Weighing personal and social information in cooperative problem solving

Principal Investigators:

Pawel Romanczuk
Ralf Kurvers
Heiko Hamann (External PI)

Project Members

David Mezey (Doctoral Researcher)
Dominik Deffner (Postdoctoral Researcher)
Research Unit 1, SCIoI Project 34

A fundamental challenge in cooperative problem solving in human, animal or robotic groups is the integration of personal and social information. Relying too heavily on the former prevents the spread of information among group members, whereas relying too heavily on the latter may hamper profitable personal exploration and reduce collective performance. P34 investigates this key process by studying how collectives of different complexities dynamically balance personal and social information use across different levels of environmental complexity to find and extract spatially distributed resources (collective foraging) from their environment and by that to achieve collective intelligence.

The first aim of the proposed work is to develop a fully vision-based mechanistic modeling framework that allows us to investigate how cooperative agents integrate personal and social cues. We aim to move beyond previous work by considering how collective intelligence in spatial search can arise mechanistically from the individual-level cognitive integration of personal information and different local social cues provided purely by the available sensory input of the individuals.

Secondly we will investigate the performance of our framework across different environments and parametrizations i.e. across different individual strategies of information weighing in agents. According to the properties of the resource environment different strategies will yield optimal results on the individual and on the collective level during collective spatial foraging. With our framework we plan to investigate the performance of a wide range of strategies in different environments for virtual agents.

Furthermore, we propose to investigate both human and robotic collective problem solving in matching experimental scenarios. Human groups are an ideal system to study cooperative problem solving, since human cooperation exceeds all other species in scale and range. To foster the integration and comparability of the agent-based simulation framework with the behavior of human and of robotic groups, we will use similar experimental paradigms.


Related Publications

Mezey, D., Deffner, D., Kurvers, R. H., & Romanczuk, P. (2023). Visual social information use in collective foraging. BioRxiv. https://doi.org/https://doi.org/10.1101/2023.11.30.569379
Deffner, D., Mezey, D., Kahl, B., Schakowski, A., Romanczuk, P., Wu, C. M., & Kurvers, R. (2023). Collective incentives reduce over-exploitation of social information in unconstrained human groups. PsyArXiv. https://doi.org/10.31234/osf.io/p3bj7