Wolfram famously developed a four-way classification of CA behaviour, with Class IV containing CAs that generate complex, localised structures. However, finding Class IV rules is far from straightforward, and can require extensive, time-consuming searches. This work presents a Convolutional Neural Network (CNN) that was trained on visual examples of CA behaviour, and learned to classify CA images with a high degree of accuracy. I propose that a refinement of this system could serve as a useful aid to CA research, automatically identifying possible candidates for Class IV behaviour and universality, and significantly reducing the time required to find interesting CA rules.

Infiltrating a swarm of artificial or living agents using a single monitoring robot could allow for the assessment of their swarm rules and parameters without the need for any external infrastructure. The inferred swarm model could then be used to control these swarms, for example to guide them to safe areas. In this study we introduce a scheme for autonomous artificial agents to extract knowledge about the interactions within a swarm of interest. By infiltrating the swarm of interest with a monitoring robot and constantly measuring the distance between the infiltrator and its nearest neighbour, the repulsion radius of the swarm agents can be estimated. Though this method works for a range of tested parameters, it is still limited to a specific model of interaction.

We consider an iterated model of agents playing a two-player game on a graph. The agents change their strategies as the game progresses based on anticipated payoffs. Using only the time series of the agents’ strategies, we determine the pairwise mutual information between all agents in the graph, and use these values as a predictors of the graph’s topology. From this, we assess the influence of various model parameters on the effectiveness of mutual information at recovering the actual causal structure. It is found that the degree to which the functional connectivity reflects the actual causal structure of the graph strongly depends on which game is being played and how the agents are changing their strategies. Further, there is evidence that the edge density of the graph may also have some impact on the accuracy of the inferred network. This approach allows us to better connect the dynamics of the systems under study with the difference in their functional and actual connectivity, and has broad implications for the interpretation and application of information-based network inference. The methods and analyses described can be generalized and applied to other types of network models.

Artificial Life models and algorithms are informed by natural and biological processes and phenomena. Artificial Life finds particular use in simulating large, complex systems such as large scale ecosystems or social networks, where the interaction between system entities may give rise to emergent behaviours. Despite the increasing popularity and ubiquitous nature of complex systems, the extent of which artificial life approaches are considered in complex systems modelling and their application across complex systems domains is still unclear. To better understand the overlap between artificial life and complex systems, we conducted a systematic literature review of last decade’s artificial life research that had a complex system focus. We performed an automated search of all relevant databases and identified 538 initial papers, with 194 in the candidate set, resulting in 115 primary studies. Our results show that the three most frequent application domains are simulation, followed by social modelling, and biological modelling. We find a plethora of paradigms that can be broadly classified into three main categories, namely, biological, social, and hybrid. We identify the artificial life paradigms that are used to generate the most common complex systems properties as well as a number of research challenges that are critical for the growth of both artificial life and complex systems modelling.

In the 1950s, the famous cyberneticists Gordon Pask and Stafford Beer conducted a series of remarkable electrochemical deposition experiments. By applying an electric potential across electrodes submerged in an acidic solution of ferrous sulfate, they could bias the growth of electrochemical deposition so as to form functional structures including sensory structures capable of distinguishing between different sounds. Unfortunately, the details of their apparatus and methods are unavailable. As a consequence, their experiment has not been replicated, and the precise mechanisms underlying their results remain unknown. As preliminary steps toward recreating their remarkable results, this paper presents a new computational model that simulates the growth and decay of dendritic structures similar to those investigated by Beer & Pask. We use this model to demonstrate a plausible mechanism through which an electrochemical system of this kind could respond to a reinforcement signal. More specifically, we investigate three strategies for varying the applied electrical current so as to guide the formation of structures into target forms. Each presented strategy succeeds at influencing the growth of the structure, with the most successful strategy involving a ‘constant-current’ feedback mechanism combined with an externally driven oscillation. In the discussion, we compare the adaptation of these structures with various biological adaptive processes, including evolution and metabolism-based adaptive behaviour.

Sexual selection is a powerful yet poorly understood evolutionary force. Research into sexual selection, whether biological, computational, or mathematical, has tended to take a top-down approach studying complex natural systems. Many simplifying assumptions must be made in order to make these systems tractable, but it is unclear if these simplifications result in a system which still represents natural ecological and evolutionary dynamics. Here, we take a bottom-up approach in which we construct simple computational systems from subsets of biologically plausible components and focus on examining the underlying dynamics resulting from the interactions of those components. We use this method to investigate sexual selection in general and the sexy sons theory in particular. The minimally necessary components are therefore genomes, genome-determined displays and preferences, and a process capable of overseeing parent selection and mating. We demonstrate the efficacy of our approach (i.e we observe the evolution of female preference) and provide support for sexy sons theory, including illustrating the oscillatory behavior that developed in the presence of multiple costly display traits.

We present recent results concerning the attractor landscape, memory, hysteresis and computation that can emerge in simple convective obstacle flows. In these systems a single phase fluid is heated from below and cooled from above. Small obstacles (one or two) are placed on the horizontal mid plane of the system and extract some fraction of the fluid’s horizontal or vertical momentum. Horizontal momentum sinks tend to attract convection plumes. Vertical momentum sinks are bistable; the obstacle will either align with a convection cell centre or convection plume depending on initial conditions and the history of the system. The resulting attractor landscape can be exploited to produce a single bit memory or even elementary Boolean logic.

An object-oriented combinator chemistry was used to construct an artificial organism with a system architecture possessing characteristics necessary for organisms to evolve into more complex forms. This architecture supports modularity by providing a mechanism for the construction of executable modules called methods that can be duplicated and specialized to increase complexity. At the same time, its support for concurrency provides the flexibility in execution order necessary for redundancy, degeneracy and parallelism to mitigate increased replication costs. The organism is a moving, self-replicating, spatially distributed assembly of elemental combinators called a roving pile . The pile hosts an asynchronous message passing computation implemented by parallel sub-processes encoded by genes distributed through out the pile like the plasmids of a bacterial cell.

Focusing on the challenge of fostering the self-assembly of socio-technical networks, we present the application of Morphogenetic Engineering principles in this domain to a 2D spatial case study involving a team of first responders. Our model and simulation illustrate how members of a rescue team could be guided via hand-held devices toward better coordination and positioning at appropriate locations, based on peer-to-peer communication and local landmarks in the environment (such as incidents or exits), without the need for a centralised control centre. Using Raspberry Pi devices, we illustrate this scenario in various situations that require quick decision-making to control and manage. Our work suggests the possibility of novel forms of bottom-up self-organisation among groups of users and machines, in contrast to top-down imposed hierarchies and policies.

Biochemical reactions underlie all living processes. Like many systems, their web of interactions is difficult to fully capture and quantify with simple mathematical objects. Nonetheless, a huge volume of research has suggested many real-world systems–including biochemical systems–can be described simply as ‘scale-free’ networks, characterized by power-law degree distributions. More recently, rigorous statistical analyses upended this view, suggesting truly scalefree networks may be rare. We provide a first application of these newer methods across two distinct levels of biological organization: analyzing an ensemble of biochemical reaction networks generated from 785 ecosystem-level metagenomes and 1082 individual-level genomes (representing all domains of life). Our results confirm only a few percent of biochemical networks meet the criteria necessary to be more than super-weakly scale-free. We perform distinguishability tests across individual and ecosystem-level biochemical networks and find there is no sharp transition in the organization of biochemistry across distinct levels of the biological hierarchy–a result that holds across network projections. This suggests the existence of common organizing principles operating across different levels of biology, which can best be elucidated by analyzing all possible coarse-grained projections of biochemistry in tandem across scales.