序列投资下的石油勘探投资最优时机选择

综合考虑石油勘探投资的不可逆性、序列性和不确定性,结合市场需求和勘探储量的不确定性刻画勘探储量转让价格的随机动态变化,运用实物期权思想下的序列投资决策方法探究了石油勘探项目的最优投资时机选择问题,得出各阶段最优投资时机临界值的解析表达式,并与一次性完成投资的决策结果进行对比,进而剖析了不确定性参数对各阶段最优投资时机的影响。结果表明,序列投资决策模型能够弥补一次性投资决策模型容易错失投资机会的局限性;各阶段最优投资时机临界值同市场需求波动率、勘探主体价格控制能力呈同向变动,同市场需求漂移率呈反向变动;随勘探进程的不断深入,最优勘探投资时机临界值对以上参数的敏感性程度不断增强。     

不同决策目标下受资金约束零售商的最优采购策略研究

在提前订购和延时采购两种情景下,分别考虑最大化期望收益和最小化违约概率两种决策目标,对受资金约束零售商的最优采购量和相匹配的融资策略进行分析,结果表明,相比于以最大化期望收益为决策目标,以最小化违约概率为决策目标的零售商在提前订购中将奉行"保守"的采购量和融资策略（仅耗尽自有资金量采购而不融资）,而在延时采购中采取"激进"的采购量和融资策略（与最大化期望收益下一致）;在此基础上,对不同决策目标下受资金约束零售商的最优采购时机问题进行研究发现,在最大化期望收益下,零售商的最优采购时机由产品采购成本、市场容量均值、市场容量方差、自有资金量、银行贷款利率等多个参数共同决定,而在最小化违约概率下,零售商将始终选择延时采购。     

基于二重随机因素的对称双头垄断期权博弈模型

本文在产品市场需求与经营成本二重随机性以及二者具有相关性的条件下,建立了一个对称双头垄断期权博弈分析框架,研究了企业最优战略投资决策问题。在导出企业投资价值函数和最优投资临界值的基础上,着重分析了企业最优投资策略均衡规则及各均衡存在的条件,并就各参数对企业最优投资临界值的影响进行比较静态分析,得出了一些有意义的结论,同时给出了经济解释。     

SMALL PANELS OF EXPERTS IN DICHOTOMOUS CHOICE SITUATIONS*

This paper analyzes an expert resolution problem under an uncertain dichotomous choice situation. The experts share a common system of norms and therefore they all prefer the alternative that best suits their purpose. The selection of such an alternative is referred to as a correct choice. Our analysis of optimal decision rules for panels of independent experts is pursued for n-member decision-making bodies, n≤ 5. The suggested optimality criterion is the maximization of the probability of the panel's making the correct choice. Within our framework, this criterion is equivalent to the more common criterion of expected-utility maximization. For three-member panels of experts, the expert resolution problem is solved and illustrated by means of a medical application. For four-member panels, we list the three relevant decision rules, specify the conditions for all possible rankings of these rules, and, finally, present an extended consulting application. We conclude by listing seven relevant decision rules in the case of five-member decision-making bodies.     

SIMULATION OF INFORMATION CHOICE

Beginning with the information economics framework and a multi-period decision model [15], this paper considers the use of computer simulation methods within an information system choice environment. Actual decision behavior is replaced by optimal decision rules, and simulation is used to evaluate the effects of parameter changes in the environmental model. Simulation is shown to be functional (1) in estimating the value of alternative information structures within a fifteen period decision model and (2) in providing sensitivity and statistical data which would be useful both for different decision maker utility functions and for a variety of information system design questions.     

