Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate of change -- irrespective of magnitude -- may also lead to system state changes due to a phenomenon known as a rate-induced transition (RIT). This study presents a novel framework that captures RITs in CASs through a local model and a network extension where each node contributes to the structural adaptability of others. Our findings reveal how RITs occur at a critical environmental change rate, with lower-degree nodes tipping first due to fewer connections and reduced adaptive capacity. High-degree nodes tip later as their adaptability sources (lower-degree nodes) collapse. This pattern persists across various network structures. Our study calls for an extended perspective when managing CASs, emphasizing the need to focus not only on thresholds of external conditions but also the rate at which those conditions change, particularly in the context of the collapse of surrounding systems that contribute to the focal system's resilience. Our analytical method opens a path to designing management policies that mitigate RIT impacts and enhance resilience in ecological, social, and socioecological systems. These policies could include controlling environmental change rates, fostering system adaptability, implementing adaptive management strategies, and building capacity and knowledge exchange. Our study contributes to the understanding of RIT dynamics and informs effective management strategies for complex adaptive systems in the face of rapid environmental change.

The Self-Optimization (SO) model is a useful computational model for investigating self-organization in "soft" Artificial life (ALife) as it has been shown to be general enough to model various complex adaptive systems. So far, existing work has been done on relatively small network sizes, precluding the investigation of novel phenomena that might emerge from the complexity arising from large numbers of nodes interacting in interconnected networks. This work introduces a novel implementation of the SO model that scales as $\mathcal{O}\left(N^{2}\right)$ with respect to the number of nodes $N$, and demonstrates the applicability of the SO model to networks with system sizes several orders of magnitude higher than previously was investigated. Removing the prohibitive computational cost of the naive $\mathcal{O}\left(N^{3}\right)$ algorithm, our on-the-fly computation paves the way for investigating substantially larger system sizes, allowing for more variety and complexity in future studies.

Recently, Dr. Ben Goertzel, CEO of SingularityNET convened the "COVID-19 Summit" to bring veterans in AI and Data Science researchers with epidemiologists, front-line doctors, and policymakers to look at how we handled the situation so far and what are the expectations going forward. One of the main themes from this Summit was the need for complex systems models such as agent-based models to inform policy. During this pandemic, at times, every policymaker around the world felt they were going into this pandemic without the information that they needed, even though we've dealt with other outbreaks such as SARS, MERS, etc.. The combined power of artificial intelligence and agent-based models in a Complex Adaptive System can give policymakers eyes, ears, and additional intelligence that can add transparency to the decision making process. Due to the technical nature of this topic, Dr. Deborah Duong, Director for AI Development at Rejuve and Director of Network Analytics at SingularityNET, gave a talk to explain the power of agent-based models combined with artificial intelligence, its usage, and the information it can provide policymakers and practical professionals in the field.

"With the growing interest in complex adaptive systems, artificial life, swarms and simulated societies, the concept of "collective intelligence" is coming more and more to the fore. The basic idea is that a group of individuals (e.g. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules." A collective mental map is developed basically by superposing a number of individual mental maps. There must be sufficient diversity among these individual maps to cover an as large as possible domain, yet sufficient redundancy so that the overlap between maps is large enough to make the resulting graph fully connected, and so that each preference in the map is the superposition of a number of individual preferences that is large enough to cancel out individual fluctuations.

We introduce a framework to study the effective objectives at different time scales of financial market microstructure. The financial market can be regarded as a complex adaptive system, where purposeful agents collectively and simultaneously create and perceive their environment as they interact with it. It has been suggested that multiple agent classes operate in this system, with a non-trivial hierarchy of top-down and bottom-up causation classes with different effective models governing each level. We conjecture that agent classes may in fact operate at different time scales and thus act differently in response to the same perceived market state. Given scale-specific temporal state trajectories and action sequences estimated from aggregate market behaviour, we use Inverse Reinforcement Learning to compute the effective reward function for the aggregate agent class at each scale, allowing us to assess the relative attractiveness of feature vectors across different scales. Differences in reward functions for feature vectors may indicate different objectives of market participants, which could assist in finding the scale boundary for agent classes. This has implications for learning algorithms operating in this domain.

A Learning System Based on Genetic Adaptive Algorithms.

Does predictability provide an overriding concept and perhaps a metric for evaluating when LAWS are acceptable or when they might be unacceptable under international humanitarian law? Arguably, if the behavior of an autonomous weapon is predictable, deploying it might be considered no different from, for example, launching a ballistic missile. This, of course, presumes that we can know how predictable the behavior of a specific autonomous weapon will be. Over the past two years, this body has focused upon ethical and legal challenges to LAWS. In addition, there are contentions that autonomous weaponry will fail to perform as expected, will behave unpredictably on occasion, and are therefore inherently risky and liable to commit acts that violate IHL, even when this is not the intention of those who deploy the systems.

This research aims to define a novel framework to employ engineering and mathematical models to study adaptive dynamics in heterarchial systems. This multi-profile descriptive platform and modeling approach is developed as a composite of conceptual behaviors and structural entity aspects of engineered complex adaptive systems (ECAS). While the US electric power system will be utilized for demonstration and validation, the framework has applicability to the general class of ECASs that are artificially created but highly interactive with natural and behavioral sciences. Conditioned on parameterization of the framework, a theorem will be presented to calibrate current structure and predict future dynamic behaviors of an ECAS. We analyze decentralized heterarchial ECASs to infer emergent behavior of the components, and evolution processes and adaptations of the whole system.

Complex adaptive systems (CAS) are composed of interacting agents, exhibit nonlinear properties such as positive and negative feedback, and tend to produce emergent behavior that cannot be wholly explained by deconstructing the system into its constituent parts. Both system dynamics (equation-based) approaches and agent-based approaches have been used to model such systems, and each has its benefits and drawbacks. In this paper, we introduce a class of agent-based models with an embedded system dynamics model, and detail the semantics of a simulation framework for these models. This model definition, along with the simulation framework, combines agent-based and system dynamics approaches in a way that retains the strengths of both paradigms. We show the applicability of our model by instantiating it for two example complex adaptive systems in the field of Computational Sustainability, drawn from ecology and epidemiology. We then present a more detailed application in epidemiology, in which we compare a previously unstudied intervention strategy to established ones. Our experimental results, unattainable using previous methods, yield insight into the effectiveness of these intervention strategies.

If computational social science is to find practical application in informing policy decisions and proportionately analyzing courses of action, then it will have to make progress in the area of composition of social models.  Since a single simulation cannot hold a world of information, policy makers need to switch in and out modules in federations of simulations to test policies against all possible social environments.  Voting processes as they occur in nature, both in the form of cognition in a human mind of disparate world views, and in the form of equilibria seeking coevolution of species, inform how to combine model results externally and deeply, respectively.  These algorithms, which use the same principles of soft computation found in nature, enable any models to mesh together, even if they have different ontologies, or their data conflict, regardless of the degree they overlap.  A whiteboard architecture in which models report in their own ontologies how other models may inform them and what they have to offer other models, is a framework for the arbitrary meshing of social models.