An improved empirical Bayes test for positive exponential families

We exhibit an empirical Bayes test δ^* _n for a decision problem using a linear error loss in a class of positive exponential families. This empirical Bayes test δ^* _n possesses the asymptotic optimality, and its associated regret converges to zero with rate n ⁻¹(ln n )⁶ This rate of convergence improves the previous results in the literature in the sense that a faster rate of convergence is achieved under much weaker conditions. Examples are presented to illustrate the performance of the empirical Bayes test δ^* _n     

Random regret minimization for consumer choice modeling: Assessment of empirical evidence

This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization (RUM) paradigm. This paper assesses empirical results based on 43 comparisons reported in peer-reviewed journal articles and book chapters, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeler's toolkit. The paper shows that RRM and hybrid RRM–RUM models outperform RUM counterparts in a majority of cases, in terms of model fit and predictive ability. Although differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to considerable differences in, for example, market share forecasts.     

GAMBLING IN CONTESTS WITH REGRET

This paper discusses the gambling contest introduced in Seel and Strack (2013, Gambling in Contests, Journal of Economic Theory, 148(5), 2033–2048) and considers the impact of adding a penalty associated with failure to follow a winning strategy. The Seel and Strack model consists of n‐agents each of whom privately observes a transient diffusion process and chooses when to stop it. The player with the highest stopped value wins the contest, and each player's objective is to maximize her probability of winning the contest. We give a new derivation of the results of Seel and Strack based on a Lagrangian approach. Moreover, we consider an extension of the problem to a behavioral finance context in the sense of regret theory. In particular, an agent is penalized when her chosen strategy does not win the contest, but there existed an alternative strategy that would have resulted in victory.     

To Justify or Not to Justify: The Role of Anticipated Regret on Consumers’ Decisions to Upgrade Technological Innovations

Prior research suggests that adoption decisions are primarily based on product features and experiential opportunities, like trial and observation. Our research follows inquiries that identify anticipated regret (AR) as an emotion integral to consumer decision making. Prior research and current retailing practice assume that AR can be alleviated by compelling product attribute-based rationales for immediate purchase. These rationales often take the form of direct attribute comparisons between the current best and the future technologies. Counter-intuitively, we find that giving consumers attribute-based justifications for immediate purchase produces a uniform level of AR and purchase delay regardless of the perceived rate of innovation (PRI). However, under conditions of low PRI and no justification, AR decreases significantly. A clear implication of our findings is that firms marketing current technology should not rush to provide consumers with justifications for immediate upgrade since such communications will remind consumers of what they might miss if they adopt now, resulting in increased AR and purchase delays. Instead, we suggest that retailers focus promotional efforts on highlighting the hedonic benefits consumers experience by adopting today.     

满意情景下的消费者后悔对口传的影响研究

后悔是消费者在消费体验之后将购买品牌同放弃的品牌进行比较所得到的负面情感反应,它对于消费者的再购意愿有着重要的影响。本文在验证了消费者可以同时体验满意和后悔这一结论的基础上,进一步研究并证实了后悔不仅会增加对购买品牌的负面口传,而且会增加对放弃品牌的正面口传。在此过程中,消费者同购买品牌的关系质量起到了显著的调节作用。     

顾客遗憾作用下的重购决策模型

顾客遗憾来源于消费者对不同企业产品或不同购买方案可感知价值的横向对比,遗憾感受程度受到交易情景因素如消费者决策现状、购买行为结果的可逆性以及对已购买产品的满意度的调节,同时会反过来影响顾客满意,最终影响顾客以后的购买行为。因此,它与顾客满意一起构成了影响消费者重购倾向的决定因素,企业在注重顾客保留和培养顾客忠诚时必须将二者同时考虑,做到需求导向和竞争导向的并重。     

网络拍卖中后悔权之适用问题研究

我国于2014年3月15日实施的《消费者权益保护法》中增加了网络买卖中买家后悔权的条款,然而网络拍卖中能否适用后悔权的规定则是现实中亟待解决的问题。首先,网络拍卖的形式主要有委托网络拍卖与自主网络拍卖两种,网络拍卖网站主要以采用自主网络拍卖为主。网络拍卖合同与传统买卖合同在意思表示、撤销权的适用等方面存在着较大的区别,在适用买卖合同的规定时需要充分考虑其特殊性。其次,后悔权是形成权,同时具有解除权和撤回权两种特质。最后,通过比较欧盟法以及德国法上关于远距离买卖后悔权的规定,区分委托网络拍卖与自行网络拍卖两种形式来分析后悔权在我国网络拍卖中的适用。     

ROBUST ASSET ALLOCATION WITH BENCHMARKED OBJECTIVES

In this paper, we introduce a new approach for finding robust portfolios when there is model uncertainty. It differs from the usual worst‐case approach in that a (dynamic) portfolio is evaluated not only by its performance when there is an adversarial opponent (“nature”), but also by its performance relative to a stochastic benchmark. The benchmark corresponds to the wealth of a fictitious benchmark investor who invests optimally given knowledge of the model chosen by nature, so in this regard, our objective has the flavor of min–max regret. This relative performance approach has several important properties: (i) optimal portfolios seek to perform well over the entire range of models and not just the worst case, and hence are less pessimistic than those obtained from the usual worst‐case approach; (ii) the dynamic problem reduces to a convex static optimization problem under reasonable choices of the benchmark portfolio for important classes of models including ambiguous jump‐diffusions; and (iii) this static problem is dual to a Bayesian version of a single period asset allocation problem where the prior on the unknown parameters (for the dual problem) correspond to the Lagrange multipliers in this duality relationship. This dual static problem can be interpreted as a less pessimistic alternative to the single period worst‐case Markowitz problem. More generally, this duality suggests that learning and robustness are closely related when benchmarked objectives are used.