- Last 7 days
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twitter.com twitter.com
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Catherine Rampell. (2020, December 4). The jobs hole remains very, very deep. Today, the U.S. economy still has a greater jobs deficit than was the case at the very worst point of every previous postwar recession, including the Great Recession https://t.co/jYH1AUqBsV [Tweet]. @crampell. https://twitter.com/crampell/status/1334856009207189507
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www.theguardian.com www.theguardian.com
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the Guardian. ‘How Modelling Covid Has Changed the Way We Think about Epidemics | Adam Kucharrski’, 4 January 2021. http://www.theguardian.com/commentisfree/2021/jan/04/covid-model-epidemic-collaboration-experiment.
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arxiv.org arxiv.org
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Gupta, Prateek, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, et al. ‘COVI-AgentSim: An Agent-Based Model for Evaluating Methods of Digital Contact Tracing’. ArXiv:2010.16004 [Cs], 29 October 2020. http://arxiv.org/abs/2010.16004.
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- Feb 2021
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www.wired.com www.wired.com
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Ogbunu, B. C. (2020, October 27). The Science That Spans #MeToo, Memes, and Covid-19. Wired. https://www.wired.com/story/the-science-that-spans-metoo-memes-and-covid-19/
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www.thelancet.com www.thelancet.com
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Li, You, Harry Campbell, Durga Kulkarni, Alice Harpur, Madhurima Nundy, Xin Wang, and Harish Nair. ‘The Temporal Association of Introducing and Lifting Non-Pharmaceutical Interventions with the Time-Varying Reproduction Number (R) of SARS-CoV-2: A Modelling Study across 131 Countries’. The Lancet Infectious Diseases 21, no. 2 (1 February 2021): 193–202. https://doi.org/10.1016/S1473-3099(20)30785-4.
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academic.oup.com academic.oup.com
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Blakely, Tony, John Lynch, Koen Simons, Rebecca Bentley, and Sherri Rose. ‘Reflection on Modern Methods: When Worlds Collide—Prediction, Machine Learning and Causal Inference’. International Journal of Epidemiology 49, no. 6 (1 December 2020): 2058–64. https://doi.org/10.1093/ije/dyz132.
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Vigfusson, Y., Karlsson, T. A., Onken, D., Song, C., Einarsson, A. F., Kishore, N., Mitchell, R. M., Brooks-Pollock, E., Sigmundsdottir, G., & Danon, L. (2021). Cell-phone traces reveal infection-associated behavioral change. Proceedings of the National Academy of Sciences, 118(6). https://doi.org/10.1073/pnas.2005241118
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- Dec 2020
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psyarxiv.com psyarxiv.com
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Rocca, R., & Yarkoni, T. (2020). Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction. PsyArXiv. https://doi.org/10.31234/osf.io/e437b
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- Nov 2020
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arxiv.org arxiv.org
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Wunderling, N., Krönke, J., Wohlfarth, V., Kohler, J., Heitzig, J., Staal, A., Willner, S., Winkelmann, R., & Donges, J. F. (2020). Modelling nonlinear dynamics of interacting tipping elements on complex networks: The PyCascades package. ArXiv:2011.02031 [Nlin, Physics:Physics]. http://arxiv.org/abs/2011.02031
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- Oct 2020
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oro.open.ac.uk oro.open.ac.uk
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Burel, Gregoire; Farrell, Tracie; Mensio, Martino; Khare, Prashant and Alani, Harith (2020). Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic. In: Proceedings of the 12th International Social Informatics Conference (SocInfo), LNCS.
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- Aug 2020
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Karl Friston: Up to 80% not even susceptible to Covid-19. (2020, June 4). UnHerd. https://unherd.com/2020/06/karl-friston-up-to-80-not-even-susceptible-to-covid-19/
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www.nber.org www.nber.org
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Atkeson, A. (2020). What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios (Working Paper No. 26867; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26867
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www.nber.org www.nber.org
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Çakmaklı, C., Demiralp, S., Kalemli-Özcan, Ṣebnem, Yesiltas, S., & Yildirim, M. A. (2020). COVID-19 and Emerging Markets: An Epidemiological Model with International Production Networks and Capital Flows (Working Paper No. 27191; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27191
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- Jun 2020
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psyarxiv.com psyarxiv.com
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Spreckelsen, P. von, Wessel, I., Glashouwer, K., & Jong, P. J. de. (2020). Preprint Averting Repulsion? Body-Directed Self-Disgust and Autobiographical Memory Retrieval. https://doi.org/10.31234/osf.io/qhc35
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www.thelancet.com www.thelancet.com
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Clark, A., Jit, M., Warren-Gash, C., Guthrie, B., Wang, H. H., Mercer, S. W., ... & Checchi, F. (2020). Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study. The Lancet Global Health.
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statmodeling.stat.columbia.edu statmodeling.stat.columbia.edu
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Tsolaki, Vasiliki, George E. Zakynthinos, and Dimosthenis Makris. ‘The ARDSnet Protocol May Be Detrimental in COVID-19’. Critical Care 24, no. 1 (December 2020): 351. https://doi.org/10.1186/s13054-020-03081-4.
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wellcomeopenresearch.org wellcomeopenresearch.org
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Friston KJ, Parr T, Zeidman P et al. Dynamic causal modelling of COVID-19 [version 1; peer review: awaiting peer review]. Wellcome Open Res 2020, 5:89 (https://doi.org/10.12688/wellcomeopenres.15881.1)
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- Jan 2020
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smethur.st smethur.st
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Mithering about the unmodellable
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- Oct 2019
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www.iste.co.uk www.iste.co.uk
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- May 2019
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topicmodels.west.uni-koblenz.de topicmodels.west.uni-koblenz.de
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- Mar 2019
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smethur.st smethur.st
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Mithering about the unmodellable. "Sometime late last year I went to the Euro IA conference with Anya and Silver to give a talk on the domain modelling work we've been doing in UK Parliament."
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- Jul 2018
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aeon.co aeon.co
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However, computers and algorithms – even the most sophisticated ones – cannot address the fallacy of obviousness. Put differently, they can never know what might be relevant.
One goal of systems science and modelling, to explore what might be relevant and give us better heuristics.
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