About Sean Devine

I was originally a research physicist in a New Zealand government laboratory, with a modest publication record.   On the way to becoming a manager in the New Zealand science system I became an economist inquiring into wealth creation through technological change.  In doing this, I saw serious difficulties with the neoclassical equilibrium view of economics, as the   view failed to recognise that an economy is far from equilibrium.   In an equilibrium situation the development path is irrelevant.   However the different paths and outcomes that occurred when the Soviet Union and China moved towards market economics, show path is critical.   Again, The 2007-2008 shock to the US and the world economy shows that the macroeconomic drivers have little to do with a neoclassical equilibrium view.
In looking for a way to approach non equilibrium issues, I stumbled across Chaitin's work on Algorithmic Information Theory (AIT)   outlined in the book  John Casti's book "Complexification".
This introduced me to the works of Gregory Chaitin and ultimately Li and Vitányi’s comprehensive book.     While this book is a treasure trove, it is difficult for an outsider to master as the context of much of the development is not clear to a non mathematician.   Nevertheless the book pointed to Kolmogorov's work on algorithmic complexity, probability theory and randomness.   So while the application to economic systems is on the back burner, I realised that AIT provided a useful tool for scientists to look at natural systems.

In short, the developments in AIT, probability and randomness seemed to me to provide a valuable tool to apply to the kind of organised complex systems studied in the natural world.   Unfortunately, these insights are not readily accessible to the scientific community, or in a form that is easy to apply. I found that the earlier work of Zurek and Bennett on algorithmic entropy provided rich understandings of non equilibrium systems- yet this work seems to have become lost in the mists of time.  

A critical issue was how to apply AIT to strings, that specified structure in the real world but also embodied significant noise or variation.   While I developed my own solution (Devine 2006). I later found that Kolmogorov’s   Algorithmic Minimum Sufficient Statistic (AMSS) approach, outlined in Vereshchagin and Vitányi, had done it all. However as the AMSS  approach addressed a different question, its application to specifying a microstate in a real world macrostate was not obvious.  Nevertheless, because variation in similar strings can be handled, the  approach is critical to real systems.  However, I use the term provisional entropy rather than AMSS, as this entropy aligns with the traditional understandings of entropy and provides a path into discussing real non equilibrium systems. See

J. L. Casti. "Complexification:Explaining a Paradoxical World Through the Science of Surprise. Harpercollins (1995).

Li, Ming, and Paul M. B. Vit´anyi, “ An Introduction to Kolmogorov Complexity and Its Applications”, 3rd. ed. New York: Springer-Verlag (2008).

S. D. Devine. The application of algorithmic information theory to noisy patterned strings. Complexity, 12(2):52–58 (2006).

N. K. Vereshchagin and P. M. B. Vitányi. "Kolmogorov's structure functions and model selection". IEEE Transactions on Information Theory, 50(12):3265–3290 (2004).

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