"Look homeward angel, now..."

Look homeward angel now, and melt with ruth.
And, O ye dolphins, waft the hapless youth.
-- John Milton, "Lycidas" (163-164)

John Milton

These lines from John Milton's poem "Lycidas" are really about the death of his friend, Edward King, who drowned in a shipwreck off the coast of Wales in 1637. But somehow, these words never carry that meaning for me. I find in them an injunction to look to the past, perhaps with sadness, and then to have my eyes and attention turned forward to a destination. They always come to me this time of year. I'm not a maker of resolutions, but I do believe in reflection and in resolve. So, those of you unlucky enough to be reading this can thank Milton.

I've been thinking a bit recently about balance and objectives. How and why this came up--other than poor Lycidas and the season--isn't important, but these thoughts turned to considering what I read and wrote this year and why. For context, though, let me go back a bit further.

Read

In 2013, I was preparing preparing myself for an unscheduled trip to Afghanistan, and as I departed learned I would take command of a squadron not long after my return. Suddenly, my reading took an instrumental turn unlike any I'd experienced since graduate school. I built for myself a plan of study that alternated deliberately between the recent and distant history of Central Asia (especially but not exclusively Afghanistan), theoretical and practical examinations of international relations and of warfare (especially counterinsurgency and "small wars"), and works on leadership. After my return from Afghanistan in 2014, the second category transformed itself a bit, and I took an interest in country studies more generally--and found an oddly special affinity for sub-Saharan Africa--but the instrumental, objective-oriented intent remained the same. So what changed in 2015?

I can't put my finger on a specific reason--perhaps I was tired or lost or seeking something--but my reading habits changed dramatically this last year. I wandered into wandering from title to title and from topic to topic with no real objective in mind. Sometimes the titles were suggested by friends. Sometimes they were the hot read of the day (my disastrous run-in with Ghost Fleet happened thus). Sometimes they were books sitting on my shelf not yet read that, on unpacking, found themselves at the top of the pile. So, in the order I read them (and not including sources such as The Strategy Bridge, From the Green Notebook, WarCouncil--now the Modern War Institute--articles ad nauseum, and a good deal of random poetry), this was my year in books:

Think

Making such a list is easy, not least because the list is short, but thinking about it is much more complicated. By and large, I don't feel my time was wasted--not even by Ghost Fleet, for reasons that will become clear in a moment, though it is perhaps the worst book I've read in many years. I learned some things about my profession, the history of conflict and the protagonists in a region that remains a hotbed of unrest, the life and work of perhaps the greatest Western theorist of war, the utility of fiction as a mirror for understanding culture, and the international system. I even set aside a bit of time for fiction...and flat enjoyed The Martian as much as any work of fiction I've read in years.

Melancholia I, Albrecht Durer

What I feel I didn't do was enough. I didn't work hard enough at my profession--which at this point has become leadership. I feel that failure keenly, and I owe the people who work for me more. I didn't work hard enough at finding my place in the world. I feel that failure keenly, and I owe myself and my family more. I didn't work hard enough at strategy, the game I'll be playing when, inevitably, they take my squadron from me. I feel that failure keenly, and fear what it means for my nation and what I will have to offer when called. And I feel the length of the list. I have many excuses and a few good reasons, but I can be better and do more...and I can do more with better balance.

Perhaps the single-minded objectives of 2013 and 2014 were a bit much, and perhaps I need to leave room for wonder and serendipity, and I CERTAINLY need to leave room for poetry and for fiction...but perhaps a bit more strategy in my approach is in order.

Write

I set myself an objective to write much more this year than the last and to explore the possibilities of "new" media--that is, publication outside the rigidly academic world in which I've spent much of my life (and new to me because I'm a bit of a Luddite). I'm not built for positive self-reflection, so I'll say I might have done more and done better...but I've also done more than I thought I could.

In this space I've spent a great deal of time exploring the history and philosophy of my profession as a mathematician and analyst, the dangers of data-driven decisions (a concept anathema to someone in my profession), wondering aloud at the possibilities of the truth we seek (and this space extols), the utility of social media (a utility it has taken me a LONG time to understand), and more. The hours I regretted reading Ghost Fleet turned into a review for The Strategy Bridge, a few more articles/posts/essays of which I'm pretty proud (this is my favorite)...and a relationship with a group of men whose company I'm proud to keep and with whom I'm proud to tilt at windmills while we change the world.

This year has also seen some fascinating things on Twitter (a forum I don't understand or like, but whose utility I appreciate) as well as the continuation of an experiment in micro-blogging on Facebook that has brought many surprises and much satisfaction--and some epic arguments with much-loved friends on a myriad of topics, to my great benefit. And all of this brought to me new acquaintances, new friends, and new possibilities.

Next

There are plans aplenty, some already afoot, to learn and apply the lessons of 2015, but I'll reflect on those in about 366 days.

Right, Wrong, and Relevance

I'd like to introduce a young man whose studies in chemistry at the University of London were interrupted by the Second World War. As a chemist, he was assigned to a chemical defense experimental station with the British Army Engineers and undertook work determining the effect of poison gas. There, he was faced with mountains of data on the effects of various doses of various compounds on rats and mice. Since the Army could provide no statistician, and since this bright lad had once read R.A. Fisher's revolutionary Statistical Methods for Research Workers, he was drafted to do the work. Thus was born a statistician who would become the Director of the Statistical Research Group at Princeton (where he married one of Fisher's daughters), create the Department of Statistics at the University of Wisconsin, and exert incredible influence in the fields of statistical inference, robustness (a word he defined and introduced into the statistical lexicon), and modelling; experimental design and response surface methodology; time series analysis and forecasting; and distribution theory, transformation of variables, and nonlinear estimation ... one might just as well say he influenced "statistics" and note that a working statistician owes much of his or her craft to this man whether they know it or not. Ladies and gentlemen, I'd like to introduce George Edward Pelham Box.

