Optimal Economic Change Detection with Imperfect Information

不完全信息下的最优经济变化检测

• 批准号: RGPIN-2014-04145
• 负责人: Lee, ChiGuhn
• 金额: $ 1.6万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=610856
• 关键词: Optimal; Economic; Change; Detection; Imperfect

We propose to address one of the most commonly encountered stochastic optimization problems, known as the optimal economic change detection. Briefly stated, this problem aims to detect changes in the given stochastic (reward or cost) process to maximize (or minimize) the given optimality criterion (total reward or cost). Uncertainties may come from a variety of sources including system state and its evolution, sojourn times before state change, white noise in the observable signal, and so on. The optimal change detection problem has a wide range of applications, including: production system control (Chryssolouris 2006, Zhang 2005), inventory management, healthcare system (Woodall 2006), maintenance optimization, and finance, to name just a few. Specific examples may include the dynamic production lot sizing, in which a production run will have to be stopped to minimize the production cost; the optimal inventory control, in which inventory threshold for ordering will have to be found; and optimal trading of financial assets, in which an asset position can be cleared once the price rises above a pre-set threshold. Despite the ample applications, the relevant literature is surprisingly limited. Most of the work is computational and analytical results are available only for simple cases. In particular, structural properties of optimal policy are available for problems with no more than two states. The existing literature can be extended by generalizing the basic problem (two states with complete information) in many directions: multiple system states, limited availability of information, finite or infinite optimization horizon, alternative stopping actions, and adaptive sampling. Our long-term objectives are (1) to develop computationally efficient algorithms for the whole spectrum of the economic change detection problem with imperfect information by characterizing the optimal policy and by utilizing the acceleration operators, and (2) to apply the developed model(s) to diverse applications such as maintenance optimization, production system control, financial optimization, security system control, and healthcare delivery optimization. Specific problems in the economic detection problem spectrum include (1) finite vs. infinite horizon problem, (2) N-state transition structure with or without an absorbing state(s), (3) complete (or as complete as possible) graph extension of the transition structure, (4) multiple stopping actions, (5) multiple types of samples, and (6) variable sampling interval. As a first step, our short-term objectives are to address two untapped problems in spectrum of Bayesian economic stopping problem and apply the model to an application. First, we will characterize the structure of optimal policy for the economic detection with optimal adaptive sampling and that with multiple post-change actions. These tasks will involve analytical investigation of the value function, derivation of bounds for the value function, investigation of the convexity and/or concavity of policy maps, understanding the relations among policy parameters such as super- and/or sub-modularity, and the analysis of impact of sampling rate on the value function. The eventual goal is to design computationally efficient algorithms for general classes of the economic detection problems. Second, on the application side, we will utilize the gained knowledge from the first short-term goal in tackling a real world problem: algorithmic trading. Specifically, we will extend the current development on pair trading to a more general dynamic portfolio management model. The trading problem requires continuous control as asset prices changing continuously and as a result will be tackled as continuous time Markov decision processes.

我们建议解决一个最常见的随机优化问题，被称为最优经济变化检测。简而言之，这个问题的目的是检测给定的随机（奖励或成本）过程中的变化，以最大化（或最小化）给定的最优性标准（总奖励或成本）。不确定性可能来自各种来源，包括系统状态及其演化、状态变化前的逗留时间、可观测信号中的白色噪声等。 最优变化检测问题有着广泛的应用，包括：生产系统控制（Solouris 2006，Zhang 2005），库存管理，医疗保健系统（Woodall 2006），维护优化和金融，仅举几例。具体例子可包括动态生产批量调整，其中必须停止生产运行以最大限度地降低生产成本;最佳库存控制，其中必须找到订购的库存阈值;以及金融资产的最佳交易，其中一旦价格上涨超过预设阈值，资产头寸就可以被清除。 尽管有大量的应用，相关文献是令人惊讶的有限。大部分的工作是计算和分析结果，只有简单的情况下。特别是，最优策略的结构性质可用于不超过两个状态的问题。现有的文献可以通过在多个方向上推广基本问题（两个状态的完整信息）来扩展：多个系统状态，有限的信息可用性，有限或无限的优化范围，替代停止动作，和自适应采样。 我们的长期目标是（1）通过表征最优策略和利用加速算子，为具有不完美信息的经济变化检测问题的整个频谱开发计算有效的算法，以及（2）将开发的模型应用于不同的应用，例如维护优化，生产系统控制，财务优化，安全系统控制，和医疗服务优化。经济检测问题谱中的具体问题包括（1）有限与无限时域问题，（2）具有或不具有吸收状态的N状态转换结构，（3）转换结构的完整（或尽可能完整）图形扩展，（4）多个停止动作，（5）多种类型的样本，以及（6）可变采样间隔。 作为第一步，我们的短期目标是解决贝叶斯经济停止问题谱中的两个未开发的问题，并将模型应用于应用程序。首先，我们将刻画最优自适应抽样的经济检测的最优策略的结构，并与多个变更后的行动。这些任务将涉及价值函数的分析调查，价值函数的界限的推导，政策地图的凸性和/或凸性的调查，了解政策参数之间的关系，如超级和/或子模块化，以及分析采样率对价值函数的影响。最终的目标是设计计算效率高的算法一般类的经济检测问题。其次，在应用方面，我们将利用从第一个短期目标中获得的知识来解决一个真实的世界问题：算法交易。具体来说，我们将扩大目前的发展对配对交易更一般的动态投资组合管理模型。交易问题需要连续控制，因为资产价格不断变化，因此将作为连续时间马尔可夫决策过程来处理。