Policymaking under uncertainty: Gradualism and robustness

Some economists have recommended the robust control approach to the formulation of monetary policy under uncertainty when policymakers cannot attach probabilities to the scenarios that concern them. One critique of this approach is that it seems to imply aggressive policies under uncertainty, contrary to the conventional wisdom of acting more gradually in an uncertain environment. This article argues that aggressiveness is not a generic feature of robust control.

Robustness and macroeconomic policy

This paper considers the design of macroeconomic policies in the face of uncertainty. In recent years, several economists have advocated that when policymakers are uncertain about the environment they face and find it difficult to assign precise probabilities to the alternative scenarios that may characterize this environment, they should design policies to be robust in the sense that they minimize the worstcase loss these policies could ever impose. I review and evaluate the objections cited by critics of this approach. I further argue that, contrary to what some have inferred, concern about worst-case scenarios does not always lead to policies that respond more aggressively to incoming news than the optimal policy would respond absent any uncertainty.Macroeconomics - Econometric models

Managing Risk of Bidding in Display Advertising

In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies