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Uncertainty Phobia and Epistemic Forbearance in a Pandemic

Published online by Cambridge University Press:  18 October 2022

Nicholas Shackel
Nicholas Shackel*
Affiliation:
Cardiff University, Oxford Uehiro Centre for Practical Ethics, Oxford University
*
[email protected]
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Abstract

In this chapter I show how challenges to our ability to tame the uncertainty of a pandemic leaves us vulnerable to uncertainty phobia. This is because, contrary to what we might hope, not all the uncertainty that matters can be tamed by our knowledge of the relevant probabilities. Unrelievable wild uncertainty is a hard burden to bear, especially so when we must act in the face of it. We are tempted to retreat into uncertainty phobia, leading to fixed definite opinions precisely when acting on sound judgement requires our opinions to be hedged and mobile. Coping with a pandemic requires us to bear the burden rather than give in to temptation: it requires us to practise the virtue of epistemic forbearance.

Type
Paper
Information
Royal Institute of Philosophy Supplements , Volume 92: Values and Virtues for a Challenging World , October 2022 , pp. 271 - 291
Copyright
Copyright © The Royal Institute of Philosophy and the contributors 2022

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References

Anastassopoulou, Cleo et al. , ‘Data-Based Analysis, Modelling and Forecasting of the Covid-19 Outbreak’, PLOS ONE, 15 (2020), e0230405. https://doi.org/10.1371/journal.pone.0230405CrossRefGoogle ScholarPubMed
Bernstein, Peter L., Capital Ideas : The Improbable Origins of Modern Wall Street, (New York: Free Press, Maxwell Macmillan Canada 1992).Google Scholar
Calafiore, Giuseppe C., et al. , ‘A Time-Varying Sird Model for the Covid-19 Contagion in Italy’, Annu Rev Control, 50 (2020) 361–372. https://doi.org/10.1016/j.arcontrol.2020.10.005CrossRefGoogle ScholarPubMed
Cepelewicz, Jordana, ‘The Hard Lessons of Modeling the Coronavirus Pandemic’, Quanta, (2021).Google Scholar
Chen, Paul Z. et al. , ‘Understanding Why Superspreading Drives the Covid-19 Pandemic but Not the H1n1 Pandemic’, The Lancet Infectious Diseases, 21 (2021), 1203–1204. https://doi.org/10.1016/S1473-3099(21)00406-0CrossRefGoogle Scholar
Cirillo, Pasquale & Taleb, Nassim Nicholas, ‘Tail Risk of Contagious Diseases’, Nature Physics, 16 (2020), 606–613. https://doi.org/10.1038/s41567-020-0921-xCrossRefGoogle Scholar
Collins, James J. & Chow, Carson C., ‘It's a Small World’, Nature, 393 (1998), 409–410. https://doi.org/10.1038/30835CrossRefGoogle ScholarPubMed
Cooper, Ian, et al. ‘A Sir Model Assumption for the Spread of Covid-19 in Different Communities’, Chaos, solitons, and fractals, 139 (2020) 110057 https://doi.org/10.1016/j.chaos.2020.110057Google ScholarPubMed
Eker, Sibel,. ‘Validity and Usefulness of Covid-19 Models’, Humanities and Social Sciences Communications, 7 (2020), 54. https://doi.org/10.1057/s41599-020-00553-4CrossRefGoogle Scholar
Endo, Hideki, et al. , ‘Conventional Risk Prediction Models Fail to Accurately Predict Mortality Risk among Patients with Coronavirus Disease 2019 in Intensive Care Units: A Difficult Time to Assess Clinical Severity and Quality of Care’, Journal of Intensive Care, 9 (2021), 42. https://doi.org/10.1186/s40560-021-00557-5CrossRefGoogle ScholarPubMed
Fama, Eugene F., ‘Efficient Capital Markets: A Review of Theory and Empirical Work’, The Journal of Finance, 25 (1970), 383–417. https://doi.org/10.2307/2325486Google Scholar
Ferguson, Neil M, et al. ‘Impact of Non-Pharmaceutical Interventions to Reduce Covid19 Mortality and Healthcare Demand’, 16 March 2020 Imperial College London, 1–20. doi.org/10.25561/77482Google Scholar
Ferrari, Luisa, et al. ‘Modelling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent Sird Model’, International Journal of Environmental Research and Public Health, 18 (2021), 6563. https://www.mdpi.com/1660-4601/18/12/6563Google Scholar
