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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656998
• 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.

我们建议解决最常见的随机优化问题之一，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随机(回报或成本)过程中的变化，以最大化(或最小化)给定的最优准则(总回报或成本)。不确定性可能来自各种来源，包括系统状态及其演变、状态改变前的逗留时间、可观测信号中的白噪声等等。**最优改变检测问题具有广泛的应用，包括：生产系统控制(Chryssolouris 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。具体的例子可能包括动态生产批量调整，其中必须停止生产以最小化生产成本；最优库存控制，其中必须找到订货的库存阈值；以及金融资产的最优交易，其中一旦价格超过预设阈值，资产头寸就可以被清理。**尽管有大量的应用，但相关文献令人惊讶地有限。大部分工作是计算性的，分析结果只适用于简单的情况。特别地，对于不超过两个状态的问题，最优策略的结构性质是可用的。我们的长期目标是：(1)通过刻画最优策略和利用加速算子，为具有不完全信息的经济变化检测问题的全谱发展出计算高效的算法；(2)将所建立的模型(S)应用于维护优化、生产系统控制、金融优化、安全系统控制和医疗保健服务优化等领域。经济检测问题谱中的具体问题包括(1)有限与无限水平问题，(2)具有或不具有吸收状态的N状态转换结构(S)，(3)转换结构的完全(或尽可能完全)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频谱中的两个未开发的问题，并将该模型应用于一个应用程序。首先，我们将刻画具有最优自适应抽样的经济检测的最优政策结构和具有多个变化后行动的最优政策的结构。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，政策映射的凸性和/或凹性的调查，理解政策参数之间的关系，如超和/或子模块，以及抽样率对价值函数的影响分析。最终目标是为一般类型的经济检测问题设计计算高效的算法。其次，在应用方面，我们将利用从解决现实世界问题的第一个短期目标中获得的知识：算法交易。具体地说，我们将把目前关于配对交易的发展扩展到更一般的动态投资组合管理模型。当资产价格不断变化时，交易问题需要连续控制，因此将被处理为连续时间马尔可夫决策过程。