Let me ask you by a question: Can you know, how is your home value affected due to Trump’s winning of the GOP nomination? Is it going to go up or down and how much will be the time lag? What other factors could affect it in the meanwhile?

It’s an interconnected world no wonder! Everything is mostly interlinked to everything else. Actions have consequences elsewhere. Luckily, Systems thinking discipline comes handy in drawing some causal maps based on what we think are causes and effect. Treat these causal maps as hypotheses.

Traditionally, it was quite difficult to test these hypotheses due to a variety of reasons (such as lack of data, handling tools, real-time analytical computation tools etc.). Thankfully, we now have big data and analytic technologies. We can put these causal maps to test on reasonableness of conclusions they make. Multiple causal maps (from often participants with conflicts of interest) results in an interconnected system. A game theory based simulation could result in various outcomes that may mimic the real world happenings. A quick regression could assign weights to various links of this map. These weights obviously are re-evaluated whenever the prediction deviates “significantly” from the actual outcome.

The next step after testing the causal maps and their interactions is to set them free to let them react to the real world situations and see how closely the adapt to the world. Add newly discovered branches or pieces of another map to address the deviations.

I’ll leave it to your imagination where you can use such a map. Once you find a use and build a live learning model, just sit back and watch this cross-disciplinary (viz., Graph theory, Systems thinking, Analytics/Machine Learning and Game Theory) asset  brings forth the value to you.

Please share your thoughts!

Some more…

Use concepts from Systems thinking to define causal relationships between various entities (causal maps). Traditional big data analytics (e.g., regression) towards assigning weights to actions (“pseudo”-independent variables) resulting from the variety of signals (“pseudo”-dependent variables) propagating through the causal graph.

Any signal (be it US Treasury raising the interest rates OR I writing this post now) will have its own strong to a “butterfly”-like effect. The essence is to assign possibilities and probabilities to various outcomes (read actions by participants – correlates with “pseudo”-independent variables) arising due to this signal propagation.

You can learn some basics on Systems Thinking this MIT courseware here