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Applying active inference to the study of collective behavior

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2024

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Zusammenfassung

In this thesis, we delve into the intricate realm of collective behavior, melding the perspectives of complex systems science with the predictive processing paradigm, and threading together our narrative within the context of active inference, a framework for designing adaptive agents that derives from information-theoretic and statistical-physical principles. Our exploration begins by setting down a theoretical foundation that casts multivariate stochastic systems as fundamentally equivalent to active Bayesian agents (Chapters 1 - 2). We then introduce software advances in deploying active inference at scale, in the form of an active inference Python library called pymdp (Chapter 3). This tool is not just an academic novelty but serves as a beacon for future empirical research, enabling a more democratic engagement with the FEP modeling processes.

Combining the theoretical innovations in Chapters 1-2 with the software tooling introduced in Chapter 3, then allows us to apply active inference to a broad spectrum of collective behavioral systems, granting us new insights into the mechanics of their emergent dynamics (Chapter 4-6).

Chapter 4 investigates how individual cognitive biases influence collective phenomena like opinion formation and polarization. The thesis further proposes that collective motion and decision-making can be effectively understood as a process of surprise minimization (Chapter 5), a concept deeply rooted in an individual-level drive for prediction error reduction. We can also use this framework to generalize notions like adaptation and information-sensitivity in the context of collective motion modelling.

Our work also bridges the gap between individual cognition and multi-agent computation, drawing parallels with the inference dynamics that emerge from spin glass systems. This offers a formal framework for the emergent information processing that arises within collective systems and elucidates explicitly the constraints for mapping between individual and collective inference.

Ultimately, the thesis paves the way for a new understanding of collective behavior, providing a cohesive picture that ties together individual-level processing with group dynamics. It showcases how individual actions, governed by the imperatives of Bayesian inference and the Free Energy Principle, scale up to complex collective phenomena, suggesting that the essence of collective behavior is rooted in the cognitive endeavors of its constituents. This comprehensive approach promises not only to enhance our theoretical grasp of complex adaptive systems in biology, but also to inform practical applications in fields ranging from robotics to social science, holding profound implications for how we conceive of and interact with the collective behaviors that shape our world.

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570 Biowissenschaften, Biologie

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ISO 690HEINS, Conor, 2024. Applying active inference to the study of collective behavior [Dissertation]. Konstanz: Universität Konstanz
BibTex
@phdthesis{Heins2024Apply-72038,
  title={Applying active inference to the study of collective behavior},
  year={2024},
  author={Heins, Conor},
  address={Konstanz},
  school={Universität Konstanz}
}
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Combining the theoretical innovations in Chapters 1-2 with the software tooling introduced in Chapter 3, then allows us to apply active inference to a broad spectrum of collective behavioral systems, granting us new insights into the mechanics of their emergent dynamics (Chapter 4-6).

Chapter 4 investigates how individual cognitive biases influence collective phenomena like opinion formation and polarization. The thesis further proposes that collective motion and decision-making can be effectively understood as a process of surprise minimization (Chapter 5), a concept deeply rooted in an individual-level drive for prediction error reduction. We can also use this framework to generalize notions like adaptation and information-sensitivity in the context of collective motion modelling.

Our work also bridges the gap between individual cognition and multi-agent computation, drawing parallels with the inference dynamics that emerge from spin glass systems. This offers a formal framework for the emergent information processing that arises within collective systems and elucidates explicitly the constraints for mapping between individual and collective inference.

Ultimately, the thesis paves the way for a new understanding of collective behavior, providing a cohesive picture that ties together individual-level processing with group dynamics. It showcases how individual actions, governed by the imperatives of Bayesian inference and the Free Energy Principle, scale up to complex collective phenomena, suggesting that the essence of collective behavior is rooted in the cognitive endeavors of its constituents. This comprehensive approach promises not only to enhance our theoretical grasp of complex adaptive systems in biology, but also to inform practical applications in fields ranging from robotics to social science, holding profound implications for how we conceive of and interact with the collective behaviors that shape our world.</dcterms:abstract>
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Prüfungsdatum der Dissertation

April 10, 2024
Hochschulschriftenvermerk
Konstanz, Univ., Diss., 2024
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