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Nguyen, Dat-Dao. "Using Social Choice Function Vs. Social Welfare Function To Aggregate Individual Preferences In Group Decision Support Systems." International Journal of Management & Information Systems (IJMIS) 18, no. 3 (2014): 167. http://dx.doi.org/10.19030/ijmis.v18i3.8703.
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In multi-criteria decision making, any Group Decision Support System (GDSS) requires a social judgment model for calculation of weights on decision alternatives, and tabulation of individual votes toward a consensus. One could assess a Social Welfare Function - such as Keeneys - to aggregate individual cardinal preferences or utilities into a group preference. Alternatively, one could use Social Choice Functions - such as Condorcet, Borda, Copeland, and Eigenvector - to aggregate individual ordinal preferences or rankings into a group ranking. This study empirically investigates the consensus between individual preferences and the group preference derived from various aggregation methods.
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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 (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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Knox, W. Bradley, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson, et al. "Learning Optimal Advantage from Preferences and Mistaking It for Reward." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10066–73. http://dx.doi.org/10.1609/aaai.v38i9.28870.
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We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments, as used in reinforcement learning from human feedback (RLHF). Most recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their partial return. Recent work casts doubt on the validity of this assumption, proposing an alternative preference model based upon regret. We investigate the consequences of assuming preferences are based upon partial return when they actually arise from regret. We argue that the learned function is an approximation of the optimal advantage function, not a reward function. We find that if a specific pitfall is addressed, this incorrect assumption is not particularly harmful, resulting in a highly shaped reward function. Nonetheless, this incorrect usage of the approximation of the optimal advantage function is less desirable than the appropriate and simpler approach of greedy maximization of it. From the perspective of the regret preference model, we also provide a clearer interpretation of fine tuning contemporary large language models with RLHF. This paper overall provides insight regarding why learning under the partial return preference model tends to work so well in practice, despite it conforming poorly to how humans give preferences.
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Zhou, Zhichun, David Burrell McAdam, Deborah Ann Napolitano, and Kathryn Douthit. "Shining a Light on the Challenging Behaviors of Adolescents with Comorbid Diagnoses: Use of Pictorial Concurrent Operant Preference Assessment." Children 8, no. 8 (2021): 683. http://dx.doi.org/10.3390/children8080683.
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Currently, there are no published studies that have used the concurrent operant preference assessment procedure to identify functions of challenging behaviors displayed by individuals with comorbid diagnoses. Four participants (aged 11–16 years) with comorbid diagnoses who displayed multiple challenging behaviors were referred to this study. We modified the standard concurrent operant preference assessment and used the new modified version, the pictorial concurrent operant preference assessment, to identify the functions of the challenging behaviors. Utilizing the triangulation mixed-methods design, we compared the indirect functional behavioral assessment (FBA) and the direct FBA with the pictorial concurrent operant preference assessment. The results obtained successfully demonstrated the concordance among these assessments in identifying the behavioral function for each participant. The results further showed that (1) the preferences served the same functional effects on both the challenging behaviors and the adaptive behaviors and (2) the pictorial concurrent operant preference assessment can be used independently to identify potential behavioral function and to specify the reinforcing potency of each behavioral function. The significance of the study results, limitations of this study, and directions for future research and clinical practice are discussed.
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Weichselbaum, Hanna, Helmut Leder, and Ulrich Ansorge. "Implicit and Explicit Evaluation of Visual Symmetry as a Function of Art Expertise." i-Perception 9, no. 2 (2018): 204166951876146. http://dx.doi.org/10.1177/2041669518761464.
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In perception, humans typically prefer symmetrical over asymmetrical patterns. Yet, little is known about differences in symmetry preferences depending on individuals’ different past histories of actively reflecting upon pictures and patterns. To address this question, we tested the generality of the symmetry preference for different levels of individual art expertise. The preference for symmetrical versus asymmetrical abstract patterns was measured implicitly, by an Implicit Association Test (IAT), and explicitly, by a rating scale asking participants to evaluate pattern beauty. Participants were art history and psychology students. Art expertise was measured using a questionnaire. In the IAT, art expertise did not alter the preference for symmetrical over asymmetrical patterns. In contrast, the explicit rating scale showed that with higher art expertise, the ratings for the beauty of asymmetrical patterns significantly increased, but, again, participants preferred symmetrical over asymmetrical patterns. The results are discussed in light of different theories on the origins of symmetry preference. Evolutionary adaptation might play a role in symmetry preferences for art experts similarly to nonexperts, but experts tend to emphasize the beauty of asymmetrical depictions, eventually considering different criteria, when asked explicitly to indicate their preferences.
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Runken, Michael Chris, and Juan Marcos Gonzalez. "Using patients’ stated preferences for immunoglobulin therapies to evaluate administration features in current treatments." Journal of Immunology 196, no. 1_Supplement (2016): 130.5. http://dx.doi.org/10.4049/jimmunol.196.supp.130.5.
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Abstract In 2012, Mohamed et al.(J Med Econ) estimated preference weights for changes in the administration features of immunoglobulin (IG) therapies among patients with self-reported primary immunodeficiency disorder (PIDD). The preference weights were estimated using a discrete-choice experiment survey that asked respondents to choose between hypothetical treatments with varying administration features. However, the authors did not discuss the implications of their results on patients’ preferences for currently available IG therapies with specific combinations of the elicited features. Transferring preference results from Mohamed et al. allowed us to evaluate current treatment options not considered during the original study. This transfer of preference information is commonplace in economics, but not in health applications where it is done almost exclusively with treatment outcomes and not treatment administration features. The aim of this study was to assess patients’ relative preferences for currently available IG treatments in terms of their administration features as elicited by Mohamed et al. To achieve this objective, we approximated a preference-weight function through visual examination of the weights plotted in the publication. This function provides a calculation of preference scores for any IG treatment described with the attributes studied in Mohamed et al. This research suggests a general tendency in PIDD patients to prefer the administration features associated with intravenous therapies over subcutaneous therapies. The evaluation of IG therapies with the approximated preference function highlights how attribute preferences can be considered jointly in clinically meaningful treatment profiles.
