1 / 21

Mergers and Collusion in All-Pay Auctions and Crowdsourcing Contests

Mergers and Collusion in All-Pay Auctions and Crowdsourcing Contests . Omer Lev, Maria Polukarov , Yoram Bachrach & Jeffrey S. Rosenschein. AAMAS 2013 St. Paul, Minnesota. All-pay auctions. Perliminaries. Bidders bid and pay their bid to the auctioneer.

ban
Download Presentation

Mergers and Collusion in All-Pay Auctions and Crowdsourcing Contests

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Mergers and Collusion in All-Pay Auctions and Crowdsourcing Contests Omer Lev, Maria Polukarov, YoramBachrach& Jeffrey S. Rosenschein AAMAS 2013 St. Paul, Minnesota

  2. All-pay auctions Perliminaries Bidders bid and pay their bid to the auctioneer Auction winner is one which submitted the highest bid

  3. Why all-pay auctions? Perliminaries Explicit all-pay auctions are rare, but implicit ones are extremely common: Competition for patents between firms Crowdsourcing competitions (e.g., Netflix challenge, TopCoder, etc.) Hiring employees Employee competition (“employee of the month”)

  4. Auctioneer types Perliminaries

  5. All-pay auction equilibrium Regular all-pay All bidders give the object in question a value of 1 A single symmetric equilibrium – for n bidders: Baye, Kovenock, de Vries

  6. All-pay auction equilibrium bidder properties Regular all-pay Expected utility: 0 Utility variance: Expected bid: Bid variance: Baye, Kovenock, de Vries

  7. All-pay auction equilibrium auctioneer properties Regular all-pay Sum profit expected profit: 1 Sum profit profit variance: Max profit expected profit: Max profit profit variance: Baye, Kovenock, de Vries

  8. Example no collusion case Regular all-pay 3 bidders Bidders’ c.d.f is and the expected bid is ⅓, with variance of . Expected profit is 0 with variance of . Sum profit auctioneer has expected profit of 1 with variance of . Max profit auctioneer has expected profit of ⅗ with variance of . Baye, Kovenock, de Vries

  9. Mergers k bidders (out of the total n) collaborate, having a joint strategy. All other bidders are aware of this. Mergers (collaboration public knowledge)

  10. Merger properties Equilibrium remains the same – but with smaller n Sum Profit Bidder Expected profit: 1 Expected Utility: 0 Profit variance: Utility variance: Max Profit Expected bid: Mergers (collaboration public knowledge) Expected profit: Bid variance: Profit variance:

  11. Example no collusion case 3 bidders Bidders’ c.d.f is and the expected bid is ⅓, with variance of . Expected profit is 0 with variance of . Sum profit auctioneer has expected profit of 1 with variance of . Mergers (collaboration public knowledge) Max profit auctioneer has expected profit of ⅗ with variance of .

  12. Example merger case 3 bidders, 2 of them merged Bidders’ c.d.f is uniform, and the expected bid is ½, with variance of . Expected profit is 0 with variance of ⅙. Sum profit auctioneer has expected profit of 1 with variance of ⅙. Mergers (collaboration public knowledge) Max profit auctioneer has expected profit of ⅔ with variance of .

  13. Collusions k bidders (out of the total n) collaborate, having a joint strategy. Other bidders are not aware of this and continue to pursue their previous strategies. Collusion(collaboration private knowledge)

  14. Collusion colluders Colluders have a pure, optimal strategy k: n: Producing an expected profit of: k: n: Collusion(collaboration private knowledge) Colluders’ profit per colluder increases as number of colluders grows Profit variance:

  15. Collusion auctioneers Sum profit: k: n: Max profit: k: n: Collusion(collaboration private knowledge) For large enough n exceed non-colluding profits

  16. Collusion non-colluding bidders Utility for non-colluding bidders is: For large enough k (e.g., ) this expression is positive.I.e., non-colluders profit from collusion Collusion(collaboration private knowledge) If a non-colluder discovers the collusion, best to bid a bit above colluders

  17. Example no collusion case 3 bidders Bidders’ c.d.f is and the expected bid is ⅓, with variance of . Expected profit is 0 with variance of . Sum profit auctioneer has expected profit of 1 with variance of . Max profit auctioneer has expected profit of ⅗ with variance of . Collusion(collaboration private knowledge)

  18. Example merger case 3 bidders, 2 of them merged Bidders’ c.d.f is uniform, and the expected bid is ½, with variance of . Expected profit is 0 with variance of ⅙. Sum profit auctioneer has expected profit of 1 with variance of ⅙. Collusion(collaboration private knowledge) Max profit auctioneer has expected profit of ⅔ with variance of .

  19. Example collusion case 3 bidders, 2 of them collude One bidder has c.d.f of (expected bid of ⅓), colluders bid ¼. Colluders’ expected profit is ¼, while the non-colluder expected profit is ⅙. Sum profit auctioneer expected profit only . Collusion(collaboration private knowledge) Max profit auctioneer has expected profit of .

  20. Future directions Adding bidders’ skills to model Detecting collusions by other bidders Designing crowdsourcing mechanisms less susceptible to collusion Adding probability to win based on effort

  21. The End Thanks for listening!

More Related