Mitnik & Lynn Scholl & Antonia Vazquez, 2019. Patricia Yañez-Pagans & Daniel Martinez & Oscar A.' Generalized Backward Induction: Justification for a Folk Algorithm,' ' Backward induction algorithm for a class of closed-loop Stackelberg games,'Įuropean Journal of Operational Research, Elsevier, vol. ' Market-based coordination of integrated electricity and natural gas systems under uncertain supply,'Įuropean Journal of Operational Research, Elsevier, vol. Ordoudis, Christos & Delikaraoglou, Stefanos & Kazempour, Jalal & Pinson, Pierre, 2020.' On Noncooperative Oligopoly Equilibrium in the Multiple Leader-Follower Game,'Ģ016-13, University of Paris Nanterre, EconomiX. Finally, we present a numerical example that validates the effectiveness of the manipulation model.

We provide the details needed to implement the extraproximal method in an efficient and numerically stable way. The result of the model is the manipulation equilibrium point. The reinforcement learning algorithm is based on an actor-critic approach responsible for evaluating the new state of the system and it determines if the cost/rewards are better or worse than expected, supported by the Machiavellian game theory solution. We employ a reinforcement learning approach for the implementation of the immorality concept providing a computational mechanism, in which, its principle of error-driven adjustment of cost/reward predictions contributes to the players' acquisition of moral (immoral) behavior. For representing the concept of immorality, we consider that rational Machiavellian players employ a combination of the deontological and utilitarian moral rules, as well as, moral heuristics. For modeling the Machiavellian views and tactics we employ a Stackelberg/Nash game theory approach. The Machiavellian game conceptualizes the Machiavellianism considering three concepts: views, tactics and immorality. This paper presents a new game theory approach for modeling manipulation behavior based on Machiavellianism (social conduct and intelligence theory).

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