Conor Heins, Senior ML Research Engineer, discusses collective behavior in complex systems, including emergence, social priors, and top-down causality. They explore mesmerizing biological phenomena like schools of fish and Starling Flocks, evolutionary pressures in the free energy principle, state-based vs path-based active inference, scene construction in inference, belief-free model selection, and the guest's learning journey in mathematics and physics.
Collective behavior in complex systems reveals emergent properties beyond individual components.
Studies on top-down causation challenge reductionist views by showing the influence of coarse-grained variables.
The trade-offs between group living benefits and individual fitness are explored in sociobiological research.
Deep dives
Understanding Collective Behavior in Complex Systems
Collective behavior in complex systems involves examining the emergent properties of a system as a whole, beyond just the individual components. Complex system science emphasizes that the whole is greater than the sum of its parts, leading to qualitative differences in collective systems. Research indicates that treating a system as an emergent entity, like a flock of birds or school of fish, reveals unique characteristics and potential causal influences that go beyond individual components.
Exploring Top-Down Causation and Coarse-Grained Variables
Studies on top-down causation have shown that coarse-grained variables derived from microscopic components can exhibit causal power that influences the lower-level constituents. Research suggests that these variables, even in simulation studies, can accumulate independent dynamics, impacting and predicting lower variables efficiently. This concept challenges traditional reductionist views and highlights the complexity of downward causal influences in complex systems.
Balancing Individual and Group Benefits in Collective Behavior
The study of collective behavior raises questions about the advantages of group living versus individual fitness. While group living offers benefits like reduced predation risk and psychological effects, individual species, like solitary predators, coexist without the need for group dynamics. Sociobiological research continues to explore the trade-offs between individual and collective benefits, showcasing the intricate interplay between group behavior and individual fitness.
Insights into Bayesian Inference and Scene Construction
Bayesian inference plays a crucial role in scene construction, where sparse sensory data is integrated to infer latent explanations for observed stimuli. The process of scene construction involves rapidly inferring context to guide the selection of potential latent variables for inference. Context provides a framework for generating suitable generative models, showcasing the efficiency of leveraging context to inform and drive inference processes.
Principles of Active Inference
Active inference involves modeling the brain's functionality as a means of adapting to various contexts. The discussion delves into the idea that evolutionary factors might influence the brain's inherent structures, thereby affecting how models are selected for tasks. The podcast reflects on the complexity of developing models that capture the brain's innate ability to infer contexts and select appropriate structures for different situations.
Biological Feasibility of Active Inference
The conversation transitions to an exploration of whether the assumptions made in active inference models align with biological feasibility. Factors like the mean field factorization and the postulation of a higher order prior for policy selection are discussed. The podcast highlights the trade-offs between simplifying models for local computations versus maintaining accurate but computationally intensive beliefs. This prompts a consideration of finding settings that balance model complexity and computational efficiency for neural implementation.
It’s all well and good being able to capture the intelligence and behaviour of a single agent, but what about collectives of them? Is that even possible and, if so, what do those models tell us not only about the individuals that belong to that group, but also the dynamic that emerges over and above their respective contributions? To answer those questions and many more, Active Inference Insights welcomes Conor Heins, Senior ML Research Engineer at Verses AI Research Lab and PhD student at the Max Planck Institute of Animal Behaviour, to the show.
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