4 research outputs found
A "Quantal Regret" Method for Structural Econometrics in Repeated Games
We suggest a general method for inferring players' values from their actions
in repeated games. The method extends and improves upon the recent suggestion
of (Nekipelov et al., EC 2015) and is based on the assumption that players are
more likely to exhibit sequences of actions that have lower regret.
We evaluate this "quantal regret" method on two different datasets from
experiments of repeated games with controlled player values: those of (Selten
and Chmura, AER 2008) on a variety of two-player 2x2 games and our own
experiment on ad-auctions (Noti et al., WWW 2014). We find that the quantal
regret method is consistently and significantly more precise than either
"classic" econometric methods that are based on Nash equilibria, or the
"min-regret" method of (Nekipelov et al., EC 2015)
ERA: A Framework for Economic Resource Allocation for the Cloud
Cloud computing has reached significant maturity from a systems perspective,
but currently deployed solutions rely on rather basic economics mechanisms that
yield suboptimal allocation of the costly hardware resources. In this paper we
present Economic Resource Allocation (ERA), a complete framework for scheduling
and pricing cloud resources, aimed at increasing the efficiency of cloud
resources usage by allocating resources according to economic principles. The
ERA architecture carefully abstracts the underlying cloud infrastructure,
enabling the development of scheduling and pricing algorithms independently of
the concrete lower-level cloud infrastructure and independently of its
concerns. Specifically, ERA is designed as a flexible layer that can sit on top
of any cloud system and interfaces with both the cloud resource manager and
with the users who reserve resources to run their jobs. The jobs are scheduled
based on prices that are dynamically calculated according to the predicted
demand. Additionally, ERA provides a key internal API to pluggable algorithmic
modules that include scheduling, pricing and demand prediction. We provide a
proof-of-concept software and demonstrate the effectiveness of the architecture
by testing ERA over both public and private cloud systems -- Azure Batch of
Microsoft and Hadoop/YARN. A broader intent of our work is to foster
collaborations between economics and system communities. To that end, we have
developed a simulation platform via which economics and system experts can test
their algorithmic implementations