Framing Effects in Narrative and Non‐Narrative Risk Messages

Narrative messages are increasingly popular in health and risk campaigns, yet gain/loss framing effects have never been tested with such messages. Three experiments examined framing in narrative messages. Experiment 1 found that only the character's decision, not framing, influenced judgments about characters in a narrative derived from a prospect theory context. Experiment 2 found that a framing effect that occurred when presented in a decision format did not occur when the same situation was presented as a narrative. Using a different story/decision context, Experiment 3 found no significant difference in preference for surgery over radiation therapy in a narrative presentation compared to a non‐narrative presentation. The results suggest that health and risk campaigns cannot assume that framing effects will be the same in narrative messages and non‐narrative messages. Potential reasons for these differences and suggestions for future research are discussed.     

Describing decision bias in the newsvendor problem: A prospect theory model

In the newsvendor setting, we consider a prospect theory model with a stochastic-subjective reference point. The reference point is based on the pay-offs corresponding to the extreme values of demand; and, is influenced by the perception of costs associated with understocking and overstocking, and the pessimism level of the decision maker. Our prospect theory model describes the recent observations in the newsvendor experiments—the non-linear ordering behavior with respect to the profit margin and behavior at the extreme profit margins, in addition to the pull-to-center effect and asymmetry in ordering.     

Hedonism versus accuracy: the influence of motivation and affect on the evaluation of multiple gains and losses

The perceived value of multiple gains and losses may be influenced by a perceiver’s goals or affective state. In this research, insights from prospect theory were combined with the heuristic-systematic model to shed light on the information-processing strategies that underlie motivated and affect-related preference formation in the context of valuating multiple gains and losses. Specifically, findings from two experiments examine the influence of motivation and affect on preferences for segregated versus integrated gains and losses. In the first experiment—consistent with hypotheses—accuracy motivation was found to induce systematic processing for gains. The mixed results in the loss condition are explained with the influence of negative affect. Overall, the evidence supports the notion that people’s value functions might be more flexible than predicted by prospect theory, depending on people’s current goals. The second experiment substantiates these findings, identifying the influence of negative versus positive affect on the valuation of gains and losses. The results suggest that mood-management determines information processing and preferences depending on the congruence of the valence of affect (e.g. negative such as sadness) and the valence event (e.g. a positive event such as a gain). From a managerial perspective these studies add to practical knowledge on price communication, bundling, surcharges, or sequences of payments. When setting prices, salaries or other compensation schemes managers should consider whether their target group tends to be more accuracy or more feeling motivated.     

A COMPARISON OF INFORMATION STRUCTURES FOR A RIGID DECISION RULE CASE

The information economics model of individual decision assumes that decision makers are capable of instantly modifying their decision rules in response to signal changes from the information structure. Individuals, however, often tend to stick to rigid decision rules, particularly when they are trained to respond with conditioned reactions, such as to a traffic light. This article modifies the individual decision model for such cases. It discusses probabilistic information structures as well as the deterministic information function. It results in an ordering of information structures that differs from the ordering imposed by the Blackwell Theorem. When deterministic information functions are considered, an ordering that is finer than the prevailing one is proposed. Transformations between deterministic and probabilistic information functions and the relationship between the proposed model and the previous theory are also discussed.     

A Framework for the Analysis of Error in Conjunctive,Multi-Criteria,Satisficing Decision Processes*

This paper examines the impact of certain types of error on ad hoc, unstructured decision making that incorporates a satisficing strategy in a multi-criteria context using the conjunctive rule. The impacts of error in evaluation and of error in minimal or satisficing levels are systematically modelled for each of the decision criteria. Error functions, analytical expressions, and procedures are developed to obtain information such as the likelihood of correct decision making in the presence of evaluation and minimal criteria level error. These are applied to a specific research design, which is then analyzed. This highlights certain features of decision making in a satisficing context, such as the importance of keeping the number of decision criteria to a minimum. The results yield various insights, some of which confirm information obtained from previous analyses. A major advantage of the framework presented is that it can provide quantitative measurements which support ideas previously couched in qualitative terms only. Although the framework is applied in the context of decision making that uses the conjunctive rule in the case of multiple criteria, other satisficing strategies can be accommodated as well.     

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.     

