Have we been thinking about warfare the wrong way all along?

One of my favourite book/movie combinations is Moneyball by Michael Lewis. In it, Lewis tells the story of Billy Beane, the General Manager of the low-budget American Baseball team Oakland Athletics. In 2002 Beane seeks to turn the tables on the baseball industry by using statistical analysis (or Sabermetrics) to give his team a competitive advantage. In doing so, he was able to counter his smaller budget with record-breaking results. My interest in this new way of looking at an old problem drew me to the book Small Wars, Big Data: The Information Revolution in Modern Conflict which uses similar approaches to challenge the way we understand small wars, particularly counter-insurgency (COIN).

As military practitioners, I find that we all tend to have strongly held beliefs about the right way to fight an insurgency. For many, this is based on personal experience, often in Iraq and Afghanistan. If we are lucky, this is also supported by some independent research on the topic. For my own part, I found myself putting pen to paper in 2013 after a deployment to Afghanistan to describe my own thoughts on the problem. As in American Baseball in 2002 though, experience and intuition does not always provide the correct answer to the challenges that we face. Consider then the research of Berman, Felter & Shapiro in Small Wars, Big Data which seeks to provide objective analysis to how we fought these wars.

Understanding violence

Fighting any war amongst the people is fundamentally concerned with seeking a decline in violence between key actors that can ultimately precipitate a political solution. Therefore, what should concern us as practitioners are the factors that cause that violence to continue and what will cause it to reduce. Without any data to inform these questions though, we often find ourselves making important decisions based on intuition rather than evidence. This is further compounded when you consider that we rarely appreciate the effect of our decisions past a six-month deployment cycle, and may never return to find out. Should consecutive military commanders at any level have differing views on many of these challenges, the result would be at best an inconsistent operational approach.

Small Wars, Big Data seeks to examine many of those notions that we have long held dear. Does a lack of employment actually drive young males to fight for an insurgency? Does aid and development actually win hearts and minds? Does winning a village eventually translate into winning a war? I have certainly held strong views on each of these questions but never bothered to wonder where or how I came to these conclusions. When I really reflect on them, they were feelings. Of course creating jobs would undermine the insurgency I would hear myself say. Who would want to fight when you can earn more money in a safer job? Such logic is hard to deny until you consider the impact of part-time fighters, foreign fighters and the realisation that an insurgency actually doesn’t need that many fighters to be effective in the first place. Yet, without data to challenge these notions, how would we expect to know any better?

How much money and effort have we potentially spent on these operations without really understanding the war that we are engaged in? Worse yet, could we have made the problem worse without even realising? Surely the provision of aid for instance, is a no-brainer. Aid helps the population and reinforces the legitimacy of both the host nation and the counter-insurgent. But what if it actually increased violence? After all, aid can easily become another commodity that needs to be controlled and undermined by the insurgent, as the authors highlight in a case study from Mogadishu in 1991-92. The challenge for the counter-insurgent is that the correlation between the rise in violence and the provision of aid might not be easily visible or the link clearly established.

Is data the answer?

These challenges are further compounded by the inevitable operational security that compartmentalises the data that we do have. Such secrecy ultimately makes it harder for the academic world to assist us in finding solutions to these problems. A further challenge is the unfortunate legacy of Robert McNamara’s obsession with data in the Vietnam War that led to the continuation of a failed strategy. There is certainly a balance to be achieved here, although the failure in Vietnam was not so much the collection of data but how the data was interpreted, whereby body counts became a key metric of progress.

The Moneyball phenomenon has already spread to some circles in the US military and there are certainly examples of the ADF using data effectively on operations; however, its use is far from widespread. If we want to be better at fighting these wars we will have to accept that we don’t have all of the answers and we will have to be willing to be proved wrong. When I reflect on my own time in Afghanistan, I can’t help but be confronted by the mistakes I may have made by providing solutions based purely on intuition. I’m willing to be proved wrong again. Are you?