Description
Date depot: 1 avril 2025
Titre: Social Learning and Social Structuring during Demographic Transitions
Directeur de thèse:
Nicolas BREDECHE (ISIR (EDITE))
Directeur de thèse:
David COHEN (ISIR (SMAER))
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Intelligence artificielle
Resumé: Supervision by Pr. Nicolas Bredeche (25%) and Pr. David Cohen (75%), ISIR, Sorbonne Université, France.
The historical shift towards sedentariness led to increasing group sizes, rendering direct acquaintance among all group members impractical. This development has pushed forward the importance of reputation for evaluating individual reliability and managing interactions within large groups. However, it remains vulnerable to manipulation and errors in judgment. Social and evolutionary psychology has underlined important individual and collective factors such as how individuals inhibit aggressiveness (the so-called opposition between fast and slow responders in the ecological evolutionary model [1]); how they infer that other individuals are efficient or popular (the so-called Stereotype content model based on in-group and out-group affiliation [2]). How evolution has selected these regulatory processes in large groups is still subject to debate although many support the importance of culture in advanced animals.
This thesis proposes to explore the adaptive dynamics of social learning as populations transition from small to large sizes using models from swarm robotics. By using local communication between robots, it is possible to implement both local innovation and diffusion of behavioural strategies within a swarm of robots [3]. Such artificial social learning algorithms can then be studied in a pseudo-realistic setup where geographical constraints are taken into account: diffusion of behavioural traits throughout the swarm depends on the relative positions of robots but also directly influences the swarm spatial configuration. We will focus on the evolution of inter-individual relationships and social structuring, emphasising reputation and cultural formation processes. We will examine how social structures, such as friendships, acquaintances, and stranger interactions, transform with demographic expansion. We will also investigate the impact of reputation on information dissemination and collective decision-making, probing the potential for discrepancies between perceived reputation and actual competence. Given the limited computation capabilities of swarm robotics, we will also investigate whether such collective adaptation may be a property that also anchors on large group collective interactive experiences and whether or not they can produce cognitive representations of the premise of culture.
We will use individual-based modelling methods to simulate and analyze the interactions and dynamics of social learning within artificial populations. The research will explore the interaction between cognitive abilities and social structures, assessing their influence on social learning. It will also analyze cognitive biases that impact reputation perception and their consequences on social structuring. Additionally, the thesis will explore the role of aggression and its inhibition mechanisms in social dynamics, particularly concerning reputation.
The thesis is hosted by the Institut des Sytèmes Intelligents et de Robotique in Paris. The local cluster and experimental swarm robotics setup will be used. The thesis starts on Oct. 1st 2025.
[1] David M. Buss (2015) The Handbook of Evolutionary Psychology. John Wiley & Sons. [2] S. T. Fiske et al. (2018) A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. in Social cognition. [3] N. Bredeche and N. Fontbonne (2022) Social learning in swarm robotics. Philosophical Transactions of the Royal Society B: Biological Sciences, Volume 377, Issue 1843.