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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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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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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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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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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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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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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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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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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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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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Birnbaum, Michael H., and Bonny Quan. "Note on Birnbaum and Wan (2020): True and error model analysis is robust with respect to certain violations of the MARTER model." Judgment and Decision Making 15, no. 5 (2020): 861–62. http://dx.doi.org/10.1017/s193029750000797x.
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AbstractThe Markov True and Error (MARTER) model (Birnbaum & Wan, 2020) has three components: a risky decision making model with one or more parameters, a Markov model that describes stochastic variation of parameters over time, and a true and error (TE) model that describes probabilistic relations between true preferences and overt responses. In this study, we simulated data according to 57 generating models that either did or did not satisfy the assumptions of the True and Error fitting model, that either did or did not satisfy the error independence assumptions, that either did or did not satisfy transitivity, and that had various patterns of error rates. A key assumption in the TE fitting model is that a person’s true preferences do not change in the short time within a session; that is, preference reversals between two responses by the same person to two presentations of the same choice problem in the same brief session are due to random error. In a set of 48 simulations, data generating models either satisfied this assumption or they implemented a systematic violation, in which true preferences could change within sessions. We used the true and error (TE) fitting model to analyze the simulated data, and we found that it did a good job of distinguishing transitive from intransitive models and in estimating parameters not only when the generating model satisfied the model assumptions, but also when model assumptions were violated in this way. When the generating model violated the assumptions, statistical tests of the TE fitting models correctly detected the violations. Even when the data contained violations of the TE model, the parameter estimates representing probabilities of true preference patterns were surprisingly accurate, except for error rates, which were inflated by model violations. In a second set of simulations, the generating model either had error rates that were or were not independent of true preferences and transitivity either was or was not satisfied. It was found again that the TE analysis was able to detect the violations of the fitting model, and the analysis correctly identified whether the data had been generated by a transitive or intransitive process; however, in this case, estimated incidence of a preference pattern was reduced if that preference pattern had a higher error rate. Overall, the violations could be detected and did not affect the ability of the TE analysis to discriminate between transitive and intransitive processes.
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Bernardo, John J., and David E. Upton. "Stochastic and Intransitive Behavior in a State-Preference Model of Asset Choice." Decision Sciences 23, no. 5 (1992): 1114–26. http://dx.doi.org/10.1111/j.1540-5915.1992.tb00438.x.
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SZWABIǸSKI, JANUSZ, ANDRZEJ PȨKALSKI, and KAMIL TROJAN. "COMPETITION AND PREDATION IN A THREE SPECIES MODEL." International Journal of Modern Physics C 17, no. 11 (2006): 1629–45. http://dx.doi.org/10.1142/s0129183106010078.
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A model of dynamics of three interacting species is presented. Two of the species are prey and one is predator, which feeds on both prey, however with some preference to one type. Prey compete for space (breeding) although they always have access to food. Predators in order to survive and reproduce must catch prey, otherwise they die of hunger. The dynamics of the model is found via differential equations in the mean-field like approach and through computer simulations for agent-based method. We show that the coexistence of the three species is possible in the mean-field model, provided the preference of the predators is small, whereas from simulation it follows that the stochastic fluctuations drive, generally, one of the prey populations into extinction. We have found a different type of behavior for small and large systems and a rather unexpected dependence of the coexistence chance of the preference parameter in bigger lattices.
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Birnbaum, Michael H., and Jeffrey P. Bahra. "Separating response variability from structural inconsistency to test models of risky decision making." Judgment and Decision Making 7, no. 4 (2012): 402–26. http://dx.doi.org/10.1017/s1930297500002758.
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AbstractIndividual true and error theory assumes that responses by the same person to the same choice problem within a block of trials are based on the same true preferences but may show preference reversals due to random error. Between blocks, a person’s true preferences may differ or stay the same. This theory is illustrated with studies testing two critical properties that distinguish models of risky decision making: (1) restricted branch independence, which is implied by original prospect theory and violated in a specific way by both cumulative prospect theory and the priority heuristic; and (2) stochastic dominance, which is implied by cumulative prospect theory. Corrected for random error, most individuals systematically violated stochastic dominance, ruling out cumulative prospect theory. Furthermore, most people violated restricted branch independence in the opposite way predicted by that theory and the priority heuristic. Both violations are consistent with the transfer of attention exchange model. No one was found whose data were compatible with cumulative prospect theory, except for those that were also compatible with expected utility, and no one satisfied the priority heuristic.
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Li, Chao, Si-Jie Cheng, and Peng-Fei Cheng. "Optimal Incentive Contract for Sales Team with Loss Aversion Preference." Symmetry 11, no. 7 (2019): 864. http://dx.doi.org/10.3390/sym11070864.