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Ajaz, Taufeeq. "Nonlinear Reaction functions: Evidence from India." Journal of Central Banking Theory and Practice 8, no. 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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Akram, Muhammad, Shumaiza, and Ahmad N. Al-Kenani. "Multi-Criteria Group Decision-Making for Selection of Green Suppliers under Bipolar Fuzzy PROMETHEE Process." Symmetry 12, no. 1 (2020): 77. http://dx.doi.org/10.3390/sym12010077.
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The preference ranking organization method for enrichment of evaluations (PROMETHEE) method considers a significant outranking class of multi-criteria decision analysis (MCDA), as it is easy to deal with its simple computations. In the PROMETHEE, different preference functions are used according to the type and nature of attributes or criteria that demonstrate the clearness and reliability of this method. This study provides a new version of the PROMETHEE method using bipolar fuzzy information, named the bipolar fuzzy PROMETHEE method. Bipolar fuzzy sets or numbers constitute an asymmetrical relationship between two judgmental factors of human reasoning. Vague and imprecise knowledge is characterized by bipolar fuzzy linguistic terms which are further represented in the form of trapezoidal bipolar fuzzy numbers. The trapezoidal bipolar fuzzy numbers are used by analysts to assign the preferences of alternatives on the basis of criteria. Further, a ranking function of bipolar fuzzy numbers is considered to access the crisp real preferences of alternatives. The entropy weighting information is employed to calculate the weights of attributes by considering the condition of normality. A numerical example such as the selection of green suppliers by using the bipolar fuzzy PROMETHEE is performed on the basis of the usual criterion preference function in order to explain the procedure of the proposed method. Comparable results are derived by using the combination of linear and level preference functions. The results obtained by using different types of preference functions are the same, representing the authenticity of the proposed bipolar fuzzy PROMETHEE method.
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Chen, Yu-ke, Yan Zou, and Zhe Chen. "Preference Integration and Optimization of Multistage Weighted Voting System Based on Ordinal Preference." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/186545.
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Multistage voting is a common voting form through which the winners are selected. By virtue of weighted multistage voting rules, in this paper, we establish a weighted voting model by analyzing the correlation between individual preference and group preference. The weights of voters in each voting stage are adjusted through preference deviation degrees between individual preferences and group preference, and the ranking among candidates in each stage is determined according to weighted Borda function value. Examples are given to verify our model, which shows that weighted information aggregation model can mine more useful information from different individual preferences of voters to quicken the aggregation of group preference.
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Esposito, Nicholas J. "Function as a Preferred Dimension among Children and Adults." Perceptual and Motor Skills 62, no. 3 (1986): 899–902. http://dx.doi.org/10.2466/pms.1986.62.3.899.
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Subjects' preferences for function vs form or for function vs color were tested using a method of triads. Children (16 girls, 24 boys) and adults (65 women, 17 men) showed a strong preference for function over color and adults also for function over form. Color-function concept identification was better when function was the relevant dimension. These data indicate that function can be assessed like physical dimensions, that function is more preferred than or as preferred as other dimensions by children and adults and that responses to preference tests are applicable over a wide range of dimensions and ages.
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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 (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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Diasakos, Theodoros M., and Georgios Gerasimou. "Preference Conditions for Invertible Demand Functions." American Economic Journal: Microeconomics 14, no. 2 (2022): 113–38. http://dx.doi.org/10.1257/mic.20190262.
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It is frequently assumed in several domains of economics that demand functions are invertible in prices. At the primitive level of preferences, however, the corresponding characterization has remained elusive. We identify necessary and sufficient conditions on a utility-maximizing consumer’s preferences for her demand function to be continuous and invertible: strict convexity, strict monotonicity, and differentiability in the sense of Rubinstein (2006). We further show that Rubinstein differentiability is equivalent to the indifference sets being smooth, which is weaker than Debreu’s (1972) notion of preference smoothness. We finally discuss implications of our analysis for demand functions that satisfy the “strict law of demand.” (JEL DO1, D11)
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Diasakos, Theodoros M., and Georgios Gerasimou. "Preference Conditions for Invertible Demand Functions." American Economic Journal: Microeconomics 14, no. 2 (2022): 113–38. http://dx.doi.org/10.1257/mic.20190262.
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It is frequently assumed in several domains of economics that demand functions are invertible in prices. At the primitive level of preferences, however, the corresponding characterization has remained elusive. We identify necessary and sufficient conditions on a utility-maximizing consumer’s preferences for her demand function to be continuous and invertible: strict convexity, strict monotonicity, and differentiability in the sense of Rubinstein (2006). We further show that Rubinstein differentiability is equivalent to the indifference sets being smooth, which is weaker than Debreu’s (1972) notion of preference smoothness. We finally discuss implications of our analysis for demand functions that satisfy the “strict law of demand.” (JEL DO1, D11)
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Dewitt, Barry, David Feeny, Baruch Fischhoff, et al. "Estimation of a Preference-Based Summary Score for the Patient-Reported Outcomes Measurement Information System: The PROMIS®-Preference (PROPr) Scoring System." Medical Decision Making 38, no. 6 (2018): 683–98. http://dx.doi.org/10.1177/0272989x18776637.
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Background. Health-related quality of life (HRQL) preference-based scores are used to assess the health of populations and patients and for cost-effectiveness analyses. The National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS®) consists of patient-reported outcome measures developed using item response theory. PROMIS is in need of a direct preference-based scoring system for assigning values to health states. Objective. To produce societal preference-based scores for 7 PROMIS domains: Cognitive Function–Abilities, Depression, Fatigue, Pain Interference, Physical Function, Sleep Disturbance, and Ability to Participate in Social Roles and Activities. Setting. Online survey of a US nationally representative sample ( n = 983). Methods. Preferences for PROMIS health states were elicited with the standard gamble to obtain both single-attribute scoring functions for each of the 7 PROMIS domains and a multiplicative multiattribute utility (scoring) function. Results. The 7 single-attribute scoring functions were fit using isotonic regression with linear interpolation. The multiplicative multiattribute summary function estimates utilities for PROMIS multiattribute health states on a scale where 0 is the utility of being dead and 1 the utility of “full health.” The lowest possible score is –0.022 (for a state viewed as worse than dead), and the highest possible score is 1. Limitations. The online survey systematically excludes some subgroups, such as the visually impaired and illiterate. Conclusions. A generic societal preference-based scoring system is now available for all studies using these 7 PROMIS health domains.
