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A firm builds its reputation not only by investing in the quality of its products, but also by controlling the information consumers observe. I consider a model in which a firm invests in both product quality and a costly signaling technology in order to build its reputation, defined as the market's belief that its quality is high. The signaling technology influences the rate at which consumers’ receive information about quality: the firm can either promote, which increases the arrival rate of signals when quality is high, or censor, which decreases the arrival rate of signals when quality is low. I study how the firm's incentives to build quality and to signal depend on its reputation and current quality. Whether the firm promotes or censors plays a key role in the structure of equilibria. Promotion and investment in quality are complements: the firm has a stronger incentive to build quality when the promotion level is high. Costly promotion can, however, reduce the firm's incentive to build quality as higher quality will lead to higher promotion expenses; this effect persists even as the cost of building quality approaches zero. Censorship and investment in quality are substitutes. The ability to censor can destroy a firm's incentives to invest in quality, because instead of building quality a firm may simply opt to reduce information about low quality products.
In many economic situations, an individual learns from the actions of others, even if she doesn't know how these individuals interpret information. This paper explores model misspecification in an observational learning framework. An agent's type specifies how she interprets signals and the actions of other agents. Misspecified types have incorrect beliefs about the signal distribution and how other agents draw inference. This framework captures behavioral biases such as confirmation bias, underweighting or overweighting information, partisan bias and correlation neglect, as well as models of inference such as level-k and cognitive hierarchy. We develop a simple criterion to identify how misspecification impacts asymptotic learning. Depending on the nature of the misspecification, beliefs may converge to the incorrect state, the correct state, or not converge at all. Agents with different models may asymptotically disagree, despite observing the same information. Finally, we establish that the correctly specified model is robust in that agents with approximately correct models have identical asymptotic learning outcomes.
Information Design in Misspecified Learning Models (Joint with Aislinn Bohren, Work in Progress)
We study how to design information to help misspecified learners learn the true state of the world. Agents learn by observing exogenous signals and the choices of others in addition to signals generated by the designer. With the correctly specified model, these agents would eventually learn the truth without any intervention, in the presence of misspecification this is no longer the case. We characterize how the degree and type of misspecification affect the optimal information policy. Depending on the misspecification, it may be optimal for the designer to release very one very precise signal or to continually release small amounts of information.
Microeconomic Theory I (graduate level)
Summer Math Camp (graduate level)
Sidney Weintraub Memorial Fellowship (2015-16)
Midwest Economic Theory Conference (2016)
North American Summer Meetings of the Econometric Society (2016)
Stony Brook Game Theory Festival (2016)
Game Theory World Congress (2016)
Pennsylvania Theory Conference (2016) (poster)