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When manufacturing enterprises employ sales team (or multiple salesmen) to sell products, there is asymmetric information such as the ability and efforts salesmen. Enterprises can use contracts to incentivize salesmen to work hard to maximize their profits. Assuming that market demand is sensitive to effort, and the salesman can exploit the market by increasing effort, a multi-agent model is established for the case of symmetrical information and asymmetrical information, in which the sales team has a loss aversion preference. In this multi-agent model, the agents’ utility function is non-concave and cannot be solved by traditional methods. We use a backward stochastic differential equation (BSDE) to represent agents’ contract through the martingale representation theorem and use the stochastic optimal control and matrix method to obtain the explicit solution of the optimal contract. Based on the conclusions of the research, an empirical analysis is made on the sales team of an enterprise.
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Skourtos, Damigos, Kontogianni, Tourkolias, and Hunt. "Embedding Preference Uncertainty for Environmental Amenities in Climate Change Economic Assessments: A “Random” Step Forward." Economies 7, no. 4 (2019): 107. http://dx.doi.org/10.3390/economies7040107.
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While there is a considerable debate regarding the choice of proper discount rates for assessing climate change projects and policies, only a tiny body of literature emphasizes “what to discount”. Usually, climate change economic assessments rely on tools and methods that employ strong simplifications, assuming, among others, given and fixed preferences about the values of man-made and environmental goods. Aiming to fill a gap in the literature, this paper leaves aside the issue of discounting and focuses on the nature and impact of preference uncertainty on the economic estimates of future climate change damages on ecosystem non-market goods and services. To this end, a general random walk-based stochastic model is proposed, combining a number of parameters, e.g., the growth of income, depletion of environmental assets, the elasticity of income and demand, and the change in preferences towards the environment. The illustrative application of the model shows that the value of environmental losses is significantly affected by the change in preferences. By doing so, the model allows the analyst to visualize future paths of preference evolutions and to bring future values of damaged environmental assets realistically to the fore. If these elements are neglected when estimating climate change-related future damages to environmental goods and services, the results may be too narrow from a policy perspective.
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Chkhartishvili, A. G. "The Problem of Finding the Median Preference of Individuals in a Stochastic Model." Automation and Remote Control 82, no. 5 (2021): 853–62. http://dx.doi.org/10.1134/s000511792105009x.
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Van den Honert, R. C. "Stochastic group preference modelling in the multiplicative AHP: A model of group consensus." European Journal of Operational Research 110, no. 1 (1998): 99–111. http://dx.doi.org/10.1016/s0377-2217(97)00243-9.
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Bodington, Jeffrey. "Disentangling Wine Judges’ Consensus, Idiosyncratic, and Random Expressions of Quality or Preference." Journal of Wine Economics 12, no. 3 (2017): 267–81. http://dx.doi.org/10.1017/jwe.2017.21.
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AbstractJudges confer various awards on wines entered in dozens of wine competitions each year. This article employs data on blind replicates to show that those awards are based on one instance of stochastic ratings assigned by wine judges; awards based on the expected values of those stochastic ratings would be different. This article recognizes the stochastic nature of ratings and builds on the work of many others to propose and test a conditional-probability model that yields maximum-likelihood estimates of judges’ latent consensus, idiosyncratic, and random assignments of scores to wines. The exact p-value for a likelihood test of the null hypothesis that the model's results are random is less than 0.001. Applying the notion of conditional probability may lead to better methods of assigning awards to entries in wine competitions and of assessing the capabilities of wine judges. (JEL Classifications: A10, C10, C00, C12, D12)
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Breitmoser, Yves. "Controlling for presentation effects in choice." Quantitative Economics 12, no. 1 (2021): 251–81. http://dx.doi.org/10.3982/qe1050.
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Experimenters make theoretically irrelevant decisions concerning user interfaces and ordering or labeling of options. Reanalyzing dictator games, I first show that such decisions may drastically affect comparative statics and cause results to appear contradictory across experiments. This obstructs model testing, preference analyses, and policy predictions. I then propose a simple model of choice incorporating both presentation effects and stochastic errors, and test the model by reanalyzing the dictator game experiments. Controlling for presentation effects, preference estimates become consistent across experiments and predictive out‐of‐sample. This highlights both the necessity and the possibility to control for presentation in economic experiments.
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Breitmoser, Yves. "Controlling for presentation effects in choice." Quantitative Economics 12, no. 1 (2021): 251–81. http://dx.doi.org/10.3982/qe1050.
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Experimenters make theoretically irrelevant decisions concerning user interfaces and ordering or labeling of options. Reanalyzing dictator games, I first show that such decisions may drastically affect comparative statics and cause results to appear contradictory across experiments. This obstructs model testing, preference analyses, and policy predictions. I then propose a simple model of choice incorporating both presentation effects and stochastic errors, and test the model by reanalyzing the dictator game experiments. Controlling for presentation effects, preference estimates become consistent across experiments and predictive out‐of‐sample. This highlights both the necessity and the possibility to control for presentation in economic experiments.
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Xue, Lian, and Xiao Xia Dai. "Research on the Vehicle Routing Problem with Fuzzy Demands." Advanced Materials Research 186 (January 2011): 570–75. http://dx.doi.org/10.4028/www.scientific.net/amr.186.570.
