Thirty years ago the Russell-Einstein Manifesto warned humanity that our survival is imperilled by the risk of nuclear war.In the spirit of that Manifesto, we now call on all scientists to expand our concerns to a broader set of interrelated dangers: destruction of the environment on a global scale, and denial of basis needs for a growing majority of humankind. The Dagomys Declaration (1988) of the Pugwash Council. Originally published in 1994

An inspiring outline of a new economics system, where justice, human dignity, compassion and reverence for life are the guiding values. The economic system under which we live not only forces the great majority of humankind to live their lives in indignity and poverty but also threatens all forms of life on Earth. Economics Unmasked presents a cogent critique of the dominant economic system, showing that the theoretical constructions of mainstream economics work mainly to bring about injustice. The merciless onslaught on the global ecosystem of recent decades, brought about by the massive increase in the production of goods and the consequent depletion of nature's reserves, is not a chance property of the economic system. It is a direct result of neoliberal economic thinking, which recognizes value only in material things. The growth obsession is not a mistaken conception that mainstream economists can unlearn, it is inherent in their view of life. But a socio-economic system based on the growth obsession can never be sustainable. This book outlines the foundations of a new economics, where we are not ruled by greed and injustice. Contrary to the absurd assumption of mainstream economists that economics is a value-free science, a new economics must make its values explicit.

First Published in 2009. Routledge is an imprint of Taylor & Francis, an informa company.

Psychological models of personality have used trait measures to index individuals with respect to specific traits or categorical types to bin individuals into defined personality types. Both of these approaches may be suboptimal for predicting performance. Categorical models rely on rough dichotomization of data and trait models may overfit variance to rigid traits and obscure trait interaction effects. This project compared predictions of midshipmen performance and outcomes at the United States Naval Academy, specifically comparing predictions derived from regression techniques with standard traits and type variables with predictions using machine learning techniques, such as k-nearest neighbors and boosted random forests. Using data from recent Naval Academy graduates (N = 725), we first applied traditional penalized regression techniques to predict performance, specifically academic and military order of merit at graduation, using standard personality traits, types, and values. These predictions served as the baseline for assessing the quality of prediction from the selected machine learning techniques. We next built, optimized, and analyzed machine learning models, finding that their accuracies were, at best, at the level of traditional penalized regression models. Finally, we examined the optimization process of the machine learning models to identify potential optimum dimensionalities for personality predictions, finding it matches currently accepted models of personality. While the machine learning models were more complex, computationally expensive, and less interpretable, we found they did not outperform regression models.