4 research outputs found
Non-Neutrality of Search Engines and its Impact on Innovation
International audienceThe search neutrality debate is about whether search engines should or should not be allowed to uprank certain results among the organic content matching a query. This debate is related to that of network neutrality, which focuses on whether all bytes being transmitted through the Internet should be treated equally. In a recent paper, we have formulated a model that formalizes this question and characterized an optimal ranking policy for a search engine. The model relies on the trade-off between short-term revenues, captured by the benefits of highly-paying results, and long-term revenues which can increase by providing users with more relevant results to minimize churn. In this article, we apply that model to investigate the relations between search neutrality and innovation. We illustrate through a simple setting and computer simulations that a revenue-maximizing search engine may indeed deter innovation at the content level. Our simple setting obviously simplifies reality, but this has the advantage of providing better insights on how optimization by some actors impacts other actors
Revenue-Maximizing Rankings for Online Platforms with Quality-Sensitive Consumers
International audienc
Optimal Experimental Design for Staggered Rollouts
Experimentation has become an increasingly prevalent tool for guiding
decision-making and policy choices. A common hurdle in designing experiments is
the lack of statistical power. In this paper, we study the optimal multi-period
experimental design under the constraint that the treatment cannot be easily
removed once implemented; for example, a government might implement a public
health intervention in different geographies at different times, where the
treatment cannot be easily removed due to practical constraints. The treatment
design problem is to select which geographies (referred by units) to treat at
which time, intending to test hypotheses about the effect of the treatment.
When the potential outcome is a linear function of unit and time effects, and
discrete observed/latent covariates, we provide an analytically feasible
solution to the optimal treatment design problem where the variance of the
treatment effect estimator is at most 1+O(1/N^2) times the variance using the
optimal treatment design, where N is the number of units. This solution assigns
units in a staggered treatment adoption pattern - if the treatment only affects
one period, the optimal fraction of treated units in each period increases
linearly in time; if the treatment affects multiple periods, the optimal
fraction increases non-linearly in time, smaller at the beginning and larger at
the end. In the general setting where outcomes depend on latent covariates, we
show that historical data can be utilized in designing experiments. We propose
a data-driven local search algorithm to assign units to treatment times. We
demonstrate that our approach improves upon benchmark experimental designs via
synthetic interventions on the influenza occurrence rate and synthetic
experiments on interventions for in-home medical services and grocery
expenditure