Calculation Behavior and Bounded Rationality in Economic Models

绝大多数的理论经济模型都对 关于代理人理性的强有力的假设。 在 在宏观经济背景下，标准假设是， 充分意识到内生性的均衡分布 变量，即，理性预期占上风。 模型中 战略互动是重要的，通常会强加一些 纳什均衡的强化形式，其中代理人知道 他们的对手的决策计划完全准确。 这些 假设，虽然中央的经济预测， 模型，可以批评的理由是，他们没有给 说明代理商如何获取此类信息。 最近 研究假设代理人参与被动适应性学习。 例如，最小二乘学习方法假设， 自适应规则采用最小二乘回归的形式， 和进化适应机制， 他们的行动基于竞争的相对适应性 战略布局 这个项目寻求发展一个中间地带 在完全理性和被动的两个极端之间 适应 首要目标是建立一个将军 分析期望形成行为的框架， 允许智能体参与主动认知的过程， 尊重经济环境，但固有的地方 他们的认知能力受到限制， 在计算技术上。 这个项目开发了一种有限理性的方法， 哪些智能体被赋予了计算技术 预期，以及对预测误差的偏好。 代理人通过权衡imp;roved的利益来形成期望 预测与计算的成本。 该项目延伸 调查人员以前的工作，包括一个更丰富的 各种经济环境和计算算法。 的 方法是用来研究恶性通货膨胀和解释令人困惑的 Marimon和Sunder的实验发现， 与理性预期或传统预期相一致 被动适应学习方法。 计算框架 被进一步扩展并应用于宏观经济学中的问题， 金融和产业组织。

The vast majority of theoretical economic models impose very strong hypotheses as to the rationality of agents. In macroeconomic contexts, the standard assumption is that agents are fully aware of the equilibrium distributions of endogenous variables, i.e., rational expectations prevails. Models in which strategic interactions are important typically impose some strengthened form of Nash equilibrium, in which agents know with complete accuracy the decision plans of their rivals. These hypotheses, although central to the economic predictions of the models, can be criticized on the grounds that they give no account of how the agents acquire such information. Recent research posits that agents engage in passive adaptive learning. For example, the least squares learning approach presumes that the adaptive rule takes the form of a least squares regression, and evolutionary adaptation schemes specific that agents adjust their actions based on the relative fitness of competing strategies. This project seeks to develop a middle ground between the opposing extremes of full rationality and passive adaptation. The overriding goal is to establish a general framework for analyzing expectation formation behavior that allows agents to engage in a process of active cognition with respect to the economic environment, but places inherent limitations on their cognitive abilities in terms of restrictions on the calculation technology. This project develops an approach to bounded rationality in which agents are endowed with a technology for calculating expectations, together with preferences over forecast errors. Agents form expectations by balancing the benefits of imp;roved forecasts against the costs of calculation. This project extends previous work by the investigators to incorporate a much richer variety of economic environments and calculation algorithms. The approach is used to study hyperinflation and to explain puzzling experimental finding by Marimon and Sunder that are difficult to reconcile with either rational expectations or conventional passive adaptive learning approaches. The calculation framework is extended further and applied to problems in macroeconomics, finance and industrial organization.