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.

Many organisms such as planaria, axolotls and deer exhibit prodigious regenerative abilities, being capable of regenerating complex organs or entire body plans. An understanding of how these organisms store and modify their morphological patterning information is necessary to identify modes of control and intervention. Insight into this process is key to the development of novel biomedical applications. In this work, we present the CANN( k ) model: an abstract computational model of pattern regeneration which couples an artificial neural network (ANN) with a k -color cellular automaton (CA). The ANN provides a global information processing system which generates state-dependent update rules for the CA. The CANN( k ) models are constructed to generate target patterns which are stable under perturbations of the pattern. We generate ensembles of CANN(4) models for each of the 4-color patterns, assess their sensitivity to changes of the ANN structure. This provides a novel model for understanding the important biological phenomenon of neural control of cellular morphogenesis in development or regeneration.

Collective decision-making systems rely on many agents to gather, process and exchange information to arrive at a group decision. Critical to group success is the transfer of information among agents and between agents and their environment. Without information transfer, no consensus can be achieved. Yet, the role of individual rules in determining information transfer at the group level is poorly understood. With the aim to shed a light on how the decision mechanism of individuals affects information transfer in collectives, we analyze the information landscape of two decision-making strategies: one based on the majority rule and one based on the voter model. For each strategy, we consider a binary site-selection scenario and use transfer entropy to measure the flow of information in a spatial, multi-agent system. We find that information transferred among agents is dependent on the decision mechanism, increases with the time necessary to make a collective decision, and is loosely modulated by the uncertainty of the final outcome. This is the first study that compares collective decision making mechanisms through the lens of information dynamics. Although this approach is limited to simulated agents, similar approaches could in principle be used to study collective decisions in biological systems.