The Problem with the Long Run

"In the long run we’re all dead." -John Maynard Keynes

The problem with the long run is that eventually it gets here.

Military planning spans the gamut from large potential near-peer conflicts, to small, asymmetrical engagements across the globe. Lately, the trend is toward thinking about China. Whether it is over the Taiwan Straits, the Spratly Island chain, Vietnam, or North Korea, China is the new pacifier for all the cold-war enthusiasts who pine for the simpler days of bipolarity in the international system.

I’ve argued elsewhere that the notion of fighting a war with China is absurd. What’s more, from a strictly military perspective, there are much more immediate and problematic issues that require greater focus, such as a Mexican failed state on our southern border. But in truth, I now believe that all of that thinking ignores the 600 pound gorilla standing in the room. What planning exists in the military to cope with a collapsed dollar?

One reason I say that the idea of a war with China is absurd is because that war has already been lost. It’s time, once again, for some economics. Let’s start with a number:

14,713,992,331,505.17

That number, at the time I’m writing this, is our national debt. Let’s have a look at what our national debt has been doing over time (according to the US Govt.):

That debt is currently increasing at a rate of 4 billion dollars PER DAY. Who is lending us all this money? Let’s have a look at who owns our national debt:

China owns seven and a half percent of our national debt, which works out to $1,103,549,424,862.89 (1.1 trillion dollars). Worse yet, oil exporters hold a large portion as well. And just who are these "oil exporters?" Why it’s Ecuador, Venezuela, Indonesia, Bahrain, Iran, Iraq, Kuwait, Oman, Qatar, Saudi Arabia, the United Arab Emirates, Algeria, Gabon, Libya, and Nigeria.

This very morning, Fed Chairman Ben Bernanke announced that the Fed would likely try even harder to lower interest rates, and since short-term rates are pretty much already zero, they’re going to go back to the 1960’s playbook and move short-term securities into longer-term holdings in an attempt to bring long-term yields closer to short-term. What is the point of that? It’s to try and push you, the consumer, into doing one thing: spend your money. For some reason, the Fed and our government have adopted the notion that the only sign of a good economy is people spending money on consumable goods. (As an aside comment, this is what happens when you put cool-aid-drinking modelers in charge of something.)

So take a wild guess what the national savings of the United States is. It’s -619,175,676,960.00 (that’s NEGATIVE six hundred BILLION dollars). There aren’t very many countries out there with negative national savings rates and the bulk of those are third world countries and failed states.

Well here’s where things start to get problematic. As of August of this year, our inflation rate according to our kind and benevolent government was 3.77 percent. Compare that to the ten-year yield on a US Treasury bill which is currently just under two percent, and perhaps you see a problem? If not, let me spell it out for you. If you bought a ten-year T-bill today, when it matures it will be worth less, in real money terms, than when you bought it. In other words, what is going on here is that the Fed and our government are purposely devaluing our currency for the express purpose getting you to consume, using borrowed money, in order to artificially pump up the economy. Do you truly think that can last? Well it can’t. Here’s why.

Let me first talk a little about the inflation rate. What I listed above as the inflation rate is called the "core" inflation rate. That rate of inflation is calculated using a basket of goods and tracing the price of those goods over time. What’s in that basket of goods is much less important than what isn’t: food and energy. Ostensibly, these two items are left out because it is feared that including them would bias the numbers because of short-term price issues. But in reality, including them always makes the rate higher, often much higher, than the core rate, which is the inflation rate that our government chooses to tell us about. In reality, our current inflation rate, accounting for food and energy, is substantially closer to ten percent. This imbalance between inflation and interest rates is why we constantly hear about how the purchasing power of US consumers has actually decreased relative to necessities, even though it has increased relative to other consumables such as electronics, etc. It is also why our currency is being devalued.

So why aren’t we seeing much more severe inflationary effects? The reason is because of trade imbalance. Most of the goods we consume are made in China, which means the money we spend for them go to banks overseas, and are not part of the money circulating in our economy, but rather in someone else’s. This is possible due to the reserve status of the US currency. Foreign nations hold US dollars due to its reserve status, and I’ve already shown you above just how much money that actually is.

Currently there are movements in the international system to move away from the US dollar to some other reserve currency. Chief among the folks wanting to do this are Russia, most of the oil producing nations and, you guessed it, China. So what happens if the US currency is no longer the reserve currency of the world?

Very simple. Nations holding US reserves will seek to spend those reserves in the only place that they can: the United States. Imagine, for a moment, the inflationary effects of a sudden infusion of several trillion dollars into our economy, as foreign nations seek to purchase anything that they can in the US. To put it simply, our economy will collapse under the weight of cripplingly high inflation rates as other nations dump their holdings in US currency, which is to say nothing of the exacerbating effects of "quantitative easing," which the Fed also announced this morning it is considering another round of. When you add to that the reality that US manufacturing has all but disappeared, the ramifications are staggering.