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Kilmer, J. T., K. D. Fowler-Finn, D. A. Gray, et al. "Describing mate preference functions and other function-valued traits." Journal of Evolutionary Biology 30, no. 9 (2017): 1658–73. http://dx.doi.org/10.1111/jeb.13122.
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ALESKEROV, FUAD T., and VYACHESLAV V. CHISTYAKOV. "THE THRESHOLD DECISION MAKING EFFECTUATED BY THE ENUMERATING PREFERENCE FUNCTION." International Journal of Information Technology & Decision Making 12, no. 06 (2013): 1201–22. http://dx.doi.org/10.1142/s021962201350034x.
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Based on the leximin and leximax preferences, we consider two threshold preference relations on the set X of alternatives, each of which is characterized by an n-dimensional vector (n ≥ 2) with integer components varying between 1 and m(m ≥ 2). We determine explicitly in terms of binomial coefficients the unique utility function for each of the two relations, which in addition maps X onto the natural 'interval' [Formula: see text], where [Formula: see text] is the quotient set of X with respect to the indifference relation I on X induced by the threshold preference. This permits us to evaluate all equivalence classes and indifference classes of the threshold order on X, present an algorithm of ordering the monotone representatives of indifference classes, and restore the indifference class of an alternative via its ordinal number with respect to the threshold preference order.
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Barzilai, Jonathan. "Measurement and preference function modelling." International Transactions in Operational Research 12, no. 2 (2005): 173–83. http://dx.doi.org/10.1111/j.1475-3995.2005.00496.x.
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Ullah, Khalil, and Muhammad Aslam. "Bayesian Analysis of the Weibull Paired Comparison Model Using Numerical Approximation." Journal of Mathematics 2020 (December 14, 2020): 1–6. http://dx.doi.org/10.1155/2020/6628379.
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The method of paired comparisons (PC) is widely used to rank items using sensory evaluations. The PC models are developed to provide basis for such comparisons. In this study, the Weibull PC model is analyzed under the Bayesian paradigm using noninformative priors and different loss functions, namely, Squared Error Loss Function (SELF), Quadratic Loss Function (QLF), DeGroot Loss Function (DLF), and Precautionary Loss Function (PLF). Numerical approximation is used to illustrate the entire estimation procedure. A real dataset showing usage preferences for different cellphone brands, Huawei (HW), Samsung (SS), Oppo (OP), QMobile (QM), and Nokia (NK), is used. Quadrature method is used to evaluate the Bayes estimates, their posterior risks, preference probabilities, predictive probabilities, and posterior probabilities to establish and verify ranking order of the competing cellphone brands under study. The results show that the paired comparison model under the study using Bayesian approach involving various loss functions can offer mathematical approach to evaluate cellphone brand preferences. The ranking provided by the model is justifiable according to the usage preference for these cellphone brands. The ranking given by the model indicates that cellphone brand Samsung is preferred the most and QMobile is the least preferred. The plausibility of the model is also assessed using the Chi square test of goodness of fit.
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Anderson, Elizabeth. "Symposium on Amartya Sen's philosophy: 2 Unstrapping the straitjacket of ‘preference’: a comment on Amartya Sen's contributions to philosophy and economics." Economics and Philosophy 17, no. 1 (2001): 21–38. http://dx.doi.org/10.1017/s0266267101000128.
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The concept of preference dominates economic theory today. It performs a triple duty for economists, grounding their theories of individual behavior, welfare, and rationality. Microeconomic theory assumes that individuals act so as to maximize their utility – that is, to maximize the degree to which their preferences are satisfied. Welfare economics defines individual welfare in terms of preference satisfaction or utility, and social welfare as a function of individual preferences. Finally, economists assume that the rational act is the act that maximally satisfies an individual's preferences. The habit of framing problems in terms of the concept of preference is now so entrenched that economists rarely entertain alternatives.
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Lou, Xingzhou, Junge Zhang, Jian Xie, Lifeng Liu, Dong Yan, and Kaiqi Huang. "Sequential Preference Optimization: Multi-Dimensional Preference Alignment with Implicit Reward Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27509–17. https://doi.org/10.1609/aaai.v39i26.34963.
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Human preference alignment is critical in building powerful and reliable large language models (LLMs). However, current methods either ignore the multi-dimensionality of human preferences (e.g. helpfulness and harmlessness) or struggle with the complexity of managing multiple reward models. To address these issues, we propose Sequential Preference Optimization (SPO), a method that sequentially fine-tunes LLMs to align with multiple dimensions of human preferences. SPO avoids explicit reward modeling, directly optimizing the models to align with nuanced human preferences. We theoretically derive closed-form optimal SPO policy and loss function. Gradient analysis is conducted to show how SPO manages to fine-tune the LLMs while maintaining alignment on previously optimized dimensions. Empirical results on LLMs of different size and multiple evaluation datasets demonstrate that SPO successfully aligns LLMs across multiple dimensions of human preferences and significantly outperforms the baselines.
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Gao, Yang, Christian M. Meyer, and Iryna Gurevych. "Preference-based interactive multi-document summarisation." Information Retrieval Journal 23, no. 6 (2019): 555–85. http://dx.doi.org/10.1007/s10791-019-09367-8.
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AbstractInteractive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound. Preference-based interactive learning has been successfully applied, but the existing methods require several thousand interaction rounds even in simulations with perfect user feedback. In this paper, we study preference-based interactive summarisation. To reduce the number of interaction rounds, we propose the Active Preference-based ReInforcement Learning (APRIL) framework. APRIL uses active learning to query the user, preference learning to learn a summary ranking function from the preferences, and neural Reinforcement learning to efficiently search for the (near-)optimal summary. Our results show that users can easily provide reliable preferences over summaries and that APRIL outperforms the state-of-the-art preference-based interactive method in both simulation and real-user experiments.
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Saidin Nainggolan, Yanuar Fitri, and Silvi Theresia. "Analysis of factors influencing production of rice farming and farmers preferences in addressing production risks in Tanjung Jabung Barat district, Jambi province." World Journal of Advanced Research and Reviews 20, no. 2 (2023): 649–55. http://dx.doi.org/10.30574/wjarr.2023.20.2.2055.