George Box

But Box hardly needs an introduction. You already know him and no doubt quote him regularly even if you are not a professional analyst or statistician. Let me prove it to you. George Box, in his work with Donald Draper on Empirical Model-Building and Response Surfaces, coined the phrase, "Essentially, all models are wrong, but some are useful."

We've all heard this aphorism, even if we do not know it springs from George Box. While true in the most fundamental sense possible and a profoundly important insight, what isn't widely understood or acknowledged is how incredibly dangerous and damaging this idea is and why, despite its veracity, we should ruthlessly suppress its use. True but dangerous? How is this possible?

Listen carefully the next time you hear this mantra produced. The key is the manner in which most use the statement, emphasizing the first half as exculpatory ("It doesn't matter that my model is wrong, since all models are") and the latter half as permissive. The forgiveness of intellectual sins implicit in the first half of the statement requires of the analyst or programmatic and planning partisan no examination of the sin and its consequences; we are forgiven, for we know not what we do ... though we should know and should not be forgiven for turning a blind eye.

Once forgiven, the utility of the model is elevated as the only criterion of interest, but this is a criterion with no definition. As such it admits all manner of pathologies that contradict the intent of Box in framing this discussion of statistical methods. Consider a somewhat more expansive discussion of the same concept. In the same work quoted above, Box and Draper wrote,

Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.

And earlier, in a marvelous 1976 paper, Box averred,

Since all models are wrong the scientist cannot obtain a 'correct' one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so over-elaboration and over-parameterization is often the mark of mediocrity.

In each case, the question of utility is tied explicitly to the question of how wrong the model is or is not. Similarly, this is precisely the emphasis in Einstein's famous injunction, often quoted "as simple as possible, but no simpler." He actually said,

It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.

In all cases, it is the phenomenon under investigation, the datum of experience, that forms the basis for evaluating the relative utility of the model that is, of necessity, not a perfect representation of the phenomenon. The question is not whether the model is right or wrong, useful or useless. The real question of interest is whether the model is right in the ways that matter. The two concepts, separated by a lowly conjunction in Box's famed quote, are not separate in his intellectual construct. Yet they are very much so in the the framing of the quote as typically (ab)used.

A Spherical Chicken and a Vacuum?

Why does this matter? As a separate idea, "useful" is applied without considering the meaning of the word. Does it mean useful in the sense that it illustrates and illuminates some natural phenomenon, as Box would have it? Or does it mean that the model is simple enough to be understood by a non-technical audience? On the other hand, it may mean that our tools appear to deliver answers to life, the universe, and everything, a chimera some see as worth chasing. Or perhaps it means that the model is simple in the sense that Herbert Weisberg uses to characterize "willful ignorance" which "entails simplifying our understanding in order to quantify our uncertainty as mathematical probability." And it is no great stretch to extend this notion of willful ignorance to the ways in which we frame underlying assumptions regarding the structure of the elements and interactions in a model to facilitate their mathematical and/or digital representation. There is a great physics joke in this vein involving a spherical chicken in a vacuum, but that's a story for another day. If any of these begin to affect our assertions regarding utility, we have crossed over into a territory where utility becomes a permissive cause for intellectual failure, and that is a dangerous territory.

So why write about these things? The answer is simple. These questions affect every aspect of nearly every problem a military analyst will face--whether that analyst is an operations research analyst, an intelligence analyst, a strategist engaged in course of action analysis,etc. Examples abound.

Consider the ubiquitous 1-n list, a model of decision making that problematically imposes a strict, transitive order in preferences, treats cost and benefit as marginal with all the path dependence possibilities that entails, and does not typically account for interaction and dependencies across the list, all of which compromise the utility of the list as a tool. The model is, however, simple, easy to explain, and conveys some sense of rigor in the construction of the list ... even if none exists. Useful indeed.

Or consider the notion of risk as an expected value expressed via the product of probability and consequence. With no meaningful characterizaion of the underlying distribution in the probability half of this formula, risk degenerates to a simple point estimate with no consideration of the heaviness of the probability tails and the relative likelihood of extremity. Or worse, it is implicitly viewed as a Gaussian distribution because that is what we've been taught to expect, and extreme outcomes are unwittingly eliminated from our calculus. On a related note, when considering a given scenario (within the scope of the various Defense Planning Scenarios) and speaking of risk, are we considering the likelihood of a given scenario (by definition asymptotically close to zero) or the likelihood of some scenario in a given class? This sounds a bit academic, but it is also the sort of subtle phenomenon that can influence our thinking based on the assessment frame we adopt. As such, characterizing the output of such a model as a description of risk is specious at best. 

John Maynard Keynes

This isn't the end of the issue vis-à-vis probability, though, and there are deeper questions about the model we use as we seek some objective concept of probability to drive our decisions. The very notion of an objective probability is (or at least once was and probably still should be) open to doubt. Consider A Treatise on Probability, a seminal work of John Maynard Keynes--a mathematician and philosopher of long before he became one of the fathers of modern macroeconomics--or Risk, Uncertainty, and Profit by Frank H. Knight, both first published in 1921. Both, in the formative days of the modern theory of probability, put forward a notion that probability is inherently subjective. Knight, for example, includes in his notion of risk (i.e., probability) the question of confidence: "The action which follows upon an opinion depends as much upon the confidence in that opinion as upon the favorableness of the opinion itself." But if subjective consequence is inherent to assessments of probability and risk, we enter into all manner of human cognitive shenanigans. Does increasing information increase the objective assessment of probability, the subjective assessment of confidence, both, or neither? There is some evidence to suggest the second and not the first, with all manner of consequences for how we conceive of risk (and for notions of information dominance and network-centric warfare). But these are central questions for models of decision making under risk.

