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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?

Intriguing post, but we should approach these topics with extreme epistemic humility. Our understanding is likely far more limited and confused than we realize:

1. Abstractions vs. reality: Concepts like "self" and "consciousness" are abstractions, not reality. As Kosoy analogizes, these might be like desktop icons - a user interface bearing little resemblance to underlying hardware.

2. Mathematical relations: Notions of "copy" may be a confused way to discuss identity. "Consciousness" could be a mathematical relation where only identities exist, with "copies" being imperfect models of that identity.

3. Cognitive limitations: Our mental architecture, optimized for our local environment, may be fundamentally ill-equipped to grasp reality. "Consciousness" itself might be an ill-defined concept arising from these limitations.

My default is epistemic humility. Acknowledge the veil of ignorance and the possibility that there is no universal resolution.