Gnanvi, Janyce Eunice, et al. ‘On the Reliability of Predictions on Covid-19 Dynamics: A Systematic and Critical Review of Modelling Techniques’, Infectious Disease Modelling, 6 (2021) 258–272. https://doi.org/https://doi.org/10.1016/j.idm.2020.12.008CrossRefGoogle ScholarPubMed
Hasan, Agus, et al. , ‘Superspreading in Early Transmissions of Covid-19 in Indonesia’, Scientific reports, 10 (2020), 22386. https://doi.org/10.1038/s41598-020-79352-5CrossRefGoogle ScholarPubMed
Holmdahl, Inga & Buckee, Caroline, ‘Wrong but Useful – What Covid-19 Epidemiologic Models Can and Cannot Tell Us’, New England Journal of Medicine, 383 (2020), 303–305. https://doi.org/10.1056/NEJMp2016822Google ScholarPubMed
Ioannidis, John P. A., et al. , ‘Forecasting for Covid-19 Has Failed’, Int J Forecast, (2020) https://doi.org/10.1016/j.ijforecast.2020.08.004Google ScholarPubMed
James, Lyndon P. et al. , ‘The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the Covid-19 Pandemic’, Medical Decision Making, 41 (2021), 379–385. https://doi.org/10.1177/0272989X21990391CrossRefGoogle ScholarPubMed
Lau, Max S. Y. et al. , ‘Characterizing Superspreading Events and Age-Specific Infectiousness of Sars-Cov-2 Transmission in Georgia, USA’, Proceedings of the National Academy of Sciences of the United States of America, 117 (2020), 22430–22435. https://doi.org/10.1073/pnas.2011802117CrossRefGoogle ScholarPubMed
Mandelbrot, Benoit B. & Hudson, Richard L., The Misbehaviour of Markets : A Fractal View of Risk, Ruin and Reward, (London: Profile, 2004).Google Scholar
Martínez, Vicente, ‘A Modified Sird Model to Study the Evolution of the Covid-19 Pandemic in Spain’, Symmetry, 13 (2021), 723. https://www.mdpi.com/2073-8994/13/4/723CrossRefGoogle Scholar
Milgram, Stanley, ‘The Small World Problem’, Psychology Today, 2 (1967) 60–67.Google Scholar
Nicholson, Alan & Wong, Yiik-Diew, ‘Are Accidents Poisson Distributed? A Statistical Test’, Accident Analysis & Prevention, 25 (1993), 91–97. https://doi.org/10.1016/0001-4575(93)90100-BCrossRefGoogle ScholarPubMed
Pareto, Vilfredo, Cours D'economie Politique, (Lausanne: Rouge, 1896).Google Scholar
Shackel, Nicholas, ‘Explaining Variation in Knowledge by Full Belief’, Proceedings of the 34th Ludwig Wittgenstein Symposium. Eds Jaeger, C. and Loffler, W. Kirchberg am Wechsel, (Austria: Ontos Verlag, 2011) 95–102. http://dx.doi.org/10.1515/9783110329018.95Google Scholar
Shackel, Nicholas, ‘Stakes and Full Belief’ (MS-a).Google Scholar
Shackel, Nicholas, ‘Wild Uncertainty and Pandemics’ (MS-b).Google Scholar
Shringi, Sakshi, et al. ‘Modified Sird Model for Covid-19 Spread Prediction for Northern and Southern States of India’, Chaos, Solitons & Fractals, 148 (2021), 111039. https://doi.org/10.1016/j.chaos.2021.111039CrossRefGoogle Scholar
Summers, Lawrence H., ‘Does the Stock Market Rationally Reflect Fundamental Values?' The Journal of Finance, 41 (1986), 591–601. https://doi.org/10.1111/j.1540-6261.1986.tb04519.xCrossRefGoogle Scholar
Sun, Kaiyuan, et al. ‘Transmission Heterogeneities, Kinetics, and Controllability of Sars-Cov-2’, Science, 371 (2021), https://doi.org/10.1126/science.abe2424CrossRefGoogle ScholarPubMed
Taleb, Nicholas Nassim, Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications, (Scribe Media, 2020 ).Google Scholar
United States Centre for Disease Control ‘Covidview-05-01-2020’ 2020. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/pdf/covidview-05-01-2020.pdfGoogle Scholar
Watts, Duncan J. & Strogatz, Steven H., ‘Collective Dynamics of ‘Small-World’ Networks’, Nature, 393 (1998), 440–42. https://doi.org/10.1038/30918CrossRefGoogle ScholarPubMed
Wong, Felix & Collins, James J., ‘Evidence That Coronavirus Superspreading Is Fat-Tailed’, Proceedings of the National Academy of Sciences, 117 (2020) 29416–29418. https://www.pnas.org/content/pnas/117/47/29416.full.pdfCrossRefGoogle ScholarPubMed