Leticia Saldain

Guadalupe Segura

The design of successful on-line auctions

Outline

E-bay basics Reputation Mechanisms Low-valued items vs. high-valued items

Pennies: US Cent and Indian Head Paul Reed Smith Guitar

Last-minute bidding

E-Bay basics

On-line auctions started in 1995 1998: e-bay had > 3 billion transactions Growth rate > 10% per month Over 3 million individual auctions / week 7 million unique individuals visit site /

month Over 2000 unique categories

E-bay auction

Second price auction Ascending-bid (English) format

- fixed time and date Reservation price Auction lasts 3-10 days “proxy bidding” system (Vickrey auction) Seller chooses

Opening bid amount Secret “reserve price” Length of auction

Sellers pays two fees: non-refundable insertion fee and final value fee

E-bay takes no risk

Reputation Mechanisms

Analyzing the Economic Efficiency of eBay-like Online Reputation Reporting MechanismsBy Chrysanthos Dellarocas

Effects of reputation

Studied by Economists Little attention to the mechanisms for

forming/communicating reputation computer scientists have focused on

design/implementation of reputations systems

On-line Reputation Reporting Systems

Goal: to induce good behavior in markets with asymmetric information

Feedback profile = reputation

Quality signal and control

Allows a market to exist!

Market for Lemons (Akerlof 1970)

Example: consider 9 used cars Quality levels: 0, ¼, ½, ¾, 1, 1 ¼, 1 ½, 1 ¾, 2

Assume cardinality (e.g. car with value 1 has twice the quality of car with value ½)

Assumptions: quality of car known to seller buyer only knows the distribution of quality

seller: reserve value = 1000*q buyer: reserve value = 1500*q

Market for Lemons (cont’)

cars sold in an auction:

Initial price $2000/car (all owners are willing to sell their car)

Buyers : average quality = 1, bid <= 1500

Auctioneer must reduce price to 1500: at this price seller 8 and 9 (best two cars) will

withdraw from market (why?) average quality of remaining cars fall (q = ¾) buyers are only willing to pay $1125 (1500* ¾ )

Auctioneer must try a lower price…and so on

NO EQUILIBRIUM SATISFYING BOTH BUYERS AND SELLERS IS FOUND!

Conclusions:

When potential buyers only know the average quality of used cars, market prices will be lower than the true value of top-quality cars

Owners of top-quality cars will withhold cars from sale

Result from asymmetric information!

GOOD CARS ARE DRIVEN OUT OF MARKET BY LEMONS!

Cars will not be sold even though potential buyers value the cars more than current owners

E-Bay Marketplace

Asymmetric information Incentive for seller to over-estimate

quality and increase profits Need to provide information to buyers

Reputation Mechanisms allow market to exist by reducing asymmetric information!

Model to analyze efficiency of binary RM

Assumptions: real quality (qr) is unknown to buyer buyer prefers high quality to low quality advertised quality (qa) is controlled by seller

Seller : Max payoff function π(x, qr, qa) = G (x, qr, qa) – c(x, qr)

x ≡ volume of saleG() ≡ gross revenue c() ≡ cost

eBay Reputation System

Feedback profile: R= (∑(+), ∑(-), ∑(no ratings))

Buyer Utility: U = θ *q – pp = priceq = level of quality after consumptionθ = buyer’s quality sensitivity

Buyer estimated quality qe = f( qa, R)qa – advertised quality R - Reputation

Buyer: max E(utility) = Ue = θ * qe – pAfter purchase: buyer observes q = qr + ε

eBay reputation system

Error term (ε) represents:

1. buyers misinterpreting qa

2. sellers may vary in actual q from one transition to another

3. buyers may have small difference in q, based on outside factors like weather

4. some aspects of q depends on factors outside seller’s control (i.e. post office delays)

eBay reputation systemBuyer satisfaction: S = U – Ue = θ (qr – qe + ε), ε ~ N(0, σ)

Rating function r(S) : ‘+’ if S>0 ‘-’ if S <= - λ no rating if –λ <S<= 0

Ratings as a function of a buyer’s satisfaction relative to expectations

λ accounts for e-bay buyers giving few ‘-’ ratings to sellersPossible Explanations:

Fear of reciprocal ratings outside network communication between sellers and buyers “culture of praise” : buyers feel a moral obligation to give “+”

ratings

Conditions for a “well-functioning” reputation mechanism (RM) :

1. If there exists an equilibrium of prices and qualities under perfect information (qe=qa=qr), then in markets where qr is private to sellers, the existence of a RM makes it optimal for sellers to settle down to steady-state pair of real and advertised qualities (qr, qa)

2. Assuming (1) holds, under all steady-state seller strategies (qr, qa) the quality of sellers as estimated by buyers before transactions take place must be equal to the true quality (i.e. qe =qr)

-in competitive markets: if qr > qe, then sellers would leave the market if qr < qe, then buyers would leave the market

Can they be well-functioning?

