Evolutive Economic Dynamics and Social Interactions: Theory and Econometrics

我们建议对三个分析相关的主题进行研究：1.预期多样性的动力学：理论和计量经济学；2.社会互动的动力学：理论和计量经济学；3.生态学中的动力系统管理。这三者都统一于由空间和时间尺度层次引起的动力学复杂性，其中活动的空间大小的测量涉及特定活动达到稳定状态的速度有多快，与较大的空间范围相关的较慢的“松弛时间”。例如，决定资产未来价值和未来通胀路径的预期多样性动态的力量，跨越时间和不同群体，人们知之甚少。然而，政策举措在监管资产市场交易和管理通胀政策方面的影响，取决于在不同时间和经济中不同群体形成预期的动态。动力学的时间/空间层次很容易导致部分不可逆性、突变、交替稳定状态等复杂现象。这些挑战了政府在管理如此复杂的动态系统的政策形成中对举证责任的传统态度。例如，在处理潜在的全球变暖等问题时，举证责任的最佳位置取决于潜在气候损害的不可逆性程度。因此，必须衡量潜在的不可逆性程度。类似的问题出现在许多不那么戏剧性的管理问题上。出于这个原因，我的研究将侧重于发展适用于具有分层时间/空间尺度的系统的计量经济学和统计推理方法。我们的研究还希望通过建立培训模型来帮助经济学家和其他政策制定者，帮助他们在动态系统管理中形成政策，以便他们可以像飞行模拟器培训飞机飞行员一样接受培训，使用此类系统。总而言之，我提议的研究是开发基本理论和统计推理工具，旨在应对由时间/空间动态联系引起的复杂性问题，并将研究结果应用于政策。

We propose research on three analytically related topics: 1. Dynamics of Expectational Diversity: Theory and Econometrics; 2. Dynamics of Social Interactions: Theory and Econometrics; 3. Management of Dynamical Systems in Ecology. All three are unified by dynamical complexities raised by a hierarchy of spatial and temporal scales where measures of spatial size of an activity relate to how fast the particular activity reaches a steady state with slower "relaxation times" associated with larger spatial ranges. The forces that determine the dynamics of expectational diversity on, for example, future values of assets and future paths of inflation, across time and across different groups are poorly understood. Yet the impact of policy initiatives in regulation of trading in asset markets and management of inflation policy, depend upon the dynamics of the formation of expectations across time and across different groups in the economy. Temporal/spatial hierarchies of dynamics easily lead to partial irreversibilities, abrupt changes, alternative stable states, and other complicated phenomena. These challenge conventional attitudes concerning burden of proof in government policy formation on managing such complicated dynamical systems. For example, in managing a problem like potential global warming, the optimal placement of the burden of proof on strength of evidence depends upon the degree of irreversibility of potential climate damage. So, the degree of potential irreversibility must be measured. Similar issues arise in many less dramatic management problems. For this reason my research will stress development of econometric and statistical inference methods appropriate to systems with hierarchical time/spatial scales.Our research also hopes to contribute to policy formation in the management of dynamic systems by building training models to assist economists and other policy makers so that they may be trained to work with such systems in much the same way that a flight simulator trains airplane pilots.In summary, my proposed research is to develop basic theory and statistical inference tools designed to confront issues raised by complexity caused by temporal/spatial dynamical connections and apply the findings to policy.