Abstract
The chapter explores the extent to which mathematical analysis helps us see the future. It looks at the challenge today’s “big data” presents to our mathematical abilities. Beginning with explanations of linear, logistic (s-shaped), and exponential growth, the chapter notes the limits to growth and where they apply. It introduces the “reaction curve” that for an industry incumbent mirrors the well-known “hype curve.” Following a discussion of price–performance curves, it addresses demographic forecasting, Kondratieff waves, and real options. The chapter concludes by showing why automating some white-collar functions can resolve the principal-agent problem.
Science has traditionally been seen as a driving force for technology, but the inverse process is equally important.
—J.P. McKelvey (1985)
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
Or a straight line when plotted on semi-log paper.
- 4.
- 5.
- 6.
- 7.
Kuznets took a slightly different slant on the long waves; see Milanovic (2016).
https://voxeu.org/article/introducing-kuznets-waves-income-inequality.
See also Coccia (2018).
- 8.
Using the expected value criterion. There are other criteria for pruning the decision tree, for example, the “minimax regret” criterion. We won’t examine those here, as they’re explained in almost any decision-making textbook, and because I hope you will aim for a positive future. Not like those grim “minimax regret” folks who try to minimize their expected maximum disappointment.
- 9.
In certain well-defined situations, the entropy of a probability function can measure knowledge. This was discovered by the famous Claude Shannon and set forth in his 1948 The Mathematical Theory of Communication, and later applied (in my own doctoral dissertation) to a kind of forecasting.
- 10.
- 11.
And involves nothing more complicated than Bayes’ Theorem. Did I say “nothing more complicated?” Well, Bayes’ theorem is very simple mathematically, but terribly contentious in its interpretation.
References
Coccia M (2018) A theory of the general causes of long waves: war, general purpose technologies, and economic change. Technol Forecast Soc Chang 128:287–295
Katzman BE, Verhoeven P, Baker HM (2009) Decision analysis and the principal–agent problem. Decis Sci J Innov Educ 7(1):51–57
Linstone HA (1999) Complexity science: implications for forecasting. Technol Forecast Soc Chang 62(1–2):79–90
McKelvey JP (1985) Science and technology: the driven and the driver. Technol Rev 88:38–74
Milanovic B (2016, February 24) Introducing Kuznets waves: how income inequality waxes and wanes over the very long run. Vox EU
Phillips F (1999) A method for detecting a shift in a trend. PICMET ‘99, Proceedings of the Portland International Conference on Management of Engineering & Technology, Portland State University, Portland, Oregon
Phillips F (2007) On S-curves and tipping points. Technol Forecast Soc Chang 74(6):715–730
Phillips F, George Hwang G, Limprayoon P (2016) Inflection points and industry change: was Andy Grove right after all? Int J Technol Manag Growing Econ 7(1):7–26
Wu G (1993) Decision analysis. Harvard Business School Case 9-894-004, rev. December 4, 1997
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Phillips, F. (2019). Analytics and the Future. In: What About the Future?. Science, Technology and Innovation Studies. Springer, Cham. https://doi.org/10.1007/978-3-030-26165-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-26165-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26164-1
Online ISBN: 978-3-030-26165-8
eBook Packages: Economics and FinanceEconomics and Finance (R0)