Antidumping and countervailing duties in a Western Hemispheric free trade agreement

Includes bibliograph

Statistically reinforced machine learning for nonlinear patterns and variable interactions

Most statistical models assume linearity and few variable interactions, even though real‐world ecological patterns often result from nonlinear and highly interactive processes. We here introduce a set of novel empirical modeling techniques which can address this mismatch: statistically reinforced machine learning. We demonstrate the behaviors of three techniques (conditional inference tree, model‐based tree, and permutation‐based random forest) by analyzing an artificially generated example dataset that contains patterns based on nonlinearity and variable interactions. The results show the potential of statistically reinforced machine learning algorithms to detect nonlinear relationships and higher‐order interactions. Estimation reliability for any technique, however, depended on sample size. The applications of statistically reinforced machine learning approaches would be particularly beneficial for investigating (1) novel patterns for which shapes cannot be assumed a priori, (2) higher‐order interactions which are often overlooked in parametric statistics, (3) context dependency where patterns change depending on other conditions, (4) significance and effect sizes of variables while taking nonlinearity and variable interactions into account, and (5) a hypothesis using parametric statistics after identifying patterns using statistically reinforced machine learning techniques

International regime conflict in trade and environment: the Biosafety Protocol and the WTO

Trade and environment constitute regimes in international relations: they are vehicles for cooperation between nation states that permit governments to address various subjects such as commercial non-discrimination, reduction of pollution, reciprocity and sustainable development. The issues of food safety and agricultural biotechnology (i.e., genetically modified organisms or GMOs) have been raised in both regimes, and have been managed in different and arguably inconsistent manners. In the trade regime, food safety and ag-biotech are mainly subject to the US-backed principle of scientific risk assessment established in the WTO s Sanitary and Phytosanitary Agreement, while in the environment regime they would likely be addressed through the more politically based precautionary principle , promoted by the EU and represented in the Cartagena Protocol on Biosafety. Both the trade and environment regimes are rules-based, but conflict between them diminishes the force of precision and obligation needed to make rules effective. Furthermore, there is a danger that regime conflict could expand, thereby reducing the opportunity to promote an optimal relationship between science and society in the future.