Relevant bibliographies by topics / Preference

Academic literature on the topic 'Preference'

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Published: 4 June 2021
Last updated: 20 July 2024

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Journal articles on the topic "Preference"

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Sánchez-Cuenca, Ignacio. "A Preference for Selfish Preferences." Philosophy of the Social Sciences 38, no. 3 (September 2008): 361–78. http://dx.doi.org/10.1177/0048393108319470.

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Purcell, A. T., R. J. Lamb, E. Mainardi Peron, and S. Falchero. "Preference or preferences for landscape?" Journal of Environmental Psychology 14, no. 3 (September 1994): 195–209. http://dx.doi.org/10.1016/s0272-4944(94)80056-1.

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McLaughlin, John P. "Aesthetic preference and lateral preferences." Neuropsychologia 24, no. 4 (January 1986): 587–90. http://dx.doi.org/10.1016/0028-3932(86)90103-x.

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Jenkins, J. Gregory, and Christine M. Haynes. "The Persuasiveness of Client Preferences: An Investigation of the Impact of Preference Timing and Client Credibility." AUDITING: A Journal of Practice & Theory 22, no. 1 (March 1, 2003): 143–54. http://dx.doi.org/10.2308/aud.2003.22.1.143.

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Explicitly stated client preferences are intended to persuade the auditor to accept a preferred outcome. This experimental study investigates two determinants of a preference's persuasiveness—timing and client credibility. Sixty-four experienced auditors completed two hypothetical cases, one involving disclosure of a contingent liability and the other involving the collectibility of a customer account. The findings suggest that audit judgments regarding contingent liability disclosure may be biased toward a client's preference if the preference is received prior to evidence evaluation (i.e., an early preference) but not if the preference is received at the end of the evidence evaluation process (i.e., a late preference). No such bias, however, is present for the collectibility judgment. Results also indicate that auditors who receive an early preference ask to examine more additional audit evidence than those who receive a late preference, although whether they seek additional evidence to confirm or disconfirm the client's preference remains unclear. Finally, no support is found for the ordinal interaction hypothesizing that only a highly credible client in the early preference condition will differentially influence contingent liability disclosure and accounts receivable collectibility judgments.
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Walden, Brian E., Rauna K. Surr, Mary T. Cord, Ken W. Grant, Van Summers, and Andrew B. Dittberner. "The Robustness of Hearing Aid Microphone Preferences in Everyday Listening Environments." Journal of the American Academy of Audiology 18, no. 05 (May 2007): 358–79. http://dx.doi.org/10.3766/jaaa.18.5.2.

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Automatic directionality algorithms currently implemented in hearing aids assume that hearing-impaired persons with similar hearing losses will prefer the same microphone processing mode in a specific everyday listening environment. The purpose of this study was to evaluate the robustness of microphone preferences in everyday listening. Two hearing-impaired persons made microphone preference judgments (omnidirectional preferred, directional preferred, no preference) in a variety of everyday listening situations. Simultaneously, these acoustic environments were recorded through the omnidirectional and directional microphone processing modes. The acoustic recordings were later presented in a laboratory setting for microphone preferences to the original two listeners and other listeners who differed in hearing ability and experience with directional microphone processing. The original two listeners were able to replicate their live microphone preferences in the laboratory with a high degree of accuracy. This suggests that the basis of the original live microphone preferences were largely represented in the acoustic recordings. Other hearing-impaired and normal-hearing participants who listened to the environmental recordings also accurately replicated the original live omnidirectional preferences; however, directional preferences were not as robust across the listeners. When the laboratory rating did not replicate the live directional microphone preference, listeners almost always expressed no preference for either microphone mode. Hence, a preference for omnidirectional processing was rarely expressed by any of the participants to recorded sites where directional processing had been preferred as a live judgment and vice versa. These results are interpreted to provide little basis for customizing automatic directionality algorithms for individual patients. The implications of these findings for hearing aid design are discussed. Los algoritmos automáticos de direccionalidad actualmente implementados en auxiliares auditivos asumen que las personas hipoacúsicas con pérdidas similares preferirán el mismo modo de procesamiento del micrófono en los ambientes cotidianos específicos de escucha. El propósito de este estudio fue evaluar la firmeza de las preferencias de micrófonos para la audición cotidiana. Dos personas hipoacúsicas establecieron juicios de preferencia en cuanto a los micrófonos (preferencia omnidireccional, preferencia direccional, sin preferencia) en una variedad de situaciones cotidianas de escucha. Simultáneamente, estos ambientes acústicos fueron registrados a través de modos omnidireccionales y direccionales de procesamiento del micrófono. Las grabaciones acústicas fueron luego presentadas en un contexto de laboratorio para preferencias del micrófono a los dos sujetos originales y a dos sujetos que diferían en su habilidad auditiva y en su experiencia con procesamiento direccional de micrófonos. Los dos sujetos originales pudieron replicar en el laboratorio sus preferencias de micrófono en vivo con un alto grado de exactitud. Esto sugiere que las bases para la preferencia original y aquella en vivo de los micrófonos fueron correctamente representadas en los registros acústicos. Otros participantes con hipoacusia y normoyentes que escucharon los registros ambientales también replicaron con exactitud las preferencias omnidireccionales originales en vivo; sin embargo, las preferencias direccionales no fueron tan consistentes entre todos ellos. Cuando la clasificación de laboratorio no replicó la preferencia direccional de micrófono en vivo, los sujetos casi siempre dejaron de expresar preferencia por ningún modo de micrófono. Por lo tanto, la preferencia para procesamiento omnidireccional raramente fue escogida por ninguno de los participantes para situaciones donde se había preferido el registro direccional como un juicio en vivo y viceversa. Se interpreta que estos resultados aportan poco en la búsqueda de adecuar automáticamente los algoritmos de direccionalidad para pacientes individuales. Se discuten las implicaciones de estos hallazgos en el diseño de auxiliares auditivos.
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Huang, Hui, Juan Zhang, Xuan Ren, and Xiang Zhou. "Greenness and Pricing Decisions of Cooperative Supply Chains Considering Altruistic Preferences." International Journal of Environmental Research and Public Health 16, no. 1 (December 26, 2018): 51. http://dx.doi.org/10.3390/ijerph16010051.

