Abstract

Bayes's theorem allows us to use subjective thinking to find numerical values to formulate assessments of risk. It is more than a mathematical formula; it can be thought of as an iterative process that challenges us to imagine the potential for "unknown, unknowns." The heuristics involved in this process can be enhanced if they take into consideration some of the established risk assessment and communication models used today in technical communication that are concerned with the social construction of meaning and the kairos involved in rhetorical situations. Understanding the connection between Bayesian analysis and risk communication will allow us to better convey the potential for risk that is based on probabilistic assumptions.

Journal
Communication Design Quarterly
Published
2020-08-12
DOI
10.1145/3394264.3394265
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References (33) · 5 in this index

  1. 10.1080/1369118X.2012.678878
  2. Callon M. Lascoumes P. & Barthe Y. (2009). Acting in an uncertain world: An essay on technical democracy. MIT…
  3. Cantor M. (2013 May 28). Filling in the blanks: The math behind Nate Silver's The Signal and the Noise. IBM. …
  4. 10.1080/02691728.2013.862880
  5. de Certeau Michel. (2012). The practice of everyday life (3rd ed). University of California Press. de Certea…
Show all 33 →
  1. Technical Communication Quarterly
  2. Technical Communication Quarterly
  3. Godfrey-Smith P. (2003). Theory and reality: An introduction to the philosophy of science. University of Chic…
  4. 10.1080/00028533.2012.11821771
  5. Technical Communication Quarterly
  6. 10.1111/j.1469-7998.2010.00700.x
  7. Guerra-Pujol F. E. (2013 August 24). What critics of Nate Silver get wrong. Prior Probability. https://priorp…
  8. Howson C. & Urbach P. (1989). Scientific reasoning: The Bayesian approach. Open Court Publishing Co. Howson …
  9. Hulme M. (2009). Why we disagree about climate change: Understanding controversy inaction and opportunity. Ca…
  10. Jaynes E. T. (2003) Probability theory: The logic of science. Cambridge University Press. Jaynes E. T. (2003…
  11. Johnson R. R. (1998). User-centered technology. A rhetorical theory for computers and other mundane artifacts…
  12. Krenchel M. & Christian M. (2014 November 4). Your big data is worthless if you don't bring it into the real …
  13. Kuhn T. (1970). The structure of scientific revolutions (2nd ed.). University of Chicago Press. Kuhn T. (197…
  14. Marcus G. & Ernest D. (2013 January 25). What Nate Silver gets wrong. The New Yorker. http://www.newyorker.co…
  15. Mayer-Schönberger V. & Cukier K. (2013). Big data: A revolution that will transform how we live work and thin…
  16. McGrayne S. B. (2011). The theory that would not die: How Bayes's rule cracked the enigma code hunted down Ru…
  17. 10.1353/con.2004.0022
  18. Morris D. (2016). Bayes theorem: A visual introduction for beginners. Blue Windmill Media. Morris D. (2016).…
  19. R. Pachauri, T. Taniguchi, and K. Tanaka (Eds), Guidance papers on the cross cutting issu…
  20. O'Hara B. (2012 November 8). How did Nate Silver predict the US election? The Guardian. https://www.theguardi…
  21. 10.1177/0162243909337121
  22. 10.1111/j.1460-2466.2004.tb02616.x
  23. Rhetoric Review
  24. 10.1080/136698798377240
  25. Silver N. (2012). The signal and the noise: Why so many predictions fail---but some don't. Penguin Press. Si…
  26. 10.1177/0162243910377624
  27. 10.1177/1075547098020001019
  28. Technical Communication Quarterly