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Study This aim For analyze influencing factors production farming lowland rice and preferences farmer respond production farming paddy fields in Tanjung Jabung Barat Regency, Jambi Province. Locus study are Sriagung Village and Rawa Medang Village taken in a way purposive . Size sample as many as 91 respondents. Withdrawal method sample use simple random sampling method. Estimation function production use function production Cobb- Douglass. Approach method preference farmer respond risk production use method Moscardi and de Janvry. Determinant factors function production farming paddy fields are Urea Fertilizer, NPK Fertilizer, Fertilizer organic and pesticides. Variable Urea fertilizer, NPK fertilizer and fertilizer organic very significant effect to production. Preference farmer respond risk production based on third variable the obtained that farmer own preference avoid risk.
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Mohsin, Ali, Qiong Shen, Xinyu Wang, and Xiaoming Zhang. "A Ranking Method for User Recommendation Based on Fuzzy Preference Relations in the Nature Reserve of Dangshan Pear Germplasm Resources." Information 9, no. 11 (2018): 291. http://dx.doi.org/10.3390/info9110291.
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Precision orchard management is an important avenue of investigation in agricultural technology and is an urgently needed part of information development in the fruit industry. Precision management based on a precision agricultural technology system involves many factors and results in users being unable to make accurate judgments. To improve user decision-making accuracy and the level of precision management, we used user preferences to achieve the recommendation function. In this paper, a ranking method based on fuzzy preference relations for user recommendation is proposed. We selected the Nature Reserve of Dangshan Pear Germplasm Resources as the research location and invited experts and representatives of different roles (government, farmers, and tourists) to give the fuzzy preference relation coefficients. Then, an optimization model was proposed based on the fuzzy preference relation. We solved the proposed model by constructing a Lagrangian function, and obtained the ranking values of the user preference recommendation function. Finally, we ranked the order of the given roles and implemented the fuzzy preference recommendation. The experimental results show that the proposed method is effective and can be conveniently applied to other problems related to user preference relations.
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Bradley, Gordon A., and Anne R. Kearney. "Public and Professional Responses to the Visual Effects of Timber Harvesting: Different Ways of Seeing." Western Journal of Applied Forestry 22, no. 1 (2007): 42–54. http://dx.doi.org/10.1093/wjaf/22.1.42.
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Abstract This study examines preferences and differences in preference among forest professionals and other forest stakeholders. Specifically, the study explores the underlying dimensions of preference and the stated rationale behind those preferences. Preferences were assessed for six different silvicultural treatments, including clearcutting, two-age cut, patch cut, group selection, commercial thin, and an unmodified control stand. Study participants were selected from six stakeholder groups: forest professionals, urban public, rural public, recreationists, environmentalists, and educators (n=210). Results show that people's preferences tend to follow the same general trend across all groups, with higher preference for less intense harvests. Foresters tended to show significantly greater preference than most other groups for treatments where tree removal left moderate to large openings; this difference was most striking with respect to clearcuts. Analysis of people's stated rationale behind the preference ratings suggests that differences in preference are a function of differences in how people interpret the scene. Foresters were more likely to see the management depicted in a scene and to interpret these signs of management in a favorable light; other groups were more likely to respond to the aesthetics of the scene or to processes perceived to be damaging to the landscape. Implications for these differences in preference and perception are discussed.
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Benabbou, Nawal, and Thibaut Lust. "A General Interactive Approach for Solving Multi-Objective Combinatorial Optimization Problems with Imprecise Preferences." Proceedings of the International Symposium on Combinatorial Search 10, no. 1 (2021): 164–65. http://dx.doi.org/10.1609/socs.v10i1.18472.
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In this paper, we develop a general interactive method to solve multi-objective combinatorial optimization problems with imprecise preferences. Assuming that preferences can be represented by a parameterized scalarizing function, we iteratively ask preferences queries to the decision maker in order to reduce the uncertainty over the preference parameters until being able to determine her preferred solution. To produce informative preference queries at each step, we generate promising solutions using the extreme points of the polyhedron representing the admissible preference parameters and then we ask the decision maker to compare two of these solutions (we propose different selection strategies). These extreme points are also used to provide a stopping criterion guaranteeing that the returned solution is optimal (or near-optimal) according to the decision maker's preferences. For the multi-objective spanning tree problem with a linear aggregation function, we provide numerical results to demonstrate the practical efficiency of our approach and we compare our results to a recent approach based on minimax regret, where preferences are asked during the construction of a solution. We show that better results are achieved by our method both in terms of running time and number of questions.
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Saidin, Nainggolan, Fitri Yanuar, and Theresia Silvi. "Analysis of factors influencing production of rice farming and farmers preferences in addressing production risks in Tanjung Jabung Barat district, Jambi province." World Journal of Advanced Research and Reviews 20, no. 2 (2023): 649–55. https://doi.org/10.5281/zenodo.12590344.
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Study This aim For analyze influencing factors production farming lowland rice and preferences farmer respond production farming paddy fields in Tanjung Jabung Barat Regency, Jambi Province. Locus study are Sriagung Village and Rawa Medang Village taken in a way <em>purposive </em>. Size sample as many as 91 respondents. Withdrawal method sample use simple random sampling method. Estimation function production use function production <em>Cobb- Douglass</em>. Approach method preference farmer respond risk production use method <em>Moscardi and de Janvry</em>. Determinant factors function production farming paddy fields are Urea Fertilizer, NPK Fertilizer, Fertilizer organic and pesticides. Variable Urea fertilizer, NPK fertilizer and fertilizer organic very significant effect to production. Preference farmer respond risk production based on third variable the obtained that farmer own preference avoid risk.
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Desjonquères, Camille, Rebecca R. Holt, Bretta Speck, and Rafael L. Rodríguez. "The relationship between a combinatorial processing rule and a continuous mate preference function in an insect." Proceedings of the Royal Society B: Biological Sciences 287, no. 1935 (2020): 20201278. http://dx.doi.org/10.1098/rspb.2020.1278.
