Academic literature on the topic 'Stochastic Preference Model'
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Journal articles on the topic "Stochastic Preference Model"
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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 (January 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 (May 1, 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 (November 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 (August 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 (August 30, 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.
Dissertations / Theses on the topic "Stochastic Preference Model"
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Siddartha, Y. R. "Learning Tournament Solutions from Preference-based Multi-Armed Bandits." Thesis, 2017. https://etd.iisc.ac.in/handle/2005/4698.
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We consider the dueling bandits problem, a sequential decision task where the goal is to learn to pick `good' arms out of an available pool by actively querying for and observing relative preferences between selected pairs of arms. The noisy observed preferences are assumed to be generated by a fixed but unknown stochastic preference model. Motivated by applications in information retrieval, e-commerce, crowdsourcing, etc., a number of bandit algorithms have been proposed in recent years for this task. These have mostly addressed restricted settings wherein the underlying preference model satisfies various structural assumptions. such as being based on a random utility function or a feature-space embedding, or satisfying transitivity or sparsity properties, or at least possessing a Condorcet winner { a single `best ‘arm that is preferred to all others. In seeking to move beyond such restricted settings, there has been a recent shift towards alternative notions of `good' arms (including Borda, Copeland and von Neumann winners).
In this work, we extend the dueling bandits problem by adopting, as the desired target set of good (or 'winning') arms, a number of tournament solutions that have been proposed in social choice and voting theory literature as embodying principled and natural criteria for identifying good arms based on preference relations. We then propose a family of upper confidence bound (UCB) based dueling bandit algorithms that learn to play winning arms from several popular tournament solutions, the top cycle, uncovered set, Banks set and Copeland set.
We derive these algorithms by first proposing a generic UCB-based framework algorithm that can be instantiated for different tournament solutions by means of appropriately designed `selection procedures. We show sufficiency conditions for the resulting dueling bandit algorithms to satisfy distribution-dependent, horizon-free bounds on natural regret measures defined w.r.t. the target tournament solutions. In contrast to previous work, these bounds do not require restrictive structural assumptions on the preference model and hold for a range of different tournament solutions.
We develop selection procedures that satisfy the sufficiency conditions for a number of popular tournament solutions, yielding dueling bandit algorithms UCB-TC, UCB-UC, UCB-BA and UCB-CO for the top cycle, uncovered set, Banks set and the Copeland set respectively.
The O_K2 ln T g2 _ bounds we derive are optimal in their dependence on the time horizon T. We show that for all of these tournament solutions, the distribution-dependent `margin' g is lower bounded by the separation or the relative advantage of top cycle arms over non-top cycle arms. While O(K ln T) bounds are known for Condorcet models, our O(K2 ln T) bounds extend to more general models as well as other tournament solutions. We empirically validate these claims and evaluate the proposed algorithms, comparing them to dueling bandit algorithms RUCB, SAVAGE and BTMB over synthetic and real-world preference models. We show that the UCB-TS algorithms perform competitively over models that possess a Condorcet winner, but out-perform the other algorithms over more general models that do not possess a Condorcet winner.
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Kučera, Adam. "Ceny aktiv v DSGE modelu s finančními frikcemi." Master's thesis, 2015. http://www.nusl.cz/ntk/nusl-333295.
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The thesis examines the ability of DSGE models with financial elements to explain financial asset prices. A neoclassical macroeconomic model is used, in- cluding a financial constraint in the form of a restriction on external financing. Moreover, the strictness of the restriction is affected by an external financial shock. It is shown, that the combination of the financial constraint and the fi- nancial shock contributes to understanding of the macroeconomic fluctuations, asset price dynamics and their mutual impact. The calibration for the United States demonstrates that the financial shock is an important source of the as- set price volatility. Contrary, when calibrated to the Czech data, the financial shock generates only moderate asset price volatility, as a consequence of a posi- tive correlation with the productivity shock. To address the issue, the model is further extended by a sector of financial intermediaries and a preference shock related to the risk-aversion of economic subjects, and the extension is shown to improve the result.
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Chammoun, Christopher James. "Production Model and Consumer Preferences for Texas Pecans." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11501.
