0542311 20240103225759.720210513235959.9 000700408500012 85103110429 10.1016/j.cnsns.2021.105805 19 s. P cav_un_epca*03149331007-570499Elsevier Hotelling’s location model Agent-based simulation Reinforcement learning Nash Q-learning cav_un_auth*0409731 Vainer J. CZ cav_un_auth*0293468 Kukačka Jiří UTIA-B Ekonometrie Department of Econometrics E E CZ 50 A Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2021/E/kukacka-0542311.pdf https://www.sciencedirect.com/science/article/pii/S1007570421001167 PRIMUS/19/HUM/17 Univerzita Karlova CZ cav_un_auth*0409019 UNCE/HUM/035 Univerzita Karlova CZ cav_un_auth*0409020 This paper examines adaptive agents’ behavior in a stochastic dynamic version of the Hotelling’s location model. We conduct an agent-based numerical simulation under the Hotelling’s setting with two agents who use the Nash Q-learning mechanism for adaptation. This allows exploring what alternations this technique brings compared to the original analytic solution of the famous static game-theoretic model with strong assumptions imposed on players. We discover that under the Nash Q-learning and quadratic consumer cost function, agents with high enough valuation of future profits learn behavior similar to aggressive market strategy. Both agents make similar products and lead a price war to eliminate their opponent from the market. This behavior closely resembles the Principle of Minimum Differentiation from Hotelling’s original paper with linear consumer costs. However, the quadratic consumer cost function would otherwise result in the maximum differentiation of production in the original model. Thus, the Principle of Minimum Differentiation can be justified based on repeated interactions of the agents and long-run optimization. WOS AH 50000 50200 50201 2022 2 RVO:67985556 http://hdl.handle.net/11104/0320103 1 cav_un_auth*0321375 Univerzita Karlova UK CZ S 105805 3+4 Article Mathematics Applied|Mathematics Interdisciplinary Applications|Mechanics|Physics Fluids Plasmas|Physics Mathematical C MATHEMATICS.INTERDISCIPLINARYAPPLICATIONS|PHYSICS.MATHEMATICAL|PHYSICS.FLUIDS&PLASMAS|MECHANICS|MATHEMATICS.APPLIED 4.015 1.54 6.1 0.01412 0.853 15955 143 1.146 42.97 4 4784 Q1 3.819 539 Q1 88 Q2 Q1 1.7 Q1 96.816 2021 85103110429 SCOPUS PUBMED 000700408500012 WOS cav_un_epca*0314933 Communications in Nonlinear Science and Numerical Simulation 1007-5704 1878-7274 Roč. 99 č. 1 2021 Elsevier