考虑消费者预期后悔的价格歧视策略研究

基于行为的价格歧视（BPD）受到越来越多的关注,企业为了更好地服务于各细分市场,需要更细致的考虑消费者事前估值的不确定性,以及因此出现的消费者预期后悔。本文在双寡头垄断市场中建立模型,探讨消费者预期后悔对企业动态价格竞争和利润的影响。研究结果表明,消费者预期后悔会对自身购买决策产生显著影响;当高值实现概率相对较低,高低值差异大且切换后悔的厌恶较小或者重复后悔的厌恶较大时,企业奖励重复购买的客户,否则奖励切换者;预期后悔对企业的利润既可以有正面的影响也可以有负面的影响。     

Adaptive Heuristics

We exhibit a large class of simple rules of behavior, which we call adaptive heuristics, and show that they generate rational behavior in the long run. These adaptive heuristics are based on natural regret measures, and may be viewed as a bridge between rational and behavioral viewpoints. Taken together, the results presented here establish a solid connection between the dynamic approach of adaptive heuristics and the static approach of correlated equilibria.     

零售商长期后悔规避的供应链网络动态均衡

本文研究了基于时变随机需求的供应链网络动态均衡。由于需求的不确定性和季节性,网络中的零售商会考虑缺货后悔和存货后悔的负效用,基于长期后悔规避效用做出最优决策。利用变分不等式,刻画了网络中制造商层、考虑长期后悔规避效用的零售商层和需求市场层的均衡决策行为。通过数值算例,基于三种长期后悔情形分析了零售商长期后悔规避行为对其均衡订购量、利润、后悔值的影响。研究结果表明,零售商的长期后悔规避程度在不断增加、保持不变,不断减少这三种情况下,对其均衡订购量、利润、后悔值的影响不同。研究结论将指导供应链网络中企业,基于长期后悔规避制定最优决策。     

基于后悔理论的混合型多属性案例决策方法

本文针对决策者的后悔规避行为对案例决策的影响,提出了一种基于后悔理论的混合型多属性案例决策方法。首先,针对案例属性的相似度测算,将案例属性的范围拓展到定性数据、清晰数、区间数、语言变量和区间直觉模糊数五种类型;然后,在案例原有效用基础上考虑决策者的后悔-欣喜值来测算各案例的感知效用;进一步,通过案例相似度对感知效用加权求和得到备选方案的综合感知效用,并依据综合感知效用大小对备选方案进行排序;最后通过一个实例,验证了上述方法的可行性和有效性。     

引入策略型消费者遗憾心理的时尚产品定价策略

通过对时尚策略型消费行为的研究,把消费者心理动机归结为两种,其一是因冲动购买而错过降价产生的“高价遗憾”;其二是因等待降价并延迟购买而遭遇缺货产生的“缺货遗憾”,并在此基础上,在一个两周期销售中分析其对企业的降低标价(Markdown, MD)与抬高标价(Markup, MU)两种定价模式的影响机理。首先把两种遗憾心理因素纳入消费者的效用函数,建模分析了需求随机下降低标价(抬高标价)两阶段销售模式中供应商如何通过考虑消费者行为设置不同阶段价格和限量配给水平的决策。并在此基础上通过不同定价模式下供应商收益参数的对比,重点分析了两种遗憾心理因素效应对销售阶段分割的综合影响。研究发现：有遗憾心理的消费者将削弱延迟购买倾向,供应商可通过制造适当的配给风险诱导顾客高价期购买;其次两种定价类型下,当满足一定条件时,面对遗憾心理的消费群体,企业均能实现比统一定价更高的最优收益,并且抬高标价比降低标价的优化效果更明显。     

