Evolution of objectives can enable prosocial behaviour without social awareness

Collective Behavior Seminar by Bernd Meyer

  • Date: Nov 7, 2025
  • Time: 11:45 AM - 12:45 PM (Local Time Germany)
  • Speaker: Bernd Meyer
  • Bernd Meyer is a professor of data science and artificial intelligence at Monash University, Australia and the deputy director of its environmental informatics hub. His team works in computational ecology developing mathematical and computational models for the interaction of organisms with their environment, including the impact our changing environment has on their lives. Most of this work is centred on the collective behaviour of social insects, such as bees and ants, in the hope that a deeper understanding of their behaviour will allow us to better protect them and the important ecosystem services they provide. Insect societies are entirely self-organised, without any central leader or master plan. How such decentralised "super-organisms" plan and coordinate their actions is still a largely open research question. Prof. Meyer’s team uses and extends a wide range of mathematical and computational techniques, including evolutionary game theory, reinforcement learning, reaction-advection diffusion and stochastic event analysis to explain this. Some of this work has interesting implications for a branch of bio-mimetic engineering and algorithm design, often referred to as “swarm intelligence." His lab also works on AI-based methods and systems for monitoring animal activity as the basis for ecosystem monitoring and for automating lab experiments.
  • Location: University of Konstanz + online
  • Room: ZT1202
  • Host: Max Planck Institute of Animal Behavior
  • Contact: mdagher@ab.mpg.de
Division of labour is fundamental to the functioning of societies and socially living organisms. While it has been central to their study for decades, no complete picture has emerged yet. Some of the most fascinating questions arise in the context of self-organised societies, such as those of social insects, which coordinate their behaviour with completely decentralised simple decision-making performed by individuals that only have local information at their disposal. Based on empirical evidence, these collectives appear to balance task engagement globally across their whole task network for the benefit of the colony overall. How can this pro-social coordination be achieved by independently acting individuals? How is a global workforce balancing possible based on only local perception with no knowledge of the global colony status or needs? How can information flow through the task network, so that a changed task demand in one part of the network can lead to adjustments in distant other parts? We detail a model that presents a potential answer to this conundrum. Our model is informed by evolutionary game theory and rests on the assumption that the perception of an individual’s sensory input can evolve. We present a conceptual argument and simulation studies to show that pro-social behaviour will evolve in a collective of agents that adjust their behaviour using primitive reinforcement mechanisms if we assume an evolving perception function

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