Frank H. Knight

Further, the question of consequence is no less problematic. What do we mean by consequence and how do we quantify it (because the probability/consequence model of risk demands an ordered consequence)? And how does the need for a quantifiable expression of consequence shape the classes of events and outcomes we consider? Does it bias the questions we ask and information we collect, shifting the world subtly into a frame compatible with the probability/consequence mode of orienting to it? What are the consequences of being wrong in such a case?

Continuum of Conflict,
2015 U.S. National Military Strategy

There is an interesting corollary relationship between the the numerical output model of risk and 1-n lists in the sense that the numerical output provides a de facto list. Et voila! The model is useful, at least in one sense.

It offers another kind of list, though, based on the Defense Planning Scenarios. Since each scenario is assigned a numerical value, and since real numbers are well ordered we suddenly have a continuum of conflict. This model may be useful--it certainly makes the complex simple--but is it right in the ways that matter? The continuum makes each of the types of conflict shown effectively similar, differing only in degree. Even the implication of such a continuum is dangerous if it leads military planners to believe the ways and means associated with these forms of conflict identical or that one form of conflict can be compartmentalized in our thinking. Perhaps some room should be made for the notion that more is not always simply more; sometimes more is different, but this is an idea explicitly excluded from an ordering like that presented here.

Blind Men Building Models of an Elephant

Another interesting question arises from the ways in which these conflicts are modeled as we seek to develop computations of the consequences in them or to develop recommendations for the force structures best aligned with the demands of give scenarios. How will we represent the scenarios, our forces, the forces of the adversary, and their respective strategies? Will attrition define the objectives, and, if so, what is the model for attrition we will use and how does that model for attrition apply across the continuum of conflict? Will our enemies be volitional, dynamic, and devious or static and inanimate? Will we make simplifying assumptions of linearity, an assumption that sounds esoteric but matters in the sense that a nonlinear model exhibits behaviors a linear model cannot replicate, may be more difficult to develop and interpret, and is also generally more reflective of reality. Stainslaw Ulam's adage--"Using a term like nonlinear science is . . . like referring to the bulk of zoology as the study of non-elephant animals”--is a trenchant reminder of this principle.

Modeling Counterinsurgency
in Afghanistan

But this does not mean linear representations are necessarily inappropriate or without value, and precise emulation can be taken too far. Will we proceed down a path of non-linear interactions and voluminous detail, toeing Box's line of "excessive elaboration," as we often do with large-scale campaign simulations or the (perhaps unfairly) infamous effort to model the dynamics of counterinsurgency in Afghanistan? What does utility mean in each of these cases, and what does "right in the ways that matter" mean here?

Or what about our models of human nature and the international system. Are we classical realists, structural realists, institutionalists, Romantics, Marxists, or something else? The structural realism of Kenneth Waltz is famously parsimonious, abstracting a great deal into billiard balls that interact on the basis of power alone (a description that is itself more parsimonious than is fair). But this leaves us with a model that cannot explain critical phenomena and necessitates expansion and refinement--see Stephen Walt's balance of threat, for example, a socially constructed concept outside the Waltz model. In the end, we are faced with a model and not with reality, with approximations of truth and not with truth itself.

This notion is particularly important in thinking about the veracity and utility of our models. They are, in fact, models. In all cases, the intent is an "adequate representation of a single datum of experience." But in studying our models we can become detached from experience and attach ourselves to the models themselves, associate our intellectual value with their form and behavior, and make them into things worthy of study unto themselves. In short, we are wont to reify them, a process Peter Berger and Thomas Luckman describe as

... the apprehension of the products of human activity as if they were something else than human products-such as facts of nature, results of cosmic laws, or manifestations of divine will. Reification implies that man is capable of forgetting his own authorship of the human world, and further, that the dialectic between man, the producer, and his products is lost to consciousness. The reified world is ... experienced by man as a strange facticity, an opus alienum over which he has no control rather than as the opus proprium of his own productive activity.

Auguste Rodin, The Thinker

This suggests an important remedy to the problem of models that are wrong in ways that matter. If we recognize them as the products of human activity, as opus proprium, and not as handed down from authority, then they can be superseded by new products of human ingenuity. They are not sacred, and when we say a model is wrong, our next though should never be to apologize for the model ("but it is useful"). Rather, our thoughts should turn to whether the model is right in the ways that matter. This is the only proper way to defend our work.

Finally, if the model is wrong, we must demand a new model more closely aligned to the question of interest, a model right enough to be useful. And this is not just a task for analysts and mathematicians, though it is our duty. This is a task for planners, strategists, operators, decision makers, and everyone else. We must seek the truth, even if we may not find it.

First, however, we should probably scrub Box's exculpatory and damaging aphorism from our decision-making discourse.

Ladies Tasting Tea, Determinism, and Comfort With Contingency

Karl Pearson, Public Domain

At the encouragement of a statistician friend of mine, I recently read David Salsburg's book, The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. This was a delightful read in a hundred ways, especially in it's effort to humanize the statistical luminaries of the 20th century, and I highly recommend it. That said, there was one idea--it seems, in fact, as if this idea is a main objective for writing the book--that left me wondering whether the author had taken leave of his senses. Bear with me while I work through this idea and it's implications for analysis.

Here is what Salsburg has to say:

Over a hundred years ago, Karl Pearson proposed that all observations arise from probability distributions and that the purpose of science is to estimate the parameters of those distributions. Before that, the world of science believed that the universe followed laws, like Newton's laws of motion, and that any apparent variation  in what was observed were due to errors ... Eventually, the deterministic approach to science collapsed because the differences between what the models predicted and what was actually observed grew greater with more precise measurements. Instead of eliminating the errors that Laplace thought were interfering with the ability to observe the true motion of the planets, more precise measurements showed more and more variation. At this point, science was ready for Karl Pearson and his distributions with parameters.

The first thing that troubles me is a strange, Platonic (metaphysical) realism in the statement that "observations arise from probability distributions" as if these distributions were actual things rather than mathematical characterizations of the observed probabilistic behavior of actual things (or the probabilistic observations of the deterministic behaviors of actual things). At the risk of labeling myself some sort of radical nominalist, this seems an odd and difficult pill to swallow. This does not mean, however, that Pearson's effort to shift our attention from individual observations to more fundamental concepts of parameters that describe the totality of observations is problematic. It only means that the notion of an unobservable pure distribution of which we observe only imperfect shadows is an infelicitous representation of Pearson's work. So, this is not the major objection to Salsburg's purpose and point, but it is the (shaky) foundation on which he proceeds to build his house, and it is the house that presents the more significant problem.

Salsburg seems to fundamentally misunderstand the concept of Kuhn's paradigm shift, the accumulation of anomalies (i.e., the growing differences between observations and expectations in planetary motions, in this case), and the relationship between these phenomena and the philosophical positions of determinism and probabilism. (Incidentally, he also seems to reify this model of science, but that's a problem for another day.) The increasing variation from prediction lamented as a flaw of worldview is in fact such a flaw, but not a flaw in determinism as such but rather a flaw in the model of planetary dynamics as derived from Newton's laws of gravity and motion. The model of planetary motion was wrong--as models are--and this manifested more clearly once methods of measurement improved. This leads not to a revolution in probabilistic worldviews but rather a revolution in the model of gravity and planetary motion (i.e., relativity). So, while the errors of measurement are probabilistic, the source of changing error is systemic. These are different, and need to be treated differently (one statistically and one deterministically).

Henri Poincare
Public Domain

That means there is no fundamental disagreement between the worldviews--probabilistic and deterministic--that Salsburg sets in opposition to each other (at least as he's characterized them ... there are deeper philosophical divides, but Salsburg is really a determinist in disguise). Henri Poincaré writes in Chapter IV of The Foundations of Science that "we have become absolute determinists, and even those who want to reserve the rights of human free will let determinism reign undividedly in the inorganic world at least." He then goes on to discuss in detail the nature of chance, or "the fortuitous phenomena about which the calculus of probabilities will provisionally give information" and describe two fundamental forms of chance: statistically random phenomena and sensitivity to initial conditions. He writes:

If we could know exactly the laws of nature and the situation of the universe at the initial instant, we should be able to predict exactly the situation of this same universe at a subsequent instant. But even when the natural laws should have no further secret for us, we could know the initial situation only approximately.

Since we can know the exact condition of the universe only approximately (because we are finite, because humans have freedom of non-rational choice, becuase we are irrational and our models shape our observations, because Heisenberg dictates that imprecision is fundamental, etc.) all phenomena are thus to some degree or another functionally probabilistic for even the most determined determinist.

Carl von Clausewitz
Public Domain

The form of chance observed is then a product of the underlying dynamics and laws of the system under observation. Are we dealing with statistically random phenomena in which, when we have eliminated large and systemic errors, "there remain many small ones which, their effects accumulating, may become dangerous" and produce results attributed "to chance because their causes are too complicated and too numerous?" (The similarity to Clausewitz's discussion of friction is no coincidence.) Or are we dealing with nonlinear phenomena in which the single small error (or the butterfly flapping it's wings) yields outcomes all out of proportion to the error? Is there a structural reason for the particular distribution we see in the chance behavior? And what parameters describe these distributions?

These are important questions for analysts, with important implications. We bound our systems in time, space and scope for the purposes of tractability, introducing error. We make assumptions regarding the structure of our systems (analogous to the application of Newton's laws to planetary motion), introducing more errors. We measure, anticipate, and assume all manner of inputs to our analytic systems, introducing yet more error.

So what does this mean for us? As analysts we must everyday ask ourselves, "What errors are we introducing, what is their character, what is their structure, and how will they interact with other errors and the system itself?" And we must become comfortable with facing these uncertainties (something occasionally difficult for those of us with too many math classes under our belts).

Reading, thinking and writing about something for analysts to consider.

Complex Adaptive Systems: A Primer

Several years ago, a friend and mentor recommended The Origin of Wealth by Eric Beinhocker, a book that looks at the economy as a complex adaptive system in which physical and social technologies are constantly evolving. This view of economics, according to Beinhocker, calls into question every fundamental assumption of classical economics and has profound implications for the ways in which we engage instrumentally with the economic environment.

I was astonished by the work and the concepts it presented, and I've been wrestling with complex adaptive systems as a minor obsession ever since, with a particular focus on the implications for my own fields of military operations research and force structure analysis (a subject on which I'll have more to say another day).

In this intellectual wrestling match, I found myself in need of a working definition of these systems, and because of the applications I was pondering I needed that definition to work from the bottom up. In other words, I needed a list of the desiderata that characterize what a complex adaptive system is and does (to provide a heuristic for classifying such a system). In the end, I arrived at the following:

A complex adaptive system is any system comprised of diverse, interdependent, adaptive elements interacting nonlinearly and exhibiting systemic behaviors including emergence, coevolution, and path dependence across multiple scales.