If given a rating function whether condition 2 is satisfied depends on the relationship between this rating function and the quality estimation function

Seller’s find it optimal to settle down to steady-state advertised quality levels if buyers are lenient when rating seller’s profiles

Estimated vs. Real Qualities in Steady State

The focus is on binary reputation mechanisms satisfying condition 2

Denote an estimated quality function qe and the deception factor ξ

If ξ > 0, buyers will overestimate seller’s true quality If ξ < 0, buyers will underestimate seller’s true

quality Let N be total no. of sale transactions [N= ∑(+) +

∑(-) + ∑(no ratings)]

Note: eBay does not specify quality assessment function f, it just publishes ∑(+) and ∑(-) allowing buyers (users) to use function they see fit. It also does not publish ∑(no ratings) thus N is not known.

Estimated vs. Real Qualities in Steady State Continued We will explore whether binary reputation

systems can be well functioning Let ξ(R) be an estimate of seller’s deception

factor based on information contained in the seller’s profile

A binary reputation system where buyers Rate according to r(S) Assess item quality according to (5) Have reliable rule for calculating ξ(R) for a given seller

satisfies condition 2 Aside: We would not expect any profit

maximizing seller to under-advertise

Estimated vs. Real Qualitiesin Steady State Continued There are three ways buyers may use ∑(+), ∑(-), and N to estimate ξ(R)

Based on the number of positives Based on the number of negatives Based on the ratio between negatives and positives

Estimate based on positives

Require a fraction of positive ratings exceed a threshold, η^

We will use statistical hypothesis, test null hypothesis Ho: η ≥ 0.5 given η^ We get new quality assessment function, qe

Method is appealing due to its relatively simplicity

Although, it is difficult without knowledge of N (Recall N is not specified by eBay)

Conclusion, method is rarely used by eBay users

Estimate based on Negatives This method is similar to previous, except we are

know looking at fraction of negative ratings, ζ Again using statistical testing

Null hypothesis, Ho’: ζ ≤ k* , where k* is a monotonically decreasing function of the leniency factor λ

New quality assessment function, qe Conclusion, satisfaction of condition 2 is always

possible. In order to find k* we need parameters λ, θ, and σ. However, this are not available to buyer’s in practice and the right k* is very important to the well-functioning of the mechanism. But parameters can be derived from ∑(+), ∑(-), and N.

Overall, this function is a rather fragile rule for assessing the seller’s quality efficiently

Estimate based on Negatives Continued Other things to consider:

What methods they use to compute threshold

Whether their trustworthiness thresholds do indeed come close to satisfying condition 2

These open questions invite to further explore empirical and experimental results to complement this paper

Estimate based on ratio between negative and positives We can just try to find a quality assessment function

between positive and negative ratings, such a function will not exists

Let ρ(ξ) = ∑(-) / ∑(+), so this function is non-negative and monotonically increasing in ξ

Again using statistical testing Null hypothesis, Ho”: ρ^ ≤ 2*Φ[-λ / (θσ)] where Φ() is

the standard normal CDF New quality assessment function qe

Once again we need knowledge of parameters λ, θ, and σ which is not known but can be substituted by N if buyers have knowledge of it.

Conclusion, this problem is just as difficult as estimation on negative ratings.

Existence of Steady-State We want to show function based on number of

negative ratings to find quality assessment function are preferred to the one’s using just the number of positive ratings

To show let’s observe what would occur if sellers oscillate between good and bad quality Let’s say in period 0 seller had N transactions with a

good reputation, q* If in period 1, she milks reputation earned during

period 0 (in order to make a little more profit since item being sold this period is not as in good conditions)

Seller’s subsequent estimate quality will fall to zero. But in order to re-gain their good reputation, seller will have to reduce the ratio ∑(-) / N and the threshold k* (this action will occur when seller’s places a good item at a lower price)

Existence of Steady-State Continued Conclusions

A profit maximizing seller will oscillate if profit of ‘deceiving’ transaction exceeds the loss from ‘redeeming’ transaction both relative to steady-state profit

However, seller’s will require many more ‘redeeming’ transactions after a ‘deceiving’ one. Thus, if λ is sufficiently large sellers will find it optimal to settle down to steady-state real and advertised quality levels

Reality Checks Assumptions:

All buyers have the same quality sensitivity, θ and leniency factor, λ

Buyers always submit ratings when satisfaction is above 0 or falls below –λ

Not likely to hold in real market!