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In this paper, the vehicle routing problem with fuzzy demands is considered, and a fuzzy chance constrained programming mathematical model is established based on fuzzy possibility theory. Then fuzzy simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy vehicle routing model. Moreover, under the target that the total driving distance of vehicles is the shortest, the influence of the decision-maker’s preference on the final objective of the problem is discussed using the method of stochastic simulation, and the rational range of the preference number is obtained.
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Li, Bohan, and Junyi Guo. "Optimal reinsurance and investment strategies for an insurer under monotone mean-variance criterion." RAIRO - Operations Research 55, no. 4 (2021): 2469–89. http://dx.doi.org/10.1051/ro/2021114.
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This paper considers the optimal investment-reinsurance problem under the monotone mean-variance preference. The monotone mean-variance preference is a monotone version of the classical mean-variance preference. First of all, we reformulate the original problem as a zero-sum stochastic differential game. Secondly, the optimal strategy and the optimal value function for the monotone mean-variance problem are derived by the approach of dynamic programming and the Hamilton-Jacobi-Bellman-Isaacs equation. Thirdly, the efficient frontier is obtained and it is proved that the optimal strategy is an efficient strategy. Finally, the continuous-time monotone capital asset pricing model is derived.
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Pan, Yuangang, Ivor W. Tsang, Avinash K. Singh, Chin-Teng Lin, and Masashi Sugiyama. "Stochastic Multichannel Ranking with Brain Dynamics Preferences." Neural Computation 32, no. 8 (2020): 1499–530. http://dx.doi.org/10.1162/neco_a_01293.
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A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.
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Yu, Qing, and Xianbin Liu. "Noise-induced transition in the Zeldovich–Semenov model with local and global bifurcations." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 1 (2022): 013207. http://dx.doi.org/10.1088/1742-5468/ac3e73.
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Abstract The noise-induced transition of the Zeldovich–Semenov model in a continuous stirred tank reactor is investigated under small random perturbations. The deterministic model will exhibit mono- and bistable characteristics via local and global bifurcations. In the bistable zone, based on the Freidlin–Wentzell large deviation theory, the stochastic preference is explained by analyzing the required action of the fluctuational path. For the case of monostability, in the weak noise limit, the emergence of the switching line gives rise to the sudden switch of the optimal path and the sliding cycle will appear via the sliding bifurcation, which is verified by numerical methods. In addition, when there is no saddle in phase space, stochastic excitation with large-amplitude spikes is studied. On the quasi-threshold manifold, the point with the minimum quasi-potential plays the same role as the saddle, which means that the optimal path will undergo a large excursion by crossing this special point. These phenomena are verified by employing stochastic simulations.
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Crott, Helmut W., and Joachim Werner. "The Norm-Information-Distance Model: A Stochastic Approach to Preference Change in Group Interaction." Journal of Experimental Social Psychology 30, no. 1 (1994): 68–95. http://dx.doi.org/10.1006/jesp.1994.1004.
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Petrucciani, Alexa, Geonsik Yu, and Mario Ventresca. "Multi-season transmission model of Eastern Equine Encephalitis." PLOS ONE 17, no. 8 (2022): e0272130. http://dx.doi.org/10.1371/journal.pone.0272130.
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Eastern Equine Encephalitis (EEE) is an arbovirus that, while it has been known to exist since the 1930’s, recently had a spike in cases. This increased prevalence is particularly concerning due to the severity of the disease with 1 in 3 symptomatic patients dying. The cause of this peak is currently unknown but could be due to changes in climate, the virus itself, or host behavior. In this paper we propose a novel multi-season deterministic model of EEE spread and its stochastic counterpart. Models were parameterized using a dataset from the Florida Department of Health with sixteen years of sentinel chicken seroconversion rates. The different roles of the enzootic and bridge mosquito vectors were explored. As expected, enzootic mosquitoes like Culiseta melanura were more important for EEE persistence, while bridge vectors were implicated in the disease burden in humans. These models were used to explore hypothetical viral mutations and host behavior changes, including increased infectivity, vertical transmission, and host feeding preferences. Results showed that changes in the enzootic vector transmission increased cases among birds more drastically than equivalent changes in the bridge vector. Additionally, a 5% difference in the bridge vector’s bird feeding preference can increase cumulative dead-end host infections more than 20-fold. Taken together, this suggests changes in many parts of the transmission cycle can augment cases in birds, but the bridge vectors feeding preference acts as a valve limiting the enzootic circulation from its impact on dead-end hosts, such as humans. Our what-if scenario analysis reveals and measures possible threats regarding EEE and relevant environmental changes and hypothetically suggests how to prevent potential damage to public health and the equine economy.
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Wang, Chunfeng, Hao Chang, and Zhenming Fang. "Optimal Consumption and Portfolio Decision with Heston’s SV Model Under HARA Utility Criterion." Journal of Systems Science and Information 5, no. 1 (2017): 21–33. http://dx.doi.org/10.21078/jssi-2017-021-13.
