发表机构
Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建系统级动态均衡框架，研究激励与学习下内生信息演化，揭示信息效率与动态可行性冲突、激励披露不可分离及单调性失效，并给出最优披露边界。
AI 中文摘要
本文研究动态经济系统的内生信息演化。基于赫维茨的概念框架，此类系统被定义为由递归反馈连接的资源与信息配置的动态联合调整过程。经济理论通过隔离特定边际而获得精确性，但当信息生产、披露、学习、激励、行动与未来可行性在这两个过程之间相互作用时，固定其他边际所得出的结论在联合决定下未必成立。本文为这种相互作用构建了一个系统层面的动态均衡框架。固定特定联系可得到动态机制设计、信息设计、实验与学习、反馈与披露以及信息获取中的熟悉环境。分析确立了相对于固定或部分外生信息环境的三个核心失效：外生可行性下的信息效率通常与内生动态可行性不相容；激励与披露设计一般不可分离；缺乏实施可复制性时信息单调性可能失效。本文还识别了相应的正面边界。最优披露可能是内部的或极端的；在附加条件下出现邦邦披露，进一步特化则产生从不透明到透明的内生转变。因此，即使信息的直接决策价值严格为正，也可能被最优地隐瞒。扩展到私人支付相关信息与有限承诺时，递归结构得以保留。
英文摘要
This paper studies the endogenous informational evolution of dynamic economic systems. Building on Hurwicz's conceptual framework, such a system is defined as a dynamic joint adjustment process of resource and information allocation linked by recursive feedback. Economic theory gains precision by isolating particular margins, but when information production, disclosure, learning, incentives, actions, and future feasibility interact across these two processes, conclusions obtained by holding other margins fixed need not survive their joint determination. The paper develops a system-level dynamic equilibrium framework for this interaction. Holding particular links fixed yields familiar environments in dynamic mechanism design, information design, experimentation and learning, feedback and disclosure, and information acquisition. The analysis establishes three central failures relative to fixed or partially exogenous informational environments: informational efficiency under exogenous feasibility is generally incompatible with endogenous dynamic feasibility; incentive and disclosure design cannot generally be separated; and informational monotonicity can fail without implementation replicability. It also identifies the corresponding positive boundaries. Optimal disclosure may be interior or extreme; bang--bang disclosure arises under additional conditions, and a further specialization generates an endogenous transition from opacity to transparency. Information may therefore be optimally withheld even when its direct decision value is strictly positive. Extensions to private payoff-relevant information and limited commitment preserve the recursive structure.
