This paper describes the results of an experiment in which human participants were required to detect degraded robot swarm behaviour and classify it as arising from either faulty or malicious robot activity in an idealised simulation of a multi-agent search and rescue task. The accuracy of participant judgements was influenced by the nature of the degradation, and between-participant differences in the extent to which they interacted with the swarm did not significantly influence their accuracy. It was found that detecting and classifying swarm degradation are challenging tasks that are likely to be strongly sensitive to task setting and will tend to require careful swarm system design and specific operator training.

The iterated learning model is an agent-based model of language evolution notable for demonstrating the emergence of compositional language. In its original form, it modelled language evolution along a single chain of teacher-pupil interactions; here we modify the model to allow more complex patterns of communication within a population and use the extended model to quantify the effect of within-community and between-community communication frequency on language development. We find that a small amount of between-community communication can lead to population-wide language convergence but that this global language amalgamation is more difficult to achieve when communities are spatially embedded.

An existing model of opinion dynamics on an adaptive social network is extended to introduce update policy heterogeneity , representing the fact that individual differences between social animals can affect their tendency to form, and be influenced by, their social bonds with other animals. As in the original model, the opinions and social connections of a population of model agents change due to three social processes: conformity, homophily and neophily. Here, however, we explore the case in which each node’s susceptibility to these three processes is parameterised by node-specific values drawn independently at random from some distribution. This introduction of heterogeneity increases both the degree of extremism and connectedness in the final population (relative to comparable homogeneous networks) and leads to significant assortativity with respect to node update policy parameters as well as node opinions. Each node’s update policy parameters also predict properties of the community that they will belong to in the final network configuration. These results suggest that update policy heterogeneity in social populations may have a significant impact on the formation of extremist communities in real-world populations.

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.

The ability of European badgers to establish communal latrines at their territory boundaries is a well-known but poorly understood example of group-level biological organisation. To what extent might we expect it to arise via self-organisation rather than as the result of specific adaptations? This paper replicates and extends a model of badger foraging and territoriality to include defecation, fcotaxis and overmarking behaviours, and shows that communal boundary latrines arise spontaneously through stigmergy in both territorial and non-territorial badgers, with no need for specific cognitive or behavioural adaptations such as spatial memory, or individual recognition. The model suggests that fcotaxis and overmarking behaviours are necessary for boundary latrine formation, that culling has little effect on the prevalence of fcal sites (implicated in the spread of bovine tuberculosis in the UK), and that the spatial micro-structure of the environment is significant to the self-organisation process.

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.

Policy-relevant scientific models are typically expected to make empirically valid predictions about policy-relevant problems. What are the consequences of shaping our science-policy interface in this way? Here, it is argued that the theoretically insecure simulation modelling pioneered within artificial life is emblematic of an important alternative approach with significance for policy-relevant modelling.

We demonstrate the emergence of spontaneous temperature regulation by the combined action of two sets of dissipative structures. Our model system comprised an incompressible, non-isothermal fluid in which two sets of Gray-Scott reaction diffusion systems were embedded. We show that with a temperature dependent rate constant, self-reproducing spot patterns are extremely sensitive to temperature variations. Furthermore, if only one reaction is exothermic or endothermic while the second reaction has zero enthalpy, the system shows either runaway positive feedback, or the patterns inhibit themselves. However, a symbiotic system, in which one of the two reactions is exothermic and the other is endothermic, shows striking resilience to imposed temperature variations. Not only does the system maintain its emergent patterns, but it is seen to effectively regulate its internal temperature, no matter whether the boundary temperature is warmer or cooler than optimal growth conditions. This thermal homeostasis is a completely emergent feature.

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.