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High prices in any industry, agricultural especially, tend to spur new investment opportunities. Recent prices for pecans have been high relative to their historical pattern, suggesting investment opportunities for pecans. Prior to any investment, the investor needs to know what products consumers are demanding and how profitable it is to grow those products. This study assessed Texas consumers' preferences for pecan products and the profitability of growing pecans in the central Texas region.
A choice experiment was conducted amongst Texas consumers to reveal consumers' preferences and determine their willingness-to-pay for the attributes comprising pecan products. A stochastic production model was formulated to determine the profitability of three different types of pecan orchards: a native orchard with no irrigation, an improved varieties orchard with irrigation, and an improved varieties orchard without irrigation.
Results from the choice experiment indicated that consumers preferred large size pecans, native variety pecans, pecan halves, United States-grown pecans, and Texas-grown pecans. The choice experiment also found that consumers were heterogeneous in their preferences for all attributes except pecan variety and U.S. origin. Results from the stochastic production model indicated that the most profitable pecan orchard in central Texas was the irrigated improved orchard.
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Shaari, Mohamad Hasni. "Analyzing Bank Negara Malaysia's Behaviour in Formulating Monetary Policy: An Empirical Approach." Phd thesis, 2008. http://hdl.handle.net/1885/49317.
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Existing studies which analyze a central banks' behaviour in formulating monetary policy, are mostly concentrated on the experience of developed economies. However, developing economies face a different institutional structure, as well as a different set of constraints and shocks, hence, it would be interesting to analyze how a central bank under this different economic environment performs its monetary policy mandate. This thesis looks at the behaviour of Bank Negara Malaysia (The Central Bank of Malaysia) in formulating monetary policy in Malaysia during the period 1975-2005. ¶ There are four major aspects of Bank Negara Malaysia's (BNM) policy behaviour that are examined in this thesis. Firstly, with regard to its policy reaction function - does BNM set interest rates according to some form of policy rule or purely on a discretionary manner? After identifying the systematic component of its policy action, we try to establish BNM's policy objectives and preferences. This will help in understanding the rationale behind its policy action. The third aspect is whether BNM's policy behaviour changes over time. Lastly, with the use of an estimated Dynamic Stochastic General Equilibrium (DSGE) model, we conduct some policy experiments to observe the possible impact on the Malaysia's economic outcomes were BNM to behave differently to what we envisaged its policy behaviour has been.
Books on the topic "Stochastic Preference Model"
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Pemberton, James. A stochastic macro model with endogenons preferences. Reading: University of Reading. Department of Economics, 1990.
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Anderson, Gordon J. Do preferences and-or skills explain gender based differences in learning? Toronto, Ont: University of Toronto, Department of Economics and Institute for Policy Analysis, 1994.
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Wagner, Udo. Vollstochastische Kaufverhaltensmodelle: Ihr Beitrag zur Analyse realer Märkte. Königstein/Ts: Hain, 1985.
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Back, Kerry E. Asset Pricing and Portfolio Choice Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.001.0001.
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This book is intended as a textbook for asset pricing theory courses at the Ph.D. or Masters in Quantitative Finance level and as a reference for financial researchers. The first two parts of the book explain portfolio choice and asset pricing theory in single‐period, discrete‐time, and continuous‐time models. For valuation, the focus throughout is on stochastic discount factors and their properties. Traditional factor models, including the CAPM, are related to or derived from stochastic discount factors. A chapter on stochastic calculus provides the needed tools for analyzing continuous‐time models. A chapter on “ex‐plaining puzzles” and the last two parts of the book provide introductions to a number of current topics in asset pricing research, including rare disasters, long‐run risks, external and internal habits, real options, corporate financing options, asymmetric and incomplete information, heterogeneous beliefs, and non‐expected‐utility preferences. Each chapter includes a “Notes and References” section and exercises for students.
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Francis, Elaine J. Gradient Acceptability and Linguistic Theory. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192898944.001.0001.
