Phd Position F - M Phd Thesis Artificial Cultural Belief Evolution Flexibility Experiments H/F - INRIA

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.
PhD Position F/M PhD thesis: Artificial cultural belief evolution: flexibility experiments
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Doctorant

Niveau d'expérience souhaité : Jeune diplômé

A propos du centre ou de la direction fonctionnelle

The Centre Inria de l'Université de Grenoble groups together almost 450 people in 26 research teams and 9 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, ...), but also with key economic players in the area.

The Centre Inria de l'Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

Mission confiée

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.

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
[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
[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
[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
[Steels, 2012] Luc Steels (ed.), , John Benjamins, Amsterdam (NL), 2012

Links:

- mOeX web site:
- ACBE web site:
- Experiment repository:

Principales activités

Doctoral school:, Université Grenoble Alpes.

Advisor: (Jerome:David#univ-grenoble-alpes:fr) and (Jerome:Euzenat#inria:fr)

Group: The work will be carried out in the team common to & . mOeX is dedicated to study knowledge evolution through adaptation. It gathers researchers which have taken an active part these past 15 years in the development of the semantic web and more specifically ontology matching and data interlinking.

Place of work: The position is located at (near Grenoble, France), a main computer science research lab, in a stimulating research environment.

Compétences

Qualification: Master or equivalent in computer science.

Researched skills:

- Curiosity and openness.
- Interaction with other researchers.
- Autonomous researcher.
- Interests in agent-based simulation and/or cultural evolution.
- Innovative.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage under condition

Rémunération

2300 euros gross salary /month

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