The siren call of probability: Dangers associated with using probability for consideration of the future

Futures

Volumn 88, Issue , 2017, Pages 43-54

  Derbyshire, James ^a  

^a MIDDLESEX UNIVERSITY   (United Kingdom)

Author keywords

Delphi; Keynes; Probability; Scenario planning; Shackle; Uncertainty

Indexed keywords

DELPHI ANALYSIS; FORECASTING METHOD; MODELING; PROBABILITY; SCENARIO ANALYSIS; UNCERTAINTY ANALYSIS;

SIREN;

EID: 85017436270     PISSN: 00163287     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.futures.2017.03.011     Document Type: Article

References (53)

1. Arrow, K.J., A difficulty in the concept of social welfare. Journal of Political Economy 58 (1950), 328–346.
2. Arrow, K.J., Social choice and individual values. 2nd ed., 1963, Yale University Press, New Haven.
3. Arrow, K.J., Social choice and multicriterion decision-making. 1986, MIT, Cambridge, Mass.
4. Aumann, R.J., Agreeing to disagree. The Annals of Statistics 4 (1976), 1236–1239.
5. Basili, M., Zappia, C., Shackle and modern decision theory. Metroeconomica 60 (2009), 245–282.
6. Basili, M., Zappia, C., Ambiguity and uncertainty in Ellsberg and Shackle. Cambridge Journal of Economics 34 (2010), 449–474.
7. Bradfield, R., Derbyshire, J., Wright, G., The critical role of history in scenario thinking: Augmenting causal analysis within the intuitive logics scenario development methodology. Futures 77 (2016), 56–66.
8. Davidson, P., Is probability theory relevant for uncertainty? A Post Keynesian perspective. Journal of Economic Perspectives 5 (1991), 129–143.
9. de Finetti, B., La prévision: Ses lois logiques, ses sources subjectives. Annales de l'Institut Henri Poincaré 7:1937 (1937), 1–68 Translated by H.E. Kyburg as Foresight: Its logical laws, its subjective sources, in: H.E. Kyburg, H.E. Smokler (Eds.), Studies in Subjective Probability, Robert E. Krieger New York, 1980, pp. 57–118.
10. Derbyshire, J., Wright, G., Preparing for the future: Development of an ‘antifragile’ methodology that complements scenario planning by omitting causation. Technological Forecasting & Social Change 82 (2014), 215–225.
11. Derbyshire, J., Wright, G., Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation. International Journal of Forecasting 33 (2017), 254–266.
12. Derbyshire, J., Potential Surprise Theory as a theoretical foundation for scenario planning. Technological Forecasting & Social Change, 2016 (in press).
13. Dewulf, A., Craps, M., Bouwen, R., Taillieu, T., Pahl-Wostl, C., Integrated management of natural resources: Dealing with ambiguous issues, multiple actors and diverging frames. Water Science & Technology 52 (2005), 115–124.
14. Earl, P.E., Littleboy, B., G. L. S. Shackle. 2014, Palgrave Macmillan, Basingstoke.
15. Embrechts, P., Resnick, S.I., Samorodnitsky, G., Extreme value theory as a risk management tool. North American Actuarial Journal 3 (1999), 30–41.
16. Feduzi, A., Runde, J., Zappia, C., De Finetti on uncertainty. Canbridge Journal of Economics 38 (2014), 1–21.
17. Franssen, M., Arrow's theorem, multi-criteria decision problems and multi-attribute preferences in engineering design. Research in Engineering Design 16 (2005), 42–56.
18. Gary, J.E., von der Gracht, H.A., The future of foresight professionals: Results from a global Delphi study. Futures 71 (2015), 132–145.
19. Gneiting, T., Editorial: Probabilistic forecasting. Journal of the Royal Statistical Society: Series A (Statistics in Society) 171 (2008), 319–321.
20. Guo, P., Tanaka, H., Decision making with interval probabilities. European Journal of Operational Research 203 (2010), 444–454.
21. Hacking, I., The taming of chance. 1990, Cambridge University Press, Cambridge, UK.
22. Harsanyi, J., Games with incomplete information played by ‘Bayesian’ players. Part I: The basic model. Management Science 14 (1967), 159–182.
