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Firms engaged in electronic commerce increasingly rely on machine learning algorithms to drive a wide array of managerial decisions. The goal of this paper is to understand how competition between firms affects their strategic choice of such algorithms. We model the interaction of two firms choosing learning algorithms as a game, and analyze its equilibria in terms of the resolution of the bias-variance tradeoffs faced by the players. We show that competition can lead to strange phenomena---for example, reducing the error incurred by a firms algorithm can be harmful to that firm---and provide conditions under which such phenomena do not occur. We also show that players prefer to incur error due to variance than due to bias. Much of our analysis is theoretical, but we also show that our insights persist empirically in several publicly-available data sets.
We present a randomized primal-dual algorithm that solves the problem $min_{x} max_{y} y^top A x$ to additive error $epsilon$ in time $mathrm{nnz}(A) + sqrt{mathrm{nnz}(A)n}/epsilon$, for matrix $A$ with larger dimension $n$ and $mathrm{nnz}(A)$ nonz
Two intimately related new classes of games are introduced and studied: entropy games (EGs) and matrix multiplication games (MMGs). An EG is played on a finite arena by two-and-a-half players: Despot, Tribune and the non-deterministic People. Despot
In this paper we introduce a novel flow representation for finite games in strategic form. This representation allows us to develop a canonical direct sum decomposition of an arbitrary game into three components, which we refer to as the potential, h
We introduce the concept of Conversion/Preference Games, or CP games for short. CP games generalize the standard notion of strategic games. First we exemplify the use of CP games. Second we formally introduce and define the CP-games formalism. Then w
One of the natural objectives of the field of the social networks is to predict agents behaviour. To better understand the spread of various products through a social network arXiv:1105.2434 introduced a threshold model, in which the nodes influenced