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.

The global COVID-19 pandemic has given rise to an unprecedented level of complex societal challenges to the humanity. Meanwhile, it also has created a huge demand for scientific expertise in modeling, simulation and analysis of epidemic and other socio-economical dynamics, to which Artificial Life researchers can make great contributions. This extended abstract presents a brief overview of projects the author has been working on in response to COVID-19 since early 2020, summarizing the lessons learned and identifying unique skills and abilities of Artificial Life researchers that have potential to help address various societal challenges.

Extreme ideas and opinions are commonly seen growing in many aspects of today's society, ranging from political ideology to healthcare choices and from dietary preferences to technological innovation. Such a trend may be understood as an outcome of a spontaneous dynamical process driven by the recent advancement of information communication technology that allows people to preferentially select their information sources. Here we study, using an agent-based model of adaptive social network dynamics, how extreme ideas may arise in society in which individuals simply try to conform to social norm within their social neighborhood. Our model assumes that each node gradually assimilates its state to local social norm, i.e., the average of its in-neighbors’ states, while also changing edge weights based on their states. Numerical simulations revealed that, when individuals tend to practice homophily by strengthening their ties selectively to neighbors with similar states, there tends to be many extreme ideas emerging in society while the network topology tends to become fragmented. Such outcomes are mitigated, however, when individuals also practice novelty-seeking behavior by increasing attention to neighbors whose ideas do not conform to the local social norm. These results paint a paradoxical picture of complex social processes — society produces difference when individuals seek sameness, or society reaches sameness when individuals seek difference.

The El Farol Bar Problem presents a coordination problem where agents must independently decide whether or not to utilize a limited resource, a bar. Given a set of decision-making strategies, the agents learn over time which strategies perform better and choose accordingly. We take a unique approach to analyzing the problem by focusing on how the distribution of utilized strategies shifts over time. To emphasize this system behavior, we create an agent-based model with a small number of decision-making strategies for the agents to use. We analyze the problem for when agents are allowed to change strategies and when they are not. The change in distribution over time is tracked, along with overall agent happiness and attendance, then compared. We find that systems with the same strategy set but different distributions tend to perform differently when agents are not allowed to switch from their initial strategy, but gravitate towards an attractor in the strategy space when switching is allowed. We also find that the approach may still work when some of the constraints of the original problem are relaxed.

Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a Lévy flight. A Lévy flight is a kind of random walk but it is composed of many small movements with a few big movements. Here, we developed an agent-based model in a square lattice where agents perform Lévy flights depending on the fraction of neighboring defectors. We focus on how the sensitivity to defectors when performing Lévy flights promotes the evolution of cooperation. Results of evolutionary simulations showed that cooperation was most promoted when the sensitivity to defectors was moderate. As the population density became larger, higher sensitivity was more beneficial for cooperation to evolve.

One of the research questions in ALife that could contribute greatly to social sustainability issues is how dynamic metastates of a complex system may be sustained through continual adaptive changes, or suppleness (Bedau, 1998). The idea of sustainability by suppleness is fundamentally different from conventional ideas of sustainability by robustness or resilience, and it is directly linked to open-endedness , a topic that has recently attracted significant attention in the ALife community (Taylor et al., 2016). Understanding and implementing mechanisms of suppleness and open-endedness may provide novel perspectives of many of today’s socio-economic, socio-ecological and socio-technological problems that call for new strategies to cope with inevitable environmental/contextual changes. This short essay provides a non-exhaustive list of research questions on this topic and encourages ALife researchers to play a leading role in this interdisciplinary collaboration endeavor.

We present a theoretical framework that mathematically formulates the evolutionary dynamics of organism-environment couplings using graph product multilayer networks, i.e., networks obtained by “multiplying” factor networks using some graph product operator. In this framework, one factor network represents different options of environments and their mutual physical reachability, and another factor network represents possible types of organisms and their mutual evolutionary reachability. The organism-environment coupling space is given by a Cartesian product of these two factor networks, and the nodes of the product network represent specific organism-environment combinations. We studied a simple evolutionary model using a reaction-diffusion equation on this organism-environment coupling space. We numerically calculated correlations between the inherent fitness of organisms and the actual average fitness obtained from the graph product-based evolutionary model, varying the spatial diffusion rate while keeping the type diffusion rate small. Results demonstrated that, when the spatial diffusion is sufficiently slow, the correlation between inherent and actual fitnesses drops significantly, where it is no longer valid to assume that fitness can be attributed only to organisms.

