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Academic literature on the topic 'Stochastic Preference Model'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Stochastic Preference Model"

1

Creal, Drew D., and Jing Cynthia Wu. "Bond risk premia in consumption‐based models." Quantitative Economics 11, no. 4 (2020): 1461–84. http://dx.doi.org/10.3982/qe887.

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Gaussian affine term structure models attribute time‐varying bond risk premia to changing risk prices driven by the conditional means of the risk factors, while structural models with recursive preferences credit it to stochastic volatility. We reconcile these competing channels by introducing a novel form of stochastic rate of time preference into an otherwise standard model with recursive preferences. Our model is affine and has analytical bond prices making it empirically tractable. We use particle Markov chain Monte Carlo to estimate the model, and find that time variation in bond term premia is predominantly driven by the risk price channel.
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2

Johnson, Joseph G., and Jerome R. Busemeyer. "A Dynamic, Stochastic, Computational Model of Preference Reversal Phenomena." Psychological Review 112, no. 4 (2005): 841–61. http://dx.doi.org/10.1037/0033-295x.112.4.841.

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3

Lau, Kin-Nam, Gerald Post, and Karuna Selvaraj. "A stochastic programming model to analyze ordinal preference data." Marketing Letters 4, no. 1 (1993): 19–29. http://dx.doi.org/10.1007/bf00994184.

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4

Hu, Hanlei, Shaoyong Lai, and Hongjing Chen. "Optimal Asset Allocation for CRRA and CARA Insurers under the Vasicek Interest Rate Model." Discrete Dynamics in Nature and Society 2022 (January 12, 2022): 1–14. http://dx.doi.org/10.1155/2022/3974488.

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This paper considers the reinsurance-investment problem with interest rate risks under constant relative risk aversion and constant absolute risk aversion preferences, respectively. Stochastic control theory and dynamic programming principle are applied to investigate the optimal proportional reinsurance-investment strategy for an insurer under the Vasicek stochastic interest rate model. Solving the corresponding Hamilton-Jacobi-Bellman equation via the Legendre transform approach, the optimal premium allocation strategies maximizing the expected utilities of terminal wealth are derived. In addition, several sensitivity analyses and numerical illustrations are given to analyze the impacts of different risk preferences and interest rate fluctuation on the optimal strategies. We find that the asset allocation and reinsurance ratio of the insurer are correlated with risk preference coefficient and interest rate fluctuation, and the insurance company may adjust the reinsurance-investment strategy to deal with interest rate risk.
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5

Zhang, Guoxing, Zhenhua Zhang, Yongjing Cui, and Chun Yuan. "Game model of enterprises and government based on the tax preference policy for energy conservation and emission reduction." Filomat 30, no. 15 (2016): 3963–74. http://dx.doi.org/10.2298/fil1615963z.

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In recent years, greater efforts in tax preference policy for energy conservation and emission reduction (ECER) have been implemented in our country. Based on the tax preference of enterprise income for comprehensive utilization of resources, the constraints to achieve completely successful equilibrium are studied in the single period and multiple periods. In the single period, the key to achieve separating equilibrium is analyzed carefully by constructing a signaling game model of enterprises and government on tax preference of enterprise income. In the multiple periods, with the stochastic evolutionary game model based on the stochastic differential equation (SDE) theory, the constraints of keeping the separating equilibrium stable and continuing in a long term will be further investigated. It gives the optimal number of tax preference of enterprise income, camouflage cost and expected cost of risk under the state of separating equilibrium. The optimal result of completely successful equilibrium is obtained in single period by the analysis of numerical example for enterprises and government signaling game model. The simulation experiment is successfully finished to test the effectiveness of the stochastic evolutionary game model by using mathematical software MATLAB.
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6

Hu, Yusheng, Wensi Zhang, Liying Zhang, and Xiao Lei. "Dual-Channel Dynamic Pricing in the Presence of Low-Carbon Preference." Discrete Dynamics in Nature and Society 2022 (November 9, 2022): 1–8. http://dx.doi.org/10.1155/2022/3881268.

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This paper investigates the dynamic pricing strategy for perishable products sold through online and offline channels with the consideration of consumers’ low-carbon preferences. The MNL stochastic utility model is used to describe the purchasing decisions of consumers with different low-carbon preferences. On this basis, we establish a dual-channel dynamic pricing model for perishable products to maximize the firm’s expected revenue by using the dynamic programming method. We also study the influence of consumers’ low-carbon preferences on optimal prices. The conclusions show that the low-carbon utility and the proportion of consumers with high low-carbon preference have positive effects on the optimal prices of the dual sales channels. Moreover, consumers are more inclined to purchase products through the online channel in the presence of low-carbon preference, so the optimal price of the online channel product is higher than that of the offline channel product.
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7

Tyson, Christopher J. "Exponential Satisficing." American Economic Journal: Microeconomics 13, no. 2 (2021): 439–67. http://dx.doi.org/10.1257/mic.20180301.

