How should market designers trade off targeting and competition? We study a natural experiment in the release of new targeting technology for online ads. A platform in our study introduced targeting into select geographic markets based on a discontinuity in local characteristics. We find that advertisers used new targeting to avoid low quality ad inventory. This led to a reduction in ad impressions. When advertisers avoided this inventory, they retreated into smaller, less competitive ad auctions featuring fewer bidders for available ad space. The reduction in competition lowered click prices in the treated areas. Nonetheless, the effects on platform revenue growth were positive. Better targeting improved the consumer experience of advertising. This led to higher consumer clickthrough rates, which raised the platform's revenue by increasing the quantity of clicks sold. The higher click volumes offset the revenue effects of the decrease in prices.

Why do biased algorithmic predictions arise, and what interventions can prevent them? We examine this topic with a field experiment about using machine learning to predict human capital. We randomly assign approximately 400 AI engineers to develop software under different experimental conditions to predict standardized test scores of OECD residents. We then assess the resulting predictive algorithms using the realized test performances, and through randomized audit-like manipulations of algorithmic inputs. We also used the diversity of our subject population to measure whether demographically non-traditional engineers were more likely to notice and reduce algorithmic bias, and whether algorithmic prediction errors are correlated within programmer demographic groups. This document describes our experimental design and motivation; the full results of our experiment are available at https://ssrn.com/abstract=3615404.

Despite the popularity of prediction markets among economists, businesses and policymakers have been slow to adopt them in decision making. Most studies of prediction markets outside the lab are from public markets with large trading populations. Corporate prediction markets face additional issues, such as thin- ness, weak incentives, limited entry and the potential for traders with ulterior motives raising questions about how well these markets will perform. We examine data from prediction markets run by Google, Ford and Firm X (a large private materials company). Despite theoretically adverse conditions, we find these markets are relatively efficient, and improve upon the forecasts of experts at all three firms by as much as a 25% reduction in mean squared error. The most notable inefficiency is an optimism bias in the markets at Google and Ford. The inefficiencies that do exist generally become smaller over time. More experienced traders and those with higher past performance trade against the identified inefficiencies, suggesting that the markets efficiency improves because traders gain experience and less skilled traders exit the market.