Markhov process

I find myself in a dualistic struggle as I try to understand the realities of what I study. On the one hand, I feel as if the only way of knowing is through observing a ‘real’ phenomenon for a very long time… as an ethnographer perhaps, or a natural scientist with longitudinal data. This side of my mind is currently very active with the book I’m just finishing, With Our Own Hands: A Celebration of Food and Life in the Afghan and Tajik Pamirs, which, if it must be classified, takes an ethnoecology approach to understanding complex social-ecological systems. So the other side of my mind, mostly related to my PhD, is searching for ways to generalise nuanced understandings to more generalisable theory, and back again. Specifically with regards to traps: What are the dominant dynamics that keep systems ‘stuck’ in an undesirable state? Rather than collecting a lot of data without knowing for what, we will create a ‘toy’ model to play with in order to further our specific hypotheses.

Modelling is one (very large) group of tools to understand complex systems and to simplify complex and perhaps even contradicting realities. So here I’ll start a small blog series on learning about modelling, and ideas that spring to mind as I dabble in this alternative reality. Mostly these blogs will act as placeholder for ideas to come back to, and hopefully for people to comment on and get involved with. I am following a free online course called Model Thinking through Michigan University. Most of the time I am very frustrated with the assumptions one must take to fit the world into a simplified model to assess a system that would never occur in reality. BUT I do also really see the value in using models to push one’s thinking in a given direction. So despite all the tedious calculations and oversimplification of what is real, here I go.

Markhov process was one such model that I learned about in last weeks lectures, which I think can have cool applications in understanding traps and transformations.

The Markhov process tells us about tendencies of a system to transform. For example, more states become democracies over time than autocracies. However, every decade a small percentage of democracies do become autocracies. Given this observation, one would logically assume that over time, the world’s states will reach an equilibrium of predominantly democratic states at any given time. However, this does not happen. A different equilibrium is reached, based on transition probabilities (the probability that a state will switch). This is based on the assumption that the system is memory-less… or predictions of the future are dependent only on the current state of the system and not of the past. This is possibly really useful for understanding traps and transformation. The Markhov model tells us that we cannot change a system’s trajectory by changing the state of the system itself, but rather that we need to change the process, or technically the transition probabilities, of moving from state to state. Process over function.  I look forward to exploring this with regards to why history matters in current system stability. Institutional theory, by limiting itself to analytical snapshots may be falling into the trap of a Markhov process.

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