研究人机协作中任务分配与顺序，发现高效组合并非让AI先行。

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 modular and sequenced task structures. Three mechanisms organize the results. First, threshold dynamics create absorbing high- and low-payoff regimes, so adaptation compounds whatever it inherits. Second, uniform memory amplifies inherited trajectories, for good and for ill, whereas recency weighting corrects locally but is volatile at scale. Third, memoryless stochastic adaptation, though inferior on average, functions as an escape mechanism when inherited trajectories are poor. Consequently, joint performance is maximized not by the prevalent "AI-first" design, but when scale-free adaptation follows a high-performing human; broad stochastic search instead rescues sequences initiated by low-performing humans. The model and its experimental validation (a separate study) offer organization designers a task-structural contingency logic for human-AI collaboration and caution against universal prescriptions for AI-first deployment.