We don't have a hundred biases, we have the wrong model
If you apply the cognitive biases model to algorithms which have superhuman performance in various games - like AlphaZero, DeepBlue, Pluribus, and so on - the natural result is to conclude that these models are predictably irrational. The reason you get this conclusion is because it turns out to be necessary to trade off theoretical optimal answers for the sake of speed. The behavioral economic view of human irrationality ought to be considered kind of dumb in view of that result. But it is actually so much worse than that for the field, because the math shows that sacrificing optimality for speed would be something that even an infinitely fast computational intelligence would be <i>forced</i> to do. It isn&#x27;t irrational; it is a fundamentally necessary tradeoff. In imperfect information games your strategy space is continuous, EV is a function of policy, and many games even have continuous action spaces. If you thought Go was high branching factor you thought wrong; Go is an example of a freakishly low branching factor. It is infinitely smaller than the branching factor in relatively trivial decision problems.<p>If you&#x27;ve never looked at cognitive biases through the lens of performance optimization you should try it. What seems like an arbitrary list from the bias perspective becomes clever approximative techniques in the performance optimization perspective.<p>I often think about why this isn&#x27;t more commonly known among people who call themselves rationalists and tend to spend a lot of time discussing cognitive bias. They seem to be trending toward a belief that general superintelligence is of infinite power, doubling down on their fallacious and hubristic appreciation for the power of intelligence.<p>I say this, because when you apply the algorithms that don&#x27;t have these biases - the behavioral economist view wouldn&#x27;t find them to be irrational  since they stick to the math, they follow things like the coherence principles for how we ought to work with probabilities as seen in works by Jayne, Finett, and so on - they either don&#x27;t terminate, or, if you force them to do so... well... they lose to humans; even humans who aren&#x27;t very good at the task.