Additive Utility in Prospect Theory

Bleichrodt, Han; Schmidt, Ulrich; Zank, Horst

doi:10.1287/mnsc.1080.0978

Cited by 131 publications

(61 citation statements)

References 48 publications

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“…We choose an additively separable model in which each good contributes to utility independently of the consumption of other goods, and having good specific reference points (Bleichrodt et al 2009). To gain insight into the variety seeking problem, we first consider the situation in which a consumer is restricted, in each time period, to the choice of one out of K goods.…”

Section: Discrete Choice Problemmentioning

confidence: 99%

Predicting Utility Under Satiation and Habit Formation

Baucells

Sarin

2010

Management Science

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W e introduce a modification of the discounted utility model that accounts for both satiation and habit formation in intertemporal choice. Preferences depend on the satiation level and the habitual consumption level. These two state variables, together with the shape of the value function, drive the properties of the model. One unique feature of our model is that it addresses the trade-off between seeking variety and maintaining acquired habits. We examine several properties of our model, such as the nontrivial patterns of desirability (willingness to pay) for an additional unit of consumption, or the effect of abstaining from consumption (craving). We explore the shape of optimal consumption patterns in discrete and continuous choice settings. If subjects underestimate the changes in satiation and habituation levels, as occurs under projection bias, our model explains why people buy more when hungry, or prefer variety in advance of consumption but stay with the same consumption good in actual use.

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Section: Discrete Choice Problemmentioning

confidence: 99%

Predicting Utility Under Satiation and Habit Formation

Baucells

Sarin

2010

Management Science

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“…Foundations for such a multi-attribute utility function were developed by [5] which is supported by several empirical studies concerning decision making under risk (e.g. [2,4,32,25,9]).…”

Section: Discussionmentioning

confidence: 99%

“…To do so we vary q 1 , q 2 , q 3 and see how this affects the expected utility of the optimal solution, tasks (3,4,5,6,7,8,10,13,14). We also investigate how large a change in the trade-off parameters it would take for this solution to no longer be optimal.…”

Section: Sensitivity To Trade-off Parametersmentioning

confidence: 99%

Allocation of tasks for reliability growth using multi-attribute utility

Wilson

Quigley

2016

European Journal of Operational Research

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In reliability growth models in particular, and project risk management more generally, improving the reliability of a system or product is limited by constraints on cost and time. There are many possible tasks which can be carried out to identify and design out weaknesses in the system under development. This paper considers the allocation problem: which subset of tasks to undertake. While the method is applicable to project risk management generally, the work has been motivated by reliability growth programmes. We utilise a model for reliability growth, based on an efficacy matrix, developed with engineering experts in the aerospace industry. We develop a general multi-attribute utility function based on targets for cost, time on test and system reliability. The optimal subset is identified by maximising the prior expected utility. We derive conditions on the model parameters for risk aversion and loss aversion based on observed properties of preference. We give conditions for multivariate risk aversion under the general form of the utility function. The method is illustrated using an example informed by work with aerospace organisations.

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“…According to decision makers' psychological and behavioral characteristics of reference dependence, loss aversion, and diminishing sensitivity, based on prospect theory [24][25][26][27][28], the calculation method of comprehensive prospect value for each grade is given below.…”

Section: Calculation Of Each Trust Grade's Comprehensive Prospect Valuementioning

confidence: 99%

“…# are nonlinear weight function for "income" and "loss" respectively. According to articles [24][25][26][27][28], function…”

Section: Calculation Of Perceived Probability For Each Evaluation Trumentioning

confidence: 99%

Consumers’ Perceived Profit for Online Trading Based on Prospect Theory

Guan¹,

Wang²,

Zhao³

2015

TOCSJ

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As decision makers of online trading, consumers make trading decisions according to their perceived profit value. Considering both the objective profit value and consumers' trust profit (or loss) value according to the evaluation of historical buyers, this paper proposed an analytic method about perceived profit value based on prospect theory. Firstly, it described the variables related to the calculation of perceived profit value. Next, based on the thought of reference dependent, loss aversion and diminishing sensitivity, it calculated consumers' perceived value according to each evaluation grade. Then, it used the distorted probability judgment thought to achieve the corresponding perceived weight of each evaluation grade according to its statistical probability. Finally, it calculated consumers' total perceived profit value based on the above. An example was illustrated to explain the concrete calculation processes.

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Additive Utility in Prospect Theory

Cited by 131 publications

References 48 publications

Predicting Utility Under Satiation and Habit Formation

Predicting Utility Under Satiation and Habit Formation

Allocation of tasks for reliability growth using multi-attribute utility

Consumers’ Perceived Profit for Online Trading Based on Prospect Theory

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