Modeling the Emergence and Convergence of Norms
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 97-101.
Abstract
In many multi-agent systems, the emergence of norms is the primary factor that determines overall behavior and utility. Agent simulations can be used to predict and study the development of these norms. However, a large number of simulations is usually required to provide an accurate depiction of the agents’ behavior, and some rare contingencies may still be overlooked completely. The cost and risk involvedwith agent simulations can be reduced by analyzing a system theoretically and producing models of its behavior. We use such a theoretical approach to examine the dynamics of a population of agents playing a coordination game to determine all the norms to which the society can converge, and develop a system of linear recurrence relations that predict how frequently each of these norms will be reached, as well as the average convergence time. This analysis produces certain guarantees about system behavior that canot be provided by a purely empirical approach, and can be used to make predictions about the emergence of norms that numerically match those obtained through large-scale simulations.
@inproceedings{ijcai2011p28,
title = {Modeling the Emergence and Convergence of Norms},
author = {Brooks, Logan and Iba, Wayne and Sen, Sandip},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {97--101},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-028},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-028},
}
TY - CPAPER TI - Modeling the Emergence and Convergence of Norms AU - Brooks, Logan AU - Iba, Wayne AU - Sen, Sandip T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 97 EP - 101 DO - 10.5591/978-1-57735-516-8/IJCAI11-028 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-028 ER -Copied
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