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Mate choice involves processing signals that can reach high levels of complexity and feature multiple components, even in small animals with tiny brains. This raises the question of whether and how such organisms deal with this complexity. One solution involves combinatorial processing, whereby different signal elements are processed as single units. Combinatorial processing has been described in several mammals and birds, and recently in a vibrationally signalling insect, Enchenopa treehoppers. Here, we ask about the relationship between combinatorial rules and mate preferences for continuously varying signal features. Enchenopa male advertisement signals are composed of two elements: a ‘whine’ followed by a set of pulses. The dominant frequency of the whine and element combination both matter to females. We presented synthetic signals varying in element order (natural [whine-pulses], reverse [pulses-whine]) and in frequency to Enchenopa females and recorded their responses. The reverse combination resulted in a decrease in attractiveness of the signals, and also slightly changed the shape of the preference for frequency. We found that females could be classified into three ‘types’: females with both a strong preference and a strong combinatorial rule, females with both a weak preference and weak rule, and females with a strong preference but a weak rule. Our results suggest that in Enchenopa signal processing, the mate preference for a continuous signal feature ‘takes precedence’ over, but also interacts with, the combinatorial rule. The relationship between the preference and the rule could evolve to take different forms according to selection on mate choice decisions. We suggest that exploring the relationship between such preferences and rules in species with more complex signals will bring insight into the evolution of the multi-component communication systems.
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McGuigan, Katrina, Anna Van Homrigh, and Mark W. Blows. "Genetic Analysis of Female Preference Functions as Function‐Valued Traits." American Naturalist 172, no. 2 (2008): 194–202. http://dx.doi.org/10.1086/588075.
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Eeckhoudt, Louis, and Harris Schlesinger. "Putting Risk in Its Proper Place." American Economic Review 96, no. 1 (2006): 280–89. http://dx.doi.org/10.1257/000282806776157777.
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This paper examines preferences toward particular classes of lottery pairs. We show how such concepts as prudence and temperance can be fully characterized by a preference relation over these lotteries. If preferences are defined in an expectedutility framework with differentiable utility, the direction of preference for a particular class of lottery pairs is equivalent to signing the nth derivative of the utility function. What makes our characterization appealing is its simplicity, which seems particularly amenable to experimentation.
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FOTSO, SIMEON, and LOUIS AIME FONO. "ON THE CONSISTENCY OF SOME CRISP CHOICE FUNCTIONS BASED ON A STRONGLY COMPLETE FUZZY PRE-ORDER." New Mathematics and Natural Computation 08, no. 02 (2012): 257–72. http://dx.doi.org/10.1142/s1793005712400145.
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This paper presents the first part of a general study of the structure of nine preference-based choice functions introduced by Barrett et al.2 More precisely, we show that, as for crisp total pre-orders, first and last alternatives exist in a finite set of alternatives equipped with a strongly complete fuzzy pre-order. We use that result to characterize each of those crisp choice functions for crisp total pre-orders and strongly complete fuzzy pre-orders. We study, by means of those characterizations, the consistency of those preference-based choice functions when preferences are strongly complete fuzzy pre-orders (thereby crisp total pre-orders), that is, we check if each choice function satisfies or violates each of six consistency conditions introduced by Sen.11
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Kirkpatrick, Mark, and Maria R. Servedio. "The Reinforcement of Mating Preferences on an Island." Genetics 151, no. 2 (1999): 865–84. http://dx.doi.org/10.1093/genetics/151.2.865.
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Abstract We develop a haploid model for the reinforcement of female mating preferences on an island that receives migrants from a continent. We find that preferences will evolve to favor island males under a broad range of conditions: when the average male display trait on the island and continent differ, when the preference acts on that difference, and when there is standing genetic variance for the preference. A difference between the mean display trait on the continent and on the island is sufficient to drive reinforcement of preferences. Additional postzygotic isolation, caused, for example, by either epistatic incompatibility or ecological selection against hybrids, will amplify reinforcement but is not necessary. Under some conditions, the degree of preference reinforcement is a simple function of quantities that can be estimated entirely from phenotypic data. We go on to study how postzygotic isolation caused by epistatic incompatibilities affects reinforcement of the preference. With only one pair of epistatic loci, reinforcement is enhanced by tighter linkage between the preference genes and the genes causing hybrid incompatibility. Reinforcement of the preference is also affected by the number of epistatically interacting genes involved in incompatibility, independent of the overall intensity of selection against hybrids.
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Maddulapalli, A. K., and S. Azarm. "Product Design Selection With Preference and Attribute Variability for an Implicit Value Function." Journal of Mechanical Design 128, no. 5 (2005): 1027–37. http://dx.doi.org/10.1115/1.2216728.
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An important aspect of engineering product design selection is the inevitable presence of variability in the selection process. There are mainly two types of variability: variability in the preferences of the decision maker (DM) and variability in attribute levels of the design alternatives. We address both kinds of variability in this paper. We first present a method for selection with preference variability alone. Our method is interactive and iterative and assumes only that the preferences of the DM reflect an implicit value function that is differentiable, quasi-concave and non-decreasing with respect to attributes. The DM states his/her preferences with a range (due to the variability) for marginal rate of substitution (MRS) between attributes at a series of trial designs. The method uses the range of MRS preferences to eliminate “dominated designs” and then to find a set of “potentially optimal designs.” We present a payload design selection example to demonstrate and verify our method. Finally, we extend our method for selection with preference variability to the case where the attribute levels of design alternatives also have variability. We assume that the variability in attribute levels can be quantified with a range of attribute levels.
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Zhu, Tianchen, Yue Qiu, Haoyi Zhou, and Jianxin Li. "Decoding Global Preferences: Temporal and Cooperative Dependency Modeling in Multi-Agent Preference-Based Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 17202–10. http://dx.doi.org/10.1609/aaai.v38i15.29666.
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Designing accurate reward functions for reinforcement learning (RL) has long been challenging. Preference-based RL (PbRL) offers a promising approach by using human preferences to train agents, eliminating the need for manual reward design. While successful in single-agent tasks, extending PbRL to complex multi-agent scenarios is nontrivial. Existing PbRL methods lack the capacity to comprehensively capture both temporal and cooperative aspects, leading to inadequate reward functions. This work introduces an advanced multi-agent preference learning framework that effectively addresses these limitations. Based on a cascading Transformer architecture, our approach captures both temporal and cooperative dependencies, alleviating issues related to reward uniformity and intricate interactions among agents. Experimental results demonstrate substantial performance improvements in multi-agent cooperative tasks, and the reconstructed reward function closely resembles expert-defined reward functions. The source code is available at https://github.com/catezi/MAPT.
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Matt, Vance, Carstens John, Gasper Michael, and Parker Phillip. "Quantitatively Exploring Human Preference." Journal of Management Science and Business Intelligence 3, no. 1 (2018): 15–30. https://doi.org/10.5281/zenodo.1246313.
