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SUMMARY:Collective motion of active Bayesian agents - Conor Heins (Max Pla
 nck Institute of Animal Behavior)
DTSTART:20230810T133000Z
DTEND:20230810T135000Z
UID:TALK201502@talks.cam.ac.uk
DESCRIPTION:Collective motion is a familiar sight in nature\; groups of di
 stinct\, self-propelled individuals appear to move as a coherent whole\, e
 xhibiting a rich behavioural repertoire that ranges from directed movement
  to milling to disordered swarming. Preeminent models of collective motion
  describe individuals in the group as self-propelled particles\, subject t
 o a combination of self-generated motion and social forces that depend on 
 the state of neighbouring particles. In this work I introduce a new approa
 ch to modelling collective movement in animal groups based on active infer
 ence\, a framework originating in theoretical biology that casts cognition
  and behaviour as consequences of a single imperative: to minimize surpris
 e. Many empirically-observed collective phenomena such as cohesion\, milli
 ng and directed motion\, naturally emerge when considering individual beha
 vior as the consequence of active Bayesian inference -- this emerges witho
 ut ever explicitly building behavioral rules or goals into individual agen
 ts. We show that active inference can recover and generalize the classic n
 otion of social forces in agent-based models of collective motion\, and nu
 merically explore the parameter space of the belief-based model. In doing 
 so we reveal non-trivial relationships between the beliefs of individuals 
 beliefs and group properties like collective polarization and the probabil
 ity of occupying different behavioural regimes. We also explore the relati
 onship of individual beliefs about uncertainty\, to the accuracy of collec
 tive decision-making. Finally\, we show how agents that can actively alter
  their generative model over time\, compared to those that can't\, form gr
 oups that are collectively more sensitive to external fluctuations and enc
 ode that information more robustly.
LOCATION:Seminar Room 1\, Newton Institute
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