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Abstract This paper studies the optimal consumption-investment strategy with Heston’s stochastic volatility (SV) model under hyperbolic absolute risk aversion (HARA) utility criterion. The financial market is composed of a risk-less asset and a risky asset, whose price process is supposed to be driven by Heston’s SV model. The risky preference of the individual is assumed to satisfy HARA utility, which recovers power utility, exponential utility and logarithm utility as special cases. HARA utility is of general framework in the utility theory and is seldom studied in the existing literatures. Legendre transform-dual technique along with stochastic dynamic programming principle is presented to deal with our problem and the closed-form solution to the optimal consumption-investment strategy is successfully obtained. Finally, some special cases are derived in detail.
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Gong, Jinnan, Nigel Roulet, Steve Frolking, et al. "Modelling the habitat preference of two key <i>Sphagnum</i> species in a poor fen as controlled by capitulum water content." Biogeosciences 17, no. 22 (2020): 5693–719. http://dx.doi.org/10.5194/bg-17-5693-2020.
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Abstract. Current peatland models generally treat vegetation as static, although plant community structure is known to alter as a response to environmental change. Because the vegetation structure and ecosystem functioning are tightly linked, realistic projections of peatland response to climate change require the inclusion of vegetation dynamics in ecosystem models. In peatlands, Sphagnum mosses are key engineers. Moss community composition primarily follows habitat moisture conditions. The known species habitat preference along the prevailing moisture gradient might not directly serve as a reliable predictor for future species compositions, as water table fluctuation is likely to increase. Hence, modelling the mechanisms that control the habitat preference of Sphagna is a good first step for modelling community dynamics in peatlands. In this study, we developed the Peatland Moss Simulator (PMS), which simulates the community dynamics of the peatland moss layer. PMS is a process-based model that employs a stochastic, individual-based approach for simulating competition within the peatland moss layer based on species differences in functional traits. At the shoot-level, growth and competition were driven by net photosynthesis, which was regulated by hydrological processes via the capitulum water content. The model was tested by predicting the habitat preferences of Sphagnum magellanicum and Sphagnum fallax – two key species representing dry (hummock) and wet (lawn) habitats in a poor fen peatland (Lakkasuo, Finland). PMS successfully captured the habitat preferences of the two Sphagnum species based on observed variations in trait properties. Our model simulation further showed that the validity of PMS depended on the interspecific differences in the capitulum water content being correctly specified. Neglecting the water content differences led to the failure of PMS to predict the habitat preferences of the species in stochastic simulations. Our work highlights the importance of the capitulum water content with respect to the dynamics and carbon functioning of Sphagnum communities in peatland ecosystems. Thus, studies of peatland responses to changing environmental conditions need to include capitulum water processes as a control on moss community dynamics. Our PMS model could be used as an elemental design for the future development of dynamic vegetation models for peatland ecosystems.
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Yang, Liu, Yao Xiong, and Xiao-jiao Tong. "A Smoothing Algorithm for a New Two-Stage Stochastic Model of Supply Chain Based on Sample Average Approximation." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/5681502.
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We construct a new two-stage stochastic model of supply chain with multiple factories and distributors for perishable product. By introducing a second-order stochastic dominance (SSD) constraint, we can describe the preference consistency of the risk taker while minimizing the expected cost of company. To solve this problem, we convert it into a one-stage stochastic model equivalently; then we use sample average approximation (SAA) method to approximate the expected values of the underlying random functions. A smoothing approach is proposed with which we can get the global solution and avoid introducing new variables and constraints. Meanwhile, we investigate the convergence of an optimal value from solving the transformed model and show that, with probability approaching one at exponential rate, the optimal value converges to its counterpart as the sample size increases. Numerical results show the effectiveness of the proposed algorithm and analysis.
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Xu, Tingliu, Yingjie Xiao, Chatchai Khiewngamdee, and Qin Lin. "Port Environmental Quality or Economic Growth? Their Relevance and Government Preference in Developing Countries." Discrete Dynamics in Nature and Society 2021 (September 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/3869125.
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In the past few years, the marine ecosystem has been a cheap resource in developing countries in the process of pursuing short-term vested interests. Therefore, high economic development of the developing countries is at a huge environmental cost. Nowadays, environmental protection is becoming a global concern. In this background, the government of the developing countries begins to formulate reasonable marine economic and environmental policies to find a balance between rapid economic development and marine environmental protection. In this contribution, a dynamic stochastic general equilibrium model (DSGE) is constructed with the environmental constraints, and Bayesian estimation is used to calibrate the main parameters. Next, the model is employed to analyze the effects of government and consumer environmental preferences on macroeconomic variables, environmental quality, and consumer and government utility. Results show that the government’s preference for environmental quality is positively related to port environmental quality and negatively related to output, capital stock, and consumption. An enhancement of the environmental quality preference of the target audience can realize balanced development of economic growth and port environmental quality. The findings are conducive to coordinating the relationship between economic development and environmental protection and achieving sustainable development of the port.
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Pan, Bin, Shih-Yung Wei, Xuanhua Xu, and Wei-Chiang Hong. "The Impact of Defense Investment on Economic Growth in the Perspective of Time Series." International Journal of Applied Evolutionary Computation 5, no. 4 (2014): 44–58. http://dx.doi.org/10.4018/ijaec.2014100104.
