Guilherme S. Y. Giardini, John F. Hardy II, Carlo R. da Cunha

Understanding the mechanisms behind emergent behaviors in multi-agent systems is critical for advancing fields such as swarm robotics and artificial intelligence. In this study, we investigate how neural networks evolve to control agents’ behavior in a dynamic environment, focusing on the relationship between the network’s complexity and collective behavior patterns. By performing quantitative and qualitative analyses, we demonstrate that the degree of network non-linearity correlates with the complexity of emergent behaviors. Simpler behaviors, such as lane formation and laminar flow, are characterized by more linear network operations, while complex behaviors like swarming and flocking show highly non-linear neural processing. Moreover, specific environmental parameters, such as moderate noise, broader field of view, and lower agent density, promote the evolution of non-linear networks that drive richer, more intricate collective behaviors. These results highlight the importance of tuning evolutionary conditions to induce desired behaviors in multi-agent systems, offering new pathways for optimizing coordination in autonomous swarms. Our findings contribute to a deeper understanding of how neural mechanisms influence collective dynamics, with implications for the design of intelligent, self-organizing systems.

Read the full article at: arxiv.org

Salva Duran-Nebreda, Blai Vidiella, Andrej Spiridonov, Niles Eldredge, Michael J. O’Brien, R. Alexander Bentley, Sergi Valverde

Palaeontology  Volume 67, Issue 5
September/October 2024
e12731

Punctuated equilibria is a theory of evolution that suggests that species go through periods of stability followed by sudden changes in phenotype. This theory has been debated for decades in evolutionary biology, but recent findings of stasis and punctuated change in evolutionary systems such as tumour dynamics, viral evolution, and artificial evolution have attracted attention from a broad range of researchers. There is a risk of interpreting punctuated change from a phenomenological, or even metaphorical, standpoint and thus opening the possibility of repeating similar debates that have occurred in the past. How to translate the lessons from evolutionary models of the fossil record to explain punctuated changes in other biological scales remains an open question. To minimize confusion, we recommend that the step-like pattern seen in many evolutionary systems be referred to as punctuated evolution rather than punctuated equilibria, which is the theory generally linked with the similar pattern in the fossil record. Punctuated evolution is a complex pattern resulting from the interaction of both external and internal eco-evolutionary feedback. The interplay between these evolutionary drivers can help explain the history of life and the whole spectrum of evolutionary dynamics, including diversification, cyclic changes, and stability.

Read the full article at: onlinelibrary.wiley.com

MICHAEL HENRY TESSLER, et al.

SCIENCE 18 Oct 2024 Vol 386, Issue 6719

To act collectively, groups must reach agreement; however, this can be challenging when discussants present very different but valid opinions. Tessler et al. investigated whether artificial intelligence (AI) can help groups reach a consensus during democratic debate (see the Policy Forum by Nyhan and Titiunik). The authors trained a large language model called the Habermas Machine to serve as an AI mediator that helped small UK groups find common ground while discussing divisive political issues such as Brexit, immigration, the minimum wage, climate change, and universal childcare. Compared with human mediators, AI mediators produced more palatable statements that generated wide agreement and left groups less divided. The AI’s statements were more clear, logical, and informative without alienating minority perspectives. This work carries policy implications for AI’s potential to unify deeply divided groups. —Ekeoma Uzogara

Read the full article at: www.science.org

Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli & Tobias Werner
Nature Human Behaviour volume 8, pages 1864–1876 (2024)

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human–machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human–machine and machine–machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.

Read the full article at: www.nature.com

Helcio Felippe, Federico Battiston & Alec Kirkley 

Communications Physics volume 7, Article number: 335 (2024)

A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.

Read the full article at: www.nature.com