. Analysis indicates that preferential foraging leads to a more even exploitation of resources and a more efficient exploration of the environment provided that information flow among robots, that results from recruitment, is regulated. On the other hand, preferential selection acts as a strong positive feedback mechanism for favouring the most popular reward source when robots exchange information rapidly in a small designated area, preventing the swarm from foraging efficiently and from responding to changes.

Designing and representing control algorithms is challenging in swarm robotics, where the collective swarm performance depends on interactions between robots and with their environment. The currently available modeling languages, such as UML, cannot fully express these interactions. We therefore propose a new, Behaviour-Data Relations Modeling Language (BDRML), where robot behaviours and data that robots utilise, as well as relationships between them, are explicitly represented. This allows BDRML to express control algorithms where robots cooperate and share information with each other while interacting with the environment.

From a multi-disciplinary point of view, research on resilience focuses on robustness, recovery, and adaptive capacity. Robustness quantifies how much damage a system can take before it breaks, whereas recovery refers to the ability of a system to recuperate within limits of time and resources, and adaptability requires a system to be able to structurally reorganize throughout time so as to improve its chances of survival when facing disturbances. In this paper, after discussing examples of models of robustness, recovery and adaptability from different scientific disciplines, is a discussion on the relationship between these three aspects of resilience, introducing a multi-level resilience hierarchy with which to relate them to each other which is termed the resilience pyramid. This paper then exemplifies this multi-level view of resilience through discussing the resilience of symbiotic networks to cascading failure in the context of modern infrastructures, and considers the introduction of infrastructure nodes with permutable roles as a possible solution.

This article introduces a special issue of Complexity dedicated to the increasingly important element of complexity science that engages with social policy. We introduce and frame an emerging research agenda that seeks to enhance social policy by working at the interface between the social sciences and the physical sciences including mathematics and computer science, and term this research area the "social science interface" by analogy with research at the life sciences interface. We locate and exemplify the contribution of complexity science at this new interface before summarizing the contributions collected in this special issue and identifying some common themes that run through them. © 2014 Wiley Periodicals, Inc. Complexity 19: 1-4, 2014

What is it about simulation models that has led some practitioners to treat them as potential sources of empirical data on the real-world systems being simulated; that is, to treat simulations as ‘artificial worlds’ within which to perform computational ‘experiments’? Here we use the work of Richard Levins as a starting point in identifying the appeal of this model building strategy, and proceed to account for why this appeal is strongest for computational modellers. This analysis suggests a perspective on simulation modelling that makes room for ‘artificial worlds’ as legitimate science without having to accept that they should be treated as sources of empirical data

Social networks characterize the set of relationships among a population of social agents. As such, their structure both constrains and is constrained by social processes such as partnership formation and the spread of information, opinions, and behavior. Models of these coevolutionary network dynamics exist, but they are generally limited to specific interaction types such as games on networks or opinion dynamics. Here we present a dynamic model of social network formation and maintenance that exhibits the characteristic features of real-world social networks such as community structure, high clustering, positive degree assortativity, and short characteristic path length. Although these macrostructural network properties are stable, the network microstructure undergoes continuous change at the level of relationships between individuals. Notably, the edges are weighted, allowing for gradual change in relationship strength in contrast to more abrupt mechanisms, such as rewiring, used in other models. We show how the structural features that characterize social networks can arise as the result of constraints placed on the interactions between individuals. Here we explore the relationship between structural properties and four idealized constraints placed on social interactions: space, affinity, time, and history. We show that spatial embedding and the subsequent constraints on possible interactions are crucial in this model for the emergence of the structures characterizing social networks.