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By considering the demand and supply effects of defense investment and the uncertainty of the stochastic process of the production and defense investment, this study proposes a stochastic endogenous growth model to explore the impact of defense investment on economic growth. The results suggest that the relationship between defense investment and economic growth rate is nonlinear and obtains the optimal percentage of defense investment to maximize economic growth. Moreover, the impact of defense investment volatility on economic growth rate is subject to production and defense investment interference term's covariance and representative private investment risk preference. Finally, the empirical data are used to illustrate the applicability of the proposed model.
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Băbeanu, Alexandru-Ionuț, and Diego Garlaschelli. "Evidence for Mixed Rationalities in Preference Formation." Complexity 2018 (2018): 1–19. http://dx.doi.org/10.1155/2018/3615476.
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Understanding the mechanisms underlying the formation of cultural traits is an open challenge. This is intimately connected to cultural dynamics, which has been the focus of a variety of quantitative models. Recent studies have emphasized the importance of connecting those models to empirically accessible snapshots of cultural dynamics. In particular, it has been suggested that empirical cultural states, which differ systematically from randomized counterparts, exhibit properties that are universally present. Hence, a question about the mechanism responsible for the observed patterns naturally arises. This study proposes a stochastic structural model for generating cultural states that retain those robust empirical properties. One ingredient of the model assumes that every individual’s set of traits is partly dictated by one of several universal “rationalities,” informally postulated by several social science theories. The second, new ingredient assumes that, apart from a dominant rationality, each individual also has a certain exposure to the other rationalities. It is shown that both ingredients are required for reproducing the empirical regularities. This suggests that the effects of cultural dynamics in the real world can be described as an interplay of multiple, mixing rationalities, providing indirect evidence for the class of social science theories postulating such a mixing.
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Regenwetter, Michel, Jean-Claude Falmagne, and Bernard Grofman. "A stochastic model of preference change and its application to 1992 presidential election panel data." Psychological Review 106, no. 2 (1999): 362–84. http://dx.doi.org/10.1037/0033-295x.106.2.362.
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Angilella, Silvia, Salvatore Corrente, and Salvatore Greco. "Stochastic multiobjective acceptability analysis for the Choquet integral preference model and the scale construction problem." European Journal of Operational Research 240, no. 1 (2015): 172–82. http://dx.doi.org/10.1016/j.ejor.2014.06.031.
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Altuzarra, Alfredo, Pilar Gargallo, José María Moreno-Jiménez, and Manuel Salvador. "Identification of Homogeneous Groups of Actors in a Local AHP-Multiactor Context with a High Number of Decision-Makers: A Bayesian Stochastic Search." Mathematics 10, no. 3 (2022): 519. http://dx.doi.org/10.3390/math10030519.
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The identification of homogeneous groups of actors in a local AHP-multiactor context based on their preferences is an open problem, particularly when the number of decision-makers is high. To solve this problem in the case of using stochastic AHP, this paper proposes a new Bayesian stochastic search methodology for large-scale problems (number of decision-makers greater than 20). The new methodology, based on Bayesian tools for model comparison and selection, takes advantage of the individual preference structures distributions obtained from stochastic AHP to allow the identification of homogeneous groups of actors with a maximum common incompatibility threshold. The methodology offers a heuristic approach with several near-optimal partitions, calculated by the Occam’s window, that capture the uncertainty that is inherent when considering intangible aspects (AHP). This uncertainty is also reflected in the graphs that show the similarities of the decision-maker’s opinions and that can be used to achieve representative collective positions by constructing agreement paths in negotiation processes. If a small number of actors is considered, the proposed algorithm (AHP Bayesian clustering) significantly reduces the computational time of group identification with respect to an exhaustive search method. The methodology is illustrated by a real case of citizen participation based on e-Cognocracy.
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Birnbaum, Michael H., Daniel Navarro-Martinez, Christoph Ungemach, Neil Stewart, and Edika G. Quispe-Torreblanca. "Risky Decision Making: Testing for Violations of Transitivity Predicted by an Editing Mechanism." Judgment and Decision Making 11, no. 1 (2016): 75–91. http://dx.doi.org/10.1017/s1930297500007609.
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AbstractTransitivity is the assumption that if a person prefers A to B and B to C, then that person should prefer A to C. This article explores a paradigm in which Birnbaum, Patton and Lott (1999) thought people might be systematically intransitive. Many undergraduates choose C = ($96, .85; $90, .05; $12, .10) over A = ($96, .9; $14, .05; $12, .05), violating dominance. Perhaps people would detect dominance in simpler choices, such as A versus B = ($96, .9; $12, .10) and B versus C, and yet continue to violate it in the choice between A and C, which would violate transitivity. In this study we apply a true and error model to test intransitive preferences predicted by a partially effective editing mechanism. The results replicated previous findings quite well; however, the true and error model indicated that very few, if any, participants exhibited true intransitive preferences. In addition, violations of stochastic dominance showed a strong and systematic decrease in prevalence over time and violated response independence, thus violating key assumptions of standard random preference models for analysis of transitivity.
