Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning

Autor: Roberto Molowny-Horas, Saeed Harati, Liliana Perez
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Technology
reinforcement learning
QH301-705.5
Computer science
QC1-999
Complex system
Principal–agent problem
Space (commercial competition)
Machine learning
computer.software_genre
ComputingMethodologies_ARTIFICIALINTELLIGENCE
0502 economics and business
050602 political science & public administration
Reinforcement learning
emergence
General Materials Science
Biology (General)
Baseline (configuration management)
complex systems
QD1-999
Instrumentation
Fluid Flow and Transfer Processes
business.industry
Physics
Process Chemistry and Technology
05 social sciences
General Engineering
Engineering (General). Civil engineering (General)
0506 political science
Computer Science Applications
social status
Chemistry
Action (philosophy)
Social system
Artificial intelligence
temporal difference learning
TA1-2040
business
Temporal difference learning
computer
050203 business & management
Zdroj: Applied Sciences
Volume 11
Issue 18
Applied Sciences, Vol 11, Iss 8368, p 8368 (2021)
ISSN: 2076-3417
DOI: 10.3390/app11188368
Popis: One of the complexities of social systems is the emergence of behavior norms that are costly for individuals. Study of such complexities is of interest in diverse fields ranging from marketing to sustainability. In this study we built a conceptual Agent-Based Model to simulate interactions between a group of agents and a governing agent, where the governing agent encourages other agents to perform, in exchange for recognition, an action that is beneficial for the governing agent but costly for the individual agents. We equipped the governing agent with six Temporal Difference Reinforcement Learning algorithms to find sequences of decisions that successfully encourage the group of agents to perform the desired action. Our results show that if the individual agents’ perceived cost of the action is low, then the desired action can become a trend in the society without the use of learning algorithms by the governing agent. If the perceived cost to individual agents is high, then the desired output may become rare in the space of all possible outcomes but can be found by appropriate algorithms. We found that Double Learning algorithms perform better than other algorithms we used. Through comparison with a baseline, we showed that our algorithms made a substantial difference in the rewards that can be obtained in the simulations.
Databáze: OpenAIRE
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