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In Gradient Acceptability and Linguistic Theory, Elaine J. Francis examines a challenging problem at the intersection of theoretical linguistics and the psychology of language: the problem of interpreting gradient judgments of sentence acceptability in relation to theories of grammatical knowledge. This problem is important because acceptability judgments constitute the primary source of data on which such theories have been built, despite being susceptible to various extra-grammatical factors. Through a review of experimental and corpus-based research on a variety of syntactic phenomena and an in-depth examination of two case studies, Francis argues for two main positions. The first is that converging evidence from online comprehension tasks, elicited production tasks, and corpora of naturally occurring discourse can help determine the sources of variation in acceptability judgments and narrow down the range of plausible theoretical interpretations. The second is that the interpretation of judgment data depends crucially on one’s theoretical commitments and assumptions, especially with respect to the nature of the syntax–semantics interface and the choice of either a categorical or a gradient notion of grammaticality. The theoretical frameworks considered in this book include derivational theories (e.g. Minimalism, Principles and Parameters), constraint-based theories (e.g. Sign-Based Construction Grammar, Simpler Syntax), competition-based theories (e.g. Stochastic Optimality Theory, Decathlon Model), and usage-based approaches. While showing that acceptability judgment data are typically compatible with the assumptions of various theoretical frameworks, Francis argues that some gradient phenomena are best captured within frameworks that permit soft constraints—non-categorical grammatical constraints that encode the conventional preferences of language users.
Book chapters on the topic "Stochastic Preference Model"
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Chambers, Robert G. "Preferences and Production under Uncertainty." In Competitive Agents in Certain and Uncertain Markets, 235–68. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190063016.003.0008.
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The Arrow-Savage-Debreu formalism (state space, consequence space, acts) for modelling a stochastic decision is introduced. Preferences over stochastic outcomes framed as maps (acts) from the state space to the consequence space are studied and related to nonstochastic preference structures. Distance function representations of preferences are developed and their superdifferential correspondences are shown to define subjective probability measures. Structural restrictions including uncertainty aversion, constant absolute uncertainty aversion, and constant relative uncertainty aversion are examined and related to parallel restrictions for nonstochastic preference or production structures. A model of a stochastic technology that has the nonstochastic production model as a special case is introduced, and distance function representations of it are discussed. Structural assumptions on the stochastic technology are discussed.
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Pavlov, Yuri P., and Rumen D. Andreev. "Preferences, Utility, and Stochastic Approximation." In Encyclopedia of Information Science and Technology, Fourth Edition, 2188–99. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch190.
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A complex system with human participation like “human-process” is characterized with active assistance of the human in the determination of its objective and in decision-taking during its development. The construction of a mathematically grounded model of such a system is faced with the problem of shortage of mathematical precise information that presents the human activity. A solution of this problem is to seek expression of different aspects of the complex system through description of the expert's preferences as an element of the system. The presentation of human preferences analytically with utility functions is an approach for their mathematical description. The objective of the article is to present an innovative approach to value driven modeling of management that bases on preference-oriented decision making. It is described a decision technology that realizes measurement of human's preferences as analytic utility function. The utility theory and stochastic approximation are possible solution of this problem that results in a value-based approach to modeling of complex systems.
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Pavlov, Yuri P., and Rumen D. Andreev. "Preferences, Utility, and Stochastic Approximation." In Advanced Methodologies and Technologies in Business Operations and Management, 734–47. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7362-3.ch055.
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A complex system with human participation like “human-process” is characterized with active assistance of the human in the determination of its objective and in decision-taking during its development. The construction of a mathematically grounded model of such a system is faced with the problem of shortage of mathematically precise information that presents the human activity. A solution of this problem is to seek expression of different aspects of the complex system through description of the expert's preferences as an element of the system. The presentation of human preferences analytically with utility functions is an approach for their mathematical description. The objective of the chapter is to present an innovative approach to value-driven modeling of management based on preference-oriented decision making. A decision technology that realizes measurement of human's preferences as an analytic utility function is described. The utility theory and stochastic approximation are possible solutions for this problem that results in a value-based approach to modeling of complex systems.
