title: Network reciprocity by coexisting learning and teaching strategies.
abstract: We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.
authors.affiliation: Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-shi, Fukuoka, Japan.
authors.forename: Jun
authors.initials: J
authors.lastname: Tanimoto
authors.forename: Markus
authors.initials: M
authors.lastname: Brede
authors.forename: Atsuo
authors.initials: A
authors.lastname: Yamauchi
mesh_terms: D003198:Computer Simulation; D005246:Feedback; D015233:Models, Statistical; D016571:Neural Networks, Computer