Academic literature on the topic 'Nonlinear preference function'
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Journal articles on the topic "Nonlinear preference function"
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Ajaz, Taufeeq. "Nonlinear Reaction functions: Evidence from India." Journal of Central Banking Theory and Practice 8, no. 1 (January 1, 2019): 111–32. http://dx.doi.org/10.2478/jcbtp-2019-0006.
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Abstract This paper uses time-series data from India and tests for asymmetries in policy preferences of the Reserve Bank of India (the Central Bank of India, hereafter RBI). The results show evidence in favour of preference asymmetries in monetary policy reaction function in India and hence nonlinearities in the Taylor-rule. Evidence of both recession avoidance preference (RAP) as well as inflation avoidance preference (IAP) is established. And it is found that RAP is dominant over IAP, thus confirming nonlinearities in reaction function which in the present case turns out to be concave in inflation and output gap. Further, the results indicate preference asymmetries in both the objectives. The coefficient weights to positive and negative inflation and output gap differ over long time horizons thus confirming asymmetric policy preferences. Specifically the RBI seems to be more averse to a negative output gap (contraction) as compared to an equal positive gap. In addition, the RBI appears to be more averse to a positive inflation gap as compared to an equal negative gap.
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Khan, Mohammad Faisal, Md Gulzarul Hasan, Abdul Quddoos, Armin Fügenschuh, and Syed Suhaib Hasan. "Goal Programming Models with Linear and Exponential Fuzzy Preference Relations." Symmetry 12, no. 6 (June 3, 2020): 934. http://dx.doi.org/10.3390/sym12060934.
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Goal programming (GP) is a powerful method to solve multi-objective programming problems. In GP the preferential weights are incorporated in different ways into the achievement function. The problem becomes more complicated if the preferences are imprecise in nature, for example ‘Goal A is slightly or moderately or significantly important than Goal B’. Considering such type of problems, this paper proposes standard goal programming models for multi-objective decision-making, where fuzzy linguistic preference relations are incorporated to model the relative importance of the goals. In the existing literature, only methods with linear preference relations are available. As per our knowledge, nonlinearity was not considered previously in preference relations. We formulated fuzzy preference relations as exponential membership functions. The grades or achievement function is described as an exponential membership function and is used for grading levels of preference toward uncertainty. A nonlinear membership function may lead to a better representation of the achievement level than a linear one. Our proposed models can be a useful tool for different types of real life applications, where exponential nonlinearity in goal preferences exists. Finally, a numerical example is presented and analyzed through multiple cases to validate and compare the proposed models. A distance measure function is also developed and used to compare proposed models. It is found that, for the numerical example, models with exponential membership functions perform better than models with linear membership functions. The proposed models will help decision makers analyze and plan real life problems more realistically.
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FEDRIZZI, MARIO, MICHELE FEDRIZZI, and R. A. MARQUES PEREIRA. "CONSENSUS MODELLING IN GROUP DECISION MAKING: DYNAMICAL APPROACH BASED ON FUZZY PREFERENCES." New Mathematics and Natural Computation 03, no. 02 (July 2007): 219–37. http://dx.doi.org/10.1142/s1793005707000744.
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The. notion of consensus plays an important role in group decision making, particularly when the collective preference structure is generated by a dynamical aggregation process of the single individual preference structures. In this dynamical process of aggregation each single decision maker gradually transforms his/her preference structure by combining it, through iterative weighted averaging, with the preference structures of the remaining decision makers. In this way, the collective decision emerges dynamically as a result of the consensual interaction among the various decision makers in the group. From the point of view of applied mathematics, the models of consensual dynamics stand in the context of multi-agent complex systems, with interactive and nonlinear dynamics. The consensual interaction among the various agents (decision makers) acts on their state variables (the preferences) in order to optimize an appropriate measure of consensus, which can be of type 'hard' (unanimous agreement within the group of decision makers) or 'soft' (partial agreement within the group of decision makers). In this paper, we study the modelling of consensus reaching when the individual testimonies are assumed to be expressed as fuzzy preference relations. Here consensus is meant as the degree to which most of the experts agree on the preferences associated to the most relevant alternatives. First of all we derive a degree of dissensus based on linguistic quantifiers and then we introduce a form of network dynamics in which the quantifiers are represented by scaling functions. Finally, assuming that the decision makers can express their preferences in a more flexible way, i.e. by using triangular fuzzy numbers, we describe the iterative process of opinion transformation towards consensus via the gradient dynamics of a cost function expressed as a linear combination of a dissensus cost function and an inertial cost function.