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Jorde, Klaus, Matthias Schneider, and Frank Zöllner. "Invited lecture: Analysis of instream habitat quality – Preference functions and fuzzy models." In Stochastic Hydraulics 2000, 671–80. CRC Press, 2020. http://dx.doi.org/10.1201/9781003078630-87.
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Falmagne, Jean-Claude, M. Regenwetter, and B. Grofman. "A Stochastic Model for the Evolution of Preferences." In Choice, Decision, and Measurement: Essays in Honor of R. Duncan Luce, 111–29. Routledge, 2019. http://dx.doi.org/10.4324/9781315789408-8.
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Herbst, Edward P., and Frank Schorfheide. "DSGE Modeling." In Bayesian Estimation of DSGE Models. Princeton University Press, 2015. http://dx.doi.org/10.23943/princeton/9780691161082.003.0001.
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This chapter discusses how dynamic stochastic general equilibrium (DSGE) models are now widely used by academics to conduct empirical research macroeconomics, as well as by central banks to interpret the current state of the economy, to analyze the impact of changes in monetary or fiscal policy, and to generate predictions for key macroeconomic aggregates. With particular emphasis on the Bayesian estimation of DSGE models, the chapter shows how the DSGE model generates a likelihood function—a joint probability distribution for the endogenous model variables such as output, consumption, investment, and inflation that depends on the structural parameters of the model. These structural parameters characterize agents' preferences, production technologies, and the law of motion of the exogenous shocks.
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Gollier, Christian. "A Theory of the Decreasing Term Structure of Discount Rates." In Pricing the Planet's Future. Princeton University Press, 2012. http://dx.doi.org/10.23943/princeton/9780691148762.003.0008.
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This chapter aims to provide a unified theoretical foundation to the term structure of discount rates. To do this the chapter develops a benchmark model based on two assumptions: individual preferences toward risk, and the nature of the uncertainty over economic growth. Previously, it was shown that constant relative risk aversion, combined with a random walk for the growth of log consumption, yields a flat term structure for efficient discount rates. In this chapter, these two assumptions are relaxed by using a stochastic dominance approach. Stochastic models of economic growth with mean-reversion, Markov switches, and parametric uncertainty all exhibit some forms of positive statistical dependence of successive growth rates. Because this tends to magnify the long-term risk, it is the driving force of the decreasing nature of the term structure.
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Pavlov, Yuri P., Rumen D. Andreev, Valentina T. Terzieva, Katia A. Todorova, and Petia I. Kademova-Katzarova. "Preferences, Utility, Value-Driven Modeling, and Decision Support." In Encyclopedia of Organizational Knowledge, Administration, and Technology, 262–78. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3473-1.ch021.
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The inclusion of organizational knowledge in the process of modeling of complex systems is an essential step in the decision-making. This needs normative description of the system structure in terms of objective and sub-objectives. In phenomena with human participation, the emphasis is on the cardinal significance as preferences. The approach to modeling such information is the utility theory. This chapter demonstrates a value-driven approach and presents two mathematical models of complex processes. The normative approach is based on stochastic-approximation methods for analytical representation of qualitative preferences. The approach is illustrated in two practical oriented applications. The first one represents modeling of exhaustible timber production by reflecting socio-economic and forest-related ecological factors. The second one concerns the determining of the optimal usage of active and passive technology-based resources in classroom teaching. The approach permits mathematical modeling and even control and prescriptive decision support in complex processes.
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Papatsimpa, C., and Jean-Paul Linnartz. "Personalized Circadian Light: A Digital Siblings Approach." In Intelligent Environments 2021. IOS Press, 2021. http://dx.doi.org/10.3233/aise210082.
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In this paper, we introduce a human-centric lighting control system optimized to support sleep and circadian coordination. We present an approach to optimize lighting that combines a “digital siblings” approach, i.e., a stochastic extension of a digital twin. It estimates and optimizes parameters in experimentally validated models of circadian and sleep regulation with a novel optimization algorithm for optimal timing of light exposure. We acknowledge that people have varying preferences for the lighting levels throughout the day based on their personal preferences and schedules and explicitly include this as a key parameter in our optimization strategy. Our results show that with the suggested lighting schedules, alignment of circadian rhythmicity to the desired sleep-wake schedule can be achieved with minimal disruption to people’s daily lives.