Bounds of Relative Regret Limit in p‐Robust Supply Chain Network Design

This research studies the p‐robust supply chain network design with uncertain demand and cost scenarios. The optimal design integrates the supplier selection together with the facility location and capacity problem. We provide a new framework to obtain the relative regret limit, which is critical in the robust supply chain design but is assumed to be a known value in the existing literature. We obtain lower and upper bounds for relative regret limit and obtain a sequence of optimal solutions for series relative regret limits between the upper and lower bounds. An algorithm for p‐robust supply chain network design is provided. A series of numerical examples are designed to find the properties of the bottleneck scenarios. A scenario with low probability and a low optimal objective function value for the scenario has a greater chance of being a bottleneck. To focus only on the influence from the relative regret, we also introduce three separate new objective functions in p‐robust design. The proposed new theories and approaches provide a sequence of options for decision makers to reduce the marketing risks effectively in supply chain network design.     

基于ART及多风险规避的交通网络均衡模型

随着对交通系统不确定性认识的深入,以绝对理性为基础的“期望效用理论”在风险环境下的路径选择分析中显示出局限性,而“预期后悔理论”则为之提供了新的分析思路.将预期后悔理论应用到风险环境下的路径选择分析中,将出行者一致风险规避的假设扩展到多风险规避,建立了基于后悔理论及多风险规避出行特征的交通网络随机用户均衡变分不等式模型,并给出了求解算法.通过算例分析发现,后悔心理对出行者的路径选择并不总是显著的.在非风险环境及极端风险环境中,后悔心理对出行者的路径选择影响是微弱的,但是当环境处于极端风险与非风险之间时,后悔心理对出行者路径选择有着较为显著的影响.     

基于竞争差分析的占线单向交易策略

针对交易者事先仅知道价格波动范围的占线单向交易问题,基于Savage后悔值准则提出了竞争差分析方法,通过引入一个假想的能够控制价格的“对手”将原来的单人决策问题转化为双人零和博弈问题.与竞争比分析相比,竞争差分析由于目标函数的数学形式更简单,因而可以直接采用逆向归纳法求解获得使最大后悔值(竞争差)最小化的稳健的占线交易策略,并找出对于交易者而言所有可能的最糟糕情况,而不必像竞争比分析那样需要事先猜测最优占线策略的特征;此外,数值模拟结果表明,基于竞争差分析的占线算法更节省计算时间,且在解决收益最大化问题时不像竞争比分析那样过于保守,一般具有更好的期望绩效.     

Dynamic Pricing to Minimize Maximum Regret

We consider a dynamic pricing problem that involves selling a given inventory of a single product over a short, two‐period selling season. There is insufficient time to replenish inventory during this season, hence sales are made entirely from inventory. The demand for the product is a stochastic, nonincreasing function of price. We assume interval uncertainty for demand, that is, knowledge of upper and lower bounds but not a probability distribution, with no correlation between the two periods. We minimize the maximum total regret over the two periods that results from the pricing decisions. We consider a dynamic model where the decision maker chooses the price for each period contingent on the remaining inventory at the beginning of the period, and a static model where the decision maker chooses the prices for both periods at the beginning of the first period. Both models can be solved by a polynomial time algorithm that solves systems of linear inequalities. Our computational study demonstrates that the prices generated by both our models are insensitive to errors in estimating the demand intervals. Our dynamic model outperforms our static model and two classical approaches that do not use demand probability distributions, when evaluated by maximum regret, average relative regret, variability, and risk measures. Further, our dynamic model generates a total expected revenue which closely approximates that of a maximum expected revenue approach which requires demand probability distributions.     

考虑决策者后悔规避的风险投资项目选择方法

在风险投资项目选择过程中,决策者通常是有限理性的,会尽量避免选择可能会令其感到后悔的风险投资项目,而在涉及多个行业的风险投资项目选择过程中如何考虑决策者后悔规避的心理行为因素,这方面的研究特别需要关注。本文提出了一种涉及多个行业的考虑决策者后悔规避的风险投资项目选择方法。在该方法中,首先计算各风险投资项目在不同市场状态下的项目价值;然后,计算各组合市场状态发生的概率;进一步地,构建后悔函数刻画决策者后悔的心理感知,并通过计算不同行业的各风险投资项目的综合效用值得到风险投资项目的排序结果。最后,通过一个算例说明了本文提出方法的可行性与有效性。     

