1. 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.

2. HICSS-41, Andreas U. Schmidt, Network Markets2 Markets for digital goods: copyright waring or economy digital goods transferable & non-rival durability no wear & tear creation can be costly free riding pirate sites Bittorrent file sharing nets warez & cracker sites p2p nets copy protection DRM impede fair use DMCA WIPO treaty EU directive national laws online music video on demand ring-tones software as a service criminalise the clientele restore features of physical goods some majors defect taxation culture flat-rate GEMA network markets for digital goods super-distribution remuneration Snocap MashBoxxPeerImpact Potato

3. HICSS-41, Andreas U. Schmidt, Network Markets3 Economic qualities of network markets Ponzi schemes Peter-and-Paul scams chain letters pyramid selling Inventory loading externalise distribution cost late entrants are penalised snowball systems externalise risk favour recruitition over resales digital goods 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 ?

4. HICSS-41, Andreas U. Schmidt, Network Markets4 The core model for expected resale revenues a flux model buyers enter continuously remaining revenues go to a collector agents compete with each other for new buyers There may be transaction costs over time until the market is saturated pay a certain price to the reseller satisfies a conservation law / zero-sum condition reparameterisation by market saturation mild error behaviour w.r.t. discrete model can be extended to multiple rewarding levels (by a Markov property) the map from price to incentive is invertible dynamical forward pricing mechanism design fair reward / incentive schedules competition against free-riders? enabling scale free! incentive is independent of absolute market size

5. HICSS-41, Andreas U. Schmidt, Network Markets5 Examples early adopters are favoured nonzero price at s=0 entails artefactual singularity early subscriber discount 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 late-comers are penalised

6. HICSS-41, Andreas U. Schmidt, Network Markets6 Modeling duopoly competition which externalities influence buyer decision? subtracting the utilities of A and B and summation yields the bias of agents to buy A exogenous factors a. priceb. popularity in a duopoly network market endogenous factors 2. genuine multiplier externality, tuned with parameter ε monopoly resale revenues estimated probability that others buy, based on popularity alone bounded rationality 1. reward expectation with bounded rationality

7. HICSS-41, Andreas U. Schmidt, Network Markets7 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 Δ B A separate them by the bias calculate the dynamics

8. HICSS-41, Andreas U. Schmidt, Network Markets8 Competition with free riders – typical dynamics price is a spike peaking at m, leading to different incentive schedules free-riders: p A =p B, π B =0 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

9. HICSS-41, Andreas U. Schmidt, Network Markets9 Competing against free riders m=0.1 m=0.5m=0.9

10. HICSS-41, Andreas U. Schmidt, Network Markets10 Open issues and further work ABCE put network marketing of digital goods with dynamical forward pricing to the real-world test! waiting costs s=1 singularity dynamical forward pricing strategies and implementation restrictions on resale explicitly scale- dependent effects rushing / sniping ? information availability market homogeneity enable marketing for resellers