Reality Checks:

1. Some buyers never rate2. Buyers differ in sensitivity and leniency3. Relax both assumptions

HOMEWORKModify r(S) to account for some buyers never rating

Conclusion Binary Reputation Systems can be well-

functioning provided buyers find the right balance between leniency and quality assessment

Finding this balance when judging seller’s profiles is necessary for the well-function of the system, otherwise the resulting market outcome will be unfair

Low-Valued vs. High-Valued Items

Pennies from eBay: the Determinants of Price in Online Auctions By: David Lucking-Reiley, Daniel Reeves and Doug Bryan/Naghi Prasad (2000 draft)

Valuing Information: Evidence from Guitar Auctions on eBay By: David H. Eaton (2002)

U.S. Cents

Collected data over 30-day period, July-Aug 1999

20,292 observations (referred to as the large set)

Subset of these used, on auctions of “U.S. Indian Head Pennies” 461 such auctions

(referred to as the small set)

Experiments Conducted

Experiments and regression analysis on three types of parameters: Effect of positive and negative feedback Effect of auction length Effect of minimum bid and reservation prices

Results Result 1-

A 1% increase in positive feedback → 0.03% increase in auction price

Effect of 1% negative feedback → 0.11% decrease in auction price (this statistically significant at 5%)

Result 2- Also found length of auction positively influenced price,

longer auctions higher prices 3 & 5-day auctions almost had same prices, but 7-day

auctions increased by 24% while 10-day auctions increased by 42% (both statistically significant)

Result 3- The presence of reserve prices increased price by 15% as minimum price bid increases by 1% final price increases

by less than 0.01%

Low-Valued vs. High-Valued Items

Pennies from eBay: the Determinants of Price in Online Auctions By: David Lucking-Reiley, Daniel Reeves and Doug Bryan/Naghi Prasad (2000 draft)

Valuing Information: Evidence from Guitar Auctions on eBay By: David H. Eaton (2002)

Paul Reed Smith Guitar Auctions High valued item (price > $1000)

Some knowledge of item based on reputation of original product ( i.e. manufacturer reputation)

Information signals: feedback profile availability of escrow services/ fraud

protection pictures

PRS Guitar Auctions

Data: auctions between January

– April 2001 four model classes 325 successful auctions

PRS Guitar Auctions

Empirical Results: pictures attract more bidding action and increase final

bid price(added value $60-232 / bid)

Use of escrow services send a negative signal to buyers

Negative feedback: - decreases the likelihood of sale - increases the final bid price for item

(added value ~ $504)

Last-minute bidding

Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet

By Alvin E. Roth and Axel Ockenfels

Late bidding

Rules for ending auction:

E-bay: fixed end-time

Amazon: automatic 10 minute extension on end time whenever bidding continues

Late bidding

Observations: the effect of experience in late bidding

More late bidding on e-bay than on Amazon

e-bay: experienced bidders submit late bids more often than less experienced bidders (opposite for Amazon)

e-bay: more late bidding for antiques than for computers

Last-minute bidding (“sniping”)

E-bay advices buyers to bid early (i.e. proxy bidding)

Late bids: risk of not being successfully transmitted

lower expected revenues for sellers Esnipe.com

“sniping” is a best response to e-Bay fixed deadline!

Last-minute bidding

Theorem 1:A bidder in continuous-time second-price private value auction doesn’t have any dominant strategies

Notation:

Notationm = min initial bids – smallest increment possibleVj – willingness to pay for bidder j ~ F

Consider two bidders : i, jShow: bidder j with value Vj > m+s has no dominant

strategy to every strategy of bidder i

Proof (cont’)Case 1: player i strategy: bid m at t=0, not bid if she remains the highest bidder bid B (with B> Vj+s) whenever she is not the highest bidder

player j best response:Not bid at any time t<1Bid Vj at t=1 (end of auction)

Payoff to player j = p*(Vj – m- s)>0, p=probability bid is transmitted

Case 2:Player i strategy: not bid at any time

If player j uses her previous strategy: E[payoff]= p*(Vj – m) < Vj –m

Player j has no dominant strategies!

