Social preferences in the public goods game–An Agent-Based simulation with EconSim

Using a reinforcement-learning algorithm, we model an agent-based simulation of a public goods game with endogenous punishment institutions. We propose an outcome-based model of social preferences that determines the agent’s utility, contribution, and voting behavior during the learning procedure. C...

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Bibliographic Details
Published in:PloS one Vol. 18; no. 3; p. e0282112
Main Authors: Bühren, Christoph, Haarde, Jan, Hirschmann, Christian, Kesten-Kühne, Janis
Format: Journal Article
Language:English
Published: United States Public Library of Science 15.03.2023
Public Library of Science (PLoS)
Subjects:
Agent-based models
Algorithms
Analysis
Biology and Life Sciences
Computer and Information Sciences
Computer Simulation
Cooperation
Cooperative Behavior
Data mining
Economic models
Economic theory
Endowment
Experiments
Game Theory
Games
Human acts
Human behavior
Human subjects
Humans
Laboratories
Machine learning
Methods
Physical Sciences
Preferences
Public good
Punishment
Research and Analysis Methods
Self interest
Simulation
Simulation methods
Social Behavior
Social Behavior Disorders
Social Sciences
Germany
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Using a reinforcement-learning algorithm, we model an agent-based simulation of a public goods game with endogenous punishment institutions. We propose an outcome-based model of social preferences that determines the agent’s utility, contribution, and voting behavior during the learning procedure. Comparing our simulation to experimental evidence, we find that the model can replicate human behavior and we can explain the underlying motives of this behavior. We argue that our approach can be generalized to more complex simulations of human behavior.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0282112