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Group C: Bidding Strategy General Issues

Group C: Bidding Strategy General Issues. Rui Dong and Adam Juda CS286r 3/2/2004. Dependencies. Group B – Agent facing proxy Responsible for eliciting information from C We will “best-respond” to their queries B may need to be stable before C

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Group C: Bidding Strategy General Issues

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  1. Group C: Bidding StrategyGeneral Issues Rui Dong and Adam Juda CS286r 3/2/2004

  2. Dependencies • Group B – Agent facing proxy • Responsible for eliciting information from C • We will “best-respond” to their queries • B may need to be stable before C • B may want to internalize into the proxy some of the issues that may otherwise be C’s • Group D • Responsible for valuations of desired bundles • Will there be values on all bundles?

  3. Bundle Space • N item available  2N Subsets • May we bid on every combination? • Probably not! • How do we decide what bundles we value, but will not bid on due to constraints?

  4. Prices in general • First-order Issues • What valuation should be declared on a bundle? • How should that range be incremented? • What can be learned from feedback? • Second-order Issues • Want others to pay high prices • Should we bid high for them? • Want to pay little for our items • Should we bid low?

  5. Specific Price/Value Issues • AB = $10, BC = $20, B = $4 • How does one break up AB into A and B? • How does one value B (it is in all bundles)? • Interim allocation has us winning A at $4 • How should we value B? • V( B | “hold” A)-V( !B | “hold” A) = 10 – 0  $10 • V(AB)-P(A) = 10 – 4  $6 • Something completely different? Hybrid [C6]? • Will expressive bidding language fix this problem? • B group may want to internalize these

  6. Closed vs. Open Strategies [C5] • How much should we play the game? • Respond to interim allocations • Respond to interim prices • How much should we play the players? • Do we want to try and coordinate bids? • Do we not want to enter markets where we think we will lose? Have to pay a high price?

  7. Equilibrium and Training • Equilibrium Analysis [C1 and C2] • Strategy  Multiply value estimate by a and submit factored estimate as bid • C’s problem may boil down to this issue • Can one-shot-game equil. provide insight? • How much training will our agents have? • Lots should help agent performance • May lead to homogenous agent strategies  • Little may / may not be realistic • Airlines could simulate other agents in house

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