Background Sexually transmitted infections (STIs) are ongoing concerns. The best method for preventing the transmission of these infections is the correct and consistent use of condoms. Few studies have explored the use of games in interventions for increasing condom use by challenging the false sense of security associated with judging the presence of an STI based on attractiveness. Objectives The primary purpose of this study was to explore the potential use of computer simulation as a serious game for sex education. Specific aims were to (1) study the influence of a newly designed serious game on self-rated confidence for assessing STI risk and (2) examine whether this varied by gender, age, and scores on sexuality-related personality trait measures. Methods This paper undertook a Web-based questionnaire study employing between and within subject analyses. A Web-based platform hosted in the United Kingdom was used to deliver male and female stimuli (facial photographs) and collect data. A convenience sample group of 66 participants (64%, 42/66) male, mean age 22.5 years) completed the Term on the Tides, a computer simulation developed for this study. Participants also completed questionnaires on demographics, sexual preferences, sexual risk evaluations, the Sexual Sensation Seeking Scale (SSS), and the Sexual Inhibition Subscale 2 (SIS2) of the Sexual Inhibition/Sexual Excitation Scales-Short Form (SIS/SES - SF). Results The overall confidence of participants to evaluate sexual risks reduced after playing the game (P

We draw together methodologies from game theory, agent based modelling, decision theory, and uncertainty analysis to explore the process of decision making in the context of pregnant women disclosing their drinking behaviour to their midwives. We employ a game theoretic framework to define a signalling game. The game represents a scenario where pregnant women decide the extent to which they disclose their drinking behaviours to their midwives, and midwives employ the information provided to decide whether to refer their patients for costly specialist treatment. This game is then recast as two games played against “nature”, to permit the use of a decision theoretic approach where both classes of agent use simple rules to decide their moves. Four decision rules are explored – a lexicographic heuristic which considers only the link between moves and payoffs, a Bayesian risk minimisation agent that uses the same information, a more complex Bayesian risk minimiser with full access to the structure of the decision problem, and a Cumulative Prospect Theory (CPT) rule. In simulation, we recreate two key qualitative trends described in the midwifery literature for all the decision models, and investigate the impact of introducing a simple form of social learning within agent groups. Finally a global sensitivity analysis using Gaussian Emulation Machines (GEMs) is conducted, to compare the response surfaces of the different decision rules in the game.

We present an agent-based model of endogenous merger formation in a market with turnover of market participants. We describe the dynamics of the model and identify the conditions under which market competition is sufficiently disrupted to prompt extended periods during which mergers are desirable. We also demonstrate how merger waves can be triggered by industry shocks and firm overconfidence.

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.

How can we understand the interaction between the social network topology of a population and the patterns of group affiliation in that population? Each aspect influences the other: social networks provide the conduits via which groups recruit new members and groups provide the context in which new social ties are formed. Given that the resources of individuals are finite, groups can be considered to compete with one another for the time and energy of their members. Such competition is likely to have an impact on the way in which social structure and group affiliation co-evolve. While many social simulation models exhibit group formation as a part of their behaviour (e.g., opinion clusters or converged cultures), models that explicitly focus on group affiliation are less common. We describe and explore the behaviour of a model in which, distinct from most current models, individual nodes can belong to multiple groups simultaneously. By varying the capacity of individuals to belong to groups, and the costs associated with group membership, we explore the effect of different levels of competition on population structure and group dynamics.

Architectural design is typically limited by the constraints imposed by physical space. If and when opportunities to attenuate or extinguish these limits arise, should they be seized? Here it is argued that the limiting influence of spatial embedding should not be regarded as a frustrating "tyranny" to be escaped wherever possible, but as a welcome enabling constraint to be leveraged. Examples from the natural world are presented, and an appeal is made to some recent results on complex systems and measures of interaction complexity.

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.

Understanding how the dynamics of language learning and language change are influenced by the population structure of language users is crucial to understanding how lexical items and grammatical rules become established within the context of the cultural evolution of human language. This paper extends the recent body of work on the development of term-based languages through signalling games by exploring signalling game dynamics in a social population with overlapping generations. Specifically, we present a model with a dynamic population of agents, consisting of both mature and immature language users, where the latter learn from the formers’ interactions with one another before reaching maturity. It is shown that populations in which mature individuals converse with many partners are more able to solve more complex signalling games. While interacting with a higher number of individuals initially makes it more difficult for language users to establish a conventionalised language, doing so leads to increased diversity within the input for language learners, and that this prevents them from developing the more idiosyncratic language that emerge when agents only interact with a small number of individuals. This, in turn, prevents the signalling conventions having to be renegotiated with each new generation of language users, resulting in the emerging language being more stable over subsequent generations of language users. Furthermore, it is shown that allowing the children of language users to interact with one another is beneficial to the communicative success of the population when the number of partners that mature agents interact with is low.

