TY - GEN
T1 - Playing the wrong game
T2 - 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
AU - Meir, Reshef
AU - Parkes, David
N1 - Publisher Copyright: © 2018 International Foundation for Autonomous Agents and Multiagent Systems.
PY - 2018
Y1 - 2018
N2 - The robustness of multiagent systems can be affected by mistakes or behavioral biases (e.g., risk-aversion, altruism, toll-sensitivity), with some agents playing the wrong game:' This can change the set of equilibria, and may in turn harm or improve the social welfare of agents in the system. We are interested in bounding what we call the biased price of anarchy (BPcsA) in populations with diverse agent behaviors, which is the ratio between welfare in the wrong equil ibrium and optimal welfare. We study nonatomic routing games, and derive an externality bound that depends on a key topological parameter of the underlying network. We then prove two general BPoA bounds for games with diverse populations: one that relies on the network structure and the average bias of all agents in the population, and one that is independent of the structure but dep ends on the maximal bias. Both types of bounds can be combined with known results to derive concrete BPOA bounds for a variety of specific behaviors (e.g., varied levels of risk-aversion).
AB - The robustness of multiagent systems can be affected by mistakes or behavioral biases (e.g., risk-aversion, altruism, toll-sensitivity), with some agents playing the wrong game:' This can change the set of equilibria, and may in turn harm or improve the social welfare of agents in the system. We are interested in bounding what we call the biased price of anarchy (BPcsA) in populations with diverse agent behaviors, which is the ratio between welfare in the wrong equil ibrium and optimal welfare. We study nonatomic routing games, and derive an externality bound that depends on a key topological parameter of the underlying network. We then prove two general BPoA bounds for games with diverse populations: one that relies on the network structure and the average bias of all agents in the population, and one that is independent of the structure but dep ends on the maximal bias. Both types of bounds can be combined with known results to derive concrete BPOA bounds for a variety of specific behaviors (e.g., varied levels of risk-aversion).
KW - Behavioral game theory
KW - Price of anarchy
KW - Selfish routing
UR - http://www.scopus.com/inward/record.url?scp=85053221461&partnerID=8YFLogxK
M3 - منشور من مؤتمر
SN - 9781510868083
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 86
EP - 94
BT - 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
Y2 - 10 July 2018 through 15 July 2018
ER -