Paper Info
Title
A Game Theory Model for Manipulation Based on Machiavellianism: Moral and Ethical Behavior.
Abstract
This paper presents a new game theory approach for modeling manipulation behavior based on Machiavellianism (social conduct and intelligence theory). The Machiavellian game conceptualizes the Machiavellianism considering three concepts: views, tactics and immorality. For modeling the Machiavellian views and tactics we employ a Stackelberg/Nash game theory approach. 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. 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. 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. The result of the model is the manipulation equilibrium point. We provide the details needed to implement the extraproximal method in an efficient and numerically stable way. Finally, we present a numerical example that validates the effectiveness of the manipulation model.
Year
DOI
Venue
2017
10.18564/jasss.3301
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION
Keywords
Field
DocType
Machiavellianism,Machiavellian Intelligence,Stackelberg/NashGame,Machine Ethics,Moral,Markov Chains,Behavioral Games
Social psychology,Machine ethics,Computer science,Repeated game,Immorality,Game theory,Stackelberg competition,Non-cooperative game,Machiavellianism,Reinforcement learning
Journal
Volume
Issue
ISSN
20
2
1460-7425
Citations 
PageRank 
References 
3
0.46
0
Authors
1
Name
Order
Citations
PageRank
Julio B. Clempner19120.11