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Dynamic Equilibrium Bunching

  • Tao Wang

    ()

    (Queen's University)

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    In this paper, we analyze the asymmetric pure strategy equilibria in a dynamic game of pure information externality. Each player receives a private signal and chooses whether and when to invest. In some of the periods, only a subgroup of the players make decisions, which we call bunching, while the rest of the players do not invest regardless of their signals. Bunching is different from herding; it occurs in the first period and recursively until herding takes place or the game runs out of undecided players. We find that any asymmetric pure strategy equilibrium is more efficient than the symmetric mixed strategy equilibrium. When players become patient enough, herding of investment disappears in the most efficient asymmetric pure strategy equilibrium, while the least efficient asymmetric pure strategy equilibrium resembles those in a fixed timing model, producing an exact match when the discount factor is equal to 1. Bunch sizes are shown to be independent of the total number of players; adding more players to the game need not change early players' behavior. All these are unique properties of the asymmetric pure strategy equilibria. We also show that the asymmetric pure strategy equilibria can accommodate small heterogeneities of the players in costs of acquiring signals, discount factors, or degree of risk aversion. In any of these environments, there exists a unique welfare maximizing equilibrium which provides a natural way for the players to coordinate.

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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1291.pdf
    File Function: First version 2011
    Download Restriction: no

    Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1291.

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    Length: 42 pages
    Date of creation: Nov 2011
    Date of revision:
    Handle: RePEc:qed:wpaper:1291
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    1. Daniel Sgroi, 2003. "The Right Choice at the Right Time: A Herding Experiment in Endogenous Time," Experimental Economics, Springer, vol. 6(2), pages 159-180, October.
    2. Banerjee, Abhijit V, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, MIT Press, vol. 107(3), pages 797-817, August.
    3. Cao, H. Henry & Han, Bing & Hirshleifer, David, 2011. "Taking the road less traveled by: Does conversation eradicate pernicious cascades?," Journal of Economic Theory, Elsevier, vol. 146(4), pages 1418-1436, July.
    4. Scharfstein, David. & Stein, Jeremy C., 1988. "Herd behavior and investment," Working papers WP 2062-88., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 2010. "A theory of Fads, Fashion, Custom and cultural change as informational Cascades," Levine's Working Paper Archive 1193, David K. Levine.
    6. Dinah Rosenberg & Eilon Solan & Nicolas Vieille, 2007. "Social Learning in One-Arm Bandit Problems," Econometrica, Econometric Society, vol. 75(6), pages 1591-1611, November.
    7. Gul, Faruk & Lundholm, Russell, 1995. "Endogenous Timing and the Clustering of Agents' Decisions," Journal of Political Economy, University of Chicago Press, vol. 103(5), pages 1039-66, October.
    8. Jianbo Zhang, 1997. "Strategic Delay and the Onset of Investment Cascades," RAND Journal of Economics, The RAND Corporation, vol. 28(1), pages 188-205, Spring.
    9. Naveen Khanna & Richmond D. Mathews, 2011. "Can herding improve investment decisions?," RAND Journal of Economics, RAND Corporation, vol. 42(1), pages 150-174, 03.
    10. Chamley, Christophe & Gale, Douglas, 1994. "Information Revelation and Strategic Delay in a Model of Investment," Econometrica, Econometric Society, vol. 62(5), pages 1065-85, September.
    11. Nicolas Vieille & Dinah Rosenberg & Eilon Solan, 2007. "Social Learning in One-Arm Bandit Problems," Post-Print hal-00464609, HAL.
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