Kinetic theory;
Active particles;
Stochastic differential games;
Social learning;
Monte Carlo particle method;
MOTIVATION LOSSES;
DYNAMICS;
SYSTEMS;
EVOLUTION;
CANCER;
D O I:
10.1016/j.plrev.2015.10.008
中图分类号:
Q [生物科学];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
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. (C) 2015 Elsevier B.V. All rights reserved.
机构:
NYU, Courant Inst Math Sci, Appl Math Lab, New York, NY 10012 USA
Princeton Univ, Dept Mech & Aerosp Engn, Princeton, NJ 08544 USANYU, Courant Inst Math Sci, Appl Math Lab, New York, NY 10012 USA
Masoud, Hassan
Shelley, Michael J.
论文数: 0引用数: 0
h-index: 0
机构:
NYU, Courant Inst Math Sci, Appl Math Lab, New York, NY 10012 USANYU, Courant Inst Math Sci, Appl Math Lab, New York, NY 10012 USA
机构:
Penn State Univ, Dept Biomed Engn, University Pk, PA 16802 USA
Penn State Univ, Dept Chem, University Pk, PA 16802 USA
Penn State Univ, Dept Math, University Pk, PA 16802 USAPenn State Univ, Dept Biomed Engn, University Pk, PA 16802 USA