Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization

AI 辅助优化下的探索性响应与适应性僵化

Abstract: This paper develops a theory of exploratory adaptation under AI-assisted optimization. The central argument is that the long-run adaptive effects of AI systems depend critically on how predictive assistance interacts with exploratory responsiveness itself.

摘要： 本文提出了一种 AI 辅助优化下的探索性适应理论。其核心论点是：AI 系统在长期内的适应性影响，关键取决于预测性辅助如何与探索性响应能力本身相互作用。

We formalize this mechanism using a dynamical framework in which cognitive, institutional, and technological systems evolve over rugged epistemic landscapes characterized by multiple locally reinforced configurations. A central state variable in the model is adaptive responsiveness, which measures the capacity of a system to traverse unfamiliar conceptual and institutional trajectories under changing conditions.

我们利用一个动力学框架将这一机制形式化，在该框架中，认知、制度和技术系统在以多种局部强化配置为特征的崎岖认知景观中演化。模型中的一个核心状态变量是“适应性响应能力”，它衡量了系统在不断变化的条件下穿越陌生概念和制度轨迹的能力。

Under convergent predictive regimes, AI systems substitute for exploratory engagement, reducing adaptive responsiveness and generating metastable trapping, hysteresis, premature convergence, and exploration-collapse dynamics in which systems become locally efficient but globally rigid.

在收敛性预测机制下，AI 系统会替代探索性参与，从而降低适应性响应能力，并产生亚稳态陷阱、滞后效应、过早收敛以及探索崩溃等动态，导致系统在局部变得高效，但在全局上变得僵化。

The framework also identifies contrasting exploration-enhancing regimes in which AI systems amplify exploratory search, conceptual traversal, and adaptive mobility. The effective substitution parameter is therefore responsiveness-dependent: systems possessing weak exploratory routines are more vulnerable to exploratory substitution, whereas systems already possessing high adaptive responsiveness may use AI assistance to expand exploratory mobility across rugged landscapes.

该框架还识别出了与之相反的“探索增强机制”，在这种机制下，AI 系统能够放大探索性搜索、概念遍历和适应性流动性。因此，有效的替代参数取决于响应能力：拥有薄弱探索常规的系统更容易受到探索性替代的影响，而那些已经具备高适应性响应能力的系统，则可以利用 AI 辅助在崎岖的景观中扩展探索的流动性。

The long-run adaptive effects of AI consequently depend not only on AI capability itself, but also on institutional structure, developmental context, and the architecture of human-machine interaction.

因此，AI 的长期适应性影响不仅取决于 AI 本身的能力，还取决于制度结构、发展背景以及人机交互的架构。