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Qualitative preference is frequently used to make significant, programmatic choices between competing suppliers and products. Our research helped identify the degree of quantitative granularity, which must be present between criteria for defensible choices, by exploring how small of a difference humans are able to reliably perceive. The response options, the type of response option (either physical weight differentiation or web-based shape/size differentiation), and the granularity of these responses were the quantitative focus of our research. From this quantitative basis, we were able to postulate corresponding qualitative-preference implications, impacts, and levels of reliability in the responses. Two primary data collection methods were designed to contrast each other: an in-person convenience sample taken on the Oklahoma State University (OSU) campus with physical weights, and an online survey of randomly sampled respondents based on pair-wise, visual-size differences of circle areas. Weber’s Law was the primary analytical exploratory tool employed. Our research showed (a) the larger a given pair-wise comparison is in magnitude (weight or size), the more reliably human preference for the larger (either heavier or larger in size) commodity can be measured, (b) visual perception of area difference was consistently more accurate than weight difference – even with small differences of just 10%, and (c) if 95% reliability in choice between alternatives was desired then the perceived difference needed to be a factor of 3.0 (the larger choice needed to be either 200% heavier or 200% larger in area).
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Gorka, Artur, and Phantipa Thipwiwatpotjana. "The Importance of Fuzzy Preference in Course Assignment Problem." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/106727.
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This paper points out the importance of fuzzy preference by using a teaching course assignment problem as a case study. The model with fuzzy teaching preference provides a more satisfactory solution to a course assignment problem than assigning arbitrary weights. A method for improving a fuzzy membership function by using sensitivity analysis is devised. The method with fuzzy preferences is compared with a model using weighted probabilities.
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Kaplan, Gisela, and Lesley J. Rogers. "Brain Size Associated with Foot Preferences in Australian Parrots." Symmetry 13, no. 5 (2021): 867. http://dx.doi.org/10.3390/sym13050867.
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Since foot preference of cockatoos and parrots to hold and manipulate food and other objects has been associated with better ability to perform certain tasks, we predicted that either strength or direction of foot preference would correlate with brain size. Our study of 25 psittacine species of Australia found that species with larger absolute brain mass have stronger foot preferences and that percent left-footedness is correlated positively with brain mass. In a sub-sample of 11 species, we found an association between foot preference and size of the nidopallial region of the telencephalon, an area equivalent to the mammalian cortex and including regions with executive function and other higher-level functions. Our analysis showed that percent left-foot use correlates positively and significantly with size of the nidopallium relative to the whole brain, but not with the relative size of the optic tecta. Psittacine species with stronger left-foot preferences have larger brains, with the nidopallium making up a greater proportion of those brains. Our results are the first to show an association between brain size and asymmetrical limb use by parrots and cockatoos. Our results support the hypothesis that limb preference enhances brain capacity and higher (nidopallial) functioning.
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FRISCH, STEFAN, PETER BEIM GRABEN, and MATTHIAS SCHLESEWSKY. "PARALLELIZING GRAMMATICAL FUNCTIONS: P600 AND P345 REFLECT DIFFERENT COST OF REANALYSIS." International Journal of Bifurcation and Chaos 14, no. 02 (2004): 531–49. http://dx.doi.org/10.1142/s0218127404009533.
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It is well-known from psycholinguistic literature that the human language processing system exhibits preferences when sentence constituents are ambiguous with respect to their grammatical function. Generally, many theories assume that an interpretation towards the subject is preferred in such cases. Later disambiguations which contradict such a preference induce enhanced processing difficulty (i.e. reanalysis) which reflects itself in late positive deflections (P345/P600) in event-related brain potentials (ERPs). In the case of phoric elements such as pronouns, a second strategy is known according to which an ambiguous pronoun preferentially receives the grammatical function that its antecedent has (parallel function strategy). In an ERP study, we show that this strategy can in principle override the general subject preference strategy (known for both pronominal and nonpronominal constituents) and induce an object preference, in case that the pronoun's antecedent is itself an object. Interestingly, the revision of a subject preference leads to a P600 component, whereas the revision of an object preference induces an earlier positivity (P345). In order to show that the latter component is indeed a positivity and not an N400-like negativity in the same time range, we apply an additional analysis based on symbolic dynamics which allows to determine the polarity of an ERP effect on purely methodological grounds. With respect to the two positivities, we argue that the latency differences reflect qualitative differences in the reanalysis processes.
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Xu, Qianqian, Jiechao Xiong, Zhiyong Yang, Xiaochun Cao, Qingming Huang, and Yuan Yao. "Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 262–69. http://dx.doi.org/10.1609/aaai.v34i01.5359.
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In recent years, learning user preferences has received significant attention. A shortcoming of existing learning to rank work lies in that they do not take into account the multi-level hierarchies from social choice to individuals. In this paper, we propose a multi-level model which learns both the common preference or utility function over the population based on features of alternatives to-be-compared, and preferential diversity functions conditioning on user categories. Such a multi-level model, enables us to simultaneously learn a coarse-grained social preference function together with a fine-grained personalized diversity. It provides us prediction power for the choices of new users on new alternatives. The key algorithm in this paper is based on Split Linearized Bregman Iteration (SplitLBI) algorithm which generates a dynamic path from the common utility to personalized preferential diversity, at different levels of sparsity on personalization. A synchronized parallel version of SplitLBI is proposed to meet the needs of fast analysis of large-scale data. The validity of the methodology are supported by experiments with both simulated and real-world datasets such as movie and dining restaurant ratings which provides us a coarse-to-fine grained preference learning.
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Wu, Chia-Ching, and Chien-Chung Chen. "Symmetry Modulates the Amplitude Spectrum Slope Effect on Visual Preference." Symmetry 12, no. 11 (2020): 1820. http://dx.doi.org/10.3390/sym12111820.
