Dividing Antarctica: The Work of the Seventh International Geographical Congress in Berlin 1899

Antarctic historians seldom look beyond the Sixth International Geographical Congress held in London in 1895 to locate the origins of the late-nineteenth-century renewal of interest in the region. Moreover, these scholars pay near-exclusive attention to Resolution 3 of that Congress, which marked the exploration of Antarctica as “the greatest piece of geographical exploration still to be undertaken.” Far-less often analyzed is the subsequent Berlin Congress of 1899, to which fell the actual coordination of the independent national expeditions proposing to set for the Far South. This paper, then, will examine the Seventh International Geographical Congress held in Berlin in 1899. It suggests that the 1899 Congress set off a period of exploration (1901-1904) in Antarctica motivated more by competition than collaboration. To organize and direct the aims of these Antarctic voyages, delegates at the 1899 Congress formulated a research program structured around a strict demarcation of each nation’s zone of activity. This essay will show how this partitioning of Antarctic space, though oft-recognized by scholars as a scheme indicative of the desire for international collaboration, betrayed the deeper international tensions and imperial priorities that had stained Antarctic deliberations during the years between the London and Berlin Congresses

Faster Rates for Policy Learning

This article improves the existing proven rates of regret decay in optimal policy estimation. We give a margin-free result showing that the regret decay for estimating a within-class optimal policy is second-order for empirical risk minimizers over Donsker classes, with regret decaying at a faster rate than the standard error of an efficient estimator of the value of an optimal policy. We also give a result from the classification literature that shows that faster regret decay is possible via plug-in estimation provided a margin condition holds. Four examples are considered. In these examples, the regret is expressed in terms of either the mean value or the median value; the number of possible actions is either two or finitely many; and the sampling scheme is either independent and identically distributed or sequential, where the latter represents a contextual bandit sampling scheme

Efficient Principally Stratified Treatment Effect Estimation in Crossover Studies with Absorbent Binary Endpoints

Suppose one wishes to estimate the effect of a binary treatment on a binary endpoint conditional on a post-randomization quantity in a counterfactual world in which all subjects received treatment. It is generally difficult to identify this parameter without strong, untestable assumptions. It has been shown that identifiability assumptions become much weaker under a crossover design in which subjects not receiving treatment are later given treatment. Under the assumption that the post-treatment biomarker observed in these crossover subjects is the same as would have been observed had they received treatment at the start of the study, one can identify the treatment effect with only mild additional assumptions. This remains true if the endpoint is absorbent, i.e. an endpoint such as death or HIV infection such that the post-crossover treatment biomarker is not meaningful if the endpoint has already occurred. In this work, we review identifiability results for a parameter of the distribution of the data observed under a crossover design with the principally stratified treatment effect of interest. We describe situations in which these assumptions would be falsifiable, and show that these assumptions are not otherwise falsifiable. We then provide a targeted minimum loss-based estimator for the setting that makes no assumptions on the distribution that generated the data. When the semiparametric efficiency bound is well defined, for which the primary condition is that the biomarker is discrete-valued, this estimator is efficient among all regular and asymptotically linear estimators. We also present a version of this estimator for situations in which the biomarker is continuous. Implications to closeout designs for vaccine trials are discussed

Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits

We study a generalization of the multi-armed bandit problem with multiple plays where there is a cost associated with pulling each arm and the agent has a budget at each time that dictates how much she can expect to spend. We derive an asymptotic regret lower bound for any uniformly efficient algorithm in our setting. We then study a variant of Thompson sampling for Bernoulli rewards and a variant of KL-UCB for both single-parameter exponential families and bounded, finitely supported rewards. We show these algorithms are asymptotically optimal, both in rateand leading problem-dependent constants, including in the thick margin setting where multiple arms fall on the decision boundary