Reality is a Game

Game thinking from Adam Clare

Tag: game theory(Page 1 of 2)

Playing Game Theory with Monkeys

The title of this post is half true, but it sounds awesome doesn’t it? This TED talk by Colin Camerer is a good illustration off the predictive power of game theory.

When two people are trying to make a deal — whether they’re competing or cooperating — what’s really going on inside their brains? Behavioral economist Colin Camerer shows research that reveals just how little we’re able to predict what others are thinking. And he presents an unexpected study that shows chimpanzees might just be better at it than we are. (Filmed at TEDxCalTech.)

We Behave Irrationally if We Don’t Understand the Rules

Game theory is not the same as game design theory but the similarities are striking when they happen. This month a paper has been published on game theory which can help explain some aspects of what happens when a game has complex rules and systems. When we can’t understand a full ruleset we make irrational decisions.

In Complex dynamics in learning complicated games by Tobias Gallaa and J. Doyne Farmer they show that by running gaming simulations with players they can expose non-rational behaviour.

When stuff like this comes out it boggles my mind that people can still stand up in favour of the Chicago School which banks on the idea that people always make rational decisions. This doesn’t even explore what is meant by rational.

Here’s the abstract from the paper:

Game theory is the standard tool used to model strategic interactions in evolutionary biology and social science. Traditionally, game theory studies the equilibria of simple games. However, is this useful if the game is complicated, and if not, what is? We define a complicated game as one with many possible moves, and therefore many possible payoffs conditional on those moves. We investigate two-person games in which the players learn based on a type of reinforcement learning called experience-weighted attraction (EWA). By generating games at random, we characterize the learning dynamics under EWA and show that there are three clearly separated regimes: (i) convergence to a unique fixed point, (ii) a huge multiplicity of stable fixed points, and (iii) chaotic behavior. In case (iii), the dimension of the chaotic attractors can be very high, implying that the learning dynamics are effectively random. In the chaotic regime, the total payoffs fluctuate intermittently, showing bursts of rapid change punctuated by periods of quiescence, with heavy tails similar to what is observed in fluid turbulence and financial markets. Our results suggest that, at least for some learning algorithms, there is a large parameter regime for which complicated strategic interactions generate inherently unpredictable behavior that is best described in the language of dynamical systems theory.

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