Optimal market timing strategies under transaction costs

In this paper, we consider optimal market timing strategies under transaction costs. We assume that the asset's return follows an auto-regressive model and use long-term investment growth as the objective of a market timing strategy which entails the shifting of funds between a risky asset and a riskless asset. We give the optimal trading strategy for a finite investment horizon, and analyze its limiting behavior. For a finite horizon, the optimal decision in each step depends on two threshold values. If the return today is between the two values, nothing needs to be done, otherwise funds will be shifted from one asset to another, depending on which threshold value is being exceeded. When investment horizon tends to infinity, the optimal strategy converges to a stationary policy, which is shown to be closely related to a well-known technical trading rule, called Momentum Index trading rule. An integral equation of the two threshold values is given. Numerical results for the limiting stationary strategy are presented. The results confirm the obvious guess that the no-transaction region increases as the transaction cost increase. Finally, the limiting stationary strategy is applied to data in the Hang Seng Index Futures market in Hong Kong. The out-of-sample performance of the limiting stationary strategy is found to be better than the simple strategy used in literature, which is based on an 1-step ahead forecast of return.     

Asymptotics for Statistical Treatment Rules

This paper develops asymptotic optimality theory for statistical treatment rules in smooth parametric and semiparametric models. Manski (2000, 2002, 2004) and Dehejia (2005) have argued that the problem of choosing treatments to maximize social welfare is distinct from the point estimation and hypothesis testing problems usually considered in the treatment effects literature, and advocate formal analysis of decision procedures that map empirical data into treatment choices. We develop large‐sample approximations to statistical treatment assignment problems using the limits of experiments framework. We then consider some different loss functions and derive treatment assignment rules that are asymptotically optimal under average and minmax risk criteria.     

基于晶格场理论和动态规划的国债期货定价研究

通过用一个二维量子场取代传统金融上的布朗运动,构建了可有效纳入国债远期利率在到期时间和日历时间两个维度上的不完全相关性的量子场理论模型,并离散化二维量子场,得到国债远期利率的晶格场理论模型,同时结合动态规划方法,将国债期货的所有交易交割规则纳入一个模型进行建模,实现在统一的模型框架下对国债期货及其内嵌的择时期权和质量期权进行定价。研究结果亦表明,所构建的国债期货定价模型的定价效果均显著优于传统的主流两因子HJM模型,且与真实市场结算价的贴合性均很强,特别地,在临近交割月份,其定价误差均降至0.05%以内。而各国债期货合约的质量期权价值都在其对应的国债期货面值的2%至6%之间,其择时期权价值大部分时间都在0附近徘徊,但随交割月份临近,择时期权价值开始迅速上升,最大时约为期货合约面值的0.6%。     

多标准决策表中发现概率规则的变精度粗糙集方法

用优势关系代替不可分辨关系,本文提出了一种可以从多标准决策表中获取概率规则的扩展变精度粗糙集模型,该模型能够处理多标准决策表中可能的不一相容性,获取由偏好对象组成的概率决策规则集,并导出事例决策系统的偏好模型。研究结果表明:基于优势关系,从多标准决策表中获取的最小概率规则集,使用的条件数量较少,且导出规则的数量较少、较强。     

Comparative Statics,Informativeness, and the Interval Dominance Order

We identify a new way to order functions, called the interval dominance order, that generalizes both the single crossing property and a standard condition used in statistical decision theory. This allows us to provide a unified treatment of the major theorems on monotone comparative statics with and without uncertainty, the comparison of signal informativeness, and a non‐Bayesian theorem on the completeness of increasing decision rules. We illustrate the concept and results with various applications, including an application to optimal stopping time problems where the single crossing property is typically violated.     

How Inventory Cost Influences Introduction Timing of Product Line Extensions

In the industry with radical technology push or rapidly changing customer preference, it is firms' common wisdom to introduce high‐end product first, and follow by low‐end product‐line extensions. A key decision in this “down‐market stretch” strategy is the introduction time. High inventory cost is pervasive in such industries, but its impact has long been ignored during the presale planning stage. This study takes a first step toward filling this gap. We propose an integrated inventory (supply) and diffusion (demand) framework and analyze how inventory cost influences the introduction timing of product‐line extensions, considering substitution effect among successive generations. We show that under low inventory cost or frequent replenishment ordering policy, the optimal introduction time indeed follows the well‐known “now or never” rule. However, sequential introduction becomes optimal as the inventory holding gets more substantial or the product life cycle gets shorter. The optimal introduction timing can increase or decrease with the inventory cost depending on the marketplace setting, requiring a careful analysis.     

