Control of chaos methods have been successfully applied to many small, closed, chaotic systems; however, there is a difficulty in expanding them to be applicable to large, open, chaotic systems. In this paper, a novel method of manipulating chaotic systems using hybrid cellular automata is proposed and evaluated. Four experiments are performed. The first experiment examines hybrid cellular automata in the presence of perturbations to the initial conditions. The second experiment analyzes the relationship between the total number of perturbations and the certainty that hybrid states will change. The third experiment analyzes the reachability of hybrid systems using complexity measures. The fourth experiment analyzes how phase transitions are affected by high-impact hybrid schemes.

Cellular automata (CAs) are used to model rule-based evolutionary systems with standard CAs applying unitary, fixed rules to an entire generation at a time. A sequential updating asynchronous cellular automaton (CA) with more than one rule for each input sequence is studied. These multiway sequential CAs (MSCAs) can model complex systems with multiple branching rule sets where changes propagate through the system. This paper examines the case of one-dimensional, two-cell, two-branch MSCAs in order to better understand their structure and the impact of parameters. The complete set of 1296 M-type rule sets possible for this type of multiway sequential CA (MSCA) is applied to a full set of 32 initial conditions, representing all possibilities of a six-cell initial condition, generating 41472 state graphs. Machine learning is used to classify a subset of these state graphs into 10 classes. Analytical data enables characterization of these classes of graphs and investigation of the role of rule sets in these state graphs. Target distribution analysis of the M-type rule sets is performed within each class of graphs to tease out intrinsic characteristics of the classes.

Computing the configuration of any one-dimensional cellular automaton at generation n can be accelerated by constructing and running a composite rule with a radius proportional to log n. The new automaton is the original one, but with its local rule function composed with itself. Consequently, the asymptotic time complexity to compute the configuration of generation n is reduced from O(n2)-time to O(n2/logn)but with O(n2/(logn)3)-space, demonstrating a time-memory tradeoff. Experimental results are given in the case of rule 30.

The agent-based model (ABM) and multi-agent system (MAS) computational approaches have gained significant attention in various scientific disciplines. While these terms are sometimes used interchangeably, an ABM and an MAS share common principles, but they differ in their underlying philosophies, modeling approaches and applications. This review paper aims to elucidate the differences between the ABM and MAS approaches, highlighting their individual strengths and exploring the potential synergies. Understanding these distinctions is crucial for researchers and practitioners seeking to employ these approaches effectively in their respective fields.

Among the major applications of network science, significant attention has been paid to modeling smart cities and mobility. Modeling cities and urban systems is also important from the perspective of policy-making toward sustainable development. By representing cities or areas in a city as nodes and the population flow among them using edges, we try to build network models that capture the essence of urban systems and growth patterns. Our simulations are based on predefined parameters and specific rules to govern area and population growth. We primarily model area expansion and population growth dynamics using two grid-based models. The first model uses real-world data, whereas the second model is inspired by cellular automata and implemented in two versions: random and contiguous. We also computed complexity metrics based on approximate entropy and found that the complexity values associated with area-population growth dynamics were always higher in the random cases as compared to their contiguous counterparts. We have also shown validation results and shared interesting insights based on our simulation runs.

Interactions among different elements of complex networks are organized in a structured manner. The collective behavior of the elements of these networks is organized according to community structure. Several methods have been defined to automatically detect these substructures in the field known as community discovery. Most of the methods have been applied to static or aggregated data. Recently the identification of evolving communities has gained more attention. Studying the relations among individuals yields insights on how communities form and evolve, but there are some limits that should be enforced to respect individuals’ privacy while sharing and collecting their data. Privacy-protection techniques have been commonly applied to static data, while there are few methods that work on dynamic data. Recently, there have been some approaches to protect dynamic graphs with local edge-differential privacy that have been tested for community discovery applications. However, the evolution of the communities over time has not been evaluated on the privacy-protected data. We test the utility considering community discovery and evolution in time-varying networks for such local-edge-ε-differential privacy methods. We show empirically how these algorithms can provide privacy while preserving the community life cycles, for their privacy-aware study.

This paper uses a theoretical approach to explore the applicability of a two-dimensional cellular automaton based on melodic and harmonic intervals in random arrays of musical notes. Our aim is to explore alternative uses for a cellular automaton in the musical context for better understanding musical creativity. We use the complex systems and humanities approaches as a framework for capturing the essence of creating music based on rules of music theory. Findings suggest that such rules matter for generating large-scale patterns of organized notes. Therefore, our formulation provides a novel approach for understanding and replicating aspects of musical creativity.

Social media data is an efficient means to understand human mobility through the spatial and temporal patterns of the users. Those patterns can help us discover and define mobility communities, which we specify as a group of users sharing the same spatiotemporal patterns. In this paper, we focus on a particular social media platform, X, formerly known as Twitter. X features geolocalized posts, also known as geolocalized tweets, that can be gathered through the platform’s API. Our goal is to gather and analyze geolocalized tweets from two different cities, namely Brasilia and London, over a one-year period, to extract common spatiotemporal patterns among users and carry out a comparative analysis between cities.

According to the Baldwin effect, learning can guide evolution. This does not suppose that information about what has been learned is transferred back into the genetic code: in the Baldwin effect, complex multi-gene characteristics are discovered through learning but acquired through standard selectionist evolutionary processes. Learning serves to improve the search by giving value to a partial, and otherwise useless, subset of the required genes. An elegant and concrete treatment of the Baldwin effect is given in “How Learning Can Guide Evolution,” a 1987 paper by G. E. Hinton and S. J. Nowlan [1]. This includes a simple but revealing simulation illustrating the effect. As a commentary on that paper, a similar simulation is used here to demonstrate that cooperation can also guide evolution. Like learning, cooperation has a clear benefit to survival, but what is proposed here is a small addition: that cooperation, like learning in the Baldwin effect, can also allow complex characteristics to be discovered and acquired much faster than they otherwise would. This suggests an additional benefit of social behavior and suggests that social animals have a broader evolutionary path toward some complex adaptations.