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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.

Typically, collective behaviour research has tended to focus on behaviour arising in populations of homogeneous agents. However, humans, animals, robots and software agents typically exhibit various forms of heterogeneity. In natural systems, this heterogeneity has often been associated with improved performance. In this work, we ask whether spatial interference within a population of co-operating mobile agents can be managed effectively via conflict resolution mechanisms that exploit the population’s intrinsic heterogeneity. An idealised model of foraging is presented in which a population of simulated ant-like agents is tasked with making as many journeys as possible back and forth along a route that includes tunnels that are wide enough for only one agent. Four conflict resolution schemes are used for determining which agent has priority when two or more meet within a tunnel. These schemes are tested in the context of heterogeneous populations of varying size. The findings demonstrate that a conflict resolution mechanism that exploits agent heterogeneity can achieve a significant reduction in the impact of spatial interference. However, whether or not a particular scheme is successful depends on how the heterogeneity that it exploits is implicated in the population-wide dynamics that underpin system-level performance.

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.

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.

The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex 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.

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self- regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms.