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Newcomb-like Problems in Algorithmic Trading: A New Angle?
I've been pondering the applicability of Newcomb-like problems in real-world systems, specifically algorithmic trading. Could these decision theory problems offer insights into the financial markets?
I'm particularly intrigued by the role of randomization in trading algorithms. Could this be seen as a strategy to 'defeat' a Newcomb-like predictor, making algorithms more robust against exploitation?
I'm new here and would value your insights. Is this a perspective worth diving deeper into?
Interesting thought, but use of randomness in adversarial games is a very old idea, and applies to CDT just as well as other decision theories. It IS part of strategies to defeat prediction, but it's not Newcomb-like.