title: Collective learning modeling based on the kinetic theory of active particles.
abstract: This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom.
authors.affiliation: Department of Mathematics and Computer Science, Università degli Studi di Perugia, Via Vanvitelli 1, 06123 Perugia, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, Italy.
authors.forename: D
authors.initials: D
authors.lastname: Burini
authors.affiliation: Department of Mathematics and Computer Science, Università degli Studi di Perugia, Via Vanvitelli 1, 06123 Perugia, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, Italy.
authors.forename: S
authors.initials: S
authors.lastname: De Lillo
authors.affiliation: Department of Mathematical Sciences, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. Electronic address: livio.gibelli@polito.it.
authors.forename: L
authors.initials: L
authors.lastname: Gibelli
mesh_terms: D005075:Biological Evolution; D005716:Game Theory; D006801:Humans; D007700:Kinetics; D007858:Learning; D008962:Models, Theoretical
keywords: Active particles; Kinetic theory; Monte Carlo particle method; Social learning; Stochastic differential games