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Wang, Jing, Bing Yan, Guohao Wang, and Liying Yu. "Rating TAs in fuzzy QFD by objective penalty function and fuzzy TOPSIS based on weighted Hamming distance." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3665–79. http://dx.doi.org/10.3233/jifs-191955.
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Quality function deployment (QFD) is an useful tool to solve Multi-criteria decision making, which can translate customer requirements (CRs) into the technical attributes (TAs) of a product and helps maintain a correct focus on true requirements and minimizes misinterpreting customer needs. In applying quality function deployment, rating technical attributes from input variables is a crucial step in fuzzy environments. In this paper, a new approach is developed, which rates technical attributes by objective penalty function and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) based on weighted Hamming distance under the case of uncertain preference characteristics of decision makers in fuzzy quality function deployment. A pair of nonlinear programming models with constraints and a relevant pair of nonlinear programming models with unconstraints called objective penalty function models are proposed to gain the fuzzy important numbers of technical attributes. Then, this paper compares the fuzzy numbers by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method based on weighted Hamming distance in consideration of the uncertain preference characteristics of decision makers. To end with, the developed method is examined with the numerical examples.
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Aragón, Edilean Kleber da Silva Bejarano, and Marcelo Savino Portugal. "Nonlinearities in Central Bank of Brazil's reaction function: the case of asymmetric preferences." Estudos Econômicos (São Paulo) 40, no. 2 (June 2010): 373–99. http://dx.doi.org/10.1590/s0101-41612010000200005.
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This paper investigates the existence of possible asymmetries in the Central Bank of Brazil's objectives. By assuming that the loss function is asymmetric with regard to positive and negative deviations of the output gap and of the inflation rate from its target, we estimated a nonlinear reaction function which allows identifying and checking the statistical significance of asymmetric parameters in the monetary authority's preferences. For years 2000 to 2007, results indicate that the Central Bank of Brazil showed asymmetric preference over an above-target inflation rate. Given that this behavior may stem from policy decisions in periods of severe crises (e.g., in 2001 and in 2002), we restricted our sample to the 2004-2007 period. We did not find any empirical evidence of any type of asymmetry in the preferences over the stabilization of inflation and of the output gap for this period.
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Pratikto, Fransiscus Rian, Gerardus Daniel Julianto, and Sani Susanto. "Preference-Based Revenue Optimization for App-Based Lifestyle Membership Plans." Jurnal Ilmiah Teknik Industri 20, no. 1 (July 2, 2021): 21–31. http://dx.doi.org/10.23917/jiti.v20i1.13312.
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The demand for a product is rooted in the consumers’ needs and preferences. Therefore, a pricing optimization model will be more valid if the demand function is represented under this basic notion. A preference-based revenue optimization model for an app-based lifestyle membership program is developed and solved in this research. The model considers competitor products and cannibalization effect from products in other fare-class, where both are incorporated using a preference-based demand function. The demand function was derived through a randomized first choice simulation that converts individual utility values into personal choices based on the random parameter logit model. Cannibalizing products are considered as competing products in the simulation scenario. In the pricing optimization, two and three fare classes based on the membership period are considered. The corresponding pricing optimization problem is a mixed-integer nonlinear programming problem with a solution-dependent objective function. Using enumeration, the three-fare-class optimal prices of Rp420,000, Rp300,000, and Rp60,000 for 12-month, 6-month, and 1-month membership, respectively, are better than those of the two-fare-class. Under this policy, the estimated total revenue is Rp30.56 billion, 41.74% greater than that of the current condition.
