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Collective Search in Networks

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Abstract

I study the dynamics of collective search in networks. Bayesian agents act in sequence, observe the choices of their connections, and privately acquire information about the qualities of different actions via sequential search. If search costs are not bounded away from zero, maximal learning occurs in sufficiently connected networks where individual neighborhood realizations weakly distort agents’ beliefs about the realized network. If search costs are bounded away from zero, maximal learning is possible in several stochastic networks, including almost-complete networks, but generally fails otherwise. When agents observe random numbers of immediate predecessors, the learning rate, the probability of wrong herds, and long-run efficiency properties are the same as in the complete network. The density of indirect connections affects convergence rates. Network transparency has short-run implications for welfare and efficiency.

Suggested Citation

  • Niccolò Lomys, 2023. "Collective Search in Networks," CSEF Working Papers 688, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    • Handle: RePEc:sef:csefwp:688
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    More about this item

    Keywords

    Networks; Bayesian Learning; Search; Speed and Efficiency of Social Learning.;
    All these keywords.

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D6 - Microeconomics - - Welfare Economics
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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