Known Knowns and Unknown Kano

Welcome to the latest edition of the Storm King Analytics newsletter, a bi-weekly update on what we’re doing, reading, and listening. In our previous newsletter, we introduced Nigeria’s Emirate of Kano, where a murky 2014 succession crisis offered a natural experiment in understanding “ungoverned spaces” — can we discern what’s happening behind the scenes, understand those relationships, predict what’s likely to happen next, and knowing that, intervene?
 
Our first few newsletters have focused on what the U.S. Army calls Ungoverned Spaces, which despite the name are neither ungoverned nor spaces — they’re dense, tangled networks of state and non-state actors competing for influence in places where formal governance is weak. In our initial installment, we talked about the four factors driving the emergence of such places: urbanization; globalization; wealthy non-state actors, and technology. Last time out, we visited a place embodying all of these trends — Nigeria’s Emirate of Kano, where a power struggle between four clans to place one of the members on the “stool of power” ended with a surprise succession reminiscent of discarded Game of Thrones subplots.
 
Why this matters: Ungoverned Spaces like Kano, which sits on the edge of the Sahel along the tenth parallel north, represents the global future of both conflict andcommerce. The shadow of Boko Haram stalks the emirate while the city of Kano’s three million residents are only just starting to draw the interest of multinational marketers. To succeed in either endeavor, you need to understand where power truly lies. And to that end, as part of our work supporting the Network Science Center at West Point, we wanted to know whether we could analyze and predict which players would succeed in situations like the succession crisis that consumed the emirate in 2014. So, without further ado…

Dramatis Personae:
 
? Emir Ado Bayero, successful clan and religious ruler who reigned for 50 years before dying in 2014.
 
? Rabiu Musa Kwankwaso, governor of the Nigerian state of Kano, who ratifies the selection of the new Emir to square the latter’s status with the state and national power structure. A Muslim, Kwankwaso later ran for president against the Christian incumbent, Goodluck Jonathan, but lost to the eventual winner in his party’s primary.
 
? Sanusi Lamido Sanusi, the Emir’s grand-nephew who was appointed the Governor of the Nigerian Central Bank in 2009. He was forced to resign in 2014 by Jonathan after alleging corruption in the state’s handling of oil revenues.
 
? The Sullubawa, Yolawa, Wudilawa, and Dambazawa clans. Each family has its own mosque, royal titles, and representative Kingmaker who helps to elect the next Emir, who is traditionally, but not always, from the Sullubawa clan. (Emir Ado Bayero fit this pattern.)
 
The Emir’s relationship with Governor Kwankwaso was known to be a tense one. It was also common knowledge that Sanusi harbored ambitions to succeed him. Following Sanusi’ departure from the Central Bank, he returned home and was given a traditional title, “Dan ‘Majen Kano,” reserved for “hardworking and courageous princes.”
 
Following the Emir’s death, the Kingmakers convened and asked each clan to advance a candidate, one of whom was Sanusi. The Emir’s youngest son (and Sullubawa nominee) Nasiru Ado Bayero was presumed by the press and the public as the front-runner — they were wrong.
 
Behind the scenes, Kwankwaso was conspiring with Sanusi to retaliate against the Bayero family for their warm relations with President Jonathan while strengthening his position for a presidential run. When Sanusi’s selection as the new Emir was announced in June 2014 just two days after Bayero’s death, crowds gathered to protest what they intuitively grasped was a rather... opaque decision-making process. Once it became clear Sanusi would get the nod, his rivals allegedly plotted to kidnap him — a scheme reportedly foiled at the last minute by Kwankwaso’s protection.
 
Their plan worked. Nasiru Ado Bayero left Kano shortly thereafter, refusing to acknowledge Sanusi’s legitimacy. But that didn’t matter, because he was stripped of his post and family title following Jonathan’s defeat by President Muhammadu Buhari in 2015. (Kwankwaso lost to Buhari, but later ran for Senate and won.)
 
That’s the end of our story. But this rather tidy resolution raises a number of questions for a military commander on the ground charged with hunting Boko Haram, foreign companies seeking to do business in a city they can barely navigate, or an investor wondering where power lies: how could we have known Sanusi was secretly the front-runner? Was Kwankwaso destined to outmaneuver the Bayeros and the Sullubawa clan? And was the root of their animus purely the result of religion and presidential politics, or were there other factors at play?
 
That’s where we come in. We build tools that quantify social capital — a currency measured in connections, reciprocity, and trust — and use them to create multi-layer network models that describe and visualize competing and cooperative actors in a social network like Kano’s. Using statistical tools like Network Kernel Density estimations, we can take these models, compare them to others, and map how they function. The last step — and this is where things truly get interesting — is to choose a goal (do you want to see Sanusi on the stool or power, or Bayero), and use our proprietary algorithms to determine how best to nudge the network toward your desired outcome. In Kano’s case, that means plotting the half-dozen steps necessary to get cozy with Kwankwaso and tip the scales in Sanusi’s favor.

An example Multi-layer Network

None of this foolproof, of course — unlike chess pieces, people have a mind of their own. Which is why we’ve added the ability to forecast the consequences of our recommendations while taking into account networks that are constantly evolving. Speaking of which, we’re currently mapping the most influential actors in the Horn of Africa and the tribes of the Maghreb for the U.S. government. Using these additional network datasets, we’ll continue to refine our methodology and algorithms.

Comparing 2 networks using Network Kernel Density. The "goal network" is on the left.

After running our algorithm for 10 steps the network on the right more closely resembles the "goal network."

Our ultimate goal is to develop an analytical engine that automates this process for decision makers for both military and commercial purposes. As you might imagine, the uses of a such a tool go far beyond military applications — knowing who to befriend is as much if not more valuable than knowing who to fight.