As a standalone definition, this has served me fairly well in pulling together the various descriptive and behavioral elements of complex adaptive systems as they've been articulated by those far more expert than I (e.g., Brian Arthur, Yaneer Bar-Yam, Eric Beinhocker, John Holland, Melanie Mitchell, Scott Page, and too many more to name). What it doesn't do, however, is lay out in detail what each of these seven characteristics mean for how we understand and engage complex systems. More depth is really needed. Then enters opportunity ...

In writing a thesis (now a short book on the subject of complex systems as a lens for understanding (material) military force structure published by Air University Press and available for free download here), I've put together a short and (I hope) useful primer on complex adaptive systems. In the interest of adding a verse to the powerful complexity play, that primer is available as a standalone document here ... Complex Adaptive Systems: A Primer.

The Challenge of Integrated Space Analysis

I've spent the past three years working to overcome these challenges.  Here is my summary in an article just published in MORS Phalanx last month.

The Challenge of Integrated Space Analysis

Ruminations on Path Dependence and Archimedes

A few days ago, I collided at a dead run with one of the hazards--one might go so far as to call it one of the nightmares--of working in the business of analysis. Having conducted a thorough assessment of a problem, applying every tool available and appropriate, carefully weighing every assumption, considering every possible solution, and expending significant manpower, a methodological course of action emerged that incontrovertibly increases both effectiveness and efficiency in the process under investigation. The results were presented to the customer, and the answer came back: "Thanks for the great effort. We love the solution you've given us. It's brilliant! We're going to stick with our current process."

I've been in this business for nearly half my life, and I shouldn't be surprised by this outcome. In this instance, though, I was floored. Then, after a few hours of frustrated muttering, I had an epiphany (or calmed down enough to recognize the dynamics in play). There was a reason for the outcome, and understanding the reason both soothed my frayed analytic psyche and helped me to understand the way ahead. That reason? A favorite concept from economics: path dependence.

So, what is path dependence? The simplest possible expression of the concept is the statement that history matters, but this doesn't do the idea justice. In path dependence, history matters in particular mathematical ways and with particular mathematical and practical consequences. Essentially, though, it boils down to the notion (contrary to classical economics) that positive feedback mechanisms create multiple stable equilibria in dynamic social, technological, and economic systems. (Scott Page describes multiple forms of path dependence in a wonderful essay on the subject, but the naive notion given here is sufficient for most purposes.)  Accidental perturbation (or human decision) at critical junctures may nudge a system's trajectory toward an outcome that is unforeseen and sub-optimal (as many, most, or all of the stable equilibria may be). If one is in a tautological mood, one might then say that these stable equilibria are difficult to escape, but the point is that positive social, technological, and fiscal returns incentize stability.

Examples of path dependent behaviors are everywhere, if we take the time to think about it. Arbitrary coherence--the idea that an arbitrary initial price affects the long-term price irrespective of intrinsic value--is a classic path dependence phenomenon. A favorite military example is low-observable technology in the Air Force's force structure. A little stealth incentivizes improvements in adversary radar that encourages more stealth, and positive feedback takes over. This is a gross oversimplification of the many social factors in play, and it leaves out a number of positive feedback mechanisms, but it illustrates the idea. (I should also note that the situation that smacked me in the face with 'thank you for your interest in national defense' had nothing to do with this example or with questions of force structure.)

So, why am I comforted by this idea? There are two reasons.

First, it helps to know that adherence to the status quo is in some sense and in some cases independent of whether the status quo is the best position available. This realization provides a partial response to the question, "What did we do wrong?" (The other parts of that response involve a close examination of the analysis to make sure we hadn't missed something critical.) We are where we are for reasons of history, and the equilibrium is stable for any number of systemic reasons. We can be both right and rejected.

Second, it gives me hope and encourages me to continue laying the intellectual groundwork for the position. Path dependence and positive returns depend on context, but a fundamental characteristic of complex adaptive (social and technological) systems is that the context is always in motion. There may come a time when the status quo is no longer attractive. There may come a time when the costs of achieving escape velocity are within reach. There may come an opportunity, and if the work is done in anticipation of that opportunity we'll be in a position to exploit it. In the end, much of the great analysis we do is anticipatory. That is, if we wait for the question to be asked (or the opportunity to prevent itself), the analysis to support the decision will almost certainly be too late. Anticipatory analysis creates the lever, and we simply wait for a fulcrum to present itself.

No matter how brilliant an idea may be, no matter how solid the recommendation, it won't always change the world. And the reasons it won't are not always rational or in our control. But that's not a reason to not do the work. In some ways, good work is its own reward (for me). More importantly, doing the work is how we change the context and prepare for the opportunity to move the world.

Social Media for Military Analysts

In response to a recent post noting a lack of social networking presence in the Air Force's analytic community I was asked a slightly surprising question. On reflection this should not have been surprising if I were taking more time to examine my own assumptions and for introspection, but here it is:

What is the value of social networking to analysts ... and the rest of us? 

It occurs to me that there is more than a little wisdom in the question. It has become an assumption and article of faith (at least for some) that social networking is a value-added activity. But unexamined assumptions are something to fear, so here we are.

I've spent some time and glucose turning over my own assumptions, and I've come up with a few interrelated ideas for why social networking technologies can not only add value to the analytic enterprise but may even be essential to the continued success of our community.