Insights from Attempts to Validate a Multi-Attribute Model of Problem Definition Quality

Problem definitions are often used as part of the problem structuring process in decision sciences. We report an attempt to validate a multi-attribute model of problem definition quality in the context of an experiment based upon the definition of the ‘alcohol problem’ on a campus. Three independent raters assessed values for the attributes of the model for 57 written problem definitions, and their rating was compared to three subjective ‘expert’ ratings made by individuals with some responsibility for alcohol policy. Our study shows that the experts exhibited considerable stakeholder bias in their assessments of the definitions, suggesting that a definition that is evaluated as good may not be subjectively acceptable to others. We present some evidence that the best definitions are those that provide direction with regard to solution, neither suggesting firm solutions to the problem nor being too open-ended. It is shown that the multi-attribute model is a reasonable tool for eliminating poorer definitions. Further, we relate our work to recent conceptual developments in understanding problem definition.     

股票市场价值函数实证研究

价值函数是前景理论的核心组成部分,用以刻画决策者对于收益和损失的主观感受.以前对前景理论的实证研究基本上通过心理学实验来进行,而且是针对决策者个体进行的.本文以股票市场整体为对象,采用EGARCH模型提取到达市场上的信息流作为财富改变的代理变量,利用两阶段幂函数型作为价值函数的表现形式,对十个国家股票市场上综合指数的日收益率数据进行了实证研究.实证结果发现各国股票市场上价值函数均呈现反S形状的,与多数心理实验中个体决策者表现价值函数呈S形状迥然有别.     

基于时间价值的交通出行方式选择行为研究

通勤出行是城市居民最基本和最重要的出行目的,通勤出行时间价值是评价通勤出行者对交通方式选择的重要参数之一,以累积前景理论为基础,将通勤出行时间价值引入广义出行成本函数中,以权重函数和改进的广义出行成本函数作为交通方式选择模型的依据,并在模型中自定义广义出行成本参考点,选择累积前景值最大的前景作为出行者最优决策。分别基于“期望效用最大化理论”和“累积前景理论”对通勤出行者在三种不同出行场景下进行仿真模拟,研究最优交通方式选择行为。研究结果表明:期望效用理论框架下,通勤出行者的交通方式选择行为不受出行场景的影响,累积前景理论更适用于出行方式选择行为的研究。构建以通勤出行时间价值为核心变量的交通方式选择模型,可帮助通勤出行者选择合理的交通方式,并为政府及相关部门制定交通管理规划和实施缓解交通拥堵政策提供依据。     

Consistency and Accuracy in Decision Aids: Experiments with Four Multiattribute Systems*

There have been a number of multiattribute decision aids developed to aid selection problems. Multiattribute value theory and the analytic hierarchy process are two commonly used techniques. Different systems can result in radically different conclusions if they inaccurately and inconsistently reflect the preference structure of decision makers, or if they are based on inappropriate theoretical models. This study examines the impact of the underlying theoretical model, the method in which preference information is elicited, and the structure of alternatives as influences on the results from using various decision aids. It was found that two systems based on the multiattribute value theory model were just as diverse in their conclusions as were results between AHP and the multiattribute value theory models. Therefore, accuracy of information reflecting decision maker preference is an important consideration. Feedback capable of assuring the decision maker that information provided is consistent is a necessary feature required of decision aids applied to selection problems. The study also found that the way in which information is elicited influenced the result more than did the underlying model. Exact numerical data for complex concepts such as attribute importance and alternative performance on attributes is not necessary, and elicitation procedures that are more natural for the user are likely to be more accurate.     

Stochastic and Intransitive Behavior in a State-Preference Model of Asset Choice*

Most models of investor behavior assume a time-state independent utility function and result in a deterministic solution where a given set of inputs uniquely specifies the decision. In contrast, a state preference model using a time-state dependent utility function is derived in this paper. The model allows the investment choice decision to be analyzed in a game theoretic context. The general solution is a mixed strategy which allows for a probabilistic interpretation of the decision. The approach presented in this paper can accommodate anomalies such as intransitivity of preference and satisficing as rational behavior. An example of a possible implementation is given along with interpretations of the outcomes.     

A Model of Consumer Inertia with Applications to Dynamic Pricing

This paper introduces a decision model of consumer inertia. Consumers exhibit inertia when they have an inherent bias to delay purchases. Inertia may induce consumers to wait even when it is optimal to buy immediately. We embed our decision model within a dynamic pricing context. There is a firm that sells a fixed capacity over two time periods to an uncertain number of both rational and inertial consumers. We find that consumer inertia has both positive and negative effects on profits: it decreases demand (in period one) but intensifies competition among consumers for the product (in period two). We show that our model of inertia is consistent with well‐established behavioral regularities, such as loss aversion and probability weighting in the sense of prospect theory, and hyperbolic time preferences. We offer practical recommendations for firms to influence the level of consumer inertia. These include offering returns policies (to mitigate potential consumer losses), providing decision aids (to avoid perception errors), and offering flexible payment options (to lower transaction costs).     

