Computational social science

Summary

Computational social science is the academic sub-discipline concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. Fields include computational economics, computational sociology, cliodynamics, culturomics, nonprofit studies, and the automated analysis of contents, in social and traditional media. It focuses on investigating social and behavioral relationships and interactions through social simulation, modeling, network analysis, and media analysis. Definitions There are two terminologies that relate to each other: Social Science Computing (SSC) and Computational Social Science (CSS). In literature, CSS is referred to the field of social science that uses the computational approaches in studying the social phenomena. On the other hand, SSC is the field in which computational methodologies are created to assist in explanations of social phenomena. Computational social science rev

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https://en.wikipedia.org/wiki/Computational_social_science

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Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to such distributions but suffer from either intrinsic bias or high variance. In this Letter, we propose a way to make this approximation unbiased and with low variance. Our method uses physical symmetries and variable-size cluster updates which utilize the structure of autoregressive factorization. We test our method for first- and second-order phase transitions of classical spin systems, showing its viability for critical systems and in the presence of metastable states.

AMER PHYSICAL SOC2021

Mathematics in the Wind

Nicola Parolini, Alfio Quarteroni

In any sport or human endeavor, coaches regularly state “play to your strengths.” One might not guess that a land-locked, mountainous country like Switzerland would have strengths that would give them a chance at winning the oldest, most competitive sailing competition in the world, the America’s Cup. But it does! In 2003, in the Harukai Gulf of New Zealand, a Swiss yacht called Alinghi was the surprise winner of the America’s up. And in 2007, in the Mediterranean sea near Valencia, Alinghi confirmed its dominance by defeating the New Zealand Team 5 to 2 in a breathtaking final match race. Mathematics and computational science were involved in Alinghi’s first successes in 2003, when it won the Louis Vuitton Cup and then the America’s Cup by defeating the defending Black Magic Team of New Zealand. Mathematics helped create the design of the boat which brought the defender Alinghi its second triumph in Valencia in 2007. Mathematical models were used in the design phase and again during the competition. Of course mathematical models alone are not enough to guarantee success in a race as competitive as the America’s Cup: a great team and much luck are needed. We know that people and explorers have been sailing literally since the stone age. It may seem astonishing that, with all the technological progress that has been achieved, sailing is still such a challenge. Alinghi has shown that part of the progress that will likely be made in the future rests on mathematical models and their numerical solution.

2009

Social processes in natural disaster management. The case of recent floods in Switzerland

Valérie November

COST Action C192008