考虑后悔规避的灰色群体偏离靶心度决策方法

针对指标值、指标权重以及状态概率均为区间灰数的风险型多指标群决策问题，考虑决策者的后悔规避的心理行为，提出基于后悔理论和区间灰数信息的群体偏离靶心度决策方法。该方法将后悔理论融入到群决策过程中，建立了各决策者的综合灰色感知效用矩阵，由此定义了群体灰色正负靶心以及灰色群体偏离靶心度；以灰色群体偏离靶心度综合值最大化为目标、同时结合极大熵原理构建优化模型并解出最优专家权重向量，利用灰色群体偏离靶心度大小对方案进行排序；最后，通过具体算例验证了该方法的有效性和实用性。     

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

Decision biases can distort cost‐benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well‐documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk‐cost‐benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk‐reducing regulations. Low‐regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low‐regret learning strategies using regulation of air pollutants with uncertain health effects as an example.     

消费者选择中后悔和不确定性的作用研究

研究当消费者对产品绩效的期望不确定性时,后悔如何影响消费选择的动态变化.数学分析和实验结果表明,当消费者观察或体验到的各个产品选项的表现与原先的期望相吻合,风险厌恶型消费者倾向于提高对初始期望绩效不确定性较高的产品的购买意愿,而风险喜好型消费者倾向于降低对该产品的购买意愿.另外,期望绩效水平的不确定性对消费选择的影响会随消费者经验值的增加而减小.研究成果弥补了现有期望差异理论对解释消费行为动态变化的不足.因此,为了更加全面地理解在多选项选择问题中后悔对消费选择的动态影响,管理者需要考虑备选产品在期望绩效水平不确定性上的差异、相关消费市场的顾客风险偏好构成、消费者的购买经验等特征.     

有限需求信息下基于回购契约的供应链鲁棒协调策略

研究了仅知需求均值和区间信息条件下,基于最小最大后悔值准则的供应链回购契约协调问题。针对未知需求具体分布形式的两级供应链系统,在回购契约框架下,建立了以鲁棒决策和最优决策下的供应链及其成员绩效之差为目标函数的供应链协调模型。在仅知需求区间和均值信息条件下,采用鲁棒优化方法求解了最小最大后悔值准则下的集成供应链鲁棒订货策略和分散供应链鲁棒契约协调策略及其绩效偏差。分析了不同服务水平和契约参数条件下,由于信息缺失而未能实现最优运作的供应链及其成员绩效损失情况。最后,进行了数值计算,验证了通过鲁棒优化方法得到的供应链回购契约协调策略的鲁棒性和有效性。结果表明,基于回购契约的供应链鲁棒协调策略能够有效抑制需求不确定性对系统及其成员运作绩效的影响,同仅知需求区间信息相比,额外获得需求均值信息能够有效改进供应链运作绩效。     

Statistical Treatment Rules for Heterogeneous Populations

An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments. This paper studies minimax‐regret treatment choice using the sample data generated by a classical randomized experiment. Consider a utilitarian social planner who must choose among the feasible statistical treatment rules, these being functions that map the sample data and observed covariates of population members into a treatment allocation. If the planner knew the population distribution of treatment response, the optimal treatment rule would maximize mean welfare conditional on all observed covariates. The appropriate use of covariate information is a more subtle matter when only sample data on treatment response are available. I consider the class of conditional empirical success rules; that is, rules assigning persons to treatments that yield the best experimental outcomes conditional on alternative subsets of the observed covariates. I derive a closed‐form bound on the maximum regret of any such rule. Comparison of the bounds for rules that condition on smaller and larger subsets of the covariates yields sufficient sample sizes for productive use of covariate information. When the available sample size exceeds the sufficiency boundary, a planner can be certain that conditioning treatment choice on more covariates is preferable (in terms of minimax regret) to conditioning on fewer covariates.     