Iterated learning takes place when the input into a particular individual’s learning process is itself the output of another individual’s learning process. This is an important feature to capture when investigating human language change, or the dynamics of culturally learned behaviours in general. Over the last fifteen years, the Iterated Learning Model (ILM) has been used to shed light on how the population-level characteristics of learned communication arise. However, until now each iteration of the model has tended to feature a single immature language user learning from their interactions with a single mature language user. Here, the ILM is extended to include a population of immature and mature language users. We demonstrate that the structure and make-up of this population influences the dynamics of language change that occur over generational time. In particular, we show that, by increasing the number of trainers from which an agent learns, the agent in question learns a fully compositional language at a much faster rate, and with less training data. It is also shown that, so long as the number of mature agents is large enough, this finding holds even if a learner’s trainers include other agents that do not yet posses full linguistic competence.

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.

Organizations face important risks with IT Outsourcing (ITO)—the practice of delegating organizational IT functions to third parties. Here, we employ a system dynamics simulator to support ITO decision-making under risk, taking a dynamic and integrated view of both capabilities management and benefits management. After briefly presenting its functionality, we use the simulator to assess how deficits in two IT capabilities—Contract Monitoring (on the customer’s side) and Service Delivery (on the supplier’s side)—affect the earned values of service orders, the ITO budget, service completion deadlines, and damage to the customer-supplier relationship. Validation is ongoing at four institutions in Brazil, including a large, state tax collecting and finance agency. Initial results are encouraging and indicate that the simulator is useful for planning and managing ITO activities.

Many artificial life researchers stress the interdisciplinary character of the field. Against such a backdrop, this report reviews and discusses artificial life, as it is depicted in, and as it interfaces with, adjacent disciplines (in particular, philosophy, biology, and linguistics), and in the light of a specific historical example of interdisciplinary research (namely cybernetics) with which artificial life shares many features. This report grew out of a workshop held at the Sixth European Conference on Artificial Life in Prague and features individual contributions from the workshop's eight speakers, plus a section designed to reflect the debates that took place during the workshop's discussion sessions. The major theme that emerged during these sessions was the identity and status of artificial life as a scientific endeavor.

The organisation of living systems is neither random nor regular, but tends to exhibit complex structure in the form of clustering and modularity. Here, we present a very simple model that generates random networks with spontaneous community structure reminiscent of living systems, particularly those involving social interaction. We extend the well-known random geometric graph model, in which spatially embedded networks are constructed subject to a constraint on edge length, in order to capture two key additional features of organic social networks. First, relationships that span longer distances are more costly to maintain. Conversely, relationships between nodes that share neighbours may be less costly to maintain due to social synergy. The resulting networks have several properties in common with those of organic social networks. We demonstrate that the model generates non-trivial community structure and that, unlike for random geometric graphs, densely connected communities do not simply arise as a consequence of an initial locational advantage.

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.

Network theory is increasingly employed to study the structure and behaviour of social, physical and technological systems — including civil infrastructure. Many of these systems are interconnected and the interdependencies between them allow disruptive events to propagate across networks, enabling damage to spread far beyond the immediate footprint of disturbance. In this research we experiment with a model to characterise the configuration of interdependencies in terms of direction, redundancy, and extent, and we analyse the performance of interdependent systems with a wide range of possible coupling modes. We demonstrate that networks with directed dependencies are less robust than those with undirected dependencies, and that the degree of redundancy in inter-network dependencies can have a differential effect on robustness depending on the directionality of the dependencies. As interdependencies between many real-world systems exhibit these characteristics, it is likely that many such systems operate near their critical thresholds. The vulnerability of an interdependent network is shown to be reducible in a cost effective way, either by optimising inter-network connections, or by hardening high degree nodes. The results improve understanding of the influence of interdependencies on system performance and provide insight into how to mitigate associated risks.

