Relational Incentive Contracts With Persistent Private Information

This paper investigates relational incentive contracts with continuous, privately observed agent types that are persistent over time. With fixed agent types, full separation is not possible when continuation equilibrium payoffs following revelation are on the Pareto frontier of attainable payoffs. This result is related to the ratchet effect in that: (1) a type imitating a less productive type receives an information rent, and (2) with full separation, one imitating a more productive type receives the same future payoff as that more productive type. However, the reason for (2) is fundamentally different than with the ratchet effect. It arises from the dynamic enforcement requirement in relational contracts, not from the principal having all the bargaining power, and applies whatever the distribution between principal and agent of the future gains from the relationship (i.e., whatever the point on the Pareto frontier). This result extends to sufficiently persistent types under certain conditions.     

Value of Persistent Information

We develop a theory of how the value of an agent's information advantage depends on the persistence of information. We focus on strategic situations with strict conflict of interest, formalized as stochastic zero‐sum games where only one of the players observes the state that evolves according to a Markov operator. Operator Q is said to be better for the informed player than operator P if the value of the game under Q is higher than under P regardless of the stage game. We show that this defines a convex partial order on the space of ergodic Markov operators. Our main result is a full characterization of this partial order, intepretable as an ordinal notion of persistence relevant for games. The analysis relies on a novel characterization of the value of a stochastic game with incomplete information.     

Decentralized Trading With Private Information

The paper studies how asset prices are determined in a decentralized market with asymmetric information about asset values. We consider an economy in which a large number of agents trade two assets in bilateral meetings. A fraction of the agents has private information about the asset values. We show that, over time, uninformed agents can elicit information from their trading partners by making small offers. This form of experimentation allows the uninformed agents to acquire information as long as there are potential gains from trade in the economy. As a consequence, the economy converges to a Pareto efficient allocation.     

The Impact of Information Sharing on Supply Chain Performance under Asymmetric Information

The use of screening contracts is a common approach to solve supply chain coordination problems under asymmetric information. One main assumption in this context is that managers without specific incentives would rather use their private information strategically than reveal it truthfully. This harms supply chain performance. This study investigates the impact of information sharing in a principal‐agent setting that is typical for many supply chain transactions. We conduct a laboratory experiment to test whether information sharing has an influence on supply chain coordination. We find that information sharing within the supply chain has two positive effects. First, information sharing reduces the inefficiencies resulting from information deficits if there is a certain amount of trust in the supply chain. Second, communication can limit out‐of‐equilibrium behavior with a small impact on the firm's own payoff, but a large impact on the supply chain partner. Furthermore, we find that both effects are amplified when communication takes place in an environment that allows the less informed supply chain party to punish or to reward the better informed party. Although our extended mechanisms substantially enhance the poor performance of the theoretically optimal coordination contract menu, we find no mechanism that implements supply chain performance superior to the theoretically predicted second‐best level.     

Equilibrium in Continuous‐Time Financial Markets: Endogenously Dynamically Complete Markets

We prove existence of equilibrium in a continuous‐time securities market in which the securities are potentially dynamically complete: the number of securities is at least one more than the number of independent sources of uncertainty. We prove that dynamic completeness of the candidate equilibrium price process follows from mild exogenous assumptions on the economic primitives of the model. Our result is universal, rather than generic: dynamic completeness of the candidate equilibrium price process and existence of equilibrium follow from the way information is revealed in a Brownian filtration, and from a mild exogenous nondegeneracy condition on the terminal security dividends. The nondegeneracy condition, which requires that finding one point at which a determinant of a Jacobian matrix of dividends is nonzero, is very easy to check. We find that the equilibrium prices, consumptions, and trading strategies are well‐behaved functions of the stochastic process describing the evolution of information. We prove that equilibria of discrete approximations converge to equilibria of the continuous‐time economy.     

