Cultural evolution is the application of evolutionary theory to culture. Like evolution it relies on variation, transmission and selection. However, these can occur in various forms which can compensate each other. Multi-agent simulations can be used to understand how this happens and how it affects agents' culture.
Cultural evolution is the application of evolution theory to culture. It has been applied to various aspects of our life in societies: from customs to languages, from boat shapes to company structures [Messoudi, 2011]. In our context, culture is the beliefs and knowledge of agents, that determine their behaviour. Cultural evolution has been the subject of multi-agent simulation [Axelrod, 1997; Steels, 2012; Acerbi et al., 2022]. Artificial cultural evolution, like artificial intelligence for intelligence, aims at considering the general principles and mechanisms governing cultural evolution.
For that purpose, we aim at defining a model of cultural evolution experiments that allows different types of agents to play different types of games. This model will be supported by a simulation environment to test cultural evolution hypotheses and ensure the reproducibility and availability of such experiments. We also seek at promoting this approach towards social scientists interested in cultural evolution.
In this context, this PhD proposal aims at instantiating this general model into specific experimental designs investigating two specific directions.
The first direction is to reproduce and extend previous experiments on knowledge and belief evolution [Bourahla et al., 2021; 2022], within a more flexible simulation framework. These experiments will study the influence of multiple populations and generations of agents on the quality and diversity of knowledge developed while playing different games. This will require a conceptual reflection on the nature of populations and generations.
The second direction aims at identifying in such experiments the source of knowledge variation, transmission and selection so that they can be controlled. Indeed, within cultural evolution, they may occur at different stages, e.g. vertical and horizontal transmission, selection by the environment and selection by the agents, and may be combined, e.g. variation or selection occurring at transmission time. Moreover, an occurrence may compensate another: variation during vertical transmission may be compensated by variation during horizontal transmission [Bourahla et al., 2022]. Experiment designs should make it explicit in order to determine that these three operations are indeed necessary for knowledge to evolve, and to characterise their relative influence on the quality and diversity of the resulting knowledge.
These experiments will directly inform the general model design.
The recruited candidate will have for main tasks to:
References:
[Acerbi et al., 2022] Alberto Acerbi, Alex Mesoudi, Marco Smolla, Individual-based models of cultural evolution. A step-by-step guide using R, Routledge, London (UK), 2022 https://albertoacerbi.github.io/IBM-cultevo/
[Axelrod, 1997] Robert Axelrod, The dissemination of culture: a model with local convergence and global polarization, Journal of conflict resolution 41:203–226, 1997.
[Bourahla et. al., 2021] Yasser Bourahla, Manuel Atencia, Jérôme Euzenat, Knowledge improvement and diversity under interaction-driven adaptation of learned ontologies, Proc. 20th AAMAS, London (UK), pp242-250, 2021 https://moex.inria.fr/files/papers/bourahla2021a.pdf
[Bourahla et. al., 2022] Yasser Bourahla, Manuel Atencia, Jérôme Euzenat, Knowledge transmission and improvement across generations do not need strong selection, Proc. 21st AAMAS, (Online), pp163–171, 2022 https://moex.inria.fr/files/papers/bourahla2022a.pdf
[Mesoudi, 2011] Alex Mesoudi, Cultural evolution: how Darwinian theory can explain human culture and synthesize the social sciences, Chicago university press, Chicago (IL US), 2011 See also: Alex Mesoudi, Andrew Whiten, Kevin Laland, Towards a unfied science of cultural evolution, Behavioral and brain sciences 29(4):329–383, 2006 https://www.alexmesoudi.com/publication/mesoudi-towards-2006/Mesoudi_Whiten_Laland_BBS_2006.pdf
[Steels, 2012] Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012
Links:
Qualification: Master or equivalent in computer science.
Researched skills:
Doctoral school: MSTII, Université Grenoble Alpes.
Advisor: Jérôme Euzenat (Jerome:David#univ-grenoble-alpes:fr) and Jérôme David (Jerome:Euzenat#inria:fr)
Group: The work will be carried out in the mOeX team common to INRIA & LIG. mOeX is dedicated to study knowledge evolution through adaptation. It gathers researchers which have taken an active part in the development of the semantic web and more specifically ontology matching and data interlinking.
Place of work: The position is located at INRIA Montbonnot (near Grenoble, France), a main computer science research lab, in a stimulating research environment.
Hiring date: October 2026.
Funding and employer: The project is funded by the ACBE projet. The employer will be INRIA; the candidate will be subject to ZRR clearance.
Duration: 36 months
Deadline: as soon as possible.
Contact: For further information, contact us.
Procedure:
Contact us (do not wait) AND apply to offer 2026-10032.
File: Provide Vitæ, motivation letter and references. It is very good if you can provide a Master report and we will ask for your marks in Master, so if you have them, you can join them.