Pushing the Prize Up, A Few Notes on Al-Qaeda's Reward Structure and the choice of Casualties

The article aims at suggesting possible conjectures on Al-Qaeda's logic and structure. Even if the organization's secrecy makes any empirical evidence difficult to find, some insight can be provided by economic theory of contests: in this terms, Al-Qaeda can be acknowledged like an agent rewarding a prize (membership) to its clients (cells and would-be cells). Although this principle makes the organization hardly visible and virtually impenetrable, we contend that in the long term such a logic is non-sustainableConflict, Al Qaeda, Terrorism, microeconomic theory, prize, contest

Adaptivity to Noise Parameters in Nonparametric Active Learning

This work addresses various open questions in the theory of active learning for nonparametric classification. Our contributions are both statistical and algorithmic: -We establish new minimax-rates for active learning under common \textit{noise conditions}. These rates display interesting transitions -- due to the interaction between noise \textit{smoothness and margin} -- not present in the passive setting. Some such transitions were previously conjectured, but remained unconfirmed. -We present a generic algorithmic strategy for adaptivity to unknown noise smoothness and margin; our strategy achieves optimal rates in many general situations; furthermore, unlike in previous work, we avoid the need for \textit{adaptive confidence sets}, resulting in strictly milder distributional requirements

An Adaptive Strategy for Active Learning with Smooth Decision Boundary

We present the first adaptive strategy for active learning in the setting of classification with smooth decision boundary. The problem of adaptivity (to unknown distributional parameters) has remained opened since the seminal work of Castro and Nowak (2007), which first established (active learning) rates for this setting. While some recent advances on this problem establish adaptive rates in the case of univariate data, adaptivity in the more practical setting of multivariate data has so far remained elusive. Combining insights from various recent works, we show that, for the multivariate case, a careful reduction to univariate-adaptive strategies yield near-optimal rates without prior knowledge of distributional parameters

Behavioral Response to an Anti Malaria Spraying Campaign, with Evidence from Eritrea

It is sometimes argued that introducing Indoor Residual Spray (IRS) in areas with high coverage of mosquito bed nets may discourage net ownership and use, which would hinder Malaria eradication rather than promote it. We analyze new data from a Randomized Control Trial conducted in Eritrea in 2009, and we show that this does not happen in practice. IRS actually induced households to acquire more nets and even led to increased net use among certain demographic groups. IRS was further not associated to any perverse behavioral response. We explore two arguments that can explain this. The IRS campaign may have conveyed information about the importance of preventing Malaria and about how to do so, and people adjusted their behavior accordingly. Alternatively, people may perceive bed nets and spray as complements, even though they are substitutes. Further research is needed to disentangle these two effects. --Malaria,Bednets,Spray,Information,Beliefs,Behavior

Rotting bandits are not harder than stochastic ones

In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary. This assumption is often violated in practice (e.g., in recommendation systems), where the reward of an arm may change whenever is selected, i.e., rested bandit setting. In this paper, we consider the non-parametric rotting bandit setting, where rewards can only decrease. We introduce the filtering on expanding window average (FEWA) algorithm that constructs moving averages of increasing windows to identify arms that are more likely to return high rewards when pulled once more. We prove that for an unknown horizon $T$, and without any knowledge on the decreasing behavior of the $K$ arms, FEWA achieves problem-dependent regret bound of $\widetilde{\mathcal{O}}(\log{(KT)}),$ and a problem-independent one of $\widetilde{\mathcal{O}}(\sqrt{KT})$. Our result substantially improves over the algorithm of Levine et al. (2017), which suffers regret $\widetilde{\mathcal{O}}(K^{1/3}T^{2/3})$. FEWA also matches known bounds for the stochastic bandit setting, thus showing that the rotting bandits are not harder. Finally, we report simulations confirming the theoretical improvements of FEWA

Intercalating cobalt between graphene and iridium (111): a spatially-dependent kinetics from the edges

Using low-energy electron microscopy, we image in real time the intercalation of a cobalt monolayer between graphene and the (111) surface of iridium. Our measurements reveal that the edges of a graphene flake represent an energy barrier to intercalation. Based on a simple description of the growth kinetics, we estimate this energy barrier and find small, but substantial, local variations. These local variations suggest a possible influence of the graphene orientation with respect to its substrate and of the graphene edge termination on the energy value of the barrier height. Besides, our measurements show that intercalated cobalt is energetically more favorable than cobalt on bare iridium, indicating a surfactant role of graphene