It is healthy, I believe, to periodically examine the way in which a field’s activity is supposed to be valuable, both internally, to those within the field itself, but also externally to the wider scientific community in which it is embedded, and ultimately society at large. Otherwise, there may be a tendency for modes or styles of work to lock in and be perpetuated uncritically. However, it is clear that such a process of critique must take care with how it defines what counts as valuable. If the definition is cast too close to the community being examined, there is a risk of swallowing a motivating story that may perhaps be internally consistent and widely accepted within the community, but does not survive scrutiny from a more critical perspective. Eugenic science might be cited as an example. Its value relies on subscribing to a set of background assumptions that should themselves be part of any critique. On the other hand, the value of many scientific activities would be difficult to discern from too remote a vantage point. What exactly is the value to future generations of investing in the study of a particular mollusk, for instance? Here, Webb’s sights are trained on the “synthetic methodology,” and in particular a specific sub-community involved in building systems that are intended to shed light on biological questions. Consequently the sense of value that Webb is concerned with is largely derived from the aims of biological science, which seems fair enough. However, I believe that there is a problem in the scoping here, which I will return to below. In large part, however, I am in agreement with Webb’s line of argument and the useful distinctions that she makes between different motivating stories that can be found throughout the ALife and adaptive behavior literatures. I share her sense that the things being built should be thought of as models, and that they are therefore models of something. This mindset should always make us wary of systems that are presented as artificial worlds worthy of study for their own sake—free-floating models that have been disconnected from the target systems that they were originally motivated by. I agree that the animats built by the community under consideration, whether simulations or fabrications, do instantiate or represent hypotheses about nature, and that, as such, their value is intimately tied up in which hypotheses are represented and to what end. Where animat behavior challenges our ideas in a way that can be empirically tested, it is, or should be, taking part in a scientific cycle of theory– hypothesis–experiment–theory (Di Paolo, Noble, & Bullock, 2000). However, I do not agree with the repeated assertion running through the article that the biological relevance of a model must be cashed out in terms of whether or not it speaks to the properties of a specific animal or species. This position seems to reflect Webb’s own interests in biological mechanisms. How does it manifest itself? In the abstract: “Animat simulations ... represent hypotheses about ... animals”. Later: “But if the results of animat models are to be relevant to biology, then ultimately some claim about the physical mechanisms of real animals must be asserted.” (sec. 4.2). This idea leads Webb to assume that because Beer claims that his work “has important implications for our understanding of perception” it must also be the case that “empirical claims are being made about the world on the basis of the model results” (sec. 3.5). I think this move is made too quickly and is overly narrow.

A working assumption that processes of natural and cultural evolution have tailored the mind to fit the demands and structure of its environment begs the question: how are we to characterize the structure of cognitive environments? Decision problems faced by real organisms are not like simple multiple-choice examination papers. For example, some individual problems may occur much more frequently than others, whilst some may carry much more weight than others. Such considerations are not taken into account when (i) the performance of candidate cognitive mechanisms is assessed by employing a simple accuracy metric that is insensitive to the structure of the decision-maker's environment, and (ii) reason is defined as the adherence to internalist prescriptions of classical rationality. Here we explore the impact of frequency and significance structure on the performance of a range of candidate decision-making mechanisms. We show that the character of this impact is complex, since structured environments demand that decision-makers trade off general performance against performance on important subsets of test items. As a result, environment structure obviates internalist criteria of rationality. Failing to appreciate the role of environment structure in shaping cognition can lead to mischaracterising adaptive behavior as irrational.

Recently, within the biology literature, there has been some interest in exploring the evolutionary function of animal displays through computer simulations of evolutionary processes. Here we provide a critique of an exploration of the evolutionary function of complex symmetrical displays. We investigate the hypothesis that complex symmetrical signal form is the product of a 'hidden preference' inherent in all sensory systems (i.e. a universal sensory bias). Through extending previous work and relaxing its assumptions we reveal that the posited 'hidden preference' for complex symmetry is in reality a preference for homogeneity. The resulting implications for further accounts of the evolutionary function of complex symmetrical patterning are considered.