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CHEN, TING-YU. "NONLINEAR ASSIGNMENT-BASED METHODS FOR INTERVAL-VALUED INTUITIONISTIC FUZZY MULTI-CRITERIA DECISION ANALYSIS WITH INCOMPLETE PREFERENCE INFORMATION." International Journal of Information Technology & Decision Making 11, no. 04 (July 2012): 821–55. http://dx.doi.org/10.1142/s0219622012500228.
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In the context of interval-valued intuitionistic fuzzy sets, this paper develops nonlinear assignment-based methods to manage imprecise and uncertain subjective ratings under incomplete preference structures and thereby determines the optimal ranking order of the alternatives for multiple criteria decision analysis. By comparing each interval-valued intuitionistic fuzzy number's score function, accuracy function, membership uncertainty index, and hesitation uncertainty index, a ranking procedure is employed to identify criterion-wise preference of alternatives. Based on the criterion-wise rankings and a set of known but incomplete information about criterion weights, a nonlinear assignment model is constructed to estimate criterion weights and to order the priority of various alternatives. Considering multiple criteria evaluation problems with preference conflict about criterion importance, an integrated nonlinear programming model is further established with regard to incomplete and inconsistent weight information. These proposed nonlinear assignment-based methods can obtain an aggregate ranking that effectively combines the relative performance of each alternative in each criterion. In addition, this overall ranking most closely agrees with the criterion-wise rankings. Finally, the feasibility of the proposed method is illustrated by a practical example of selecting a suitable bridge construction method.
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Liu, Huazhen, Wei Wang, Yihan Zhang, Renqian Gu, and Yaqi Hao. "Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback." Computational Intelligence and Neuroscience 2022 (January 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/9593957.
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Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.
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Izawa, Andrew, and Matthias Fripp. "Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance." Energies 11, no. 9 (September 8, 2018): 2373. http://dx.doi.org/10.3390/en11092373.
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A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, nonlinear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a “brick wall” preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 1% reduction in costs vs. a similar “brick-wall” MPC approach with the same comfort and 6–11% reduction in costs vs. other control strategies in the literature. CCPSO can also be used to operate the building with much greater comfort and costs or much lower costs and comfort than the “brick-wall” approach, depending on user preferences. CCPSO also reduced peak-hours demand by 3% vs. the “brick-wall” strategy and 4–14% vs. other strategies. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% or more vs. other control methods. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours.
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Wang, Hua Kai. "Research of Resonant Machine Nonlinear Vibrating Characteristics." Applied Mechanics and Materials 494-495 (February 2014): 641–44. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.641.
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Studying the core component of the resonant machine by building the bench test system, utilizing the nonlinear analysis function of Abaqus/Explicit to solve the problem that the rigid-flexible coupling model can`t take the material nonlinearity into adequate consideration. Simulating the nonlinearity of the vibrating beam by doing time integration over the kinematic equation to obtain an accurate and reliable result. Firstly, simplifying the bench testing system to simulating the low frequent vibration; And then, comparing the acceleration root value between the test data and the simulating result on the characteristically point to check the reliability and accuracy of the simulation; By studying the hammer vibrating amplitude curve and shear stress curve changing along the rubber spring thickness, providing a preference for the optimization.
Dissertations / Theses on the topic "Nonlinear preference function"
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Lyubchyk, Leonid, and Galina Grinberg. "Nonlinear expert preference function concordance identification for multiple criteria decision making." Thesis, ТВіМС, 2014. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36757.
