Alternative Machine Learning Approaches for an Agent-Based Model of the Ultimatum Game Using R

Pedro Campos (), José Matos () and Joaquim Margarido
Additional contact information
Pedro Campos: University of Porto, FEP, LIAAD-INESC TEC
José Matos: University of Porto, FEP
Joaquim Margarido: ISEP

Chapter Chapter 8 in Machine Learning Perspectives of Agent-Based Models, 2025, pp 189-223 from Springer

Abstract: Abstract We provide a straightforward demonstration of the potential of different types of agent-based learning in the context of the Ultimatum Game. A recurring iteration of the Ultimatum Game is explored through Agent-Based Models (ABM), in which agents—representing the players—engage in repeated interactions following predefined rules. Leveraging the capabilities of Machine Learning we aim to harness the agents’ capacity to acquire strategies for optimizing their earnings within this game. Illustrative simplified examples of Fictitious Play, Reinforcement Learning, and Classifier systems are developed in R. The Classifier systems are based on Decision Trees that enable agents to learn from previous interactions and use background knowledge. The prior knowledge is based on the previous results of Reinforcement Learning in two different ways: on one hand, agent decisions are random (emphasizing exploration), and on the other hand, agent decisions are “epsilon-greedy”, emphasizing exploitation. We compare the gains of the agents in the different setups. We then assume that agents are placed in networks to develop a more sophisticated setup and explore the possibilities of Transfer Learning among agents, where some teach others how to learn.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-73354-3_8

Ordering information: This item can be ordered from
http://www.springer.com/9783031733543

DOI: 10.1007/978-3-031-73354-3_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().