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We propose the exponential satisficing model of boundedly rational decision-making, a general-purpose tool designed for use in typical microeconomic applications. The model posits that the preferences perceived and acted upon by the agent are a stochastic coarsening of his or her true, welfare-significant preferences. The decision-maker’s perceptual capabilities are controlled by a preference resolution parameter, which smoothly varies the impact of cognitive constraints on choice. To demonstrate the implementation of the model, it is applied to duopolistic price competition with satisficing consumers and to normal-form games with satisficing players. (JEL D01, D80, D90)
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8

Birnbaum, Michael H. "Reanalysis of Butler and Pogrebna (2018) using true and error model." Judgment and Decision Making 15, no. 6 (2020): 1044–51. http://dx.doi.org/10.1017/s1930297500008238.

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AbstractButler and Pogrebna (2018) devised triples of three-branch gambles theorized to violate transitivity of preference according to a most probable winner model. According to this model, a person chooses the option that has the higher probability to yield a better outcome than the other alternative. They tested 11 triples with 100 participants and found cases that appeared to violate weak stochastic transitivity and the triangle inequality. But tests of weak stochastic transitivity and the triangle inequality do not provide a proper method to compare transitive and intransitive models that allow mixtures of preference patterns and random errors. Those older methods can yield false conclusions regarding transitivity, for example, if different participants have different true preferences or if different choice problems have different rates of error. This paper reanalyzes their data using a true and error (TE) model, which does not require these unrealistic assumptions, and which provides estimates of the incidence of transitive and intransitive behavior in a mixture. Reanalysis indicated that 3 of the 11 triples showed convincing evidence of violations of transitivity in the opposite direction of the predictions of the most probable winner model. Further, these and other triples showed other significant violations of the most probable winner model. Despite some violations of the true and error model, the data of Butler and Pogrebna appear to contradict not only transitive utility models but they also refute the most probable winner model as a descriptive theory of choice behavior.
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9

Zhao, Lei, Hongzhi Guan, Xinjie Zhang, and Xiongbin Wu. "A regret-based route choice model with asymmetric preference in a stochastic network." Advances in Mechanical Engineering 10, no. 8 (2018): 168781401879323. http://dx.doi.org/10.1177/1687814018793238.

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In this study, a stochastic user equilibrium model on the modified random regret minimization is proposed by incorporating the asymmetric preference for gains and losses to describe its effects on the regret degree of travelers. Travelers are considered to be capable of perceiving the gains and losses of attributes separately when comparing between the alternatives. Compared to the stochastic user equilibrium model on the random regret minimization model, the potential difference of emotion experienced induced by the loss and gain in the equal size is jointly caused by the taste parameter and loss aversion of travelers in the proposed model. And travelers always tend to use the routes with the minimum perceived regret in the travel decision processes. In addition, the variational inequality problem of the stochastic user equilibrium model on the modified random regret minimization model is given, and the characteristics of its solution are discussed. A route-based solution algorithm is used to resolve the problem. Numerical results given by a three-route network show that the loss aversion produces a great impact on travelers’ choice decisions and the model can more flexibly capture the choice behavior than the existing models.
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10

Permana, Yudistira, Giovanni Van Empel, and Rimawan Pradiptyo. "Investigation of the Stochastic Choice under Risk using Experimental Data." Gadjah Mada International Journal of Business 22, no. 2 (2020): 137. http://dx.doi.org/10.22146/gamaijb.34446.

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This paper extends the analysis of the data from the experiment undertaken by Pradiptyo et al. (2015), to help explain the subjects’ behaviour when making decisions under risk. This study specifically investigates the relative empirical performance of the two general models of the stochastic choice: the random utility model (RUM) and the random preference model (RPM) where this paper specifies these models using two preference functionals, expected utility (EU) and rank-dependent expected utility (RDEU). The parameters are estimated in each model using a maximum likelihood technique and run a horse-race using the goodness-of-fit between the models. The results show that the RUM better explains the subjects’ behaviour in the experiment. Additionally, the RDEU fits better than the EU for modelling the stochastic choice.
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