Thomas More
Wikimedia Commons

To begin, and to make sure we're all on the same argumentative page, what do we mean by social networking? The dictionary, that most reliable of sources (and a favorite), defines social networking as "the development of social and professional contacts; the sharing of information and services among people with a common interest." Like a lot of definitions, this doesn't necessarily clear things up, not least because under this definition prolific and brilliant correspondents such as Thomas More and Desiderius Erasmus were a social network, and while this is true it also isn't the focus of the contemporary conversation. These legacy networks all still exist, but there are new technologies that facilitate the creation of new (or if not entirely new then at least new in scale) networks. So, what we're talking about here are the new, technologically-enabled forms of social networks and social media. ("Social media" isn't much better as a terminology, since the same objections apply. More and Erasmus interacted in a social network via the available social media of letters.)

Desiderius Erasmus
Wikimedia Commons

Without trying to create a precise definition of the technologies and forums involved, I include in the model outlets such as Twitter, Facebook, blogs (like this one), communities of practice (consider the Military Writers Guild as a fine new example), and other online publications with relatively low cost of entry (e.g., The Strategy Bridge, War Council, The Constant Strategist, The Complex Systems Channel, etc.). The line blurs with more traditional forms based in an online medium (look to ForeignPolicy.com and War on the Rocks as examples), and gets really fuzzy from a social network perspective when we start talking about traditional media with an online presence (e.g., the Air & Space Power Journal, the MOR Journal, etc.). These are all very much in the mix, but it's really the first category that's of most interest here.

One thing you may notice about this list is a general absence of robust exploitation of the digital social media by the Air Force's analytic enterprise. But these efforts take ... effort ... and one wouldn't expect a wholesale plunge into the available medium if there weren't tangible benefits that outweigh the costs, opportunity and otherwise, and I suppose this is the fundamental motivation for this post. Here are four broad reasons for social networking in our community that resonate with me.

Lifelong Learning

There are effective models for distance learning, but in general the value of an educational experience is multiplied by active, daily engagement with those around you (professors and fellow students). This engagement enhances the pedagogical experience in any number of ways. 

For any given concept we are almost certain to hear alternative views and questions we never thought to ask, increasing our understanding of it. These alternatives can be found without that engagement (e.g., by actively seeking and reading competing visions of a given topic), but the costs associated with this sort of study are high and the human  proclivities for delightful cognitive biases present a non-trivial barrier. When each member of the group is exploring the possibilities in their own way and informed by their own background, we distribute the effort in and increase the likelihood of finding constructive gems. 

Gerhard von Scharnhorst
Wikimedia Commons

In addition, I happen to think that writing our ideas down and putting them out in the world forces us to consider our positions more carefully even than conversational academic interaction. As Scharnhorst said, "The preparation of a short essay is often more instructive for the author than the reading of a thick book" because a requirement to present and discuss an issue drives a deeper study. Thus, we are enjoined to read, think, and write, a common theme in this space (here and here, for example). This achieves the noted first order pedagogical outcomes and also yields second order benefits (e.g., improved communication skills). We further experience the tertiary benefit of permanence, since ideas captured and promulgated in writing are not lost. Conversation in the classroom is ephemeral, but discourse in social media is accessible, searchable, and cross-linkable. (Big data, anyone?) This last alone is powerful enough to motivate alternatives to learning and interaction driven exclusively by conversation. 

Finally, the inevitable divergence of intellectual ideas, with equally serious, well-intended, and well-informed students arriving at divergent conclusions, teaches us something important about contingency and uncertainty. In a perfect world, this also inculcates a certain amount of intellectual humility. 

All of these may be available to an individual among their local peers, but they may also be difficult to access ... and well-formed social networks in a digital medium can fill the resulting gap. We can also exploit the revolution in massive online open courses (the Santa Fe Institute has some fantastic things going on in complexity studies, for example). These are easy to access and bring in the flavor of social networks, but why would we leave another low-cost rock un-turned?

Carefully-constructed social networks are one way of replicating the values of brick-and-mortar education and augmenting the available alternatives in an environment that does not permit face-to-face engagement in the context of life-long rather than episodic learning. And we earn many second-order benefits in the process. With blogs we get comfortable with and better at writing. With Twitter we learn the value of a bottom line and elevator speeches. And so on ...

Access to Expertise

The second idea that occurs to me is one I've mentioned elsewhere in this space: expertise. Effective analysis encompasses a myriad of disciplines (military history, military theory, sociology, psychology, international relations, economics, mathematics, statistics, religion, regional history, anthropology, computer science, military technology and capabilities, etc.), and analogies from each of these can inform our understanding of the others in the context the decisions we inform. This is deeply ingrained in our humanity, but it is also in the nature of humanity to make mistakes, and we do so with alarming frequency and occasionally unpleasant consequences. The likelihood of mistakes is amplified when the export of these metaphors is overseen by dilettantes since amateurs by definition lack the tools in either the source or destination context to evaluate the utility of those metaphors. So, expertise is important. On the other hand, it is difficult for any one individual, or even any one organization, to include deep expertise in the diverse areas that encompass effective analysis ... and we are left with a conundrum.

As an antidote, social networking offers access to the extended mind, the notion that the environment plays an active role in cognitive processes. Social networking facilitates the creation of an environment in which experts can interact and access the expertise of others, and our own ideas are shaped and improved by the process. In a sense, anyone who has worked on a headquarters staff has seen this concept made concrete. A staff is essentially an extended mind for the commander, improving his or her memory, facilitating multi-tasking, and enabling decisions across a broader array of activities than would be possible for the commander alone. Social networks simply extend this idea to digital interaction.

