基于效应的随机灰规划模型和应用

为了解决同时含有随机因素和灰色因素的不确定规划问题,通过结合区间灰数所属区间两个端点的随机性,给出随机区间灰数和随机区间灰函数的定义,提出了随机灰规划模型。通过综合效应函数理论用随机变量期望值和方差综合量化表示灰数所属区间的两个端点值。应用该理论对综合量化后的两个端点值继续进行综合量化,从而将随机灰规划转化为确定型规划问题。应用遗传算法进行求解。通过综合效应函数的理念,综合随机变量的期望和方差,同时综合区间灰数的区间因素,将随机灰规划数学模型转化为确定型规划模型即基于效应的随机灰规划模型。通过选取不同的综合效应函数,得到了关于不同决策意识下的随机灰规划的最优解。这个方法可为决策者进行不确定决策提供参考。     

More pessimism than greediness: a characterization of monotone risk aversion in the rank-dependent expected utility model

Summary. This paper studies monotone risk aversion, the aversion to monotone, mean-preserving increase in risk (Quiggin [21]), in the Rank Dependent Expected Utility (RDEU) model. This model replaces expected utility by another functional, characterized by two functions, a utility function u in conjunction with a probability-perception function f. Monotone mean-preserving increases in risk are closely related to the notion of comparative dispersion introduced by Bickel and Lehmann [3,4] in Non-parametric Statistics. We present a characterization of the pairs (u,f) of monotone risk averse decision makers, based on an index of greediness G _u of the utility function u and an index of pessimism P _f of the probability perception function f: the decision maker is monotone risk averse if and only if . The index of greediness (non-concavity) of u is the supremum of taken over . The index of pessimism of f is the infimum of taken over 0 < v < 1. Thus, , with G _u = 1 iff u is concave. If then , i.e., f is majorized by the identity function. Since P _f = 1 for Expected Utility maximizers, forces u to be concave in this case; thus, the characterization of risk aversion as is a direct generalization from EU to RDEU. A novel element is that concavity of u is not necessary. In fact, u must be concave only if P _f = 1.Received: 10 April 2001, Revised: 18 November 2003, JEL Classification Numbers: D81. Correspondence to: Michéle CohenAlain Chateauneuf, Michéle Cohen, Isaac Meilijson: We are most grateful to Mark Machina, Peter Wakker and two anonymous referees for very helpful suggestions and comments.     

Optimal insurance under rank‐dependent utility and incentive compatibility

Bernard, He, Yan, and Zhou (Mathematical Finance, 25(1), 154–186) studied an optimal insurance design problem where an individual's preference is of the rank‐dependent utility (RDU) type, and show that in general an optimal contract covers both large and small losses. However, their results suffer from the unrealistic assumption that the random loss has no atom, as well as a problem of moral hazard that provides incentives for the insured to falsely report the actual loss. This paper addresses these setbacks by removing the nonatomic assumption, and by exogenously imposing the “incentive compatibility” constraint that both indemnity function and insured's retention function are increasing with respect to the loss. We characterize the optimal solutions via calculus of variations, and then apply the result to obtain explicitly expressed contracts for problems with Yaari's dual criterion and general RDU. Finally, we use numerical examples to compare the results between ours and Bernard et al.     

Arrow–Debreu equilibria for rank‐dependent utilities with heterogeneous probability weighting

We study Arrow–Debreu equilibria for a one‐period‐two‐date pure exchange economy with rank‐dependent utility agents having heterogeneous probability weighting and outcome utility functions. In particular, we allow the economy to have a mix of expected utility agents and rank‐dependent utility ones, with nonconvex probability weighting functions. The standard approach for convex economy equilibria fails due to the incompatibility with second‐order stochastic dominance. The representative agent approach devised in Xia and Zhou (2016) does not work either due to the heterogeneity of the weighting functions. We overcome these difficulties by considering the comonotone allocations, on which the rank‐dependent utilities become concave. Accordingly, we introduce the notion of comonotone Pareto optima, and derive their characterizing conditions. With the aid of the auxiliary problem of price equilibria with transfers, we provide a sufficient condition in terms of the model primitives under which an Arrow–Debreu equilibrium exists, along with the explicit expression of the state‐price density in equilibrium. This new, general sufficient condition distinguishes the paper from previous related studies with homogeneous and/or convex probability weightings.     

