Innovation Adoption by Forward-Looking Social Learners

Frick, Mira; Ishii, Yuhta

doi:10.2139/ssrn.2565769

Cited by 16 publications

(14 citation statements)

References 58 publications

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“…Private information about project quality is studied by Gomes et al (2015) in experimentation without moral hazard, and in a different setting by Gerardi and Maestri (2012). Another possibility would be non-common priors between the principal and the agent, which would involve quite distinct considerations.…”

Section: A1 Step 1: Low Type Always Workmentioning

confidence: 99%

Optimal Contracts for Experimentation

Halac

Kartik

Liu

2016

Review of Economic Studies

125

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This paper studies a model of long-term contracting for experimentation. We consider a principalagent relationship with adverse selection on the agent's ability, dynamic moral hazard, and private learning about project quality. We find that each of these elements plays an essential role in structuring dynamic incentives, and it is only their interaction that generally precludes efficiency. Our model permits an explicit characterization of optimal contracts.

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Section: A1 Step 1: Low Type Always Workmentioning

confidence: 99%

Optimal Contracts for Experimentation

Halac

Kartik

Liu

2016

Review of Economic Studies

125

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“…for which such equilibria exist (this interval is non-empty by our assumption that b < 1/4). 18 But note that they are all worse than the original two-partition equilibrium of CS, as the higher value pushes t 1 above what it would have been if y 1 = t 1 /2. The problem is that the lower interval is already too large, compared to the higher one.…”

Section: Negative Bias (Bias For Action)mentioning

confidence: 96%

Learning, Experimentation, and Information Design

Hörner

Skrzypacz

2017

Advances in Economics and Econometrics

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for detailed comments. 2 Equilibrium Interactions 2.1 Strategic Bandits Strategic bandit models are game-theoretic versions of standard bandit models. While the standard "multi-armed bandit" describes a hypothetical experiment in which a player faces several slot machines ("one-armed bandits") with potentially different expected payouts, a strategic bandit involves several players facing (usually, identical) copies of the same slot machine. Players want to stick with the slot machine if and only if the best payout rate makes it worth their time, and learn not only from their own outcomes but also from their neighbors. Equilibrium strategies are not characterized by simple cutoffs in terms of the common belief. As a result, solutions to strategic bandits are only known for a limited class of distributions, involving two states of the world only. In Bolton and Harris (1999, BH), the observation process (of payoffs) follows a Brownian motion, whose drift depends on the state. In Keller, Rady and Cripps (2005, KRC), it follows a simple Poisson process, with positive lump-sums ("breakthroughs") occurring at random (exponentially distributed) times if and only if the arm is good. Keller and Rady (2015, KR15) solve the polar opposite case in which costly lump-sums ("breakdowns") occur at random times if and only if the arm is bad. Keller and Rady (2010, KR10) consider the case in which breakthroughs need not be conclusive. These models share in common, in addition to the binary state framework, their focus on symmetric Markov perfect equilibria (MPE). 1 Throughout, players are Bayesian. Given that they observe all actions and outcomes, they share a common belief about the state, which serves as the state variable. They are also impatient and share a common discount rate. BH and KR10 are the most ambitious models and offer no closed-form solutions for the equilibrium. Remarkably, however, they are able to prove uniqueness and tease out not only their structure, but their dependence on parameters. While it is BH that first develops both the ideas (including the concepts of free-riding and encouragement effects) as well as the methods used throughout this literature, the most interesting insights can already be gleaned from the simple exponential bandits in KRC and KR15. What makes these two models tractable is that these models can be viewed as deterministic: unless a breakdown or breakthrough ("news") occurs, the (conditional) posterior belief follows a known path. If news arrives, the game is over, since if it is commonly known that the state is good (or bad), informational externalities cease to matter, and each player knows the strictly dominant action to take. Let us consider here a simplified version combining the good and bad news models. The state is ω ∈ {G, B}. Each player i = 1,. .. , I controls the variable u i t ∈ [0, 1], which is the fraction allocated to the risky arm at time t ≥ 0 (the complementary fraction being allocated to the safe arm). The horizon is infinite. This leads to a total realized payoff of ...

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“…This allows for a simpler inference problem and mitigates the issue of cascades (herding on a suboptimal alternative). More generally, there exists wide literature on social learning (e.g., Frick and Ishii [2016], Acemoglu, Makhdoumi, Malekian, and Ozdaglar [2017]) which focuses on the assumptions about players' information structures which enable social learning of an unknown state.…”

Section: Social Learningmentioning

confidence: 99%

Bad News Turned Good: Reversal Under Censorship

Smirnov

Starkov

2018

SSRN Journal

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Sellers often have the power to censor the reviews of their products. We explore the effect of these censorship policies in markets where some consumers are unaware of possible censorship. We find that if the share of such "naive" consumers is not too large, then rational consumers treat any bad review that is revealed in equilibrium as good news about product quality. This makes bad reviews worth revealing and allows the high-type seller to use them as a costly signal of his product's quality to rational consumers.

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Innovation Adoption by Forward-Looking Social Learners

Cited by 16 publications

References 58 publications

Optimal Contracts for Experimentation

Optimal Contracts for Experimentation

Learning, Experimentation, and Information Design

Bad News Turned Good: Reversal Under Censorship

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