Ludic Fallacy: Why It’s a Mistake to Use Games to Model Real-Life Situations

In your adult life, if you have talked to enough people, or have attended few conferences and keynotes, you must have heard people starting a sentence with, “Like in a game of chess,” or “Think of it as a game of poker.” Analogies are helpful in communication, but life is neither a game of chess, nor a game of poker.

In The Black Swan, Nassim Nicholas Taleb explains the difference between everyday randomness, and randomness in the context of games or casinos. Taleb coined the term “ludic fallacy” to refer to “the misuse of games to model real-life situations.”

Ludic Fallacy: It is a capital mistake to use games to model real-life situations. The attributes of the uncertainty we face in real life have little connection to the sterilized ones we encounter in games.

Games are far more constrained, and follow certain rules. Games have clearly defined probabilities. For example, an AI system can be used to examine all possible outcomes of a chess game at any point by backwards induction to determine future moves that are likely to win.

Real-life situations, on the other hand, are full of uncertainty, and don’t comply to our probabilistic models. Unlike a game of poker that has a fixed set of cards known to all parties involved, in life an unknown card can pop-up at any point randomly.

There is a key distinction between Knightian risks, which are computable because we have enough information to calculate the odds, and Knightian uncertainty, which is non-computable because we don’t have enough information to calculate odds accurately. Games fall into the former category. Real life is in the latter. If we take the concept literally and only plan for the expected, we will run into some serious problems.

The ludic fallacy is more like an acknowledgement that real-life is far more complex than their paper models. Even if you conclude that all systems could theoretically be expressed through sufficiently complex models, there’s always the problem of creating a model complex enough to express the system.

Because we understand that a coin flip turning out to be heads has roughly 0.5 probability, we reasonably predict that flipping the coin 1000 times will yield roughly 500 heads. But the fallacy is to assume that this is true in real-life when the coin being used can be loaded, or doubled-headed.

“In real life you do not know the odds; you need to discover them, and the sources of uncertainty are not defined.”

In Taleb’s context, most things in real life are this way. They look like they’re going to follow your probabilistic model, but you frequently don’t account for true randomness, which isn’t modelled this way. Also, you may not have complete information most of the time—you think you’re flipping a fair coin because you see two possible outcomes, but in reality the odds are 0.99 against you.

In The Black Swan, Taleb introduces two characters viz. the street smart Fat Tony, and the ‘nerd’ Dr. John.

They are asked to “assume that a coin is fair, i.e., has an equal probability of coming up heads or tails when flipped. I flip it ninety-nine times and get heads each time. What are the odds of my getting tails on my next throw?”

Dr. John says that the odds are not affected by the previous outcomes so the odds must still be 50:50.

Fat Tony says, “You are either full of crap or a pure sucker to buy that ’50 pehcent’ business. The coin gotta be loaded. It can’t be a fair game.” Taleb writes:

“Organised competitive fighting trains the athlete to focus on the game and, in order not to dissipate his concentration, to ignore the possibility of what is not specifically allowed by the rules, such as kicks to the groin, a surprise knife, et cetera. So those who win gold medal might be precisely those who will be most vulnerable in real life. Likewise, you see people with huge muscles (in black T-shirts) who can impress you in the artificial environment of the gym but are unable to lift a stone.”

The moral: don’t be a nerd. Taleb further elaborates:

“In an IQ test, as well as in any academic setting (including sports), Dr. John would vastly outperform Fat Tony. But Fat Tony would outperform Dr. John in any other possible ecological, real-life situation. In fact, Tony, in spite of his lack of culture, has an enormous curiosity about the texture of reality, and his own erudition-to me, he is more scientific in the literal, though not in the social, sense than Dr. John.”
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