We present multilayer gene regulatory networks (GRNs) consisting of an intercellular layer and an intracellular layer. A network in an intercellular layer represents interactions between cells, and a network in an intracellular layer indicates interactions between genes. All the nodes of an intercellular network have identical random Boolean networks (RBNs) as intracellular GRNs. We introduce genetic perturbations (e.g., mutations) to the intracellular GRNs. Varying the properties of the intracellular GRNs from ordered, through critical, to chaotic regimes, we investigate how criticality of GRNs affects the robustness and evolvability of multilayer GRNs against the genetic perturbations. We found that the robust and evolvable multilayer GRNs were generated with the highest probability when intracellular GRNs were critical. Based on our findings, we conclude that the criticality of GRNs plays an important role in determining the robustness and evolvability of multilayer GRNs at a hierarchical level.

Self-organization has been an important concept within a number of disciplines, which Artificial Life (ALife) also has heavily utilized since its inception. The term and its implications, however, are often confusing or misinterpreted. In this work, we provide a mini-review of self-organization and its relationship with ALife, aiming at initiating discussions on this important topic with the interested audience. We first articulate some fundamental aspects of self-organization, outline its usage, and review its applications to ALife within its soft, hard, and wet domains. We also provide perspectives for further research.

We studied the long-term dynamics of evolutionary Swarm Chemistry by extending the simulation length ten-fold compared to earlier work and by developing and using a new automated object harvesting method. Both macroscopic dynamics and microscopic object features were characterized and tracked using several measures. Results showed that the evolutionary dynamics tended to settle down into a stable state after the initial transient period, and that the extent of environmental perturbations also affected the evolutionary trends substantially. In the meantime, the automated harvesting method successfully produced a huge collection of spontaneously evolved objects, revealing the system’s autonomous creativity at an unprecedented scale.

We present morphogenetic systems using Kauffman’s NK random Boolean network (RBN) as a gene regulatory network (GRN) and spring-mass-damper kinetics for cellular movements. We investigate what role the criticality of GRNs plays in morphogenetic pattern formation. Our model represents a cell aggregation, where all cells have identical GRNs. The properties of GRNs are varied from ordered, through critical, to chaotic by node in-degree K. For cellular behaviors, cell fates, specifically, proliferation, differentiation, apoptosis, and quiescence, are assigned to the attractors of RBNs. We obtained diverse morphologies from our morphogenetic systems. We found that nontrivial spatial patterns were generated most frequently when the GRNs were critical. Our finding indicates that the criticality of GRNs facilitates the formation of nontrivial morphologies in GRN-based morphogenetic systems.

Cooperation is ubiquitous in every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. Until recently, it has been difficult to predict whether cooperation can evolve at a network (population) level. To address this problem, Pinheiro et al. proposed a numerical metric, called Average Gradient of Selection (AGoS) in 2012. AGoS can characterize and forecast the evolutionary fate of cooperation at a population level. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we analyzed the evolution of cooperation using AGoS where mutation may occur to strategies of individuals in networks. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the fraction of cooperators and network structures. Moreover, we found that mutation affects the fitness of cooperation differently on different social network structures.

Gene regulatory network (GRN)-based morphogenetic systems have recently attracted an increasing attention in artificial life and morphogenetic engineering research. However, the relationship between microscopic properties of intracellular GRNs and collective properties of morphogenetic systems has not been fully explored yet. Thus, we propose a new GRN-based framework to elucidate how critical dynamics of GRNs in individual cells affect cell fates such as proliferation, apoptosis, and differentiation in resulting morphogenetic systems. Our model represents an aggregation of cells, where each cell has a GRN in it. We used Kauffman's NK Boolean networks for GRNs. Specifically, we randomly assigned three cell fates to the attractors. Varying the properties of GRNs from ordered, through critical, to chaotic regimes, we observed the process that cells are aggregated. We found that the criticality of a GRN made an optimal partition of basins of attraction, which led to a maximum balance between cell fates. Based on the result, we can conclude that the criticality of a GRN is an important controller to determine the frequencies of cell fates in morphogenetic systems.