Last-minute bidding

Recall Theorem 1:A bidder in continuous-time second-price private value auction doesn’t have any dominant strategies

Last-minute bidding

Theorem 2:There can exist equilibria in which bidders do not bid their true values until last moment (t=1), at which time there is only a probability p (p<1) that a bid will be transmitted

Proof:

A mutual delay until the last-minute of the auction can raise the E [profit] of all bidders because of the positive probability that another bidder’s last-minute bid will not be successfully transmitted

At this equilibrium, E [bidder profits]> than at equilibrium at which each player bids his true values early

Strategic Reasons for Late Bidding

To avoid “bidding wars” with incremental bidders

To avoid “bidding wars” with other like-minded bidders

To protect information

Non-strategic Reasons for Late Bidding

Procrastination

To retain flexibility to bid in other auctions (same item)

Comparisons of eBay & Amazon

Most differences come from the different auction rules

Data Description Both make data publicly available Downloaded data in two categories

Computers: retail price of most items are usually available

Antiques: retail prices are not usually known and value is in most cases ambiguous

Comparisons of eBay & Amazon Continued

Data set Auctions were randomly selected during a certain

time period Criteria

Two or more bidders Auctions with reserve price were selected if it was met

Selected 480 auctions with 2279 bidders 120 eBay and 120 Amazon Computer Auctions 120 eBay and 120 Amazon Antiques Auctions

Comparisons of eBay & Amazon Continued

Timing Data on seconds last bid submitted by each bidder

before auction closed (if bid was within last 12 hours) For Amazon computed ‘hypothetical’ deadline

Feedback Number For eBay as explained before For Amazon, users (both buyers and sellers) place a 1-

5 rating. The sum of these ratings is the ‘feedback number’ in Amazon

Results

Results (cont’)

When are last bids submitted?

Survey Included a survey consisting of 8 questions

Targeted at buyers who have been successful last moment bidders

Questions: Do you plan early on to be a late bidder? Why? Bid by hand or bidder software? What % of your late bids were not submitted due to

auction closing? Due to something else coming up? On average number of bids per auction? Idea of max you are willing to pay for an item, early

on What % of time do you wish you had bid higher (when

not highest bidder)?

Survey Results Conclusion, most often late bidding is part of a

planned strategy even knowing of late bidding risks. 91% confirm late bidding is planned early (Q1, N=65) 65% say its to avoid ‘bidding wars’ or to keep prices

down (some experienced Antique-bidders use it to avoid sharing valuable info) (Q1, N=49)

88% know early on what they are willing to pay (Q6, N=65)

Survey Results Continued Amateurs late bidding due to confusing eBay with

English auction (< 10%). Also some do feel regret for not bidding higher. (Q7)

Most bidders, 93%, do not use sniping software but have many windows open to improve late bidding performance. (Q2, N=67)

86% say at least once they were not able to make bid (Q3, N=65) and 90% say sometimes something else comes up (Q4, N=63)

Conclusions from Prior Experiment

Many causes for multiple and late bidding Differences in the number of late bids on eBay and

Amazon is evidence that rational strategic considerations play a significant role

Additional differences between categories suggests bidders respond to strategic incentives for late bidding in markets with unknown values

The large number of late bidding on Amazon also shows non-strategic causes for late bidding

Further Research

Differences in auction outcomes due to negative feedback related to seller characteristics as compared to negative feedback related to product characteristics

Analyze impact of internet-based companies that provide price information to auction participants

U-Q: Reputation Systems

How should users use information provided for their decision-making?

Are Reputation Mechanisms truly reliable?

Do they promote efficient market outcomes?

To what extent are they manipulated by strategic users?

What is the best way to design reputation systems?

U-Q: Rules for Ending Auctions Does a fixed-deadline auction of a private value good

raise less revenue than one with the same number of bidders in automatic extended deadline auction?

How about for a public value good?

Could the increased entertainment value of a fixed deadline attract sufficiently many bidders to overcome this?

Sourceso Dellarocas, Chrysanthos, Analying the Economic Efficiency of eBay-like Online Reputaion Reporting Mechanisms, MIT, 2001

o Eaton, David H., Valuing Information: Evidence from Guitar auctions on eBay, Murray State, 2002

o Lucking-Reiley, David, Bryan Doug, and Reeves, Daniel, Pennies from eBay: The Determinants of Price in Online Auctions, Vanderbilt, 2000

o Melnik, Mikhail I. and Alm, James, Does a Seller’s eCommerce Reputation Matter? Evidence from eBay Auctions, Georgia State

o Roth, Alvin and Ockenfels, Axels, Last-Minute bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions, Harvard, 1999

o Wilcox, Ronald T., Experts and Amateurs: The Role of Experience in Internet Auctions, Carnegie Mellon, 2000