零售商主导下生鲜农产品供应链的定价策略对比研究

运用RFID技术来监控与管理产品,零售商可以依据记录、传递的产品实时价值信息制定更为灵活的定价和订货策略,提升自身以及供应链的利润。针对生鲜农产品在价值损耗及消费者需求等方面进行特征分析,分别在固定定价、动态定价以及带有降价时点考虑的定价三种不同模式下,构建了单周期零售商的决策模型,得到各自的最优定价与订货策略,通过分析最优策略与关键参数之间的关系,发现:价值衰减系数越大,库存成本参数越大,三种模式下的总体零售价格都越小,价格的下调幅度越大;而两个参数对降价折扣时间点的影响却截然相反。进一步,着力在最优销售价格、订货总量与利润值三个方面对三种定价策略进行对比分析,结果显示基于RFID技术的灵活定价模式有助于零售商获取更大的市场需求量及更大的收益。数值实验进一步验证了理论研究的结果,为生鲜农产品供应链中对RFID技术的应用以及相应定价模式的选择提供了参考依据。     

Expedient and Monotone Learning Rules

This paper considers learning rules for environments in which little prior and feedback information is available to the decision maker. Two properties of such learning rules are studied: absolute expediency and monotonicity. Both require that some aspect of the decision maker's performance improves from the current period to the next. The paper provides some necessary, and some sufficient conditions for these properties. It turns out that there is a large variety of learning rules that have the properties. However, all learning rules that have these properties are related to the replicator dynamics of evolutionary game theory. For the case in which there are only two actions, it is shown that one of the absolutely expedient learning rules dominates all others.     

Competing on Time: An Integrated Framework to Optimize Dynamic Time‐to‐Market and Production Decisions

This study develops a comprehensive framework to optimize new product introduction timing and subsequent production decisions faced by a component supplier. Prior to market entry, the supplier performs process design activities, which improve manufacturing yield and the chances of getting qualified for the customer's product. However, a long delay in market entry allows competitors to enter the market and pass the customer's qualification process before the supplier, reducing the supplier's share of the customer's business. After entering the market and if qualified, the supplier also needs to decide how much to produce for a finite planning horizon by considering several factors such as manufacturing yield and stochastic demand, both of which depend on the earlier time‐to‐market decision. To capture this dependency, we develop a sequential, nested, two‐stage decision framework to optimize the time‐to‐market and production decisions in relation to each other. We show that the supplier's optimal market entry and qualification timing decision need to be revised in real time based on the number of qualified competitors at the time of market‐entry decision. We establish the optimality of a threshold policy. Following this policy, at the beginning of each decision epoch, the supplier should optimally stop preparing for qualification and decide whether to enter the market if her order among qualified competitors exceeds a predetermined threshold. We also prove that the supplier's optimal production policy is a state‐dependent, base‐stock policy, which depends on the time‐to‐market and qualification decisions. The proposed framework also enables a firm to quantify how market conditions (such as price and competitor entry behavior) and operating conditions (such as the rate of learning and inventory/production‐related costs) affect time‐to‐market strategy and post‐entry production decisions.     

基于多主体仿真的大群体应急决策风险致因分析

大群体应急决策风险来源众多,且对决策的影响不容忽视。本文从个体因素和群体因素两方面对大群体应急决策风险进行系统识别,并将各风险因素与两类群体效应(认知冲突和关系冲突)进行关联,建立大群体应急决策风险致因体系。在此基础上,设置由个体认可度、群体结构、沟通方式、决策策略和外部影响组成的仿真变量,然后基于观点动力学利用Netlogo工具建立大群体应急决策风险致因多主体仿真模型,最后通过案例模拟得出各风险因素致因机理的一般规律。仿真结果表明:控制高认可度决策主体的比例,增加聚集间交互,采取必要的预见性措施,对降低决策风险,提高决策共识速度,应对决策环境的高动态性具有积极作用。研究有助于掌握大群体应急决策风险因素的组成及其影响规律,为应急决策的策略引导提供参考和借鉴。     