23. Harsanyi, J., Games with incomplete information played by ‘Bayesian’ players. Part II: Bayesian equilibrium points. Management Science 14 (1968), 320–334.
24. Harsanyi, J., Games with incomplete information played by ‘Bayesian’ players. Part III: The basic probability distribution of the game. Management Science 14 (1968), 486–502.
25. Jefferson, M., Shell scenarios: What really happened in the 1970 and what may be learned for current world prospects. Technological Forecasting and Social Change 79 (2012), 186–197.
26. Jefferson, M., The passage of time: Shackle, Shell and scenarios. Earl, P.E., Littleboy, B., (eds.) G.L.S shackle, 2014, Palgrave Macmillan, Basingstoke, 198–214.
27. Keynes, J.M., Treatise on probability. 1921, Macmillan, London.
28. Keynes, J.M., The general theory of employment, interest and money. 1936, Palgrave Macmillan, London.
29. Keynes, J.M., The general theory and after: Defence and development. The collected writings of John Maynard Keynes, Vol. 14, 1937, Palgrave Macmillan, London.
30. Knight, F.H., Risk, uncertainty and profit. 1921, Schaffner & Marx, Boston, MA.
31. Kolmogorov, A.N., Foundations of the theory of probability. 1956, Chelsea Publishing Company, New York 2nd English ed.
32. Koutsoyiannis, D., Montanari, A., Negligent killing of scientific concepts: The stationary case. Hydrological Sciences Journal 60 (2015), 1174–1183.
33. Koutsoyiannis, D., Hurst-Kolmogorov dynamics and uncertainty. Journal of the American Water Resources Association 47 (2011), 481–495.
34. Maier, H.R., Guillaume, J.H.A., van Delden, H., Riddell, G.A., Haasnoot, M., Kwakkel, J.H., An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?. Environmental Modelling & Software 81 (2016), 154–164.
35. Maislin, M., Cascading uncertainty in climate change models and its implications for policy. The Geography Journal 179 (2013), 264–271.
36. Makridakis, S., Hogarth, R.M., Gaba, A., Forecasting and uncertainty in the economic and business world. International Journal of Forecasting 25 (2009), 794–812.
37. Morgan, M.G., Use (and abuse) of expert elicitation in support of decision making for public policy. Proceedings of the National Academy of Sciences of the United States of America 20 (2014), 7176–7184.
38. Morris, S., The common prior assumption in economic theory. Economics and Philosophy 11 (1995), 227–253.
39. Ramsey, F.P., Truth and probability. Braithwaite, R.B., (eds.) The foundations of mathematics and other logical essays, 1931, Routledge, London 1976.
40. Runde, J., Shackle on probability. Earl, P.E., Frowen, S.F., (eds.) Economics as an art of thought: Essays in memory of G. L. S. Shackle, 2000, Routledge, London, 214–241.
41. Savage, L.J., The foundation of statistics. 1954, Wiley, New York.
42. Shackle, G.L.S., Uncertainty in economics and other reflections. 1955, Cambridge University Press, Cambridge.
43. Shackle, G.L.S., Decision, order and time in human affairs. 1961, Cambridge University Press, Cambridge.
44. Skidelsky, R., Keynes: The return of the master. 2009, Penguin, London.
45. Stern, N., Current climate models are grossly misleading. Nature, 531(7592), 2016 31-31.
46. Taleb, N.N., Fooled by randomness. 2001, Random House, London.
47. Taleb, N.N., The black swan. 2008, Penguin, London.
48. Taleb, N.N., AntiFragile. 2012, Allen Lane, London.
49. Tversky, A., Kahneman, D., Judgement under uncertainty: Heuristics and biases. Science 185 (1974), 1124–1131.
50. Walker, W.E., Lempert, R.J., Kwakkel, J.H., Deep uncertainty. 2012, Delft University of Technology and RAND, Delft and Santa Monica.
51. Wallis, K.F., Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts. International Journal of Forecasting 19 (2003), 165–175.
52. Wynne, B., Uncertainty and environmental learning. Global Environmental Change 2 (1992), 111–127.
53. Zappia, C., Non-Bayesian decision theory ahead of its time. Cambridge Journal of Economics 38 (2014), 1133–1154.