Competition and Herding in Breaking News
Abstract: I present a dynamic model of breaking news. News firms are rewarded for reporting before their competitors but also for making reports that are credible to consumers. Errors occur when firms fake, reporting a story despite lacking evidence. While firms make errors even under a monopoly, competition and observational learning exacerbate errors and give rise to rich dynamics in firm behavior. Competition intensifies faking by engendering a preemptive motive, but the haste-inducing effect of preemption is endogenously mitigated by gradual improvement in report credibility over the course of a news cycle. Meanwhile, observational learning causes existing errors to propagate through the market. This is driven by a copycat effect, in which one report triggers an immediate surge in faking by others. This behavior is consistent with herding on the decision to report a story as well as herding on the timing of reports.
Reputation in News Media: Speed vs. Accuracy
Abstract: We study news firms’ reporting behavior, including their propensity to misreport, when they are reputation driven. In our model, a news firm (sender) dynamically learns about a state and reports to a consumer (receiver). Senders are concerned with their reputation at the end of the game, and must choose when to time their report. We find that in equilibrium, the sender fakes, i.e., report despite being ignorant of the state, with positive probability in every period. This faking in turn leads to a higher level of misreporting than if the sender were instead truthful. We further find the sender’s reputations is endogenously rewarded for both speed and accuracy, and thus we provide a microfoundation to the speed-accuracy tradeoff in the news media setting. Finally, we consider the dynamics in the sender’s strategy, finding that the sender becomes more truthful, and thus less prone to misreporting, as time passes.
Dynamic Reputation-Driven Media Bias
Abstract: We study the dynamics of reputation-driven media bias. To this end, we present a dynamic model of reputation-driven media bias. A firm privately learns about an issue in increments and reports to a consumer with each new piece of information. With each new report, the consumer updates her beliefs about the firm’s information quality, i.e., the firm’s reputation. Firms are forward-looking and thus take into account both their immediate and future reputations when reporting. Nonetheless, we establish that equilibrium reporting behavior is identical for myopic and forward-looking firms. In equilibrium, firms bias their reports, and this bias is shown to be driven by two separate factors. First, firms can appear more reputable by appealing to a consumer’s prior bias (the prior effect). Separately firms with reports that are more consistent across time are viewed more favorably (the consistency effect). The relative importance of the consistency effect grows over time as the firm accumulates a richer history of reports.
Works In Progress
Optimal Stopping and Reputation
News Accuracy and Speed: Theory and Experiment (with Silvio Ravaioli)
Preemption and Private Learning