China will have won the war without ever firing a shot. In truth, they already have. All they need do is pull the trigger. Which gets me back to the opening points in this discussion. What plans are being made in the US military to help this nation survive that trauma? I’m fairly certain that the answer is none. The US military industrial complex currently operates under a single over-riding assumption: that the gravy train of the defense budget will never end. I’ve got bad news. It’s about to.

As much as we love our expensive gadgets, they are simply untenable. If the US Military does not start planning on how to remain an effective force using a fraction of their current budget, then they do a disservice to our country.

It is my belief that at this stage it is simply too late to prevent what is going to happen to our economy. It will happen, and it will happen sooner rather than later (and the longer we put it off by artificially propping up the economy, the worse it will be). We need to start planning on how to survive the event, and come out the other end a stronger, more agile, and more fiscally responsible nation. The members of our military take an oath to defend our constitution. It is time for them to do some soul searching over what that really means when the financial status-quo changes drastically for the worse.

Standing Inside the Outside Box

In his book Empirical Model-Building and Response Surfaces, George Box made the now semi-famous statement "All models are wrong, some are useful." In summing up mathematical modeling and statistics, this comes about as close to ground truth on the subject as possible. Aside from those of us suffering from the side effects of cool-aide poisoning (dispensaries have sprung up at universities across the country) many of us who have worked through the detailed mathematics that underlie statistics and formal modeling realize the metaphoric nature of the structure and the perilousness of the assumptions behind it. Despite that, we continue to oversell the "truth" of what we do.

We excuse this behavior with a simple expedient; give the customer what he wants. The general wants an answer, so he is provided with a point solution to a broad spectrum question. He’s happy, we’re happy, everyone’s happy. At least until they start bringing the boys home in boxes. But don’t worry, how will the bullets in those corpses ever be traced back to that product handed to the general. It’s what he wanted, after all. Conscience clear.

Not in my book.

We're accosted on all sides. In the press we continually see where someone or other has invented some new method for predicting the future (which usually turns out to be something already done wrapped in new clothing, e.g. Gourley, BMD, etc.) and the money starts to flow. Hard not to be envious of that.

When we read reports about some new wizbang method for predicting the future, our first reaction should not be one of optimistic hope and excitement. Instead, our first impulse should be to reach for our crap detector and to start figuring out what it's doing, what context it works within (and what context it doesn’t), and then to firmly place it within our hierarchy of tools in such a way that it is optimized to perform only on the issues on which it is effective.

Our other challenge comes in the form of poorly defined problems being posed by the technically incompetent (who, unfortunately, usually hold the checkbook). "If I could predict what the terrorists were going to do next, I could stop them and better defend the country." How many times have you seen some formulation of that sentence/sentiment?

It's mostly our fault. In the mad dash for social sciences to gain respectability by donning the trappings of the scientific method, we've made promises we can’t actually keep. Of the list of things that all good models are supposed to do, predict is one of them. This comes right out of formal methods 101, and it's based upon a gross misunderstanding of reality. As Stephen Downes-Martin has so effectively pointed out, F=MA is a very effective model at predicting things, except that it is not a social science model.

In quantitative academic literature on civil wars, insurgencies, and non-state actor violence we see a broad range to empirical evidence used to back all sorts of claims. One of my favorites is the notion that suicide bombings are primarily the result of occupations in foreign countries. As a result of this correlation, Robert Pape has recommended rather broad policy prescriptions that have gotten traction in some circles. Except that the correlation is so obviously spurious and driven by a particular political perspective. Beyond that, try and reproduce the results. You can't. Pape won’t share his data (I’ve asked him more than once for it, and so have other professors. Pape won’t even respond to the request). Another favorite is the work of James Fearon and David Laitin out of Stanford. They claim to have empirical evidence that the presence of mountainous terrain is a primary determinant for insurgency. This author has gotten their data and run their regressions. If the original R Squared value of low twenty percent doesn’t give you pause, then when I tell you that if you take the lagged dependent variable out of the model the whole thing collapses it should at least raise an eyebrow.

But beyond the shortcomings of quantitative work being done in the social sciences (there is good work out there, it’s not a complete calamity), we have a problem with approach. "If I could only predict what the terrorist is going to attack next" is really not the problem we need to be thinking about. What if we instead ask "what can the terrorist do that can really hurt me?" and it’s corollary "what are the responses to bad events that are most appropriate?" These are questions we can actually answer with some degree of confidence, and by doing so, mitigate against their occurrence. Similarly, if I ask "what COA will defeat whatever Red does against it?" I have an intractable problem. But instead what if I ask "How can Red defeat my COA?" then I have a problem that is more tractable by not relying on attempting to predict what he might do, and instead causing me to think about making my own actions more flexible and responsive to potentially changing situations. The wargame response to the latter question is not to have accurate predictive PolMil models informing the game, but rather to harness the creativity of a large number of red cells as they attack a single Blue COA.