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The proposal generalization of expert estimates concordance idea for the case of nonlinear preferance function guaranties on optimal concordance of mesuarement and expert data, whereas machine learning approach ensure the possibility of more accurate approximation expert preference function with complex structure.Предложен подход согласования экспертных оценок для случая нелинейных функций предпочтения, который гарантирует оптимальное согласование данных измерений и экспертных данных, который при использовании методов машинного обучения обеспечивает возможность построения более точной аппроксимационной функции предпочтений эксперта.
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Hansen, Silva Erwin Guillermo. "Nonlinear conditional risk-neutral density estimation in discrete time with applications to option pricing, risk preference measurement and portfolio choice." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/nonlinear-conditional-riskneutral-density-estimation-in-discrete-time-with-applications-to-option-pricing-risk-preference-measurement-and-portfolio-choice(0369c1bb-0873-42c8-a0cf-d18356b3643e).html.
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In this thesis, we study the estimation of the nonlinear conditionalrisk-neutral density function (RND) in discrete time. Specifically, weevaluate the extent to which the estimated nonlinear conditional RNDvaluable insights to answer relevant economic questions regarding to optionpricing, the measurement of invertors' preferences and portfolio choice.We make use of large dataset of options contracts written on the S&P 500index from 1996 to 2011, to estimate the parameters of the conditional RNDfunctions by minimizing the squared option pricing errors delivered by thenonlinear models studied in the thesis.In the first essay, we show that a semi-nonparametric option pricing modelwith GARCH variance outperforms several benchmarks models in-sample andout-of-sample. In the second essay, we show that a simple two-state regimeswitching model in volatility is not able to fully account for the pricingkernel and the risk aversion puzzle; however, it provides a reasonablecharacterisation of the time-series properties of the estimated riskaversion.In the third essay, we evaluate linear stochastic discount factormodels using an out-of-sample financial metric. We find that multifactormodels outperform the CAPM when this metric is used, and that modelsproducing the best fit in-sample are also those exhibiting the bestperformance out-of-sample.
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Fiodendji, Komlan. "Monetary Policy, Asset Price and Economic Growth." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22725.
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The relations between monetary policies, asset prices, and economic growth are important and fundamental questions in macroeconomics. To address these issues, several empirical works have been conducted to investigate these relations. However, few of them have documented whether these relations differ across regimes. In this context, the general motivation of this thesis is to use dependent regime models to examine these relations for the Canadian case.
Chapter one empirically analyzes the interest rate behaviour of the Canadian monetary authorities by taking into account the asymmetry in the loss function. We employ a switching regime framework using two estimation strategies: First, we follow Caner and Hansen (2004) Threshold approach. Under this procedure we estimate the threshold values, using the Taylor empirical rules. Second, we estimate the asymmetric policy reaction function following Favero and Rovelli’s (2003) approach. The results reveal that the monetary authorities showed asymmetric preferences and that its reaction function can be better modeled with a nonlinear model. The main contribution of this chapter is to successfully interpret the parameters associated with the Bank of Canada preferences, something that Rodriguez (2008) could not do.
Chapter two tries to estimate the interest rate behaviour of the Canadian monetary authorities by expanding the arguments of the loss function for fluctuations in asset prices. Using the same methodology as in the first chapter, our findings suggest that the augmented nonlinear reaction function is a good fit for the data and gives new relevant insights into the influence of asset prices on Canadian monetary policy. These findings about the role of asset prices in the reaction function of the Bank of Canada provide relevant insights regarding the opportunities and limitations of incorporating financial indicators in monetary policy decision making. They also provide financial market participants, such as analysts, bankers and traders, with a better understanding of the impact of stock market index prices on Bank of Canada policy. Stock market stabilization plays a larger role in the interest rate decisions of the Bank of Canada than it is willing to admit.