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Vemić, Milan B. "A Further Look at Working Capital Optimization in Medium-Sized Firms." In Optimal Management Strategies in Small and Medium Enterprises, 144–77. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1949-2.ch008.
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SMEs battling with financial volatility require financial and business ambidexterity. To them working capital optimization is one partial solution to improve performance. Examined concepts will not develop a spectacular model but rather quantitatively - qualitatively upgrade existing theories. The treatise will demonstrate that in stochastic working capital decisions important are varieties of alternatives. A structural model of working capital optimization is developed with practical examples for financial decision making under uncertainty. Emphasis will be placed on structuring management problems with the analysis of preferences using probability tools. Additional light is shed on coupling static and dynamic indicators, systemically approaching inventory and cash management, sales and purchasing, costs and profitability. Working capital optimization can contribute to management of financial risk and have an overall impact in medium sized enterprises. Conclusions and recommendations will aim to contribute to the overall body of knowledge on optimized financial decision making.
Conference papers on the topic "Stochastic Preference Model"
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Slon, Christopher, and Vijitashwa Pandey. "Reconciling Co-Evolving Engineering and Customer Requirements With a Looped Bayesian Model." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97524.
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Abstract Engineering and manufacturing abilities of firms evolve with every passing year and so do the preferences of the customers buying their products. Reconciling this coevolution is essential to staying competitive in the marketplace. In this paper, we provide a looped Bayesian framework to accomplish this so that designs can evolve as engineering capabilities increase and customer preferences change. We begin with an approach to incorporating the voice of the customer through the multi-attribute utility function, the core of decision-based design. We consider the utility to be a stochastic function governed by shape parameters that are random variables. Typically, a representative preference or utility function is used or the function is aggregated over many decision makers and regarded as a deterministic function of specified shape parameters. In our approach, the shape parameters represent the stochastic nature of preference behavior either due to variation in a decision maker’s state of mind from one decision to another, or due to a multiplicity of decision makers. The novelty of this approach is in taking a Bayesian perspective on the stochastic utility function. We consider the utility distribution in the design phase as a prior distribution and we update the prior to a posterior with feedback on the actual product in production. The method is valuable in providing a means to improve the level of informativeness of the design level utility function for adjustments to the design or for the next design revision in the cycle of continuous improvement. We present our approach on a real-life assembly problem in an automotive manufacturing floor.
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Leahu, Haralambie, Michael Kaisers, and Tim Baarslag. "Automated Negotiation with Gaussian Process-based Utility Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/60.
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Designing agents that can efficiently learn and integrate user's preferences into decision making processes is a key challenge in automated negotiation. While accurate knowledge of user preferences is highly desirable, eliciting the necessary information might be rather costly, since frequent user interactions may cause inconvenience. Therefore, efficient elicitation strategies (minimizing elicitation costs) for inferring relevant information are critical. We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate it into a (belief) Markov Decision Process paradigm which formalizes automated negotiation processes with incomplete information. Our utility model, which naturally maps ordinal preferences (inferred from the user) into (random) utility values (with the randomness reflecting the underlying uncertainty), provides the basic quantitative modeling ingredient for automated (agent-based) negotiation.
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Shin, Jaekwan, and Scott Ferguson. "Product Line Design Search Considering Reliability and Robustness Under Uncertainty in Discrete Choice Methods." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59535.
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Point-estimates of part-worth values in customer preference models have been used in market-based product design under the simplying assumption that customer preferences can be treated as deterministic. However, customer preferences are not only inherently stochastic, but are also statistical estimates that exhibit random errors in model formulation and estimation. Ignoring uncertainty in customer preferences and variability in estimates has caused concern about the reliability and robustness of an optimal product design solution. This study quantitatively defines reliability and robustness of a product design under uncertainty when using discrete choice methods. These metrics are then integrated into a multi-objective optimization problem to search for product line solutions considering reliability and robustness under uncertainty when using discrete choice methods.
