Network markets for digital goods
Free riding and competition
Andreas U. Schmidt
Fraunhofer Institute for Secure Information Technology SIT,
Darmstadt, Germany
The material contained is for educational purpose only. All trademarks are property of their respective holders.
Markets for digital goods: copyright waring or economy
warez & cracker sites
DMCA
criminalise
the clientele
EU directive
national laws
pirate sites
free riding
WIPO treaty
taxation
GEMA
network markets
culture
for digital goods
flat-rate
super-distribution remuneration
Snocap
MashBoxx
digital goods
durability
no wear & tear
creation can
be costly
transferable &
non-rival
p2p nets
Bittorrent
file sharing nets
Potato
PeerImpact
video on demand
software as ring-tones
a service
online music
some
majors defect
DRM
impede
restore features of
fair use
physical goods
copy protection
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Economic qualities of network markets
favour recruitition
over resales
Ponzi schemes
pyramid selling
Peter-and-Paul scams
externalise
risk
Inventory loading
digital
goods externalise
distribution cost
snowball systems
chain letters
late entrants
are penalised
if there is a free version and it is common knowledge that
some (late-comers) pay the rent, then an inductive
argument shows that the market is empty
as with pure chain letters, the success of network markets
for digital goods may depend on bounded rationality of
buyers
what revenues from resale can an agent expect ?
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The core model for expected resale revenues
a flux
model
remaining revenues
go to a collector
buyers enter
continuously
agents compete with each
other for new buyers
There may be
transaction costs
pay a certain price
to the reseller
over time until the
market is saturated
reparameterisation
by market saturation
satisfies a conservation
law / zero-sum condition
mild error behaviour
w.r.t. discrete model
can be extended to multiple
rewarding levels (by a Markov
property)
scale free!
incentive is independent of
absolute market size
the map from price to
incentive is invertible
mechanism design
dynamical forward pricing
enabling
fair reward / incentive schedules
competition against free-riders?
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Examples
early adopters are favoured
nonzero price at s=0 entails
artefactual singularity
early subscriber discount
late-comers are penalised
rebate for late adopters
mitigates the penalty
letting π(1)=0 effectively
closes the market
invitation to enter during
an initial period
taxation by the collector
does not hurt the incentive
too much
multiple levels benefit all
agents
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Modeling duopoly competition
in a duopoly network market
which externalities influence buyer decision?
endogenous factors
1. reward expectation with bounded rationality
estimated probability that
others buy, based on
popularity alone
bounded rationality
monopoly resale
revenues
exogenous factors
2. genuine multiplier externality,
tuned with parameter ε
a. price
b. popularity
subtracting the utilities of A and B and
summation yields the bias of agents to buy A
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From bias to probabilities to dynamics
given the bias Δ to buy A, how to calculate actual probabilities?
choose a ‘natural’ distribution of
the subjective utility of both goods
separate them
by the bias
Δ
B
A
calculate the dynamics
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Competition with free riders – typical dynamics
free-riders: pA=pB, πB=0
price is a spike peaking
at m, leading to different
incentive schedules
shares, turnovers
and collector’s shares
can be observed
initial invitation to enter
yields strong initial growth
more extended and amplified
by multiplier effect as m
increases
while large m are optimal
w.r.t shares, turnovers suffer
from the long rebate period
substantial growth at high s
due to the multiplier effect
mitigates this
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Competing against free riders
m=0.1
m=0.5
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m=0.9
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Open issues and further work
waiting costs
dynamical forward pricing
strategies and implementation
explicitly scaledependent effects
s=1 singularity
ABCE
rushing /
sniping ?
information
availability
market
homogeneity
enable marketing
for resellers
restrictions
on resale
put network marketing of digital goods with dynamical forward
pricing to the real-world test!
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