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Information Aggregation and Innovation in Market Design

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Abstract

The literature on information aggregation predicts that market growth unambiguously reduces uncertainty about the value of traded goods. The results were developed within the classical model, which assumes that traders’ values for the exchanged good are determined by fundamental (common) shocks. At the same time, design innovation in contemporaneous markets seems to exploit demand interdependence among agents with similar tastes or common information sharing (e.g., Facebook ads, the practice of customer targeting). This paper demonstrates that with heterogeneous interdependence among agents’ values or noise in signals about values, opportunities to innovate in smaller or less connected (in the network-theoretic sense) markets may dominate those in larger or better connected markets.

Suggested Citation

  • Mariann Ollar & Marzena Rostek, 2011. "Information Aggregation and Innovation in Market Design," Working Papers 11-12, NET Institute.
    • Handle: RePEc:net:wpaper:1112
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    References listed on IDEAS

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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Laura L. Veldkamp, 2011. "Information Choice in Macroeconomics and Finance," Economics Books, Princeton University Press, edition 1, number 9621.
    3. Marzena Rostek & Marek Weretka, 2012. "Price Inference in Small Markets," Econometrica, Econometric Society, vol. 80(2), pages 687-711, March.
    4. Xavier Vives, 2007. "Information and Learning in Markets," Levine's Bibliography 122247000000001520, UCLA Department of Economics.
    5. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
    6. Michael Ostrovsky, 2012. "Information Aggregation in Dynamic Markets With Strategic Traders," Econometrica, Econometric Society, vol. 80(6), pages 2595-2647, November.
    7. Jonathan Levin & Paul Milgrom, 2010. "Online Advertising: Heterogeneity and Conflation in Market Design," American Economic Review, American Economic Association, vol. 100(2), pages 603-607, May.
    8. Dirk Bergemann & Alessandro Bonatti, 2011. "Targeting in advertising markets: implications for offline versus online media," RAND Journal of Economics, RAND Corporation, vol. 42(3), pages 417-443, September.
    9. Susan Athey & Joshua S. Gans, 2010. "The Impact of Targeting Technology on Advertising Markets and Media Competition," American Economic Review, American Economic Association, vol. 100(2), pages 608-613, May.
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    More about this item

    Keywords

    Interdependent values and noise; Network; Link Formation; Innovation; Information Aggregation; Divisible Good Auction; Commonality;
    All these keywords.

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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