Adam Smith
Wikimedia Commons

The emphasis placed on expertise here does not mean we should each be narrowly or perfectly stove-piped in our education and experience, storing all expertise in each field of interest in distinct socially-networked nodes. There are efficiencies to be gained from this kind of specialization "and the division of labour is in this, as in all other cases, advantageous to all the different persons employed in the various occupations into which it is subdivided," but we each need enough breadth to inform the creation of potentially useful metaphors, applying our individual fields to other fields and to common problems. We need enough commonality and sufficient linguistic overlap to communicate our ideas to each other. Interestingly, this is also something social networking can facilitate, though it comes with a corollary danger in that we may choose our socially-networked associations unwisely or narrowly and create digital echo chambers for ourselves.

This notion of collaborative expertise makes me consider my own profession. I come from an Air Force community culturally dominated by Operations Research, a discipline "employing techniques from other mathematical sciences, such as mathematical modeling, statistical analysis, and mathematical optimization" to arrive "at optimal or near-optimal solutions to complex decision-making problems." This is a relatively young and inherently cross disciplinary field (with all the depth-and-breadth-balance problems that entails) that grew out of efforts by scientists, mathematicians, computer scientists, etc., to solve operational problems in World War II. These folks were experts (in some cases luminaries) in their respective fields, and together they were able to do incredible things that might have been impossible for any subset to accomplish on their own. Social networking is one way to access and connect that kind of expertise.

Expertise is obviously available through scholarly journals and other professional reading (and we should be reading them). But that places the onus of expertise back on us. It is also something we can access in forums like the annual Military Operations Research Society Symposium, but the cost of entry in a forum like this one is surprisingly high and in times of budget constraint (now?) this is at least problematic.

Networks and Requisite Variety

The third idea is related to Ross Ashby's law of requisite variety (from cybernetics/control theory). Basically the ability of a system to influence outcomes in the environment is contingent on the number of possible disturbances in the environment and the number of responses available to the system. More responses available reduces the variability of outcomes. This is very much related to John Boyd's so-called OODA "loop" (nothing more than a cybernetic feedback process).

The OODA "Loop"
Adapted from Frans B. Osinga
Science,Strategy, and War

To survive in an environment, a system must be able to orient to disturbances in the environment (i.e., understand its causes and effects) and have as many responses available as there are potential disturbances. What happens when a system doesn't have the wherewithal? It can die out, it can change, or it can organize into a higher-order system with greater complexity and more available responses. A cell may not be able to defend against a contagion, so some cells organize as organisms (e.g., people). Organisms may not be able survive in the environment alone. So organisms organize as families, tribes, and nations. Another way of saying this is that an system must be as complex as its environment.

(The language I'm using here is a little sloppy, and I don't mean to imply that there is consciousness in the cells and that they choose to organize. There are energy efficiencies and synergistic outcomes from organization that make these states structural basins of attraction and the outcomes emerge. They are not designed, as such, in either a bottom up or top down way. A more agency-oriented argument can be made when talking about collections of rational organisms--i.e., us.)

Social networking is one factor creating an environment in which new and more disturbances are possible. But networks create social structures with increasing complexity and associated with this complexity we can achieve greater potential for appropriate orientation and add potential responses, giving us more capability to influence and respond to the environment. (I hesitate to use the word "control" in this context for fear the semantics of that word are too loaded.)

There is an interesting aspect of this network concept. We have to be careful about the networks we create. Connecting everyone to everyone else is a recipe for entropy and white noise. Some of the necessary controls tend to happen in an emergent way. But some of it will generally come from conscious choices by those involved as well.

Lt Gen Glenn A. Kent
Courtesy of USAF

In any case, it is clear that the world is changing under us, and changing rapidly. As it does so, the tools of analysis must change as well. Glenn Kent is and should be a hero to our profession, but the world he faced was different, and optimizing against an analytically stable and monolithic adversary is no longer an option. It is our professional obligation to help our senior leaders understand the interactions of the dynamic environment and evolving force structure in the context of adversaries large and small, shifting alliances, changing footprints and geopolitical realities, new opportnities and threats in the domains of space and cyberspace, and more. These are nontrivial problems with the future in the balance and muddling through in isolation is not an option.

Because I Must
There is a final reason for pursuing the possibilities of social media and social networking in our community that touches on the personal. Rainer Maria Rilke's advice to a young poet is resonant for me.

"Search for the cause, find the impetus that bids you write. Put it to this test: Does it stretch out its roots in the deepest place of your heart? Can you avow that you would die if you were forbidden to write? Above all, in the most silent hour of your night, ask yourself this: Must I write? Dig deep into yourself for a true answer. And if it should ring its assent, if you can confidently meet this serious question with a simple, ‘I must,’ then build your life upon it.”

Not everyone may feel the same, but I feel I have a voice. I have things to say about things that matter ... and so do you. Social media gives us an opportunity to give those things voice, to touch the lives and minds of others, and to have our lives and minds touched in return. Social media, if you use it with deliberate intent, enables your humanity.

So What?
I've asked before, and I'll ask again. I see forums all about dedicated to collaboration in national security endeavors. If there is value in these endeavors, then where are the options for the analytic community?

Analysis and the Innovation Imperative

The defense community is recently (and not-so-recently) awash in calls for innovation to ensure that we are militarily postured to meet future challenges and possess the agility necessary to turn toward those challenges we did not anticipate.