Pareto Optimal Trade in an Uncertain World: GMOs and the Precautionary Principle

Optimal trade policy in an uncertain world is examined. Decision-maker attitudes toward uncertainty are represented in terms of the Gilboa–Schmeidler (1989) maximin expected-utility (MMEU) model. The central result is that in a two-country, general-equilibrium setting with both trading partners possessing an MMEU preference structure, Pareto optimality can require one trading partner to absorb all uncertainty in the economy if its set of priors is a subset of its trading partners. An immediate corollary is that autarky is Pareto optimal if the trading partner with the more inclusive set of priors either chooses or is endowed with a nonstochastic technology.     

基于非线性效应的第三方支付平台兼容性研究

本文在设定网络外部性效用为符合梅特卡夫法则的非线性函数条件下,构建双寡头支付平台兼容和不兼容两种情况下的优化模型,探讨双寡头支付平台的兼容性策略选择问题。研究发现,规模对称的双寡头支付平台兼容之后,两家支付平台的利润、消费者效用及社会福利均会增加;银联成立后中国工商银行的银行卡业务利润得以提升也验证了模型结果。建议政府借鉴银联的成功经验,使银联在线成为各支付平台的聚合型平台,提高第三方支付清算系统的效率。     

BEHAVIORAL PORTFOLIO SELECTION: ASYMPTOTICS AND STABILITY ALONG A SEQUENCE OF MODELS

We consider a sequence of financial markets that converges weakly in a suitable sense and maximize a behavioral preference functional in each market. For expected concave utilities, it is well known that the maximal expected utilities and the corresponding final positions converge to the corresponding quantities in the limit model. We prove similar results for nonconcave utilities and distorted expectations as employed in behavioral finance, and we illustrate by a counterexample that these results require a stronger notion of convergence of the underlying models compared to the concave utility maximization. We use the results to analyze the stability of behavioral portfolio selection problems and to provide numerically tractable methods to solve such problems in complete continuous‐time models.     

Value creation through stakeholder synergy

Our “stakeholder synergy” perspective identifies new value creation opportunities that are especially effective strategically because a single strategic action (1) increases different types of value for two or more essential stakeholder groups simultaneously, and (2) does not reduce the value already received by any other essential stakeholder group. This result is obtainable because multiple potential sources of value creation exist for each essential stakeholder group. Actions that meet these criteria increase the size of the value “pie” available for essential stakeholder groups, and thereby serve to attract exceptional stakeholders and obtain their increasing effort and commitment. The stakeholder synergy perspective extends stakeholder theory further into the strategy realm, and offers insights for realizing broader value creation that is more likely to produce sustainable competitive advantage. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Supporting ALARP decision making by cost benefit analysis and multiattribute utility theory

Current guidance in the UK and elsewhere indicate upper and target risk limits for the operation of nuclear plant in terms of individual risk per annum. ‘As low as reasonably practicable’ (ALARP) arguments are used to justify the acceptance or rejection of policies that lead to risk changes between these limits. The suitability of cost‐benefit analysis (CBA) and multiattribute utility theory (MAUT) are assessed for performing ALARP (‘as low as reasonably possible’) assessments, in particular within the nuclear industry. Four problems stand out in current CBA applications to ALARP, concerning the determination of prices of safety gains or detriments, the valuation of group and individual risk, calculations using ‘disproportionality’, and the use of discounting to trade‐off risks through time. This last point has received less attention in the past but is important because of the growing interest in risk‐informed regulation in which policies extend over several timeframes and distribute the risk unevenly over these, or in policies that lead to a nonuniform risk within a single timeframe (such as maintenance policies). The problems associated with giving quantitative support to such decisions are discussed. It is argued that multiattribute utility methods (MAUT) provide an alternative methodology to CBA which enable the four problems described above to be addressed in a more satisfactory way. Through sensitivity analysis MAUT can address the perceptions of all stakeholder groups, facilitating constructive discussion and elucidating the key points of disagreement. It is also argued that by being explicitly subjective it provides an open, auditable and clear analysis in contrast to the illusory objectivity of CBA. CBA seeks to justify a decision by using a common basis for weights (prices), while MAUT recognizes that different parties may want to give different valuations. It then allows the analyst to explore the ways in which different parties might (or might not) come to the same conclusion even when weighting items differently.