Abstract
The best way to sell n items to a buyer who values each of them independently and uniformly randomly in [ c, c +1] is to bundle them together, as long as c is large enough. Still, for any c , the grand bundling mechanism is never optimal for large enough n , despite the sharp concentration of the buyer's total value for the items as n grows. Optimal multi-item mechanisms are rife with unintuitive properties, making multi-item generalizations of Myerson's celebrated mechanism a daunting task. We survey recent work on the structure and computational complexity of revenue-optimal multi-item mechanisms, providing structural as well as algorithmic generalizations of Myerson's result to multi-item settings.
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