On the Preference to Avoid Ex Post Inventory Errors

The value of demand information underlies many supply chain strategies that aim at better matching supply and demand. This study reports on the results of a laboratory experiment designed to estimate the behavioral value of demand information. Relative to the commonly assumed benchmark of a rational risk‐neutral decision maker, we find that decision makers are consistently willing to pay too much for the option to eliminate the risk of supply not matching demand. Contrary to intuition, we show that risk aversion does not explain this result. We posit that demand information provides behavioral value because it mitigates regret from ex post inventory errors.     

基于后悔理论和DEMATEL的语言型多属性决策方法

针对目前语言型多属性决策方法大多基于期望效用理论且不考虑指标间影响关系的不足，提出了一种将后悔理论和决策试验与评价试验法相结合的语言型多属性决策方法。首先，依据后悔理论的思想，定义了语言后悔-欣喜函数，给出了方案感知效用值的计算公式；然后，利用决策试验与评价试验法分析指标间的影响关系，给出了基于语言DEMATEL的指标权重确定方法，再通过指标总容量最大优化模型给出了基于注水原理的指标权重确定方法，并在此基础上求解方案的综合感知效用值，据此对方案进行排序择优；最后，通过两个应用实例来验证所提方法的可行性和有效性。实例结果表明，由于该方法同时考虑了决策者的心理行为和指标间的影响关系，因此可使决策结果更加贴近现实且更为可靠。     

Collusion in Second‐Price Auctions under Minimax Regret Criterion

Collusion in auctions, with different assumptions on distributions of bidders' private valuation, has been studied extensively over the years. With the recent development of on‐line markets, auctions are becoming an increasingly popular procurement method. The emergence of Internet marketplaces makes auction participation much easier and more convenient, since no physical presence of bidders is required. In addition, bidders in on‐line auctions can easily switch their identities. Thus, it may very well happen that the bidders in an auction have very little, if any, prior knowledge about the distributions of other bidders' valuations. We are proposing an efficient distribution of collusive profit for second‐price sealed bid auctions in such an environment. Unlike some known mechanism, which balance the budget only in expectation, our approach (which we call Random k) balances the budget ex‐post. While truth‐telling is not a dominant strategy for Random k, it is a minimax regret equilibrium.     

Route vs. Segment: An Experiment on Real‐Time Travel Information in Congestible Networks

We report the results of an experimental study of route choice in congestible networks with a common origin and common destination. In one condition, in each round of play network users independently committed themselves at the origin to a three‐segment route; in the other condition, they chose route segments sequentially at each network junction upon receiving en route information. At the end of each round, players received ex‐post complete information about the distribution of the route choices. Although the complexity of the network defies analysis by common users, traffic patterns in both conditions converged rapidly to the equilibrium solution. We account for the observed results by a Markov adaptive learning model postulating regret minimization and inertia. We find that subjects' learning behavior was similar across conditions, except that they exhibited more inertia in the condition with en route information.     

基于最小最大后悔值的应急救灾网络构建鲁棒优化模型与算法

应急救灾过程分为两个阶段：第一阶段启动应急救灾网络构建,在灾区附近设立临时应急配送中心,并由应急资源供应方向其紧急调配应急资源;第二阶段将应急资源从临时应急配送中心向灾区受灾点进行调度,以保证救灾过程顺利进行。本文研究第一阶段应急救灾网络的构建问题,考虑到突发灾害初期灾情相关参数概率分布情况难以获取,建立了基于情景的最小最大后悔值准则的应急救灾网络构建鲁棒优化模型。求解模型时,利用有限情景集表示第二阶段的不确定性数据,并将模型化为与其等价的混合整数规划模型,利用情景松弛的迭代算法进行求解。数值试验中给出相应的绝对鲁棒模型与本文偏差鲁棒模型作了比较,结果表明基于最小最大后悔值准则的应急救灾网络优化模型具有良好的鲁棒性,而且算法也是有效的。