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Cho, Shin-Hyung, and Seung-Young Kho. "Exploring Route Choice Behaviours Accommodating Stochastic Choice Set Generations." Journal of Advanced Transportation 2021 (April 8, 2021): 1–14. http://dx.doi.org/10.1155/2021/5530814.
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Modelling route choice behaviours are essential in traffic operation and transportation planning. Many studies have focused on route choice behaviour using the stochastic model, and they have tried to construct the heterogeneous route choice model with various types of data. This study aims to develop the route choice model incorporating travellers’ heterogeneity according to the stochastic route choice set. The model is evaluated from the empirical travel data based on a radio frequency identification device (RFID) called dedicated short-range communication (DSRC). The reliability level is defined to explore the travellers’ heterogeneity in the choice set generation model. The heterogeneous K-reliable shortest path- (HK α RSP-) based route choice model is established to incorporate travellers’ heterogeneity in route choice behaviour. The model parameters are estimated for the mixed path-size correction logit (MPSCL) model, considering the overlapping paths and the heterogeneous behaviour in the route choice model. The different behaviours concerning the chosen routes are analysed to interpret the route choice behaviour from revealed preference data by comparing the different coefficients’ magnitude. There are model validation processes to confirm the prediction accuracy according to travel distance. This study discusses the policy implication to introduce the traveller specified route travel guidance system.
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Liu, Jinpei, Mengdi Fang, Feifei Jin, Chengsong Wu, and Huayou Chen. "Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development." Sustainability 12, no. 6 (2020): 2375. http://dx.doi.org/10.3390/su12062375.
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Multi-attribute decision making (MADM) is a cognitive process for evaluating data with different attributes in order to select the optimal alternative from a finite number of alternatives. In the real world, a lot of MADM problems involve some random and ordinal variables. Therefore, in this paper, a MADM method based on stochastic data envelopment analysis (DEA) cross-efficiency with ordinal variable is proposed. First, we develop a stochastic DEA model with ordinal variable, which can derive self-efficiency and the optimal weight of each attribute for all decision making units (DMUs). To further improve its discrimination power, cross-efficiency as a significant extension is proposed, which utilizes peer DMUs’ optimal weight to evaluate the relative efficiency of each alternative. Then, based on self-efficiency and cross-efficiency of all DMUs, we construct corresponding fuzzy preference relations (FPRs) and consistent fuzzy preference relations (FPRs). In addition, we obtain the priority weight vector of all DMUs by utilizing the row wise summation technique according to the consistent FPRs. Finally, we provide a numerical example for evaluating operation performance of sustainable development of 15 listed banks in China, which illustrates the feasibility and applicability of the proposed MADM method based on stochastic DEA cross-efficiency with ordinal variable.
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Lyu, Jiajun, and Aya Hagishima. "Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach." Buildings 13, no. 2 (2023): 521. http://dx.doi.org/10.3390/buildings13020521.
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Occupant behavior (OB) has a significant impact on household air-conditioner (AC) energy use. In recent years, bottom-up simulation coupled with stochastic OB modeling has been intensively developed for estimating residential AC consumption. However, a comprehensive analysis of the diverse behavioral preference patterns of occupants regarding AC use is hampered by the limited availability of large-scale residential energy demand data. Therefore, this study aimed to develop a prediction model for the residential household’s AC usage considering various OB-related diversity patterns based on monitoring data of appliance-level electricity use in a residential community of 586 households in Osaka, Japan. First, individual operation schedules and thermal preferences were identified and quantitatively extracted as the two main factors for the diverse behaviors across the whole community. Then, a clustering analysis classified the target households, finding four typical patterns for schedule preferences and three typical patterns for thermal preferences. These results were used, with time and meteorological data in the summer seasons of 2013 and 2014, as inputs for the proposed prediction model using Extreme Gradient Boosting (XGBoost). The optimized XGBoost model showed a satisfactory prediction performance for the on/off state in the testing dataset, with an F1 score of 0.80 and an Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.845.
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Birnbaum, Michael H., and Lucy Wan. "MARTER: Markov True and Error model of drifting parameters." Judgment and Decision Making 15, no. 1 (2020): 47–73. http://dx.doi.org/10.1017/s1930297500006902.
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AbstractThis paper describes a theory of the variability of risky choice that describes empirical properties of choice data, including sequential effects and systematic violations of response independence. The Markov True and Error (MARTER) model represents the formation and fluctuation of true preferences produced by stochastic variation of parameters over time, which produces changing true preference patterns. This model includes a probabilistic association between true preferences and overt responses due to random error. Computer programs have been developed to simulate data according to this model, to fit data to the TE model, and to test and analyze violations of iid (independent and identical distributions) that are predicted by the model. Data simulated from MARTER models show properties that are characteristic of real data, including violations of iid similar to those observed in previous empirical research. This paper also illustrates how methods based on analysis of binary response proportions do not and in many cases cannot correctly diagnose what model was used to generate the data. The MARTER model is extremely general and neutral with respect to models of risky decision making. For example, the transitive transfer of attention exchange (TAX) model and intransitive Lexicographic Semiorder (LS) models can both be represented as special cases of MARTER, and they can be tested against each other, even when binary choice proportions cannot discriminate which model was used to simulate the data. Software to simulate data according to this model, and to fit data to this model, to test this model, and to compare special case theories are included or linked to this article.