Complex network approaches have been used to analyse physical or social networks. Previous research has tended to focus on studying single, isolated systems and ignores the fact that many of these systems are developing into a “network of networks”. Over the years these systems have become increasingly interconnected. Due to this interdependence the failure of one network component may propagate across the system of systems, resulting in cascading failure. This research studies interdependent networks from a “system-of-systems” perspective, and a suite of tools have been developed to analyse infrastructure networks and their associated interdependencies. The aim of this research is to facilitate system-scale understanding of infrastructure networks and the implications of interdependencies between them. This understanding could be used to improve the design of infrastructure that is more resilient and adaptable to natural and manmade hazards.

Objectives: Judgements of attractiveness have been shown to influence the character of social interactions. The present study sought to better understand the relationship between perceived attractiveness, perceived sexual health status and condom use intentions in a heterosexual male population.Setting: The study employed an electronic questionnaire to collect all data, during face-to-face sessions.Participants: 51 heterosexual, English-speaking men aged between 18 and 69 years.Outcome measures Men were asked to rate the attractiveness of 20 women on the basis of facial photographs, to estimate the likelihood that each woman had a sexually transmitted infection (STI) and to indicate their willingness to have sex with or without a condom with each woman.Results: The more attractive a woman was judged to be on average, the more likely participants would be willing to have sex with her (pConclusions: Male perceptions of attractiveness influence their condom use intentions; such risk biases could profitably be discussed during sex education sessions and in condom use promotion interventions.

The UK’s population is aging, which presents a challenge as older people are the primary users of health and social care services. We present an agent-based model of the basic demographic processes that impinge on the supply of, and demand for, social care: namely mortality, fertility, health-status transitions, internal migration, and the formation and dissolution of partnerships and households. Agent-based modeling is used to capture the idea of “linked lives” and thus to represent hypotheses that are impossible to express in alternative formalisms. Simulation runs suggest that the per-taxpayer cost of state-funded social care could double over the next forty years. A key benefit of the approach is that we can treat the average cost of state-funded care as an outcome variable, and examine the projected effect of different sets of assumptions about the relevant social processes.

We introduce a distinction between algorithm performance and algorithm competence and argue that bio-inspired computing should characterize the former rather than the latter. To exemplify this, we explore and extend a bio-inspired algorithm for collective construction influenced by paper wasp behavior. Despite its being provably general in its competence, we demonstrate limitations on the algorithm's performance. We explain these limitations, and extend the algorithm to include pheromone-mediated behavior typical of termites. The resulting hybrid waspmite algorithm shares the generality of the original wasp algorithm, but exhibits improved performance and scalability.

A game-theoretic model of handicap signalling over a pair of signalling channels is introduced in order to determine when one channel has an evolutionary advantage over the other. The stability conditions for honest handicap signalling are presented for a single channel and are shown to conform with the results of prior handicap signalling models. Evolutionary simulations are then used to show that, for a two-channel system in which honest signalling is possible on both channels, the channel featuring larger advertisements at equilibrium is favoured by evolution. This result helps to address a significant tension in the handicap principle literature. While the original theory was motivated by the prevalence of extravagant natural signalling, contemporary models have demonstrated that it is the cost associated with deception that stabilises honesty, and that the honest signals exhibited at equilibrium need not be extravagant at all. The current model suggests that while extravagant and wasteful signals are not required to ensure a signalling system's evolutionary stability, extravagant signalling systems may enjoy an advantage in terms of evolutionary attainability.

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.

