184,270 research outputs found
Strategy and Sample Selection -- A Strategic Selection Estimator
The development and proliferation of strategic estimators has narrowed the gap between theoretical models and empirical testing. But despite recent contributions that extend the basic strategic estimator, researchers have continued to neglect a classic social science phenomenon: selection. Compared to non-strategic estimators, strategic models are even more prone to selection effects. First, external shocks or omitted variables can lead to correlated errors. Second, because the systematic parts of actors? utilities usually overlap on certain key variables, the two sets of explanatory variables are correlated. As a result, both the systematic and the stochastic components can be correlated. However, given that the estimates for the first mover are computed based on the potentially biased predicted probabilities of the second actor, we also generate biased estimates for the first actor. In applied work researchers neglect the potential shortcomings due to selection bias. This paper presents an alternative strategic estimator that takes selection into account and allows scholars to obtain consistent, unbiased, and efficient estimates in the presence of both selection and strategic action. I present a Monte Carlo analysis as well as a real world application to illustrate the superior performance of this estimator relative to the standard practice
Corrupted Contextual Bandits with Action Order Constraints
We consider a variant of the novel contextual bandit problem with corrupted
context, which we call the contextual bandit problem with corrupted context and
action correlation, where actions exhibit a relationship structure that can be
exploited to guide the exploration of viable next decisions. Our setting is
primarily motivated by adaptive mobile health interventions and related
applications, where users might transitions through different stages requiring
more targeted action selection approaches. In such settings, keeping user
engagement is paramount for the success of interventions and therefore it is
vital to provide relevant recommendations in a timely manner. The context
provided by users might not always be informative at every decision point and
standard contextual approaches to action selection will incur high regret. We
propose a meta-algorithm using a referee that dynamically combines the policies
of a contextual bandit and multi-armed bandit, similar to previous work, as
wells as a simple correlation mechanism that captures action to action
transition probabilities allowing for more efficient exploration of
time-correlated actions. We evaluate empirically the performance of said
algorithm on a simulation where the sequence of best actions is determined by a
hidden state that evolves in a Markovian manner. We show that the proposed
meta-algorithm improves upon regret in situations where the performance of both
policies varies such that one is strictly superior to the other for a given
time period. To demonstrate that our setting has relevant practical
applicability, we evaluate our method on several real world data sets, clearly
showing better empirical performance compared to a set of simple algorithms
Structured penalized regression for drug sensitivity prediction
Large-scale {\it in vitro} drug sensitivity screens are an important tool in
personalized oncology to predict the effectiveness of potential cancer drugs.
The prediction of the sensitivity of cancer cell lines to a panel of drugs is a
multivariate regression problem with high-dimensional heterogeneous multi-omics
data as input data and with potentially strong correlations between the outcome
variables which represent the sensitivity to the different drugs. We propose a
joint penalized regression approach with structured penalty terms which allow
us to utilize the correlation structure between drugs with group-lasso-type
penalties and at the same time address the heterogeneity between omics data
sources by introducing data-source-specific penalty factors to penalize
different data sources differently. By combining integrative penalty factors
(IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We
present a unified framework to transform more general IPF-type methods to the
original penalized method. Because the structured penalty terms have multiple
parameters, we demonstrate how the interval-search Efficient Parameter
Selection via Global Optimization (EPSGO) algorithm can be used to optimize
multiple penalty parameters efficiently. Simulation studies show that
IPF-tree-lasso can improve the prediction performance compared to other
lasso-type methods, in particular for heterogenous data sources. Finally, we
employ the new methods to analyse data from the Genomics of Drug Sensitivity in
Cancer project.Comment: Zhao Z, Zucknick M (2020). Structured penalized regression for drug
sensitivity prediction. Journal of the Royal Statistical Society, Series C.
19 pages, 6 figures and 2 table
Finding Any Nontrivial Coarse Correlated Equilibrium Is Hard
One of the most appealing aspects of the (coarse) correlated equilibrium
concept is that natural dynamics quickly arrive at approximations of such
equilibria, even in games with many players. In addition, there exist
polynomial-time algorithms that compute exact (coarse) correlated equilibria.
In light of these results, a natural question is how good are the (coarse)
correlated equilibria that can arise from any efficient algorithm or dynamics.
In this paper we address this question, and establish strong negative
results. In particular, we show that in multiplayer games that have a succinct
representation, it is NP-hard to compute any coarse correlated equilibrium (or
approximate coarse correlated equilibrium) with welfare strictly better than
the worst possible. The focus on succinct games ensures that the underlying
complexity question is interesting; many multiplayer games of interest are in
fact succinct. Our results imply that, while one can efficiently compute a
coarse correlated equilibrium, one cannot provide any nontrivial welfare
guarantee for the resulting equilibrium, unless P=NP. We show that analogous
hardness results hold for correlated equilibria, and persist under the
egalitarian objective or Pareto optimality.
To complement the hardness results, we develop an algorithmic framework that
identifies settings in which we can efficiently compute an approximate
correlated equilibrium with near-optimal welfare. We use this framework to
develop an efficient algorithm for computing an approximate correlated
equilibrium with near-optimal welfare in aggregative games.Comment: 21 page
Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting
We consider the distributed channel selection problem in the context of
device-to-device (D2D) communication as an underlay to a cellular network.
Underlaid D2D users communicate directly by utilizing the cellular spectrum but
their decisions are not governed by any centralized controller. Selfish D2D
users that compete for access to the resources construct a distributed system,
where the transmission performance depends on channel availability and quality.
This information, however, is difficult to acquire. Moreover, the adverse
effects of D2D users on cellular transmissions should be minimized. In order to
overcome these limitations, we propose a network-assisted distributed channel
selection approach in which D2D users are only allowed to use vacant cellular
channels. This scenario is modeled as a multi-player multi-armed bandit game
with side information, for which a distributed algorithmic solution is
proposed. The solution is a combination of no-regret learning and calibrated
forecasting, and can be applied to a broad class of multi-player stochastic
learning problems, in addition to the formulated channel selection problem.
Analytically, it is established that this approach not only yields vanishing
regret (in comparison to the global optimal solution), but also guarantees that
the empirical joint frequencies of the game converge to the set of correlated
equilibria.Comment: 31 pages (one column), 9 figure
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