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The design of successful on-line auctions. Leticia Saldain Guadalupe Segura. 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

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The design of successfulon-line auctions

Leticia Saldain

Guadalupe Segura


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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 l.jpg
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


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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


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Reputation Mechanisms

Analyzing the Economic Efficiency of eBay-like Online Reputation Reporting Mechanisms

By Chrysanthos Dellarocas


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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


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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!


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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


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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!


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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

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

Result from asymmetric information!


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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!


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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 sale

    G() ≡ gross revenue

    c() ≡ cost


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eBay Reputation System

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

Buyer Utility: U = θ *q – p

p = price

q = level of quality after consumption

θ = buyer’s quality sensitivity

Buyer estimated quality qe = f( qa, R)

qa – advertised quality

R - Reputation

Buyer: max E(utility) = Ue = θ *qe – p

After purchase: buyer observes q = qr + ε


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eBay reputation system

Error term (ε) represents:

  • buyers misinterpreting qa

  • sellers may vary in actual q from one transition to another

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

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


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eBay reputation system

Buyer 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 sellers

    Possible Explanations:

    • Fear of reciprocal ratings

    • outside network communication between sellers and buyers

    • “culture of praise” : buyers feel a moral obligation to give “+” ratings


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

  • 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)

  • 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


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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


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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.


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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


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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


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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


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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


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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


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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 parametersλ, 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.


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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)


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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


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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!


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Reality Checks:

  • Some buyers never rate

  • Buyers differ in sensitivity and leniency

  • Relax both assumptions

HOMEWORK

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


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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


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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)


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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)


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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


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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%


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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)


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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


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PRS Guitar Auctions

Data:

  • auctions between January – April 2001

  • four model classes

  • 325 successful auctions


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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)


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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


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Late bidding

Rules for ending auction:

  • E-bay: fixed end-time

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


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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


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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!


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Last-minute bidding

  • Theorem 1:

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


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Notation:

Notation

m = min initial bid

s – smallest increment possible

Vj – willingness to pay for bidder j ~ F

Consider two bidders : i, j

Show: bidder j with value Vj > m+s has no dominant strategy to every strategy of bidder i


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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<1

Bid 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!


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Last-minute bidding

  • Recall Theorem 1:

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


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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


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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


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Strategic Reasons for Late Bidding

  • To avoid “bidding wars” with incremental bidders

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

  • To protect information


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Non-strategic Reasons for Late Bidding

  • Procrastination

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


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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


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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


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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





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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)?


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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)


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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)


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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


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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


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U-Q: Reputation Systems

  • Are Reputation Mechanisms truly reliable?

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

  • Do they promote efficient market outcomes?

  • To what extent are they manipulated by strategic users?

  • What is the best way to design reputation systems?


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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?


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Sources

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

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

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

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

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

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


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