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Hason, B., and Y. Y. Zeevi. "Interaction between lateral and eccentricity-dependent preferences in saccadic responses." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.fq3.
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Eye movement responses to dual targets provide insight into mechanisms of visual information acquisition. It was previously shown1 that symmetric point targets stimulating both hemispheres require extra processing time compared to either a single or a dual unidirectional stimulus. These so-called bifurcating point stimuli reveal the existence of preferred lateral direction. We substantiate and refine these results incorporating the effects of eccentricity. In the case of a unidirectional dual target an absolute preference exists for the target closer to the fovea. Responses to asymmetric bihemifield stimuli show that the lateral direction preference is contingent on target eccentricities. Being close to the fovea enchances, whereas being away from the fovea inhibits, the preference level of the point target. If one of the points is delayed, the overall lateral, spatial, and temporal preferences determine the response direction. At large interpoint delays for which the second point is still the preferred one, the response time measured with respect to the onset of this target is much shorter than the average latency. We propose a stochastic model accounting for the temporal aspect of our observations. Each point stimulus is processed in a separate channel in two stages, the first providing a general intermediate output utilizable in the processing of any additional input. Interaction between opposite hemispheric channels results in longer response duration due to the second stage processing time. The model reproduces the timing of events in responses to dual targets.
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Chkhartishvili, Alexander. "Stochastic Preferences Model." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247771.
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Dai, Zhihuang, Michael J. Scott, and Zissimos P. Mourelatos. "Incorporating Epistemic Uncertainty in Robust Design." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48713.
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There are two sorts of uncertainty inherent in engineering design, random uncertainty and epistemic uncertainty. Random, or stochastic, uncertainty deals with the randomness or predictability of an event. It is well understood, easily modelled using classical probability, and ideal for such uncertainties as variations in manufacturing processes or material properties. Epistemic uncertainty deals with our lack of knowledge, our lack of information, and our own and others’ subjectivity concerning design parameters. While there are many methods to incorporate random uncertainty in a design process, there are fewer that consider epistemic uncertainty. There are fewer still that attempt to incorporate both sorts of uncertainty, and those that do usually attempt to model both sorts using the same uncertainty model. Two methods, a range method and a fuzzy sets approach, are proposed to achieve designs that are robust to both epistemic uncertainty and random uncertainty. Both methods incorporate preference aggregation methods to achieve more appropriate trade-offs between performance and variability when considering both sorts of uncertainty. The proposed models for epistemic uncertainty are combined with existing models for stochastic uncertainty in a two-step process. An illustrative example incorporating subjectivity concerning design parameters is presented.
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Durocher, Loke, Panagiotis Karras, Andreas Pavlogiannis, and Josef Tkadlec. "Invasion Dynamics in the Biased Voter Process." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/38.
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The voter process is a classic stochastic process that models the invasion of a mutant trait A (e.g., a new opinion, belief, legend, genetic mutation, magnetic spin) in a population of agents (e.g., people, genes, particles) who share a resident trait B, spread over the nodes of a graph. An agent may adopt the trait of one of its neighbors at any time, while the invasion bias r quantifies the stochastic preference towards (r>1) or against (r<1) adopting A over B. Success is measured in terms of the fixation probability, i.e., the probability that eventually all agents have adopted the mutant trait A. In this paper we study the problem of fixation probability maximization under this model: given a budget k, find a set of k agents to initiate the invasion that maximizes the fixation probability. We show that the problem is NP-hard for both regimes r>1 and r<1, while the latter case is also inapproximable within any multiplicative factor that is independent of r. On the positive side, we show that when r>1, the optimization function is submodular and thus can be greedily approximated within a factor 1-1/e. An experimental evaluation of some proposed heuristics corroborates our results.
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Pandey, Vijitashwa, and Zissimos P. Mourelatos. "Decision-Based Design Using Time-Varying Preferences Represented by Stochastic Processes." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70558.
