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Dynamics of Network Technology Competition The Role of Gateways

Dynamics of Network Technology Competition The Role of Gateways. Soumya Sen Dept. Elec. & Sys. Eng University of Pennsylvania Technical Report: “Modeling the dynamics of network technology adoption and the role of converters”, http://repository.upenn.edu/ese_papers/496/. Acknowledgements.

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Dynamics of Network Technology Competition The Role of Gateways

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  1. Dynamics of Network Technology Competition The Role of Gateways Soumya Sen Dept. Elec. & Sys. Eng University of Pennsylvania Technical Report: “Modeling the dynamics of network technology adoption and the role of converters”, http://repository.upenn.edu/ese_papers/496/ New York Computer Science & Economics Day, Nov 9, 2009

  2. Acknowledgements • Joint work with: Youngmi Jin (Penn, ESE) Roch Guerin (Penn, ESE) Kartik Hosanagar (Penn, Wharton) • In collaboration with: Andrew Odlyzko (U. Minn) Zhi-Li Zhang (U. Minn) New York Computer Science & Economics Day

  3. One (out of many) Motivating Example • Two competing video-conf service offerings • Incumbent: Low-def video (has low quality, low price) • Entrant: High-def (has high quality, high price) • Technology characteristics • User value depends on who they can reach • Higher externality benefits for High-def than for Low-def • Gateways/converters can allow inter-operability with some limitations • Simplex, asymmetric, unconstrained • Asymmetric: encoding is hard, decoding is easy • Low-def subscribers can display high-def signals but not generate them • Modeling the evolution and outcome of technology competition New York Computer Science & Economics Day

  4. Model • Users individually continuously evaluate their technology choice • Decision based on technology utility Technology 1: U1(,x1,x2) =  q1+(x1+α1β x2) – p1 Technology 2: U2(,x1,x2) =  q2+(βx2+α2x1) – p2 • User decisions are rational (but myopic – based on current adoption) • No technology if U1< 0, U2<0 • Technology 1 if U1>0, U1> U2 • Technology 2 if U2>0, U1< U2 • Adoption dynamics can be captured with a standard diffusion model • Main complexity is keeping track of combinations of decision regions New York Computer Science & Economics Day

  5. What can this model achieve? • Identify feasible combinations of possible equilibria • Characterize diffusion trajectories • Insight into possible adoption patterns can be extracted from the solution New York Computer Science & Economics Day

  6. Key Findings • Gateways can introduce instabilities (“boom and bust” cycles) • Impossible without gateways • Gateways create unpredictable outcomes • Allow inferior technologies to persist • Can hurt or help the incumbent • Can hurt or help overall market penetration New York Computer Science & Economics Day

  7. Example 1: “Boom & bust Cycles”(From Stable to Unstable- Asymmetric Gateways) x1: Fraction of Technology 1 adopters, x2: Fraction of Technology 2 adopters • As the efficiency of Tech. 1 gateway increases, system goes from dominance of Tech. 2 to a system with no stable state • No stable equilibrium for 1=1 and 2=0 New York Computer Science & Economics Day

  8. Example 2: Hurting Overall Market(Asymmetric Gateways – Entrant) • Tech. 2 fails to gain market share without gateways • Tech. 2 introduces gateways of increasing efficiency • Tech. 2 gains market share, but at the cost of a lower overall market penetration New York Computer Science & Economics Day

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