On Learnable Mechanism Design
Computation is increasingly distributed across open networks and performed by self-interested autonomous agents that represent individuals and businesses. Given that these computational agents are often in situations of strategic interaction, it is natural to turn to economics for ideas to control these systems. Mechanism design is particularly attractive in this setting, given its focus on the design of optimal rules to implement good systemwide outcomes despite individual self-interest. Yet these rich computational environments present new challenges for mechanism design, for example, because of system dynamics and because the computational cost of implementing particular equilibrium outcomes is also important. We discuss some of these challenges and provide a reinterpretation of the mathematics of collective intelligence in terms of learnable mechanism design for bounded-rational agents.
KeywordsEquilibrium Strategy Social Choice Function Combinatorial Auction Collective Intelligence Congestion Game
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