Slide 1

Slide 1 text

MANOLO VALDÉS, Ivy, 2012 https://www.artsy.net/artwork/manolo-valdes-ivy-5 2017 Daniel Doubrovkine | [email protected] | @dblockdotorg Auctions and bidding: A guide for computer scientists.

Slide 2

Slide 2 text

A Paper I Love — http://curatorsintl.org

Slide 3

Slide 3 text

What’s in an Auction? —

Slide 4

Slide 4 text

A Louis XVI Sofa —

Slide 5

Slide 5 text

Auctions Everywhere! —

Slide 6

Slide 6 text

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

Slide 7

Slide 7 text

TOC — - Auction Families - Analyzing Auctions - Abstract Process View - Computer Science - Actual Implementation

Slide 8

Slide 8 text

Auction Families —

Slide 9

Slide 9 text

When were auctions invented? — - Auction = Exchange of Money for Goods
 Money ~ 700 B.C. 
 Rome and Greece ~ 5th Century B.C.

Slide 10

Slide 10 text

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).

Slide 11

Slide 11 text

Single vs. Multi-Dimensional — - Single Dimensional: price only - Multi Dimensional: other aspects

Slide 12

Slide 12 text

One vs. Two-Sided — - One-Sided: either buyer or seller bids - Two-Sided: both sides submit bids
 - The auctioneer decides

Slide 13

Slide 13 text

Open Cry vs. Sealed Bid — - Open Cry: every bidder “hears” bids - Sealed Bid: only auctioneer sees bids

Slide 14

Slide 14 text

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!

Slide 15

Slide 15 text

Single Unit vs. Multi Unit — - Single Unit: bid for a single good - Multi Unit: bid for a set of goods

Slide 16

Slide 16 text

Combinatorial Auction — - Multiple, heterogeneous goods - Bids on arbitrary combinations

Slide 17

Slide 17 text

“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

Slide 18

Slide 18 text

“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

Slide 19

Slide 19 text

“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

Slide 20

Slide 20 text

“Vickrey” Auction — - Second price, silent - Bidder is free to bid max - Possible trouble maximizing seller revenue

Slide 21

Slide 21 text

“Buy Side” Auctions — - Sellers offering various terms - Credit cards, mortgages, procurement

Slide 22

Slide 22 text

“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

Slide 23

Slide 23 text

“Continuous Double Auction”, Stock Market —

Slide 24

Slide 24 text

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…

Slide 25

Slide 25 text

Analyzing Auctions —

Slide 26

Slide 26 text

Concerns — - Efficiency: achieve allocation of money and goods that maximizes the total value of all participants
 - Optimality: maximizing the revenue of the bid taker

Slide 27

Slide 27 text

Auction Models — - Independent private values model - Correlated values model - Almost common values model

Slide 28

Slide 28 text

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

Slide 29

Slide 29 text

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).”

Slide 30

Slide 30 text

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

Slide 31

Slide 31 text

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

Slide 32

Slide 32 text

Generalized Analysis w/ Mechanisms — - Agents - Outcomes - Decision rules (efficient or dictatorial) - Transfers - Social choice functions (skip …)

Slide 33

Slide 33 text

Collusion, lying, and other sharp practice — - Rings of bidders, also called “pies” and “kippers” - Bidding “clubs” - Sniping - Bid shielding - Retaliation

Slide 34

Slide 34 text

Auctioneer (seller) fraud — - Inserting fake bids to raise the price of a second bid auction - Fixing commissions - “Shills” or “puffers” - “Book” bids

Slide 35

Slide 35 text

Abstract Models —

Slide 36

Slide 36 text

Parametric Model — - Common auction characteristics - Auction parameter space - Matching functions

Slide 37

Slide 37 text

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

Slide 38

Slide 38 text

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

Slide 39

Slide 39 text

Big Questions — - Can automated trading outperform humans? - Can a market-based method compute the outcome of a distributed problem?

Slide 40

Slide 40 text

http://www.sci.brooklyn.cuny.edu/~parsons/projects/mech-design/publications/bluffers-final.pdf

Slide 41

Slide 41 text

Thank you. Daniel Doubrovkine | [email protected] | @dblockdotorg | code.dblock.org