Predicting Trade Expansion under FTAs and Multilateral Agreements

This paper examines the historical record of eight recent free trade agreements (FTAs). It also investigates the predictive power of two popular quantitative world trade models—the single-equation gravity model and the multiequation comput-able general equilibrium (CGE) model—as applied to three major trade liberalization agreements adopted during the 1990s: Mercosur, NAFTA, and the Uruguay Round Agreement, using the Rose gravity model and the GTAP general equilibrium model. Both models are found accurate in some instances, but intervening influences in the wake of trade liberalization episodes confound the challenge of drawing a strong conclusion in favor of one modeling approach over the other. Between the “naïve” gravity model and “naïve” CGE model predictions, we find that the former tends to overpredict intrabloc trade expansion (especially over horizons of five years and less) while the latter tends to underpredict. CGE models remain favored for ex post analysis of welfare impacts and the direct and indirect linkages between policy reforms and the numerous other economic variables of concern to policymakers and the public at large.gravity models, CGE models, regional trading arrangements

The West: slowing, but still strong

Federal Reserve District, 12th

Fuzzy State Aggregation and Off-Policy Reinforcement Learning for Stochastic Environments

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function approximation presented here is that of fuzzy state aggregation. This article presents the use of fuzzy state aggregation with the current policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF), exceeding the learning rate and performance of the combined fuzzy state aggregation and Q-learning reinforcement learning. Results of testing using the TileWorld domain demonstrate the policy hill climbing performs better than the existing Q-learning implementations

Fuzzy State Aggregation and Policy Hill Climbing for Stochastic Environments

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the operating environment changes. Additionally, by applying reinforcement learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the fastest policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF). The combination of fast policy hill climbing and fuzzy state aggregation function approximation is tested in two stochastic environments: Tileworld and the simulated robot soccer domain, RoboCup. The Tileworld results demonstrate that a single agent using the combination of FSA and PHC learns quicker and performs better than combined fuzzy state aggregation and Q-learning reinforcement learning alone. Results from the multi-agent RoboCup domain again illustrate that the policy hill climbing algorithms perform better than Q-learning alone in a multi-agent environment. The learning is further enhanced by allowing the agents to share their experience through a weighted strategy sharing

Parity-Projected Shell Model Monte Carlo Level Densities for fp-shell Nuclei

We calculate parity-dependent level densities for the even-even isotopes 58,62,66 Fe and 58 Ni and the odd-A nuclei 59 Ni and 65 Fe using the Shell Model Monte Carlo method. We perform these calculations in the complete fp-gds shell-model space using a pairing+quadrupole residual interaction. We find that, due to pairing of identical nucleons, the low-energy spectrum is dominated by positive parity states. Although these pairs break at around the same excitation energy in all nuclei, the energy dependence of the ratio of negative-to-positive parity level densities depends strongly on the particular nucleus of interest. We find equilibration of both parities at noticeably lower excitation energies for the odd-A nuclei 59 Ni and 65 Fe than for the neighboring even-even nuclei 58 Ni and 66 Fe.Comment: 5 pages, 4 figures, submitted to Phys. Rev.

Assessing surrogate endpoints in vaccine trials with case-cohort sampling and the Cox model

Assessing immune responses to study vaccines as surrogates of protection plays a central role in vaccine clinical trials. Motivated by three ongoing or pending HIV vaccine efficacy trials, we consider such surrogate endpoint assessment in a randomized placebo-controlled trial with case-cohort sampling of immune responses and a time to event endpoint. Based on the principal surrogate definition under the principal stratification framework proposed by Frangakis and Rubin [Biometrics 58 (2002) 21--29] and adapted by Gilbert and Hudgens (2006), we introduce estimands that measure the value of an immune response as a surrogate of protection in the context of the Cox proportional hazards model. The estimands are not identified because the immune response to vaccine is not measured in placebo recipients. We formulate the problem as a Cox model with missing covariates, and employ novel trial designs for predicting the missing immune responses and thereby identifying the estimands. The first design utilizes information from baseline predictors of the immune response, and bridges their relationship in the vaccine recipients to the placebo recipients. The second design provides a validation set for the unmeasured immune responses of uninfected placebo recipients by immunizing them with the study vaccine after trial closeout. A maximum estimated likelihood approach is proposed for estimation of the parameters. Simulated data examples are given to evaluate the proposed designs and study their properties.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS132 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Emancipation Proclamation and Arbitrary Arrests!!

Speech of Hon. Gilbert Dean, of New York, on the governor\u27s annual message, delivered in the House of assembly of the state of New York, February 12, 1863.https://scholarsjunction.msstate.edu/fvw-pamphlets/1460/thumbnail.jp