Long‐Term Contracting: The Role of Private Information in Dynamic Supply Risk Management

We examine the critical role of evolving private information in managing supply risk. The problem features a dyadic channel where a dominant buyer operates a multiperiod inventory system with lost sales and fixed cost. He replenishes from a supplier, whose private state of production is vulnerable to random shocks and evolves dynamically over time. We characterize the optimal inventory policy with a simple semi‐stationary structure; it distorts order quantity for limiting information rent only in the initial period; the optimal payment compensates for production cost in every period but concedes real information rent only in the initial period. These properties allow us to derive an easy‐to‐implement revenue‐sharing contract that facilitates ex ante strategic planning and ex post dynamic execution. This work advances our understanding on when and how to use private information in dynamic risk management.     

考虑随机利率和通货膨胀的连续时间资产组合选择

通过随机控制技术、Bellman最优性原理和HJB方程研究了通货膨胀、随机利率和交易成本等因素影响下的连续时间投资组合选择的最优化问题,将利率假定为服从Vasicek利率模型的随机过程,应用连续时间的动态均值-方差方法得到符合实际意义的HJB方程,通过多重网格的数值逼近方法求解相应的HJB方程得到双目标优化问题的最优投资策略。用实证方法与国内证券市场上代表性指数基金进行对比研究,发现通货膨胀和利率变动以及经济环境和投资者的异质信念等因素均会对最优策略产生影响,有效前沿会随之发生变化,债券与股票之间的投资比例并不是简单维持固定比例就可以保证总资产最优,拓展了基金分离定理。考虑通货膨胀和交易成本等因素的资产组合选择模型可以实实在在为机构投资者提供客观的实践指导和科学的理论依据。     

A Bayesian Inventory Model Using Real‐Time Condition Monitoring Information

Lack of coordination between machinery fault diagnosis and inventory management for spare parts can lead to increased inventory costs and disruptions in production activity. We develop a framework for incorporating real‐time condition monitoring information into inventory decisions for spare parts. We consider a manufacturer who periodically replenishes inventory for a machine part that is subject to deterioration. The deterioration is captured via condition monitoring and modeled using a Wiener process. The resulting degradation model is used to derive the life distribution of a functioning part and to estimate the demand distribution for spare parts. This estimation is periodically updated, in a Bayesian manner, as additional information on part deterioration is obtained. We develop an inventory model that incorporates this updated demand distribution and demonstrate that a dynamic base‐stock policy, in which the optimal base‐stock level is a function of some subset of the observed condition monitoring information, is optimal. We propose a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute. Computational experiments indicate that this heuristic performs quite well relative to the optimal policy. Adaptive inventory policies such as these can help manufacturers to increase machine availability and reduce inventory costs.     

Contracting and Coordination under Asymmetric Production Cost Information

We analyze a supply chain consisting of a supplier and a retailer. The supplier's unit production cost, which characterizes his type, is only privately known to him. When trading with the retailer, the supplier demands a reservation profit that depends on his unit production cost. We model this problem as a game of adverse selection. In this model, the retailer offers a menu of contracts, each of which consists of two parameters: the ordering quantity and the supplier's share of the channel profit. We show that the optimal contract depends critically on a surrogate measure—the ratio of the types’ reservation profit differential to their production cost differential. An important implication from our analysis is that information asymmetry alone does not necessarily induce loss in channel efficiency. The optimal contract can coordinate the supply chain as long as the low‐cost supplier's cost efficiency is neither much overvalued nor much undervalued in the outside market. We further discuss the retailer's preference of the supplier's type under different market conditions, as well as evaluate the effects of the supplier's reservation profit, the retail price, and the demand uncertainty on the optimal contract.     

Optimal Ordering Policies for Stochastic Inventory Problems with Observed Information Delays

Information delays exist in an inventory system when it takes time to collect, process, validate, and transmit inventory/demand data. A general framework is developed in this paper to describe information flows in an inventory system with information delays. We characterize the sufficient statistics for making optimal decisions. When the ordering cost is linear, the optimality of a state‐dependent base‐stock policy is established even when information flows are allowed to cross over time. Additional insights into the problem are obtained via a comparison between our models and the models with stochastic order lead times. We also show that inventory can substitute for information and vice versa.     

