High-performance bidding agents for the continuous double auction
Proceedings of the 3rd ACM conference on Electronic Commerce
Strategic sequential bidding in auctions using dynamic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Proceedings of the twenty-second annual symposium on Principles of distributed computing
A novel method for automatic strategy acquisition in N-player non-zero-sum games
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Empirical game-theoretic analysis of the TAC Supply Chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Strategic bidding in continuous double auctions
Artificial Intelligence
Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Approximate strategic reasoning through hierarchical reduction of large symmetric games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Evolutionary stability of behavioural types in the continuous double auction
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
Agent-based analysis of asset pricing under ambiguous information
Proceedings of the 2011 Workshop on Agent-Directed Simulation
Efficient nash computation in large population games with bounded influence
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Strategic formation of credit networks
Proceedings of the 21st international conference on World Wide Web
| Hi-index | 0.00 |
Multiagent simulation extends the reach of game-theoretic analysis to scenarios where payoff functions can be computed from implemented agent strategies. However this approach is limited by the exponential growth in game size relative to the number of agents. Player reductions allow us to construct games with a small number of players that approximate very large symmetric games. We introduce deviation-preserving reduction, which generalizes and improves on existing methods by combining sensitivity to unilateral deviation with granular subsampling of the profile space. We evaluate our method on several classes of random games and show that deviation-preserving reduction performs better than prior methods at approximating full-game equilibria.