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

The design of successful on-line auctions
Leticia Saldain
Guadalupe Segura
Outline
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E-bay basics
Reputation Mechanisms
Low-valued items vs. high-valued items
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Pennies: US Cent and Indian Head
Paul Reed Smith Guitar
Last-minute bidding
E-Bay basics
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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
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Second price auction
Ascending-bid (English) format
- fixed time and date
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Reservation price
Auction lasts 3-10 days
“proxy bidding” system (Vickrey auction)
Seller chooses
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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 Mechanisms
By Chrysanthos Dellarocas
Effects of reputation
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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
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Feedback profile = reputation
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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
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seller: reserve value = 1000*q
buyer: reserve value = 1500*q
Market for Lemons (cont’)
cars sold in an auction:
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Initial price $2000/car (all owners are willing
to sell their car)
Buyers : average quality = 1, bid <= 1500
Auctioneer must reduce price to 1500:
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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:
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When potential buyers only know the average quality of used
cars, market prices will be lower than the true value of topquality cars
Owners of top-quality cars will withhold cars from sale
GOOD CARS ARE DRIVEN OUT OF MARKET BY LEMONS!
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Cars will not be sold even though potential buyers value the cars
more than current owners
Result from asymmetric information!
E-Bay Marketplace
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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:
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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
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
eBay reputation system
Error term (ε) represents:
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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)
eBay reputation system
Buyer satisfaction: S = U – Ue = θ (qr – qe + ε), ε ~ N(0, σ)
Rating function r(S) :
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‘+’
‘-’
no rating
if S>0
if S <= - λ
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:
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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?
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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 steadystate advertised quality levels if buyers are
lenient when rating seller’s profiles
Estimated vs. Real Qualities
in Steady State
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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
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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
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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 Qualities
in Steady State Continued
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There are three ways buyers may use ∑(+),
∑(-), and N to estimate ξ(R)
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Based on the number of positives
Based on the number of negatives
Based on the ratio between negatives and positives
Estimate based on positives
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Require a fraction of positive ratings exceed a
threshold, η^
We will use statistical hypothesis,
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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
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This method is similar to previous, except we are
know looking at fraction of negative ratings, ζ
Again using statistical testing
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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
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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
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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 nonnegative and monotonically increasing in ξ
Again using statistical testing
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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
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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
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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
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Conclusions
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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
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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:
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Some buyers never rate
Buyers differ in sensitivity and leniency
Relax both assumptions
HOMEWORK
Modify r(S) to account for some buyers never rating
Conclusion
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Binary Reputation Systems can be wellfunctioning 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
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Collected data over 30day 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”
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461 such auctions
(referred to as the
small set)
Experiments Conducted
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Experiments and regression analysis on
three types of parameters:
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Effect of positive and negative feedback
Effect of auction length
Effect of minimum bid and reservation prices
Results
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Result 1
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Result 2
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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%)
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
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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
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High valued item (price > $1000)
Some knowledge of item based on
reputation of original product
( i.e. manufacturer reputation)
Information signals:
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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:
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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:
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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
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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”)
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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
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Theorem 1:
A bidder in continuous-time
second-price private value auction
doesn’t have any dominant
strategies
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
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!
Last-minute bidding
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Recall Theorem 1:
A bidder in continuous-time
second-price private value auction
doesn’t have any dominant
strategies
Last-minute bidding
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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:
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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
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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
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Procrastination
To retain flexibility to bid in other
auctions (same item)
Comparisons of eBay & Amazon
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Most differences come from the different
auction rules
Data Description
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Both make data publicly available
Downloaded data in two categories
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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
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Data set
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Auctions were randomly selected during a certain
time period
Criteria
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Two or more bidders
Auctions with reserve price were selected if it was met
Selected 480 auctions with 2279 bidders
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120 eBay and 120 Amazon Computer Auctions
120 eBay and 120 Amazon Antiques Auctions
Comparisons of eBay & Amazon Continued
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Timing
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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
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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
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Included a survey consisting of 8 questions
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Targeted at buyers who have been successful last
moment bidders
Questions:
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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
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Conclusion, most often late bidding is part of a
planned strategy even knowing of late bidding
risks.
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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
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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
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Many causes for multiple and late bidding
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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
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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
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Are Reputation Mechanisms truly reliable?
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Do they promote efficient market outcomes?
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To what extent are they manipulated by strategic users?
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What is the best way to design reputation systems?
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How should users use information provided for their
decision-making?
U-Q: Rules for Ending Auctions
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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?
Sources
o 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