(Roughly) Daily

… Maybe, as Greg Costikyan explains, none more so than Rochambeau (or “Rock-Paper-Scissors” as it’s also known)…

Unless you have lived in a Skinner box from an early age, you know that the outcome of tic-tac-toe is utterly certain. At first glance, rock-paper-scissors appears almost as bad. A four-year-old might think there’s some strategy to it, but isn’t it basically random?

Indeed, people often turn to rock-paper-scissors as a way of making random, arbitrary decisions — choosing who’ll buy the first round of drinks, say. Yet there is no quantum-uncertainty collapse, no tumble of a die, no random number generator here; both players make a choice. Surely this is wholly nonrandom?

All right, nonrandom it is, but perhaps it’s arbitrary? There’s no predictable or even statistically calculable way of figuring out what an opponent will do next, so that one choice is as good as another, and outcomes will be distributed randomly over time — one-third in victory for one player, one-third to the opponent, one-third in a tie. Yes? Players quickly learn that this is a guessing game and that your goal is to build a mental model of your opponent, to try to predict his actions. Yet a naïve player, once having realized this, will often conclude that the game is still arbitrary; you get into a sort of infinite loop. If he thinks such-and-so, then I should do this-and-that; but, on the other hand, if he can predict that I will reason thusly, he will instead do the-other-thing, so my response should be something else; but if we go for a third loop — assuming he can reason through the two loops I just did — then . . . and so on, ad infinitum. So it is back to being a purely arbitrary game. No?

No…

Read on for an explanation in this excerpt from veteran game designer Greg Costikyan’s book Uncertainty in Games: “The Psychological Depths of Rock-Paper-Scissors,” from @mitpress.

* Eric Berne

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As we play, we might send carefully-plotted birthday greetings to Vilfredo Pareto; he was born on this date in 1848. An engineer, mathematician, sociologist, economist, political scientist, and philosopher, he made significant contributions to math and sociology. But he is best remembered for his work in economics and socioeconomics– particularly in the study of income distribution, in the analysis of individuals’ choices, and in his studies of societies, in which he popularized the use of the term “elite” in social analysis.

He introduced the concept of Pareto efficiency (zero-sum situations in which no action or allocation is available that makes one individual better off without making another worse off) and helped develop the field of microeconomics. He was also the first to discover that income follows a Pareto distribution, which is a power law probability distribution. The Pareto principle ( the “80-20 rule”) was built on his observations that 80% of the wealth in Italy belonged to about 20% of the population. 

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