Catálogo de publicaciones - revistas

<jats:p>Can internet data outdo costly and unreliable polls in predicting election outcomes?</jats:p>

<jats:p>This Essay provides an introduction to the general challenges of predicting political violence, particularly compared with predicting other types of events (such as earthquakes). What is possible? What is less realistic? We aim to debunk myths about predicting violence, as well as to illustrate the substantial progress in this field.</jats:p>

<jats:p>The desire to predict discoveries—to have some idea, in advance, of what will be discovered, by whom, when, and where—pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the “science of science” and what it teaches us about the predictability of scientific discovery. We then discuss future opportunities for improving predictions derived from the science of science and its potential impact, positive and negative, on the scientific community.</jats:p>

<jats:p>Political debates often suffer from vague-verbiage predictions that make it difficult to assess accuracy and improve policy. A tournament sponsored by the U.S. intelligence community revealed ways in which forecasters can better use probability estimates to make predictions—even for seemingly “unique” events—and showed that tournaments are a useful tool for generating knowledge. Drawing on the literature about the effects of accountability, the authors suggest that tournaments may hold even greater potential as tools for depolarizing political debates and resolving policy disputes.</jats:p>

<jats:p>Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.</jats:p>

<jats:p>Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.</jats:p>

<jats:p>Machine learning has provided researchers with new tools for understanding human behavior. In this article, we briefly describe some successes in predicting behaviors and describe the challenges over the next few years.</jats:p>