How Did I get Here?

During my public defence the questions was raised at what point I got interested in complexity science. I answered that this happened in my first job, and that my introduction to chaos theory had led up to it. The writings of Gleick (1988) about how deterministic (non-stochastic) systems can generate randomness showed that simple systems are already capable of generating a mess! Come to think of it, that encounter with chaos was in turn made possible by my university study, financial economics.

To my mind the economic models I was taught seemed unduly neat. Left to themselves systems (many things interacting) were assumed to automatically and stabilise and settle into an equilibrium. A perfect version is assumed to be immanent to them. In this view variation can only originate from external influences keeping them away from this immanent propensity to become perfect. Mitigation of the impact of such variations allows a system to reach its ideal. One example is provided by markets, which ‘perform’ best when equilibrated because left unrestricted, helping distribute utility freely and fairly.

In another example of this essentialist stance the firm is viewed first as an imperfect version of its ideal ‘urtype’ (ia Mintzberg 1993), next that it is possible shed the variation from its current make-up (messy) to achieve that ideal, and last that individual persons are capable of getting firms from here to there. To me this perspective, although corrected for strong assumptions like bounded rationality, intentionality and perfect information, and solidly mathematicized, remained uncomfortable, perhaps because the firms (and the people) I knew were nothing like that.

That said, also from the discipline of economics and business and management science emerged sub-disciplines like evolutionary economics (e.g. Nelson & Winter, 1982), systems views on the firm (e.g. Boulding, 1956), industrial ecology, capability theory (e.g. Pitelis & Teece, 2009). However, deep-seated axioms such as human teleology (intrinsic intentionalism), individualism, essentialism, and anthropomorphism (people-centeredness) are foundational even for those scientific endeavours. In order to enable further advancement, I believe these axioms need further revision.

To my mind chaos theory paved a path enabling presupposition of irregularity and variation, instead of equilibrium and stability. That endowed me with an intuition for non-equilibrium systems, at best temporarily stable, and generating novelty. Call me nerdy but I still believe this is cool! Ever since I tend to gravitate towards such systems by that intuition. In the final stages of my studies I took a math course in chaos modelling resulting in a thesis about chaotic behavior on the currency markets.

And the day after defending my master thesis I met a cool retail strategy consulting boutique and got a combined consulting and research position. We focused on theorising, modelling and advising of retail companies as complex systems. There, among the first books I got to read were Waldrop (1992), Kauffman (1993), and Holland (1982) and early publications of the Santa Fe Institute. Chaos theory got me the job, where I was then immersed in complexity. Complex systems generate novel orderly behavior from chaos. Thereby they add a level to chaotic systems, which create novelty as random behavior.

Complex system can show emergent behavior, or self-organisation, depending on your perspective. As a multitude they can generate behavior of which their component parts (or processes) are individually incapable. Or in other words, that global behavior is not immanent in the components: the human body is an organisation of molecules, but that organisation is not immanent in those molecules. I find this general notion so intriguing today, that I ended up doing a PhD on the topic, 30 years after having been introduced to chaos theory. The topic is the view that the firm is an emergent phenomenon: multitudes of people showing organised behavior which is not imminent in them.

Turing Machines and Beyond

To put to bed the discussion about companies being the computer – and for me to finalize the invention of yet another existing wheel, find attached this document. The author surveys the latest in computational logic, in the process describing Natural Computation. This is apparently an existing name for the beast I described in the posts categorized under Turing Machines so far!

Networks are capable of processing information in parallel, while interacting dynamically with their changing environment, asynchronously if necessary (companies!). TM as defined here compute solutions for given problems using algorithms and as such are a special case for the general principle of Natural Computation.