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Within the spectrum of a natural image, the amplitude of modulation decreases with spatial frequency. The speed of such an amplitude decrease, or the amplitude spectrum slope, of an image affects the perceived aesthetic value. Additionally, a human observer would consider a symmetric image more appealing than they would an asymmetric one. We investigated how these two factors jointly affect aesthetic preferences by manipulating both the amplitude spectrum slope and the symmetric level of images to assess their effects on aesthetic preference on a 6-point Likert scale. Our results showed that the preference ratings increased with the symmetry level but had an inverted U-shaped relation to amplitude spectrum slope. In addition, a strong interaction existed between symmetry level and amplitude spectrum slope on preference rating, in that symmetry can amplify the amplitude spectrum slope’s effects. A quadratic function of the spectrum slope can describe such effects. That is, preference is an inverted U-shaped function of spectrum slope whose intercept is determined by the number of symmetry axes. The modulation depth of the quadratic function manifests the interaction between the two factors.
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Arntzen, Erik, and Mateusz Mysior. "Expansion of Classes, Transfer of Function, and Preference Tests in Adult Participants." Perspectivas em Análise do Comportamento 15, no. 2 (2024): xx. https://doi.org/10.18761/pac071024.
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The purpose of the experiment was to study the effect of transfer of function on preferences on different stimuli conditions in 15 adults. Participants were trained on six conditional discriminations arranged as one-to-many training (AB/AC) and tested for the emergence of three 3-member equivalence classes. Fourteen of 15 participants passed the equivalence test. For the 14 participants, the classes were expanded by training three new stimuli GOOD (D1), NEUTRAL (D2), and BAD (D3), to A1, A2, and A3 and followed by a test equivalence class formation including all relations. The participants were also tested for the preference of the B-stimuli before the conditional discrimination training and after the test for expansion of classes. In the preference test after the expansion test, participants were presented with the three B-stimuli mounted on pictures of identical objects in groups of three (three stimulus conditions). The pictures of objects were three identical soda cans, cars, and mobile phones. The main findings were that 3 of 14 participants picked B1 in the pretest while 8 of 14 participants picked B1 for all stimulus sets in the preference test.
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Kannan, Hanumanthrao, Garima V. Bhatia, Bryan L. Mesmer, and Benjamin Jantzen. "Theoretical Foundations for Preference Representation in Systems Engineering." Systems 7, no. 4 (2019): 55. http://dx.doi.org/10.3390/systems7040055.
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The realization of large-scale complex engineered systems is contingent upon satisfaction of the preferences of the stakeholder. With numerous decisions being involved in all the aspects of the system lifecycle, from conception to disposal, it is critical to have an explicit and rigorous representation of stakeholder preferences to be communicated to key personnel in the organizational hierarchy. Past work on stakeholder preference representation and communication in systems engineering has been primarily requirement-driven. More recent value-based approaches still do not offer a rigorous framework on how to represent stakeholder preferences but assume that an overarching value function that can precisely capture stakeholder preferences exists. This article provides a formalism based on modal preference logic to aid in rigorous representation and communication of stakeholder preferences. Formal definitions for the different types of stakeholder preferences encountered in a systems engineering context are provided in addition to multiple theorems that improve the understanding of the relationship between stakeholder preferences and the solution space.
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Okuyama, Teruhiro, Saori Yokoi, Hideki Abe, et al. "A Neural Mechanism Underlying Mating Preferences for Familiar Individuals in Medaka Fish." Science 343, no. 6166 (2014): 91–94. http://dx.doi.org/10.1126/science.1244724.
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Social familiarity affects mating preference among various vertebrates. Here, we show that visual contact of a potential mating partner before mating (visual familiarization) enhances female preference for the familiarized male, but not for an unfamiliarized male, in medaka fish. Terminal-nerve gonadotropin-releasing hormone 3 (TN-GnRH3) neurons, an extrahypothalamic neuromodulatory system, function as a gate for activating mating preferences based on familiarity. Basal levels of TN-GnRH3 neuronal activity suppress female receptivity for any male (default mode). Visual familiarization facilitates TN-GnRH3 neuron activity (preference mode), which correlates with female preference for the familiarized male. GnRH3 peptides, which are synthesized specifically in TN-GnRH3 neurons, are required for the mode-switching via self-facilitation. Our study demonstrates the central neural mechanisms underlying the regulation of medaka female mating preference based on visual social familiarity.
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Cheung, Stephen L. "Comment on “Risk Preferences Are Not Time Preferences”: On the Elicitation of Time Preference under Conditions of Risk." American Economic Review 105, no. 7 (2015): 2242–60. http://dx.doi.org/10.1257/aer.20120946.
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Andreoni and Sprenger (2012a, b) report evidence that distinct utility functions govern choices under certainty and risk. I investigate the robustness of this result to the experimental design. I find that the effect disappears completely when a multiple price list instrument is used instead of a convex time budget design. Alternatively, the effect is reduced by half when sooner and later payment risks are realized using a single lottery instead of two independent lotteries. The result is thus at least partially driven by intertemporal diversification, supporting an explanation in terms of concavity of the intertemporal, and not only atemporal, utility function. (JEL C91, D81, D91)
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Neelon, Daniel P., Rafael L. Rodríguez, and Gerlinde Höbel. "On the architecture of mate choice decisions: preference functions and choosiness are distinct traits." Proceedings of the Royal Society B: Biological Sciences 286, no. 1897 (2019): 20182830. http://dx.doi.org/10.1098/rspb.2018.2830.
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Mate choice is an important cause of sexual selection; it can drive the evolution of extravagant ornaments and displays, and promote speciation through the reproductive isolation generated by rapid divergence of sexual traits. Understanding mate choice requires knowledge of the traits involved in generating mate-choice decisions, and how those traits may interact with each other. It has been proposed that mate-choice decisions are the outcome of two components that vary independently: the preference function (the ranking of the attractiveness of prospective mates) and choosiness (the effort invested in mate assessment). Here we test this hypothesis by examining individual variation in female preference functions and choosiness in green treefrogs ( Hyla cinerea ). We show that measures describing preference functions and choosiness are not correlated. We also show that both components are influenced differently by variation in female body size, and that preference function shape (closed and preferring intermediate values or open-ended and preferring extremes) has a strong influence on this relationship: function traits are positively correlated with body size only for individuals with closed functions, while choosiness is positively correlated with body size for individuals with open functions, but negatively for those with closed functions.
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Kim, Jin Bai, and Kern O. Kymn. "Rational choice function derived from a fuzzy preference." International Journal of Mathematics and Mathematical Sciences 11, no. 1 (1988): 43–45. http://dx.doi.org/10.1155/s0161171288000080.
