Recently, there has been an increasing interest in the control community in studying large-scale distributed systems. Several techniques have been developed to address the main challenges for these systems, such as the amount of information needed to guarantee the proper operation of the system, the economic costs associated with the required communication structure, and the high computational burden of solving for the control inputs for largescale systems.

Source: ieeexplore.ieee.org

We have tried to predict the future since ancient times when shamans looked for patterns in smoking entrails. As this special section explores, prediction is now a developing science. Essays probe such questions as how to allocate limited resources, whether a country will descend into conflict, and who will likely win an election or publish a high-impact paper, as well as looking at how standards should develop in this emerging field.

Prediction and its limits
Barbara R. Jasny, Richard Stone
Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 468-469
DOI: 10.1126/science.355.6324.468

Source: science.sciencemag.org

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