For example, we have the Pentagon's Third Offset Strategy and Air University's call for "Airmen to offer innovative solutions to address problems facing the Air Force in a time of increasingly daunting global and fiscal challenges." We also see what B. J. Armstrong describes as a small movement

... growing across the defense community which realizes that the challenges of the new century are going to require innovative and creative solutions. Parts of this movement, inspired from the junior ranks of our services, look to embrace the ideals of innovation and entrepreneurship from the business world. These dedicated women and men recognize that the budget, manpower, and resource challenges in a post-war drawdown mean that new ways of doing things will be required.

The Red Queen and Alice

The ways in which this small (but growing) movement manifests are legion, including formal organizations such as the Defense Entrepreneurs Forum and  CIMSEC, online venues for the exchange of ideas (The Strategy Bridge, War Council, The Constant Strategist, etc.), and the growth of peer-mentoring communities of practice (whether on the model of Scharnhorst's Militarische Gesellschaft or something less formal). I am a huge fan of these efforts, and I think the powers-that-be should support them in any way they can. They will, I believe, be a critical part of creating innovation going forward.

Why should the powers-that-be support such an effort, though, especially in the face of limited resources? In times of plenty, we have the resources but lack the imperatives for innovation. In times of need, we lack the resources but the necessity is much more clear. This is the conundrum.

That innovation (or at least the ability to innovate) is necessary in the abstract seems self-evident (and if not self-evident then compelling arguments can be made for its necessity). The world changes around us. There are adversaries, potential adversaries, allies, and potential allies all about us who all seek to increase their own relative advantage, and every change they make (whether they intend it or we like it) affects the calculus of our own continuing advantage. We are all trapped in a Red Queen Race, and survival depends upon our capacity to find new solutions to new problems, more efficient solutions to old problems, and the creation of new problems for our competitors.

But innovation for the sake of innovation is a mistake. The folks at the Havok Journal make a fine argument that not all "outside the container" thinking is worthwhile. Paraphrasing egregiously, they claim that the container was built by someone for some reason, sometimes the container is just fine, and those looking to operate outside its confines often "don’t really understand the fundamentals of [their] profession and don’t want to take the time to learn." (The full article is well worth reading, and I highly recommend it. My self-serving paraphrasing does not do it justice.)

Art, Science, and Engineering

There is a real and important tension here, described in the fields of expert systems and evolution as a trade-off between exploration and exploitation. If a particular strategy works, it makes sense to exploit that strategy. But we should also explore the strategic environment for approaches that work better and for changes in that environment that might compromise the approaches we've exploited so successfully. A nice metaphor for this tension has been described elsewhere in this space as the interaction between art, science, and engineering. (I won't lie. The parallel with Clausewitz's wonderful trinity and the chaotic dynamics of the three-body problem add an undeniable attraction to the model.)  The balance between these is the difference between evolutionary success and failure.

So, why post this thought in a forum all about analysis? I thought you'd never ask.

We (analysts, mathematicians, statisticians, modelers, computer scientists, etc.) are a part of this environment, too. Our worldviews and tools must be no less adaptable than those of the doctrine writers, planners, and strategists of the world. More important (and a little frightening) is the possibility of reified and static analytic ideas becoming framing concepts for the rest of the strategic world.

Optimality is a favorite and appropriate example. For what do we optimize force structure? A world and a worldview. What is the consequence if either situation fails to meet our assumptions? Something less than optimal. How do we minimize the probability that failures of optimality (or analysis in general) are catastrophic? Welcome to the problem of framing analysis in a way that supports the decision needs of leadership in a way that meets the needs of today, tomorrow, and the future. And welcome to the need for innovation in our own community.

So, what does this all mean? How are art, science, engineering, innovation, expertise, exploration, and exploitation to be managed? Not surprisingly, I have some thoughts on the matter:

1. Expertise matters. The problems we face as military analysts are trivial in neither their costs not their consequences. If military operations research is to synthesize inputs and techniques from diverse disciplines (math, statistics, economics, computer science, etc.) expertise in those disciplines is important. Destruction and creation can produce positive results by accident, but will produce innovation far more reliably if the agents involved really understand the underlying philosophical and technical principles.
2. Diversity is more than important. Without diversity of experience and expertise, it is difficult to find and exploit the skis, handlebars, outboard motors, and tank treads in our experience to create effective snowmobiles. This is an interesting concept for military operations research. Ours is an academic discipline that is inherently interdisciplinary, but an interdisciplinary field will always struggle with sufficient isolated expertise to facilitate effective interdisciplinary exploration.
3. Tolerance for individual failure is critical. Evolution and innovation are bottom-up processes, and there must be room for both exploration and exploitation. In an ecosystem, failure is fatal, but that is a system relying on chance to create the necessary genetic and phenotypic variations that facilitate adaptation to changing environments at a population level. The loss of individuals is not important, since the adaptation is neither social nor volitional. On the other hand, analytic innovation is a volitional act by rational social agents, and intellectual variation leading to dead ends cannot lead to deadly individual ends (unless the objective is abject conformity). This is the primary purpose of a community/institution in the context of organizational innovation. It exists to exploit the known, incentivize exploration, and protect the explorers from censure. We need to be free to disagree, argue, and explore.

For our community these observations result in some imperatives for action. Simply put, a structure that prizes expertise and diversity in that expertise (not expertise in that diversity) while facilitating exploration of new ideas and analytic approaches is what we should seek. 

Commission for Military
Reorganization at Konigsberg, 1807

This blog notwithstanding, I'm left wondering why there aren't analytic equivalents of CIMSEC, The Strategy Bridge, War Council, The Constant Strategist, the Militarische Gesellschaft, etc. I'm left wondering why so many of our analysts attend the same school (singular) to receive the same graduate education. I'm left wondering if we will find a way to be relevant in the face of a future that looks decidedly unlike the world in which our "discipline" emerged.