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Eschenauer, H., G. Kneppe, and K. H. Stenvers. "Deterministic and Stochastic Multiobjective Optimization of Beam and Shell Structures." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 1 (1986): 31–37. http://dx.doi.org/10.1115/1.3260780.
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This paper presents application examples of vector optimization (Pareto-optimization) especially for the design of beam and shell components. A deterministic as well as a stochastic optimization model is formulated. Stochastic models will be important in the future because many factors in fabrication and operation are of random nature (fabrication tolerances, loads, etc.). For the special case of a sandwich beam these influences are demonstrated by means of a sensitivity analysis. For all examples presented (shell-flange structure, sandwich beam, panel-truss structure) the solution of the optimization problem results from transformation into scalar substitute problems by means of preference functions. In the examples, various optimization algorithms (e.g., sequential linear programming) were tested.
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DeSarbo, Wayne S., Geert De Soete, and Jehoshua Eliashberg. "A new stochastic multidimensional unfolding model for the investigation of paired comparison consumer preference/choice data." Journal of Economic Psychology 8, no. 3 (1987): 357–84. http://dx.doi.org/10.1016/0167-4870(87)90029-8.
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Chakraborty, Arya. "Perceptron Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 437–47. http://dx.doi.org/10.22214/ijraset.2023.49044.
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Abstract: While multivariate logistic regression classifiers are a great way of implementing collaborative filtering - a method of making automatic predictions about the interests of a user by collecting preferences or taste information from many other users, we can also achieve similar results using neural networks. A recommender system is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. A perceptron or a neural network is a machine learning model designed for fitting complex datasets using backpropagation and gradient descent. When coupled with advanced optimization techniques, the model may prove to be a great substitute for classical logistic classifiers. The optimizations include feature scaling, mean normalization, regularization, hyperparameter tuning and using stochastic/mini-batch gradient descent instead of regular gradient descent. In this use case, we will use the perceptron in the recommender system to fit the parameters i.e., the data from a multitude of users and use it to predict the preference/interest of a particular user
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Medina Guevara, María Guadalupe, Héctor Vargas Rodríguez, Pedro Basilio Espinoza Padilla, and José Luis Gozález Solís. "Evolution of Electoral Preferences for a Regime of Three Political Parties." Discrete Dynamics in Nature and Society 2018 (October 24, 2018): 1–9. http://dx.doi.org/10.1155/2018/2989851.
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In this article, we use a discrete system to study the opinion dynamics regarding the electoral preferences of a nontendentious group of agents. To measure the level of preference, a continuous opinion space is used, in which the preference (opinion) can evolve from any political option, to any other; for a regime of three parties, a circle is the convenient space. To model a nonbiased society, new agents are considered. Besides their opinion, they have a new attribute: an individual iterative monoparametric map that imitates a process of internal reflection, allowing them to update their opinion in their own way. These iterative maps introduce six fixed points on the opinion space; the points’ stability depends on the sign of the parameter. When the latter is positive, three attractors are identified with political options, while the repulsors are identified with the antioptions (preferences diametrically opposed to each political choice). In this new model, pairs of agents interact only if their respective opinions are alike; a positive number called confidence bound is introduced with this purpose; if opinions are similar, they update their opinion considering each other’s opinion, while if they are not alike, each agent updates her opinion considering only her individual map. In addition, agents give a certain level of trust (weight) to other agent’s opinions; this results in a positive stochastic matrix of weights which models the social network. The model can be reduced to a pair of coupled nonlinear difference equations, making extracting analytical results possible: a theorem on the conditions governing the existence of consensus in this new artificial society. Some numerical simulations are provided, exemplifying the analytical results.
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Ming, Jian, Azamat Rajapov, and Saidjahon Hayrutdinov. "Three-Echelon Supply Chain Contractual Coordination with Loss-Averse Multiple Retailer Preference." Mathematical Problems in Engineering 2019 (September 30, 2019): 1–11. http://dx.doi.org/10.1155/2019/4927302.
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In this paper, we propose a supply chain contract model aimed to coordinate a three-echelon supply chain, which is based on the revenue-sharing allocation with loss-aversion preference. We consider a three-echelon supply chain consisting of a risk-neutral manufacturer, a risk-neutral distributor, and loss-averse multiple retailers. To address this model, we consider a shortage product produced and sold within a single period in the stochastic market. The model allows the system efficiency to be achieved as well as it will improve the profits of all supply chain members by tuning the contract parameters. We used the expected utility function to describe the loss-aversion member’s influence coefficient. The decisions of chain members under different conditions are studied by simulation analyses. The paper also analysed the relationship between different revenue-sharing coefficient combinations with multiple retailers in the supply chain system. Furthermore, the study has addressed the supply chain coordination decision bias in the centralized and decentralized systems.