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We shall prove that every fuzzy rational choice function is fuzzy regular (see Richter [6, p. 36] ), count the total number of the fuzzy rational choice ftmctions on a set of four elements and consider a semigroup of all fuzzy rational choice functions on a set.
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Rogers, Lesley J. "Development of Hand and Paw Preferences and Their Association with Other Patterns of Behaviour and Cognition." Symmetry 15, no. 4 (2023): 926. http://dx.doi.org/10.3390/sym15040926.
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Hand preference in non-human primates has been studied extensively with the aim of understanding the evolution of hemispheric asymmetry and hand preferences in humans. However, the focus has been on hand preferences expressed in adulthood, with a surprising lack of studies on hand preferences in infants and changes that occur during the development of other, potentially associated, asymmetries in the brain and behaviour. This paper reports on the development of hand preference for grasping food and taking it to the mouth in common marmosets. It considers the development of other types of behaviour, such as head cocking and anogenital licking, that parallel and might influence the development of hand preferences during the first months of life. It then discusses behavioural differences between left- and right-handed adult marmosets, including response to novel stimuli, social behaviour and cognitive bias. The need to study the development of hand preferences together with the development of these other expressions of cognitive function is highlighted. The question to be addressed by empirical studies is whether hand preference is a downstream manifestation of the development of hemispheric differences in sensory processing and cognition, or whether it is instrumental in the development of functional differences between the hemispheres. Comparison is made to paw preference and associated behaviour in non-primate species.
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Benabbou, Nawal, Cassandre Leroy, and Thibaut Lust. "An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2335–42. http://dx.doi.org/10.1609/aaai.v34i03.5612.
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We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinatorial optimization problems with unknown preferences. For the purpose of elicitation, we assume that the decision maker's preferences can be represented by a parameterized scalarizing function but the parameters are initially not known. Instead, the parameter imprecision is progressively reduced by asking preference queries to the decision maker during the search to help identify the best solutions within a population. Our algorithm, called RIGA, can be applied to any multi-objective combinatorial optimization problem provided that the scalarizing function is linear in its parameters and that a (near-)optimal solution can be efficiently determined when preferences are known. Moreover, RIGA runs in polynomial time while asking no more than a polynomial number of queries. For the multi-objective traveling salesman problem, we provide numerical results showing its practical efficiency in terms of number of queries, computation time and gap to optimality.
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Chen, Tinggui, Qianqian Li, Peihua Fu, et al. "Public Opinion Polarization by Individual Revenue from the Social Preference Theory." International Journal of Environmental Research and Public Health 17, no. 3 (2020): 946. http://dx.doi.org/10.3390/ijerph17030946.
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Social conflicts occur frequently during the social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social preferences of Internet users (network nodes) into three categories: egoistic, altruistic, and fair preferences, and adopt the revenue function to define the benefits obtained by individuals with different preferences among their interaction process so as to analyze their decision-making behaviors driven by the revenue. Secondly, the revenue function is used to judge the exit rules of nodes in a network, and then a dynamic network of spreading public opinion with the node (individual) exit mechanism is built based on a BA scale-free network. Subsequently, the influences of different social preferences, as well as individual revenue on the effect of public opinion polarization, are analyzed through simulation experiments. The simulation results show that (1) Different social preferences demonstrate different influences on the evolution of public opinions, (2) Individuals tend to interact with ones with different preferences, (3) The network with a single preference or a high aggregation is more likely to form public opinion polarization. Finally, the practicability and effectiveness of the proposed model are verified by a real case.
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Jiang, Yiheng, Yuanbo Xu, Yongjian Yang, Funing Yang, Pengyang Wang, and Chaozhuo Li. "Auto Encoding Neural Process for Multi-interest Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11879–87. https://doi.org/10.1609/aaai.v39i11.33293.
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Multi-interest recommendation constantly aspires to an oracle individual preference modeling approach, that satisfies the diverse and dynamic properties. Fueled by the deep learning technology, existing neural network (NN)-based recommender systems employ single-point or multi-point interest representation strategy to realize preference modeling,and boost the recommendation performance with a remarkable margin. However, as parameterized approximate functions, NN-based methods remain deficiencies with respect to the adaptability towards distinctive preference patterns cross different users and the calibration over the individual current intent. In this paper, we revisit multi-interest recommendation with the lens of stochastic process and Bayesian inference. Specifically, we propose to learn a distribution over functions to depict the individual diverse preferences rather than a unified function to approximate preference. Subsequently, the recommendation is encouraged with the uncertainty estimation which conforms to the dynamic shifting intent. Along these lines, we establish the connection between multi-interest recommendation and neural processes by proposing NP-Rec, which realizes the flexible multiple interests modeling and uncertainty estimation, simultaneously. Empirical study on 4 real world datasets demonstrates that our NP-Rec attains superior recommendation performances to several state-of-the-art baselines, where the average improvement achieves up to 13.94%.
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Xiao, Xingxing, and Jianzhong Li. "rkHit: Representative Query with Uncertain Preference." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–26. http://dx.doi.org/10.1145/3589271.
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A top-k query retrieves the k tuples with highest scores according to a user preference, defined as a scoring function. It is difficult for a user to precisely specify the scoring function. Instead, obtaining the distribution on scoring functions, i.e., the preference distribution, has been extensively explored in many fields. Motivated by this, we introduce the uniform (r,k)-hit (UrkHit) problem. Given a preference distribution, UrkHit aims to select a representative set of r tuples to maximize the probability of containing a tuple attractive to the user. We say a tuple attracts a user, if it is a top-k tuple for the scoring function adopted by the user. Further, we generalize UrkHit and propose the (r,k)-hit (rkHit) problem with an additional penalty function to model the user satisfaction with the tuple ranked i-th. rkHit aims to maximize the expected user satisfaction with the representative set. In 2D space, we design an exact algorithm 2DH for rkHit, indicating rkHit is in P for d=2. We show that rkHit is NP-hard when d\ge3. In 3D space, assuming a uniform preference distribution, we propose a (1-1/e)-approximation algorithm 3DH based on space partitioning. In addition, we propose an approximate algorithm MDH suitable for any dimension and distribution, which creatively combines the ideas of sampling and clustering. It relaxes the approximation guarantee slightly. Comprehensive experiments demonstrate the efficiency and effectiveness of our algorithms.