(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.
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