Saeed Alaei
I am a research scientist in the market algorithms team in Mountain View. I received my Ph.D
in Computer Science from University of Maryland - College Park. I was a post doctoral
research associate at Cornell University prior to joining Google. My research interests
include mechanism design/algorithmic game theory, combinatorial and convex optimization,
and online algorithms. The focus of my research is on developing general algorithms and
techniques for optimization problems involving strategic agents arising in online markets.
Authored Publications
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Inspect or Guess? Mechanism Design with Unobservable Inspection
Azarakhsh Malekian
Ali Daei Naby
The 21st Conference on Web and Internet Economics (WINE) (2025) (to appear)
Preview abstract
We study the problem of selling $k$ units of an item to $n$ unit-demand buyers to maximize revenue, where buyers' values are independently (and not necessarily identically) distributed. The buyers' values are initially unknown but can be learned at a cost through inspection sources. Motivated by applications in e-commerce, where the inspection is unobservable by the seller (i.e., buyers can externally inspect their values without informing the seller), we introduce a framework to find the optimal selling strategy when the inspection is unobservable by the seller. We fully characterize the optimal mechanism for selling to a single buyer, subject to an upper bound on the allocation probability. Building on this characterization and leveraging connections to the \emph{Prophet Inequality}, we design an approximation mechanism for selling $k$ items to $n$ buyers that achieves $1-1/\sqrt{k+3}$ of the optimal revenue. Our mechanism is simple and sequential and achieves the same approximation bound in an online setting, remaining robust to the order of buyer arrivals. Additionally, in a setting with observable inspection, we leverage connections to index-based \emph{committing policies} in \emph{Weitzman's Pandora's problem with non-obligatory inspection} and propose a new sequential
mechanism for selling an item to $n$ buyers that significantly improves the existing approximation factor to the optimal revenue from $0.5$ to $0.8$.
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Response Prediction for Low-Regret Agents
Ashwinkumar Badanidiyuru Varadaraja
Sadra Yazdanbod
Web and Internet Economics 2019
Preview abstract
Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on the revenue of the company and the welfare of the advertisers and the users. Therefore, any change requires careful evaluation of its potential impacts. Currently, such impacts are often evaluated by running simulations or small controlled experiments. This, however, misses the important factor that the advertisers respond to changes. Our goal is to build a theoretical framework for predicting the actions of an agent (the advertiser) that is optimizing her actions in an uncertain environment. We model this problem using a variant of the multi armed bandit setting where playing an arm is costly. The cost of each arm changes over time and is publicly observable. The value of playing an arm is drawn stochastically from a static distribution and is observed by the agent and not by us. We, however, observe the actions of the agent. Our main result is that assuming the agent is playing a strategy with a regret of at most f(T) within the first T rounds, we can learn to play the multi-armed bandits game without observing the rewards) in such a way that the regret of our selected actions is at most O(k^4 (f(T) + 1) log(T)).
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