By understanding and cataloging our own vulnerabilities, we don’t get a tool to predict, but we do get a tool that anticipates and mitigates. But there’s no panacea here. We live in a sea of risk, and there’s no real way to create certainty out of chaos. In trying to form a future based upon predicted outcomes we create a system highly vulnerable to black swan events that could easily destroy the very future we wish to create. But by anticipating events by understanding what we are vulnerable to and then mitigating against them, we create a ship that can weather most storms.

Overcoming the PolMil Prediction Addiction

(from the intro to a paper I’m writing)

The dealer calmly dealt two cards to each of the eight players who sat around the table. I look at my cards and see a pair of queens. My first impulse is to fold, as pocket pairs seldom win hands in Texas Holdem, but they’re face cards so I pay the blind and play the hand. No one raises the ante, and the flop reveals two more queens. I have four of a kind, the best hand I’ve ever had and am certain that I will win this round. Yet, try as I may, I am unable to slow-roll anyone into raising my bets and ultimately the best hand of cards I’ve ever had nets little more than the antes of the players who chose to stay in.

A few hands later I’m dealt an ace and a queen. The flop reveals another ace and queen. I’ve got the top pair with good odds of winning the hand. The turn card is of no help to anyone, and I’m successful in slow-rolling another player into investing into the pot. The river card reveals a king. The odds are well in my favor that the other player doesn’t have the cards to beat my two pair, so I go all in and he calls. Thinking I’ve just won a big pot, he turns over an ace and a king. He beat me on the river card.

Two important lessons to be learned here: the best hand does not equate to the best payoff, and if you play the odds rather than the player, you lose.

I account myself a pretty good poker player, having participated in numerous tournaments, and even won a few. I have several strategies I use as guidelines to play, and they serve me fairly well. But there’s one thing I know with certainty: no matter how much I study the game, the player I own today may own me tomorrow. Poker is a game of psychology so much more than it is a game of chance, and to win you have to play the player, not the game. Thus, despite having calculable odds and bounded rules, poker is an unbounded game, where the best hand doesn’t always win, and the very best hands often net poor payoffs.

What does this have to do with prediction in PolMil you ask? The analogy of poker is, in fact, very applicable. With poker, as in PolMil, I have numerous indicators about my opponent’s likely choices. I can observe his play, discern his patterns of cautious vs. aggressive play, observe how he bets when he has a good hand, or bluffs, or I can simply watch for tells. All of the observations give me insights into what he is likely to do in any given situation. So in some limited respect, I can attempt to predict what he will do and adjust my play to suit. Given enough observations about a single opponent, I can even build a mathematical model that will predict his play in any given situation.

But there’s a problem. That problem is that he is doing the same thing to me. It’s not a one-way system, but rather a complex interchange of observation and adjustment to suit what we each believe the other is likely to do. That model I’ve constructed tells me what he will do in the aggregate. It does not tell me what he is likely to do relative to me and my particular style of play. What’s more, if I adapt my play to rigidly adhere to my model’s predictions, I am certain to lose, as my play will become, in turn, predictable. Give me an opponent with a deterministic (read numeric) view of play any day; I will get rich off of him in short order. To defeat an opponent who believes they’ve predicted my behavior, I need do little more than roll dice.

The key notion to understand is that politics, like poker, is an activity in which the ones who are most successful are the players who are the most adaptable to any given situation, and, most importantly, understand their own vulnerabilities. Stated simply, players whose actions are predictable lose (and players who strictly play the odds are always predictable).

In the example above where the hand was lost by paired aces and kings, I was playing the odds. But here’s the thing: so was the other guy. He knew he’d paired the ace, so his chances already looked pretty good. When the king hit the table on the river, he knew the odds were very high in his favor (just like me). This is black swan country. From our individual perspectives, we viewed the probability of the other guy having a hand better than ours as a very low one. For him the probability paid off. For me, I was bitten by that shady swan, as the low probability event took the entirety of my chip stash. Thus, another reflection of reality is revealed. Despite the fact that the odds of particular hands appearing have very defined probabilities, those are modified by the fact that players are interacting and making conscious decisions about risk due to necessarily incomplete information. So while the math may look very well behaved, the reality is that the tails are, in fact, very fat.

So the overall point of this lengthy preamble is two things. First, where we intend to interact with an opponent and that opponent is anticipatory and adaptive, accurate prediction is simply not possible. Second, if we fool ourselves into believing that it is possible, we add more vulnerability to our portfolio. In more scientific parlance, the problem is not generalizable, and no amount of data makes it tractable. This isn’t to say that there aren’t some very good ways to model specific situations or anticipate the actions of an opponent (anticipate is not the same as predict), but it is to say that the traditional thought methodology of hypothesis testing is very likely to be misleading due to the afore mentioned lack of generalizability.