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Xie, Y. L., and G. H. Huang. "An optimization model for water resources allocation risk analysis under uncertainty." Journal of Hydroinformatics 16, no. 1 (2013): 144–64. http://dx.doi.org/10.2166/hydro.2013.239.
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In order to deal with the risk of low system stability and unbalanced allocation during water resources management under uncertainties, a risk-averse inexact two-stage stochastic programming model is developed for supporting regional water resources management. Methods of interval-parameter programming and conditional value-at-risk model are introduced into a two-stage stochastic programming framework, thus the developed model can tackle uncertainties described in terms of interval values and probability distributions. In addition, the risk-aversion method was incorporated into the objective function of the water allocation model to reflect the preference of decision makers, such that the trade-off between system economy and extreme expected loss under different water inflows could be analyzed. The proposed model was applied to handle a water resources allocation problem. Several scenarios corresponding to different river inflows and risk levels were examined. The results demonstrated that the model could effectively communicate the interval-format and random uncertainties, and risk aversion into optimization process, and generate inexact solutions that contain a spectrum of water resources allocation options. They could be helpful for seeking cost-effective management strategies under uncertainties. Moreover, it could reflect the decision maker's attitude toward risk aversion, and generate potential options for decision analysis in different system-reliability levels.
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Ma, Junhai, Weiya Di, and Hao Ren. "Complexity Dynamic Character Analysis of Retailers Based on the Share of Stochastic Demand and Service." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1382689.
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Apart from the price fluctuation, the retailers’ service level becomes another key factor that affects the market demand. This paper depicts a modified price and demand game model based on the stochastic demand and the retailer’s service level which influences the market demand decided by customers’ preference, while the market demand is stochastic in this model. We explore how the price adjustment speed affects the stability of the supply chain system with respect to service level and stochastic demand. The dynamic behavior of the system is researched by simulation and the stability domain and the bifurcation phenomenon are shown clearly. The largest Lyapunov exponent and the chaotic attractor are also given to confirm the chaotic characteristic of the system. The simulation results indicate that relatively small price adjustment speed may maintain the system at stable state. With the price adjustment speed gradually increasing, the price system gets unstable and finally becomes chaotic. This chaotic phenomenon will perturb the product market and this phenomenon should be controlled to keep the system stay in the stable region. So the chaos control is done and the chaos can be controlled completely. The conclusion makes significant contribution to the system referring to the price fluctuation based on the service level and stochastic demand.
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Imagawa, Hikaru, Hom Bahadur Rijal, and Masanori Shukuya. "Study on the Stochastic Model for Excessive Air Conditioning Use in Japanese Dwellings." Journal of the Institute of Engineering 15, no. 3 (2020): 153–58. http://dx.doi.org/10.3126/jie.v15i3.32173.
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Thermal adjustment is one of the most important behaviours. In daily life, we use some behavioural adjustments. It is not always obtained the optimal condition by the thermal adjustments. For example, it happens to use excessive air conditioning for thermal adjustments. Especially, the excessive air conditioning use is important issue not only for the human comfort but also for the energy use. The occupant behaviour stochastic model was proposed by some previous researches around the world. However, these models show only the state of the occupant behaviour, and thus the excessive adjustments are not known. The objective of this research is to clarify quantitatively the excessive thermal adjustments which are happened to be in the stochastic model. The thermal measurement and occupant behaviour survey was conducted in 120 dwellings during 4 years period in Kanto region of Japan. The number of samples collected was 36,114. We analysed the occupant behaviour stochastic model of the excessive cooling and heating use. The thermal sensation vote (TSV) and the thermal preference vote (TP) were used to classify the excessive air conditioning use. The proportion of excessive cooling use were increased when outdoor air temperature was increased. In addition, there were no significant difference between TSV and TP for calculating the proportion of the excessive air conditioning use in Japanese dwellings. These models are useful to understand the possibility of the energy saving. In the future, the model will be implemented in the building thermal simulation to predict the energy use in building.
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Aguiar, Victor H., Maria Jose Boccardi, Nail Kashaev, and Jeongbin Kim. "Random utility and limited consideration." Quantitative Economics 14, no. 1 (2023): 71–116. http://dx.doi.org/10.3982/qe1861.
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The random utility model (RUM, McFadden and Richter (1990)) has been the standard tool to describe the behavior of a population of decision makers. RUM assumes that decision makers behave as if they maximize a rational preference over a choice set. This assumption may fail when consideration of all alternatives is costly. We provide a theoretical and statistical framework that unifies well‐known models of random (limited) consideration and generalizes them to allow for preference heterogeneity. We apply this methodology in a novel stochastic choice data set that we collected in a large‐scale online experiment. Our data set is unique since it exhibits both choice set and (attention) frame variation. We run a statistical survival race between competing models of random consideration and RUM. We find that RUM cannot explain the population behavior. In contrast, we cannot reject the hypothesis that decision makers behave according to the logit attention model (Brady and Rehbeck (2016)).