Browsing by Author "Kolb, A"

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    A Delegation-Based Theory of Expertise
    (Economic Research Initiatives at Duke (ERID), 2015-09-17) Ambrus, A; Baranovskyi, V; Kolb, A
    We investigate competition in a delegation framework, with a coarsely informed principal. Two imperfectly informed and biased experts simultaneously propose action choices. A principal with a diffuse prior, and only being able to ordinally compare the two proposals, has to choose one of them. The selected expert might receive a bonus payment. We show that having a second expert benefits the principal, even if the two experts have the same biases and the bonus of the winner is zero. In contrast with other models of expertise, in our setting the principal prefers experts with equal rather than opposite biases. Increasing the bonus brings experts closer to truthful reporting, but this only benefits the principal up to a threshold level, with further increases in the bonus strictly decreasing her payoffs. A methodological contribution of our paper is characterizing restrictions on the set of strategies which allows a formal generalization of ex ante expected payoffs to games with diffuse prior.
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    Supplementary Appendix to 'A Delegation-Based Theory of Expertise'
    (Economic Research Initiatives at Duke (ERID), 2015-09-17) Ambrus, A; Baranovskyi, V; Kolb, A
    This supplement provides welfare results not contained in the main text and a proof of Lemma A.1. For small bonuses, a mixed equilibrium exists if and only if a downward equilibrium exists; if so, it is unique. For large bonuses, we find a unique candidate for mixed equilibrium and show that mixed and upward equilibria cannot co-exist. Also, we give an example for equal biases, where this candidate is indeed a mixed equilibrium. However, when biases are different enough and the bonus is high, a mixed equilibrium does not exist. Though a general analytical comparison is infeasible, we show that mixed equilibria are inferior to upward equilibrium or simple delegation in various special cases.