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.     

中希神话英雄寻异

英雄神话是各民族早期文学中共有的内容,神话英雄却风采各异。本文简析了中国与希腊神话英雄在某些方面的差异及形成的原因。     

不安抗辩权试析

<中华人民共和国合同法>虽然确立了不安抗辩权制度,但在实践中如何正确贯彻实施,尚有许多需要研究的问题.弄清发生不安抗辩权法定事由的判定标准,弄清难为对待给付的发生原因、发生时间以及不安抗辩权的法律效果,对于正确实施不安抗辩权制度,保护债权人的正当合法权益,稳定社会主义市场经济秩序,都具有十分重要的意义.     

Tempered Regrets Under Total Ignorance

Several decision rules, including the minimax regret rule, have been posited to suggest optimizing strategies for an individual when neither objective nor subjective probabilities can be associated to the various states of the world. These all share the shortcoming of focusing only on extreme outcomes. This paper suggests an alternative approach of tempered regrets which may more closely replicate the decision process of individuals in those situations in which avoiding the worst outcome tempers the loss from not achieving the best outcome. The assumption of total ignorance of the probabilities associated with the various states is maintained. Applications and illustrations from standard neoclassical theory are discussed.     

汉武帝的思想演变

少年刘彻受儒家思想教育，登位后由怀疑天命到倾信天命、天瑞，将自己视为与众不同的超人，实施他的文治武功，并几十年不懈地追求长生不死。晚年因家庭悲剧、求仙及对外作战的惨败，醒悟过来，下了著名的罢轮台屯田罪己诏，承认自己的过错，由有为政治转入到息兵富民的轨道上来，为昭宣中兴奠定了思想基础。     

论违约损害赔偿中的可预见性理论

可预见性理论用于确定损害赔偿事实与违约行为之间的因果关系。文章系统介绍了可预见性理论的法理基础及在两大法系的适用,提出了我国司法实践中适用可预见性理论应注意的一些问题,并认为这一理论所体现的合理价值可为其他法律借鉴采纳。     

我国合同法上预期违约制度新探

预期违约是英美合同法通过判例发展起来的特有制度,现已为绝大多数国家的合同法和国际合同公约所普遍接受而成为现代合同法上的重要制度。面向21世纪的我国新《合同法》在参酌他国他地区成功的判例、学说与先进的立法经验之基础上,第一次规制了预期违约制度,扩大了对当事人的权益保护,体现了合同法的价值关怀。从理论上,正确解析预期违约的价值取向,科学认定预期违约的形态,进一步完善预期违约的救济制度,对于准确适用这一制度,意义十分重大。     

On Some Optimal Bayesian Nonparametric Rules for Estimating Distribution Functions

In this paper, we present a novel approach to estimating distribution functions, which combines ideas from Bayesian nonparametric inference, decision theory and robustness. Given a sample from a Dirichlet process on the space (𝒳, A), with parameter η in a class of measures, the sampling distribution function is estimated according to some optimality criteria (mainly minimax and regret), when a quadratic loss function is assumed. Estimates are then compared in two examples: one with simulated data and one with gas escapes data in a city network.     

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.     

Robust Bayesian prediction under a general linear-exponential posterior risk function and its application in finite population

We consider robust Bayesian prediction of a function of unobserved data based on observed data under an asymmetric loss function. Under a general linear-exponential posterior risk function, the posterior regret gamma-minimax (PRGM), conditional gamma-minimax (CGM), and most stable (MS) predictors are obtained when the prior distribution belongs to a general class of prior distributions. We use this general form to find the PRGM, CGM, and MS predictors of a general linear combination of the finite population values under LINEX loss function on the basis of two classes of priors in a normal model. Also, under the general ε-contamination class of prior distributions, the PRGM predictor of a general linear combination of the finite population values is obtained. Finally, we provide a real-life example to predict a finite population mean and compare the estimated risk and risk bias of the obtained predictors under the LINEX loss function by a simulation study.