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Soliciting and expressing the preferences of a decision maker in engineering design is critical. In general, the preferences vary through time, complicating the design of engineering systems. In this article, we propose that if parameterized utility functions are used to model the preferences, the time-varying characteristics of the parameters can provide valuable information on the likely decisions the decision maker can make at a future time. To model the time-dependent uncertainty in preferences, we use parameterized utility functions with the parameters characterized by stochastic processes and demonstrate how the design process is affected by stationarity properties of the random parameters. We work in the normative utility theoretic domain and show a property of the multiplicative utility function that allows us to use the common Black-Scholes-Merton options pricing model from finance, to account for variability in preferences with time. Finally, we discuss how to modify the design process so that optimal products are ready when there is a future need for them. The applicability of our approach is demonstrated using a cell phone example.
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Haladuick, Shane, and Markus R. Dann. "Risk Based Inspection Planning for Deteriorating Pressure Vessels." In ASME 2016 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/pvp2016-63138.
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Pressure vessels are subject to deterioration processes, such as corrosion and fatigue. If left unchecked these deterioration processes can lead to failure; therefore, inspections and repairs are performed to mitigate this risk. Oil and gas facilities often have regular scheduled shutdown periods during which many components, including the pressure vessels, are disassembled, inspected, and repaired or replaced if necessary. The objective of this paper is to perform a decision analysis to determine the best course of action for an operator to follow after a pressure vessel is inspected during a shutdown period. If the pressure vessel is inspected and an unexpectedly deep corrosion defect is detected an operator has two options: schedule a repair for the next shutdown period, or perform an immediate unscheduled repair. A scheduled repair is the preferred option as it gives the decision maker lead time to accommodate the added labour and budgetary requirements. This preference is accounted for by a higher cost of immediate unscheduled repairs relative to the cost of a scheduled repair at the next shutdown. Depending on the severity of deterioration either option could present the optimal course of action. In this framework the decision that leads to the minimum expected cost is selected. A stochastic gamma process was used to model the future deterioration growth using the historical inspection data, considering the measurement error and uncertain initial wall thickness, to determine the probability of pressure vessel failure. The decision analysis framework can be used to aid decision makers in deciding when a repair or replacement action should be performed. This method can be used in real time decision making to inform the decision maker immediately post inspection. A numerical example of a corroding pressure vessel illustrates the method.
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Kretzschmar, Florian, Matthias Beggiato, and Alois Pichler. "Detection of Discomfort in Autonomous Driving via Stochastic Approximation." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002437.
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One of the most important goals in the field of autonomous driving development is to make the experience for the passenger as pleasant and comfortable as possible. In addition to traditional influence factors on passenger comfort, new aspects arise due to the transfer of control from the human to the vehicle. Some of these are apparent safety, motion sickness, user preferences regarding driving style and information needs. Ideally, the vehicle and the passenger should form a team, whereby the vehicle should be able to detect and predict situations of discomfort in real time and take measures accordingly. This requires not only the continuous monitoring of the passengers state but also the implementation of adequate mathematical models. To investigate how this teaming of human and automated agents can be shaped in the most effective way is a key topic of the Collaborative Research Center “Hybrid Societies (https://hybrid-societies.org/). In this framework, driving simulator data from the previous project “KomfoPilot” (https://bit.ly/komfopilot) is re-analyzed using new mathematical models. The participants in the study completed several automated drives and reported continuously situations of discomfort using a handset control. Sensor data was collected simultaneously using eye tracking glasses, a smart band, seat pressure sensors and video cameras for motion and face tracking. While pupil diameter, heart rate, interblink intervals, skin conductance and head movement have already been identified as potential single indicators of discomfort, it is now necessary to integrate these and other findings of the project into a functional multivariate model. In this paper, we investigate how such a model can be shaped to offer high prediction accuracy and viable practical implementation. The first important question – which arises from the heterogeneity of the participants – is whether to work with training data on an individual or aggregated level. We compare both possibilities by applying techniques from the field of stochastic approximation for clustering of the chosen training set and subsequent classification of the test data. In the case of an individual model for each participant, we furthermore divide the participants into subgroups and analyze whether there is a connection between the physiological reactions of a passenger and his/her demographic characteristics and driving experience. Finally, we discuss the potential of our method as a reliable prediction model as well as implications for future driving simulator studies and related research.