Distributed algorithmic mechanism design: recent results and future directions
DIALM '02 Proceedings of the 6th international workshop on Discrete algorithms and methods for mobile computing and communications
Co-evolutionary Auction Mechanism Design: A Preliminary Report
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Integer programming and arrovian social welfare functions
Mathematics of Operations Research
An Algorithm for Automatically Designing Deterministic Mechanisms without Payments
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Regret minimizing equilibria and mechanisms for games with strict type uncertainty
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Developing adaptive auction mechanisms
ACM SIGecom Exchanges
Empirical mechanism design: methods, with application to a supply-chain scenario
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Evolution of market mechanism through a continuous space of auction-types
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Searching for stable mechanisms: automated design for imperfect players
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Methods for boosting revenue in combinatorial auctions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Towards modeling trust based decisions: a game theoretic approach
ESORICS'07 Proceedings of the 12th European conference on Research in Computer Security
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Automated mechanism design (AMD) seeks to find, using algorithms, the optimal rules of interaction (a mechanism) between selfish and rational agents in order to get the best outcome. Here optimal is defined by the objective function of the designer of the mechanism where the function has usually some desirable properties (e.g., Pareto optimal). A difficulty with AMD lies in the size of the optimization problem that one needs to solve in order to select the best mechanism: there is a huge number of variables (and constraints but to a lesser extent) even for AMD instances of relatively small size. We study how to adapt the column generation techniques in order to solve the linear programming LP formulation of the AMD problem and compare its efficiency with the classical simplex algorithm for linear programs, on a bartering of goods example. We show that the resulting column generation algorithm is very quickly faster than the simplex algorithm for a fixed number of types (i.e., preference relations) on the goods as the number of goods increases, and then for a fixed number of goods as the number of types increases. Moreover, we show that, as the number of goods increases, the percentage of variables that need to be explicitly considered by the column generation techniques comes down very fast while the simplex algorithm must always consider explicitly all variables.