כאשר יש ל-AI לעקוב: תקבולת והתאמה רצפית של סוכני אנוש ו-AI

תקציר מקורי באנגליתarXiv:2504.20903v4 Announce Type: replace-cross Abstract: How should organizations divide and sequence decision tasks between human and artificial agents? We develop a computational model of joint sequential adaptation in which two agents differ in a single, precisely specified way: the memory regime governing how past decisions shape subsequent ones. A recency-weighted regime, motivated by behavioral evidence on human adaptation, privileges recent outcomes; a uniform-memory regime, motivated by the scale-free consistency of algorithmic updating, weights a window of past outcomes equally. Situated in the lineage of NK/NKC models but developed on its own terms as a sequential-adaptation model, the framework varies task scope (N), within-task coupling (K), and cross-agent coupling (C) across