考虑广告主投资竞争的关键词拍卖研究

关键词拍卖是搜索引擎盈利手段之一，同时给广告主带来高额回报。在搜索引擎注重质量权重的拍卖规则的推动下和广告主自身利益的驱使下，广告主通过投资来提高自身表现水平，赢得更好排位增加点击量。引入广告主投资，并用连续可变的努力水平来表征参与竞价的广告主投资过程中所付出的各种要素和资源投入，考虑投资和竞价两阶段模型，分析高低两类广告主的努力水平决策及均衡竞价策略。研究表明当满足初始投入最低努力水平时的边际成本大于边际收益的条件时，决定低类型广告主类型转换的估价阈值存在且唯一，并且在情形一中，潜力广告主的最优努力水平总是大于高类型广告主，在情形二中，随着估价的增大，两类广告主的最优努力水平趋于一致；同时，搜索引擎拍卖规则会影响广告主投资过程中最优努力水平的决策：搜索引擎给低类型广告主的质量权重越大，转换类型的估价阈值就越高，且潜力广告主取内点解时的最优努力水平和高类型广告主的最优努力水平均随之减少。最后，通过数值算例分析了两类广告主最优努力水平以及拍卖规则对广告主努力水平的影响。     

ECONOMIC PROCESS CONTROL UNDER UNCERTAINTY

The future of the global industry lies in the continuous improvement of both products and processes, a renewed commitment to competition, and an aggressive approach to satisfying customers needs in quality, quantity, and timing. In quality management, the degree of customer satisfaction for a given product may be measured in the form of the loss to society. This loss is formulated as a function of the deviation from the target for each of the product's quality characteristics. The greater the variability of uncontrolled factors during manufacturing or production the larger will be that loss. In this paper, we develop a form of the loss function that takes into account the variability of a production process, the decision loss, and the costs of sampling and inspection. Specifically, we consider monitoring a production process, which may undergo continuous mean shift and variance deterioration during a production run. We then examine decision rules for continuing production or stopping and adjusting the production process.     

Inventory Commitment and Prioritized Backlogging Clearance with Alternative Delivery Lead Times

We propose a model where customers are classified into two groups: short lead‐time customers who require the product immediately and long lead‐time customers to whom the supplier may deliver either immediately or in the next cycle. Unmet orders are backlogged with associated costs. Specifically, the supplier faces two problems: how the on‐hand inventories should be allocated between the two classes of customers and how the backlogged orders should be cleared when replenishments arrive. We treat the former as an inventory commitment problem and handle the latter with priority rules. We characterize and compare the inventory commitment policies with three priority rules in clearing backlogs. We also explore the optimal inventory replenishment decision and evaluate the performance of each priority rule.     

Dissaggregation of deviations in cost analysis

The decisions on investigation of cost deviations have been generally discussed in the cost control literature assuming deviations are monitored and reported at a certain level of aggregation. This paper focuses on the relationship between the level of aggregation of the reporting system and the effectiveness of the investigating decisions. It suggests that, by incorporating into the decision process the prior knowledge managers have about the deviation sources, the deviation report could be further disaggregated leading to improved investigation decisions. This is done by using the reported deviation to update the prior distribution of its possible sources in a Bayesian manner. The resulting system is less costly to monitor than a system which monitors all the possible sources of a deviation, although it is less exact. The paper illustrates how the selection of the optimal level of aggregation should be made simultaneously with the information system and the decision rules.     

A Warning Zone Method for the Multiperiod Cost Variance Investigation Problem

This paper develops a warning zone approach to make variance investigation decisions for a multiperiod process. The assumed cost generation process varies between an in-control and out-of-control state. These states cannot be directly observed, but must be inferred from the reported cost variances. Using the warning zone method of inference, the manager investigates the process whenever an upper threshold is exceeded or a lower threshold is exceeded for two consecutive periods. A four-state Markov chain models the resulting decision process. Steady state probabilities are derived for this chain and are used to obtain explicit formulas for the effectiveness and efficiency of the decision process. These formulas permit computation of the cost savings attainable by the warning zone method. Compared to other decision rules, the warning zone method is much simpler than the theoretically optimal Bayesian revision method, but uses more information than the Markovian control limit method. Numerical comparison of results shows that the warning zone method usually captures most of the available cost savings, even in cases where the Markovian control limit method does not perform well.