Generation Risk: Assessing Political Instability in the Business World


Photo by Derek Gavey

We are Generation Risk. Just think of the number of man-made and nature-made catastrophes humankind has experienced in the last two decades: tremendous technological changes, global economic downturns, ecological disasters, wars and massive terror attacks. In this turbulent environment, organizations, firms and individuals are seeking to find the new crystal ball that will enable them to not only improve their situational awareness but also (and even more importantly) create better early warning mechanisms.

More concretely, risk management has become a crucial part of corporate activities and best practices in risk management policy is constantly pursued.

Assesing political risk — specifically on the assessment of regime stability — is not a theoretical issue, nor is it something that should concern only international relations wonks. In a globally hyper-connected world, assessing regime stability is a crucial practice: First, most multinational corporations have complex operations all over the world, including in underdeveloped and unstable countries. Second, in such a global world, even the clap of a butterfly wing in the southern hemisphere is likely to influence the northern one (and vice versa). So it is necessary to track changes in the strategic environment, it the broader sense of the word. Geopolitical stability is a crucial part of that.

Attempts to develop generic models to enable decision-makers to assess regime stability (and even more importantly, to preemptively identify political changes) can be traced back to as early as the 1950s. The events surrounding the Arab Spring – especially the inability of literally any Western intelligence organization to predict this political tsunami – gave these efforts a renewed push.

Most methodologies and services aimed at measuring political (in)stability suffer from what I call “risky dichotomies”: They are either quantitative or qualitative in nature, are either generic or context-heavy, are either machine-based or human-based, and so on. In short, they suffer from two main problems. First, the need to create a model as generic as possible (and therefore one which is scalable) contradicts the fact that every country (or at least every geographic region) has its own unique characteristics. The need for contextualization takes the edge off of any generic model. Second, the need to weight quantitative and qualitative indicators under one model creates measurements, validity and credibility problems — which in turn harms such models’ predictive effectiveness.

Given these methodological complexities, and the market’s desperate need for an efficient tool to enable decision-makers to manage political risks by being fully aware of related trends, Wikistrat has developed a tool that does exactly that. As the company’s Chief Strategy Officer, I have led a team of analysts, economists, statisticians and data scientists with a highly ambitious objective: to develop a model that (a) doesn’t fall into the methodological pitfalls we identify in existing methodologies and services, and (b) presents analysis, trends and early warning in a simple and intuitive manner, thus enabling decision-makers to quickly and simply consume information and apply it in the decision-making processes.

The Regime Stability Model (RSM) we have developed brings the power of subject-matter expert collaboration and infuses data collection and interpretation capabilities into a service offering. Used principally to perform real-time monitoring of country-level risk and instability, this capability combines qualitative crowdsourced analysis with quantitative datamining to provide a real-time, 24-7-365 system for monitoring and anticipating social unrest and destabilization potential in countries around the world.

The RSM has several main advantages: First, it combines quantitative and qualitative methods. Second, it utilizes a wide array of sources (e.g., open sources, social media, big data and expert opinions). Third, it is able to compare output across time. Fourth, it is able to differentiate between events and trends, and to analyze events within a broad strategic context. Finally, independent analysis generated by one method is used to validate results of another in a symbiotic relationship.

This altogether ensures a wider and more accurate perspective on the stability of a regime, as well as evolution and improvement of the model itself. Most importantly, it works. Among other things, the model was able to predict several occurrences regarding a third-world country — including the exact locations of riots in several cities, the judiciary as a new terror target, a period of growth in foreign investment, and a devaluation of the local currency several months before it was officially announced.

Want to hear more? Don’t hesitate to contact me.

About the author


Dr. Shay Hershkovitz
Wikistrat Chief Strategy Officer
Director of Analytic Community

This article was originally published on LinkedIn.

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