The mOeX team investigates the cultural evolution of knowledge and beliefs on a computational basis. For that purpose, we design and perform agent-based simulations. The goal of this position (32 months) is to develop and maintain a simulator based on novel models allowing agents to play different games and games to be played by different types of agents using the same knowledge.
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.
The recruited candidate will have for main task to develop and maintain an agent-based simulator allowing users to develop their own games and their own agents or to choose from a library of such. It would allow to describe experiment setups involving them that can then be performed and analysed.
The simulator may be a largely reworked version of our Lazy lavender simulator or a completely new design, possibly using an existing basis.
Beyond this core task, the candidate will participate to related tasks such a participating to the definition of the model to be implemented, offering support for analysing and publishing results and promoting the resulting system.
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.
[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. The position could be requalified as post-doctoral.
Researched skills:
Environnement: The work will be carried out in the mOeX team common to INRIA & University Grenoble Alpes within the Laboratoire d'Informatique de Grenoble (UGA - CNRS - INRIA - Grenoble INP).
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.
Duration: 24 months, extensible
Funding and employer: The project is funded by the ACBE projet. The employer will be INRIA; the candidate will be subject to ZRR clearance.
Deadline: as soon as possible.
Contact: For further information, contact Jerome:Euzenat#inria:fr.
Procedure:
Contact us (do not wait) AND apply to offer 2026-10031.
File: Provide Vitæ, motivation letter and references. It is very good if you can provide previous work of yours (software, projects, papers).