An Information-Maximizing Interactive Procedure for Scenario Probability Elicitation

Various approaches have been proposed for determining scenario probabilities to facilitate long-range planning and decision making. These include microlevel approaches based on the analysis of relevant underlying events and their interrelations and direct macrolevel examination of the scenarios. The determination of a unique solution demands excessive consistency and time requirements on the part of the expert and often is not guaranteed by these procedures. We propose an interactive information maximizing scenario probability query procedure (IMQP) that exploits the desirable features of existing methods while circumventing their drawbacks. The approach requires elicitation of cardinal probability assessments and bounds for only marginal and first-order conditional events, as well as ordinal probability comparisons (probability orderings or rankings) of carefully selected scenario subsets determined using concepts of information theory. Guidelines for implementation based on simulation results are also developed. A goal program for handling inconsistent ordinal probability responses is also integrated into the procedure. The results of behavioral experimentation (which compared our approach to Expert Choice and showed that the IMQP was viable) compared favorably in terms of ease of use and time requirements, and works best for problems with a large number of scenarios. Design modifications to IMQP learned from the experiments, such as incorporating interactive graphics, are also in progress.     

考虑排污权市场价格不确定性的企业生产决策

目前有关不确定情境下排污权交易企业生产决策问题研究通常都是采纳的期望效用理论。针对排污权市场价格不确定性普遍存在的客观事实,重点引入前景理论,考察排污权市场价格不确定情境下的企业生产决策问题。首先通过建立特定排污权市场价格下的生产决策优化模型,提取了企业在产品生产、污染削减、排污权交易等方面决策行为与排污权市场价格之间的关联性;基于此,运用前景理论分析框架,分析、推导了排污权市场价格不确定情境下企业生产决策的价值函数、主观概率与决策权重函数,建立了考虑心理参考点与决策偏好的生产决策模型。结果表明：由于受信息局限、资源禀赋、心理预期、行为偏好等多方面的影响,企业的实际生产决策会系统性地偏离期望最优决策;算例分析也从多个角度清晰刻画了企业面向排污权市场价格不确定性的复杂决策行为,所得结果更贴近现实情景,并充分说明了基于前景理论的模型分析能够更好地描述排污权交易企业的实际生产决策行为。     

考虑决策者损失规避的异质信息多属性变权决策方法

现实中存在大量异质信息（或数据）和需要考虑权重随属性值变化的多属性决策问题。针对这类异质信息多属性决策问题，本文提出了一种基于前景理论的变权综合求解方法。首先，构建了异质信息的统一距离计算公式，进而计算各个决策方案的相对贴近度；然后，提出基于不同类型效用函数的变权向量构造方法；其次，以初始权重为参考点，计算变权向量相对于参考点的益损决策矩阵，进而计算考虑决策者权重损失和收益的风险态度的各个决策方案的前景综合值，据此确定方案优劣排序和最优方案。通过数值例子的计算分析说明，文中所提决策模型与方法具有较好的有效性和合理性，可为解决复杂情景的决策问题提供理论依据与方法支持。     

基于前景理论的跨市场状态转移多阶段资产配置研究

在行为金融前景理论框架下研究跨市场间的状态转移资产配置问题,构建隐Markov——混合正态分布模型描述股票、债券和商品混合市场间的状态特征,用Baum-Welch算法估计模型参数,并利用状态转移思想进行情景生成建立多阶段随机优化模型。进一步,以我国股票、债券和商品混合市场的实际数据为背景,利用滚动窗口方法实证分析基于状态转移的多阶段随机模型的表现,并与忽略状态转移特征的基准模型、等权重组合、沪深300指数的结果进行对比。结果表明,与其他组合相比,基于状态转移的投资组合有助于规避风险,且混合市场间的状态转移信息能够对前景理论投资者的最优投资决策产生影响。     

Data and Model Management in a Generalized MCDM-DSS*

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.     

基于前景理论的多阶段随机多准则决策方法

研究一种基于动态参考点的多阶段随机多准则决策方法。考虑多阶段决策过程中决策者的风险偏好,建立了基于前景理论的多阶段随机多准则决策分析框架,提出了一种基于阶段发展特征的动态参考点设置方法;构建准则权重的目标规划模型,结合阶段参考点动态变化的特征测算各阶段备选方案的综合前景值;设计方案综合前景值的范围估算模型,以反映决策风险对评价结果的影响;案例研究验证了上述方法的可行性和实际效果。