预算报酬方案和线性报酬方案的一个比较分析

在代理理论的框架下 ,分析比较企业实务中最为常见的两种报酬方案 :预算报酬方案和线性报酬方案 .通过建立一个具体模型进行定量模拟分析 ,探讨影响企业报酬方案的激励成本的主要因素 .根据模拟的数值结果 ,具体描述了企业应该如何根据其业绩评价信息系统的质量来选择和设计报酬激励方案 ,以较低的成本达到预定的控制和激励目标     

非对称信息下业务外包中的质量评价决策

研究了不同信息下供应链业务外包的产品质量评价决策问题,考虑购买商作为委托人和供应商作为代理人分别建立了他们的质量决策模型,重点研究了非对称信息下的决策问题已经变成最优控制问题,运用极大值原理推导了供应商质量预防信息隐匿情况下购买商的质量评价的最优解,并进行了仿真计算和分析,对比了不同信息环境下的决策结果。     

Electricity Time‐of‐Use Tariff with Stochastic Demand

In this article, we study the electricity time‐of‐use (TOU) tariff for an electricity company with stochastic demand. The electricity company offers the flat rate (FR) and TOU tariffs to customers. Under the FR tariff, the customer pays a flat price for electricity consumption in both the peak and non‐peak periods. Under the TOU tariff, the customer pays a high price for electricity consumption in the peak period and a low price for electricity consumption in the non‐peak period. The electricity company uses two technologies, namely the base‐load and peak‐load technologies, to generate electricity. We derive the optimal capacity investment and pricing decisions for the electricity company. Furthermore, we use real data from a case study to validate the results and derive insights for implementing the TOU tariff. We show that in almost all the cases, the electricity company needs less capacity for both technologies under the TOU tariff than under the FR tariff, even though the expected demand in the non‐peak period increases. In addition, except for some extreme cases, there is essentially no signicant reduction in the total demand of the two periods, although the TOU tariff can reduce the demand in the peak period. Under the price‐cap regulation, the customer may pay a lower price on average under the TOU tariff than under the FR tariff. We conduct an extensive numerical study to assess the impacts of the model parameters on the optimal solutions and the robustness of the analytical results, and generate managerial implications of the research findings.     

Supply Chain Strategies and International Tax Arbitrage

What motivates the geographic footprint of the supply chains that multinational firms (MNFs) deploy? Traditional research in the operations and supply chain management literature tends to recommend locations primarily based on differentials in production costs and the ramifications of physical distance ignoring the role of taxation. MNFs that strategically position parts of their supply chains in low‐tax locations can allocate the profits across the divisions to improve post‐tax profits. For the profit allocation to be defensible to tax authorities, the divisional operations must possess real decision authority and bear meaningful risks. Generally speaking, the greater the transfer of risk and control, the larger the allowable allocation of profit. These transfers may also create inefficiencies due to misalignment of business goals and attitudes toward risk. We model these trade‐offs in the context of placing in a low‐tax region a subsidiary that oversees product distribution (as a limited risk distributor commissionnaire, limited risk distributor, or fully fledged distributor). Our analysis demonstrates that the MNF's preferences regarding the operating structures are not necessarily an obvious ordering based on the amount of risk and decision authority transferred to the division in the low‐tax jurisdiction. We derive and analyze threshold values of the performance parameters that describe the main trade‐offs involved in selecting an operating structure. We find some of the optimal decisions to exhibit interesting non‐monotone behavior. For instance, profits can increase when the tax rate in the low‐tax jurisdiction increases. Numerical analysis shows that the Limited‐Risk Distributor structure is rarely optimal and quantifies when each alternative dominates it.     

The Impact of External Demand Information on Parallel Supply Chains with Interacting Demand

This paper considers two parallel supply chains with interacting demand streams. Each supply chain consists of one supplier and one retailer. The two demand streams are jointly described with a vector autoregressive time‐series process in which they interact and their respective innovation errors correlate contemporaneously. For each supply chain, we develop insights into when and how much the supplier and the retailer can improve on their forecasting accuracy if the external demand history of the other supply chain is utilized. When this external demand history is not available or made available after a time lag, we develop a partial process and a delayed process to characterize the demand structure that the retailer can recover from the available demand histories. Our results show that the external demand history of the other supply chain always helps the retailer make better forecasts when demand streams interact; however, the enhanced information alters the retailer's order process, which may produce larger forecasting errors for the supplier. Conditions are established for the supplier to benefit from the external demand history of the other supply chain.     

