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.

 

 

Data-driven predictions in the science of science
Aaron Clauset, Daniel B. Larremore, Roberta Sinatra

Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 477-480
DOI: 10.1126/science.aal4217

Source: science.sciencemag.org

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.

 

Prediction and explanation in social systems
Jake M. Hofman, Amit Sharma, Duncan J. Watts

Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 486-488
DOI: 10.1126/science.aal3856

Source: science.sciencemag.org

This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data. We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds. We also participated in two live forecasting experiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that, with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.

 

 

Improving election prediction internationally
Ryan Kennedy, Stefan Wojcik, David Lazer

Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 515-520
DOI: 10.1126/science.aal2887

Source: science.sciencemag.org

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language.

 

Cornish, H., Dale, R., Kirby, S. & Christiansen, M.H. (2017). Sequence memory constraints give rise to language-like structure through iterated learning. PLoS ONE 12(1): e0168532.

Source: journals.plos.org

With the large-scale penetration of the internet, for the first time, humanity has become linked by a single, open, communications platform. Harnessing this fact, we report insights arising from a unified internet activity and location dataset of an unparalleled scope and accuracy drawn from over a trillion (1.5$\times 10^{12}$) observations of end-user internet connections, with temporal resolution of just 15min over 2006-2012. We show how these data can be used to provide scientific insights in diverse fields such as technological diffusion, chronobiology and economics.  To our knowledge, our study is the first of its kind to use online/offline activity of the entire internet to infer such insights, demonstrating the potential of the internet as a quantitative social data-science platform.

Source: arxiv.org