Abstract
Models so far discussed are all deterministic, meaning that, if the present state were perfectly known, it would be possible to predict exactly all future states. Now we try to face the Babylonian lottery considering models that can describe the possible infusion of chaos (but we would rather say chance) into the cosmos, within a probabilistic framework that can take care of all circumstances of events like birth and death.
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.
1 If the Lottery is an intensification of chance, a periodic infusion of chaos into the cosmos, then is it not appropriate that chance intervene in every aspect of the drawing, not just one? Is it not ludicrous that chance should dictate a person’s death while the circumstances of that death–whether private or public, whether drawn out for an hour or a century–should not be subject to chance? (Translation by Andrew Hurley)
References
Anderson, W.J.: Continuous-time Markov Chains. Springer, New York (1991)
Ethier, S.N., Kurtz, T.G.: Markov processes: characterization and convergence. John Wiley & Sons (2009)
Kurtz, T.G.: Approximation of population processes. SIAM (1981)
Norris, J.R.: Markov Chains. Cambridge University Press, Cambridge (1997)
Taylor, H.M., Karlin, S.: An Introduction to Stochastic Modeling. Academic Press, San Diego (1998)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Iannelli, M., Pugliese, A. (2014). Stochastic modeling of population growth. In: An Introduction to Mathematical Population Dynamics. UNITEXT(), vol 79. Springer, Cham. https://doi.org/10.1007/978-3-319-03026-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-03026-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03025-8
Online ISBN: 978-3-319-03026-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)