Chapter three provides new evidence on the relation between inflation, relative price variability and economic growth to a panel of Canadian provinces over the period 1981-2008. We use the Bick and Nautz (2008) modified version of Hansen’s (1999) Panel Threshold Model. The evidence strongly supports the view that the relationship between inflation and economic growth is nonlinear. Further investigation suggests that relative price variability is one of the important channels through which inflation affects economic performance in Canadian provinces. When taking into account the cross-section dependence, we find that the critical threshold value slightly changes. It is desirable to keep the inflation rate in a moderate inflation regime because it may be helpful for the achievement of sustainable economic growth. The results seem to indicate that inflation that is too high or too low may have detrimental effects on economic growth.
Books on the topic "Nonlinear preference function"
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Gordon, Robert. “Old Situations, New Complications”. Edited by Robert Gordon. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780195391374.013.0004.
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Forumrepresents an idiosyncratic attempt to reconcile the principles of musical comedy with Sondheim’s avowed preference for writing integrated musical drama. The sources of its plot in Roman farce become a pretext for a camp pastiche of the vulgar clichés of American burlesque and vaudeville. By analyzing the dramaturgical function of the individual songs, the chapter illustrates the various ways in which their evocation of the thought processes of type characters motivates the causal logic of the plot. The ingenuity of their placement and form is shown to shape the mood and pace of the action, while their stylistic cleverness is revealed as an enhancement of the metatheatricality of Shevelove and Gelbart’s book, producing a play of self-reflexive ironies that foreshadows Sondheim’s later experiments with the nonlinear structure of the postmodern musical.
Book chapters on the topic "Nonlinear preference function"
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"Multicriteria Problems in Complex Systems Management." In Multi-Criteria Decision Making for the Management of Complex Systems, 17–44. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2509-7.ch002.
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Chapter 2 provides a general formulation of multicriteria problems. Formalization of the utility function and analysis of the utility function are considered. Nonlinear scheme of compromise and its unification is proposed. Accounting for the decision maker preferences is reviewed. The dual approach to the accounting is proposed. Pareto optimality and axiomatics of the nonlinear scheme of compromise are considered. A nested scalar convolutions method is proposed. Multicriteria evaluation problems are reviewed. Principle of rational organization is proposed.
Conference papers on the topic "Nonlinear preference function"
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Wan, Jie, and Sundar Krishnamurty. "Towards a Consistent Preference Representation in Engineering Design." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/dtm-5675.
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Abstract Multiattribute utility theory is commonly used to define and represent the decision-maker’s preferences under conditions of uncertainty and risk. A major issue in implementing this approach deals with the identification and generation of appropriate utility functions, especially in an often nonlinear and complex engineering design environment. Typically, the decision-maker’s preferences are provided through lottery questions rather than based on deductive reasoning to reflect the nonlinear tradeoffs among the attributes. The use of such an intuitive procedure can lead to inconsistent and inexact preference information that may result in inaccuracy and rank reversal problems. Alternatively, this paper presents an Interactive Preference-Modeling (IPM) method towards a consistent preference representation in engineering design. Focusing on the preference orientation by implicitly articulating the designer’s priorities, this method provides a methodical framework to check and eliminate inconsistency in preference information, and to accurately express preferences through rational pairwise comparisons. The development of IPM method and its utilization in the determination of the system utility function from a consistent set of local utility functions are presented in the context of a beam design problem and the results are discussed.
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Frischknecht, Bart, Katie Whitefoot, and Panos Papalambros. "Methods for Evaluating Suitability of Econometric Demand Models in Design for Market Systems." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87165.
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This paper articulates some of the challenges for what has been an implicit goal of design for market systems research: To predict demand for differentiated products so that counterfactual experiments can be performed based on changes to the product design (i.e., attributes). We present a set of methods for examining econometric models of consumer demand for their suitability in product design studies. We use these methods to test the hypothesis that automotive demand models that allow for nonlinear horizontal differentiation perform better than the conventional functional forms, which emphasize vertical differentiation. We estimate these two forms of consumer demand in the new vehicle automotive market, and find that using an ideal-point model of size preference rather than a monotonic model has model fit but different attribute substitution patterns. The generality of the evaluation methods and the range of demand model issues to be explored in future research are highlighted.