Why 'Omega' muddies causal ordering in decision problems, and how to fix it
jessi_cata · x · 2026-10-23
The author argues that introducing 'Omega' into decision problems (e.g., the superpredictor in Newcomb-like scenarios) makes the causal ordering and full joint distribution unclear.
Better practice: sketch out what physical process 'Omega' actually is—it needn't be realistic physics, a stochastic Turing machine world suffices—to clarify the causal structure. A methodological critique of staple thought experiments in decision theory and AI alignment.
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