Florin Constantin

Theory research at Google

Through the history of Computer Science, new technologies have emerged and generated fundamental problems of interest to theoretical computer scientists. From the era of telecommunications to computing and now, the Internet and the web, there are many such examples. This article is derived from the emergence of web search and associated technologies, and focuses on the problems of research interest to theoretical computer scientists that arise, in particular at Google.

Dynamic Incentive Mechanisms

Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter” by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in real-world settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic incentive mechanisms, which provide rules for the coordination of agents in sequential decision problems. The framework of dynamic mechanism design embraces coordinated decision-making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences. In addition to tracing some recent developments, we point to ongoing research challenges.

The snowball effect of uncertainty in potential games

Uncertainty is present in different guises in many settings, in particular in environments with strategic interactions. However, most game-theoretic models assume that players can accurately observe interactions and their own costs. In this paper we quantify the effect on social costs of two different types of uncertainty: adversarial perturbations of small magnitude to costs (effect called the Price of Uncertainty (PoU) [3]) and the presence of several players with Byzantine, i.e. arbitrary, behavior (effect we call the Price of Byzantine behavior (PoB)). We provide lower and upper bounds on PoU and PoB in two well-studied classes of potential games: consensus games and set-covering games.

Game couplings: Learning dynamics and applications

Modern engineering systems (such as the Internet) consist of multiple coupled subsystems. Such subsystems are designed with local (possibly conflicting) goals, with little or no knowledge of the implementation details of other subsystems. Despite the ubiquitous nature of such systems very little is formally known about their properties and global dynamics.

Sequential item pricing for unlimited supply

We study prior-free revenue maximization for a seller with unlimited supply of n item types facing m myopic buyers present for k < log n days. We show that a certain randomized schedule of posted prices has an approximation factor of O(log m+log n/k). This algorithm relies on buyer valuations having hereditary maximizers, a novel natural property satisfied for example by gross substitutes valuations. We obtain a matching lower bound with multi-unit valuations. In light of existing results [2], k days can thus improve the approximation by a Θ(k) factor. We also provide a posted price schedule with the same factor for positive affine allocative externalities, despite an increase in the optimal revenue.