Bias by Necessity: Impossibility Theorems for Sequential Processing with Convergent AI and Human Validation
Are certain cognitive biases mathematically inevitable consequences of sequential information processing? We prove that primacy effects, anchoring, and order-dependence are architecturally necessary in autoregressive language models due to causal masking constraints. Our three impossibility theorems establish: (1) primacy bias arises from asymmetric attention accumulation; (2) anchoring emerges from sequential conditioning with provable information bounds; and (3) exact debiasing by permutation marginalization requires factorial-time computation, with Monte Carlo approximation feasible at constant per-tolerance overhead. We validate these bounds across 12 frontier LLMs ($R^2 = 0.89$; $Δ$B...
| Authors | Jikun Wu, Dongxin Guo, Siu-Ming Yiu |
|---|---|
| Primary category | cs.AI |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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