信息非对称条件下的质量预防决策分析

研究了不同信息条件下供应链的质量预防问题,在文献[1,2]建立的有关质量签约问题的优化模型基础上,把供应商作为委托人和购买商作为代理人建立了供应链的质量预防决策委托代理模型。其中,质量预防水平和供应商要求购买商的前向支付是供应商的决策变量,质量评价水平是购买商的决策变量。本文重点研究了了非对称信息下供应链的质量预防和前向支付问题,考虑了购买商质量评价信息隐匿情况,运用极大值原理推导了供应商的质量预防和前向支付的最优解。最后结合一个农机公司拖拉机销售问题进行了仿真计算和分析,对比了不同信息环境下的决策结果。     

风险投资主体、银行和创业企业家三方委托代理研究

研究创业企业家、风险投资主体努力程度的影响因素和他们之间的双向道德危害风险,并从风险管理的角度解释创业企业家难以从银行得到贷款的原因,在Casamana的研究基础上,通过赋予纯金融机构明确的现实定义,构建风险投资主体、创业企业家、银行三方参与的委托代理模型.得出双向道德风险下的最优权益分配.研究表明,若只有银行投资或银行和风险投资主体同时投资时,存在风险限制,当风险高于限制时,创业企业家不可能贷款成功;若只有风险投资主体投资时,则没有风险限制;若银行和风险投资主体同时投资,他们权益分配的最高限制将低于只有一方投资时所得权益分配的最高限制,风险限制高于只有银行投资时的风险限制;若机构在既无资金也无非资金贡献的情况下瓜分风险投资利益,将导致创业企业家努力程度降低;银行、风险投资主体和创业企业家三方参与风险投资时,风险投资主体的最优权益安排与风险投资主体和创业企业家的生产力有关,随着风险投资主体生产力的增大而增大,随着创业企业家生产力的增大而减小.     

非对称信息条件下业务外包的质量评价和转移支付决策

研究了不同信息条件下业务外包的产品质量评价问题,在文献[l,2]基础上考虑购买商作为委托人和供应商作为代理人建立了业务外包的委托代理模型。其中,质量预防水平为供应商的决策变量,购买商对供应商的转移支付和质量评价水平为购买商的决策变量。本文考虑了供应商质量预防信息隐匿情况,重点研究了非对称信息下业务外包的质量评价和转移支付问题,运用极大值原理推导了购买商的质量评价和转移支付的最优解。最后结合一个农机公司拖拉机生产业务外包问题进行了仿真计算和分析,对比了不同信息环境下的决策结果。     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

Managing the Revenue of Sales Platforms: Insights into Service Fees

Online sales platforms have grown substantially in recent years. These platforms assist sellers to conduct sales, and in return, collect service fees from sellers. We study the fee policies by considering a fee‐setting platform, on which a seller may conduct a sale with a reserve price to a group of potential buyers: the seller retains the object for sale if the final trading price is below the reserve price. The platform may charge two types of fees as in current practice: a reserve fee as a function of the seller's reserve price and a final value fee as a function of the sale's final trading price. We derive the optimality condition for fee policies, and show that the platform can use either just a final value fee or just a reserve fee to achieve optimality. In the former case, the optimal final value fee charged by the platform is independent of the number of buyers. In the latter case, the optimal reserve fee is often a decreasing, instead of increasing, function of the seller's reserve price. An increasing reserve fee may make the seller reluctant to use a positive reserve price and hurt the platform's revenue. In general, the optimal fees are nonlinear functions, but in reality, linear fees are commonly used because of their simplicity for implementation. We show that a linear fee policy is indeed optimal in the case that the seller's valuation follows a power distribution. In other cases, our numerical analysis suggests close‐to‐optimal performance of the linear policy.