Different animals employ different strategies for sampling sensory data. In animals that can see, differences in sampling strategy manifest themselves as differences in field of view and in spatially variant sampling (so-called foveal vision). In analyzing adaptive behavior in animals, or attempting to design autonomous robots, mechanisms for exploring variations in sensory sampling strategy will be required. This article describes our work exploring a minimal system for investigating the effects of variations in patterns of sensory sampling. We have reimplemented Wilson's animat (Wilson, 1985b) and then experimented with altering its sensory sampling pattern (i.e., its sensory field). Empirical results are presented which demonstrate that alterations in the sensory field pattern can have a significant effect on the animat's observable behavior. Analysis of our results involves characterizing the interaction between the animat's sensory field and the environment within which the animat resides. We found that the animat's observed behavior can, at least in part, be explained by the animat cautiously moving in a manner that attempts to maximize the generation of new information from the environment over time. We demonstrate that similar explanations can be offered for behavioral patterns in real animals. The article concludes with a discussion of the generality of the results and reflections on the prospects for further work.

Recent artificial neural networks for machine learning have exploited transient dynamics around globally stable attractors, inspired by the properties of cortical microcolumns. Here we explore whether similarly constrained neural network controllers can be exploited for embodied, situated adaptive behaviour. We demonstrate that it is possible to evolve globally stable neurocontrollers containing a single basin of attraction, which nevertheless sustain multiple modes of behaviour. This is achieved by exploiting interaction between environmental input and transient dynamics. We present results that suggest that this globally stable regime may constitute an evolvable and dynamically rich subset of recurrent neural network configurations, especially in larger networks. We discuss the issue of scalability and the possibility that there may be alternative adaptive behaviour tasks that are more `attractor hungry'.

Software agent technology is currently based on fixed ontologies and languages, hand-crafted for a particular application. The advent of massively distributed systems however calls for not only a common language between all agents involved but also the ability to autonomously adapt and form concepts about novel experiences and events. We propose a method by which agents can autonomously form new concepts grounded in their own experience. This is an improvement on previous approaches because it can tackle a much wider range of conceptual types and provides an efficient, accurate representation which can be used in a rule based system. Furthermore, our method allows an agent to simultaneously learn new concepts and the rules to govern its behaviour whilst providing a more robust system which adapts well to both new and changeable environments.

The "order for free" exhibited by some classes of system has been exploited by natural selection in order to build systems capable of exhibiting complex behaviour. Here we explore the impact of one ordering constraint, spatial embedding, on the dynamical complexity of networks. We apply a measure of functional complexity derived from information theory to a set of spatially embedded network models in order to make some preliminary characterisations of the contribution of space to the dynamics (rather than mere structure) of complex systems. Although our measure of dynamical complexity hinges on a balance between functional integration and segregation, which seem related to an understanding of the small-world property, we demonstrate that smallworld structures alone are not enough to induce complexity. However, purely spatial constraints can produce systems of high intrinsic complexity by introducing multiple scales of organisation within a network.

Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In this paper we introduce a model framework to analyze the mediation of network structure by spatial embedding; specifically, we model connectivity as dependent on the distance between network nodes. Our spatially embedded random networks construction is not primarily intended as an accurate model of any specific class of real-world networks, but rather to gain intuition for the effects of spatial embedding on network structure; nevertheless we are able to demonstrate, in a quite general setting, some constraints of spatial embedding on connectivity such as the effects of spatial symmetry, conditions for scale free degree distributions and the existence of small-world spatial networks. We also derive some standard structural statistics for spatially embedded networks and illustrate the application of our model framework with concrete examples.

Utility computing exemplifies a novel kind of solution to the increasing scale and complexity of modern IT systems. Here, the “on-demand” provisioning of computing resources is managed via a population of independent software agents that query and negotiate with one another in an open system of resource providers and consumers that has no fixed organisation and is free to change and grow organically. Where centralised executive control of agent activity is relaxed or removed, such systems have the potential to deliver scalable, flexible computing. However, major design and control challenges must be overcome if multi-agent systems are to achieve efficient, decentralised resource allocation that delivers reliable and robust performance. In this paper we introduce a minimally complex multi-agent system, where individual agents rely on simple, local strategies to perform resource allocation. We explore the relationship between local and global behaviour as system size, load, heterogeneity and reliability are varied. We identify generic feedbacks underlying system behaviour that must be balanced if decentralised control is to become an effective technique for preserving stable functionality across utility computing infrastructures.