Daniel Doubrovkine on Auctions and bidding: A guide for computer scientists

There is a veritable menagerie of auctions — single dimensional, multi-dimensional, single sided, double sided, first price, second price, English, Dutch, Japanese, sealed bid — and these have been extensively discussed and analyzed in the economics literature. We will survey this literature from a computer science perspective, primarily from the viewpoint of computer scientists who are interested in learning about auction theory, and to provide pointers into the economics literature for those who want a deeper technical understanding. We'll also take a peek into how the paper was used to build a new online auction platform Artsy.

Transcript

1. MANOLO VALDÉS, Ivy, 2012 https://www.artsy.net/artwork/manolo-valdes-ivy-5 2017 Daniel Doubrovkine | dblock@dblock.org | @dblockdotorg Auctions and bidding: A guide for computer scientists.
2. A Paper I Love — http://curatorsintl.org
3. What’s in an Auction? —
4. A Louis XVI Sofa —
5. Auctions Everywhere! —
6. A Menagerie of Auctions — - Single Dimensional - Multi Dimensional - Single Sided - Double Sided - First Price - Second Price - Kth Price - English - Dutch - Japanese - Open-Cry - Sealed Bid - Combinatorial
7. TOC — - Auction Families - Analyzing Auctions - Abstract Process View - Computer Science - Actual Implementation
8. Auction Families —
9. When were auctions invented? — - Auction = Exchange of Money for Goods
 Money ~ 700 B.C. 
 Rome and Greece ~ 5th Century B.C.
10. Independent Features (Zoology) — - Single dimensional, or 
 multi-dimensional. - One sided or two sided. - Open-cry or sealed bid. - First price or kth price. - Single-unit or multi-unit. - Single-item or multi-item 
 (combinatorial).
11. Single vs. Multi-Dimensional — - Single Dimensional: price only - Multi Dimensional: other aspects
12. One vs. Two-Sided — - One-Sided: either buyer or seller bids - Two-Sided: both sides submit bids
 - The auctioneer decides
13. Open Cry vs. Sealed Bid — - Open Cry: every bidder “hears” bids - Sealed Bid: only auctioneer sees bids
14. First Price vs. K-th Price — - First Price: winning bidder pays the price - Second Price: winning bidder pays the
 underbidder price - … - 16th price? Risk seekers!
15. Single Unit vs. Multi Unit — - Single Unit: bid for a single good - Multi Unit: bid for a set of goods
16. Combinatorial Auction — - Multiple, heterogeneous goods - Bids on arbitrary combinations
17. “English” (or “Silent”) Auction — - A single dimensional, one sided, single good, open-cry auction - May have reserve price - Bids are made in ascending order - Auctioneer calls the next bid - Ends when nobody accepts a higher bid or at a given time
18. “Dutch” Auction — - A single dimensional, one sided, 
 single good, open-cry auction - May have reserve price - Bids are made in descending order - Auctioneer calls the next bid - Ends when someone accepts a bid 
 or at reserve 18
19. “Japanese” Auction — - A single dimensional, one sided, single good, open-cry auction - May have reserve price - Bids are made in ascending order - Auctioneer calls increasing bids - Bidders indicate that they drop out - Auction ends when there’s a single bidder
20. “Vickrey” Auction — - Second price, silent - Bidder is free to bid max - Possible trouble maximizing seller revenue
21. “Buy Side” Auctions — - Sellers offering various terms - Credit cards, mortgages, procurement
22. “Double Auction”, “Call Market” or “Clearing House” — - Ask = a request from someone to buy a good from the asker at a price - Bid = a request from a buyer to buy a good from the seller at a price - Offer = a general term for a “bid” or an “ask” trade price = k · bid price + (1 − k) · ask price
23. “Continuous Double Auction”, Stock Market —
24. Multi-Dimensional & Multi-Attribute Auctions — … although auctions with side constraints on overall assignments can be cleared in polynomial time using network flow algorithms, when the demand is indivisible, assessing an optimal allocation becomes computationally intractable and requires solving np-hard optimization problems such as the generalized assignment problem, the multiple knapsack problem and the bin packing problem…
25. Analyzing Auctions —
26. Concerns — - Efficiency: achieve allocation of money and goods that maximizes the total value of all participants
 - Optimality: maximizing the revenue of the bid taker
27. Auction Models — - Independent private values model - Correlated values model - Almost common values model
28. Independent Private Values Model — - Many buyers bid for a single, invisible object - Each buyer is risk neutral - Each buyer bids up to the private value of the object Dutch and First Price Sealed Bid Auctions are equivalent: a bidder only has one choice, pick a price to bid English and Second Price Sealed Bid Auctions are equivalent: bid up to the value to the bidder this model is Pareto optimal this model gives almost identical revenue to the seller leads to winner’s curse where the winner overpays
29. Revenue Equivalence Theorem (RET) — - All major types of auctions generate the same expected revenue “Assume each of a given number of risk neutral potential buyers has a privately known valuation independently drawn from a strictly increasing atomless distribution, and that no buyer wants more than one of the k identical indivisible objects. Then any mechanism in which (i) the objects always go to the k buyers with the highest valuations and (ii) any bidder with the lowest feasible valuation expects zero surplus, yields the same expected revenue (and results in each bidder making the same expected payment as a function of her valuation).”
30. Correlated Values Model — - Generalization of the independent private values model - Not all buyers have an accurate value of the goods sold - English auction will lead to higher prices than a Second Bid sealed auction - Revealing information will increase prices overall, even if the information is bad
31. Almost Common Values Model — - Bidders have almost common values, but different estimates - A bidder with a higher value will bid more aggressively - Competing bidders with lower value will bid more conservatively, increase winner’s curse - A small information advantage is magnified - There’re advantages to appear aggressive
32. Generalized Analysis w/ Mechanisms — - Agents - Outcomes - Decision rules (efficient or dictatorial) - Transfers - Social choice functions (skip …)
33. Collusion, lying, and other sharp practice — - Rings of bidders, also called “pies” and “kippers” - Bidding “clubs” - Sniping - Bid shielding - Retaliation
34. Auctioneer (seller) fraud — - Inserting fake bids to raise the price of a second bid auction - Fixing commissions - “Shills” or “puffers” - “Book” bids
35. Abstract Models —
36. Parametric Model — - Common auction characteristics - Auction parameter space - Matching functions
37. Abstract Process Model — - Bid call - Ask call - Bid collection - Ask collection - Bid retraction - Ask retraction - Winner determination - Clearing - Information revelation - Tie breaking - Stage switch - Closing
38. Practical Implementations — - Building an English Auction w/ MongoDB
 http://artsy.github.io/blog/2014/04/17/building-an-english-auction-with-mongodb/ 
 - Build a Real-Time Auction Engine in Scala using Event Sourcing
 https://www.youtube.com/watch?v=szgmgBfvDrs&index=19
39. Big Questions — - Can automated trading outperform humans? - Can a market-based method compute the outcome of a distributed problem?
40. http://www.sci.brooklyn.cuny.edu/~parsons/projects/mech-design/publications/bluffers-final.pdf
41. Thank you. Daniel Doubrovkine | db@artsy.net | @dblockdotorg | code.dblock.org