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With the development of the economy and science technology, global resource shortages and environmental pressures have become the focus of attention. More and more consumers tend to buy non-polluting and environmentally friendly green products, and many manufacturers and retailers are beginning to produce or sell green products to enhance their competitive advantage in the market. Considering the green preference attributes of consumers, the altruistic preference is introduced into the supply chain, and we establish four models: two cooperative manufacturers and one retailer are completely self-interested, one retailer has altruistic preference, two cooperative manufacturers have altruistic preferences, and two cooperative manufacturers and one retailer have altruistic preferences. We address the optimal greenness and pricing decisions of supply chain members, and analyze the impact of altruistic preferences on supply chain decision-making and profits. The results show that the altruistic preference coefficient can significantly affect the decision-making and the profits of supply chain members, and when two manufacturers and one retailer consider altruistic preferences, the altruistic preference coefficients adopted by the three parties are in the certain ranges, the supply chain members’ altruistic preference coefficients can increase the whole supply chain profit. Through analysis, in the three cases where the retailer has altruistic preferences, two manufacturers have altruistic preferences, and two manufacturers and one retailer have altruistic preferences, two manufacturers should adopt higher altruistic preference coefficients, and the retailer should adopt a lower altruistic preference coefficient, and the product greenness under the three altruistic preferences is higher than the product greenness when there is no altruistic preference.
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Fishburn, Peter C. "Ordered preference differences without ordered preferences." Synthese 67, no. 2 (May 1986): 361–68. http://dx.doi.org/10.1007/bf00540076.

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Warren, Caleb, A. Peter McGraw, and Leaf Van Boven. "Values and preferences: defining preference construction." Wiley Interdisciplinary Reviews: Cognitive Science 2, no. 2 (July 9, 2010): 193–205. http://dx.doi.org/10.1002/wcs.98.

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Turley, Marianne, Susan Wang, Di Meng, Michael H. Kanter, and Terhilda Garrido. "An information model for automated assessment of concordance between advance care preferences and care delivered near the end of life." Journal of the American Medical Informatics Association 23, e1 (November 13, 2015): e118-e124. http://dx.doi.org/10.1093/jamia/ocv149.

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Abstract Objective To develop an information model for automating evaluation of concordance between patient preferences and end-of-life care. Methods We modeled and validated 15 end-of-life care preference option domains, to which we mapped preferences recorded in standardized advance care planning documents and 232 end-of-life care events defined by procedure and medication codes. Patient preferences and end-of-life care events were available in electronic health records. Data from Kaiser Permanente Southern California modeling and testing populations were evaluated for concordance between patients’ preferences and the end-of-life care events they experienced. Results The information model successfully assessed concordance between patient preferences and end-of-life care events. Among 388 expired patients in the modeling population, 4164 care events occurred, 4100 (98%) of which were preference-concordant, and 64 (2%) of which were preference-discordant. Including end-of-life care events that did not occur increased the number of observations to 6029; 99% were preference-concordant. At the level of individuals, 72% (278) of patients experienced only preference-concordant care events, 13% (50) experienced at least one preference-discordant care event, and 15% (60) experienced no preference-related care events. Discussion Model limitations pertain to assumptions that are required to match advance care planning documents with patient preference options and exclusion of preferred care that did not occur. Further research is required to apply the model to larger populations and to investigate the need for additional preference options. Conclusion An information model for automating the assessment of the concordance between patients’ advance care planning preferences and the end-of-life care they received was effective in a small population and has the potential to assess population-level preference-concordance on an ongoing basis.
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Tudor, Scarlett, and Molly Morris. "Variation in male mate preference for female size in the swordtail Xiphophorus malinche." Behaviour 146, no. 6 (2009): 727–40. http://dx.doi.org/10.1163/156853909x446172.

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AbstractMale and female mate preferences have the potential to influence one another, and such interactions could help explain variation in female mate preferences. In Xiphophorus malinche, larger females prefer asymmetrical males while smaller females prefer symmetrical males. We used a two-part preference test to determine if there were differences in mate preferences between symmetrical and asymmetrical males for female size that could influence female mate preference. We found no significant difference between symmetrical and asymmetrical male's preferences. A preference for large female size was detected during the time males directly interacted with females and in standard dichotomous choice tests that followed, as long as the males had been isolated less than 30 days. We did detect variation in male preference for female size depending on male size and the amount of time a male was isolated. These results suggest that variation in male mate preference is not likely to have produced the difference in female preference for symmetry between large and small females, but should be considered where females vary in their preference for male size. In addition, our results suggest that males may shift their preferences from large to small females depending on time since last mating opportunity.
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Dissertations / Theses on the topic "Preference"

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Zhao, Jinhua 1977. "Preference accommodating and preference shaping : incorporating traveler preferences into transportation planning." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54221.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2009.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 212-220).
This dissertation examines the psychological factors that influence travel behavior such as people's personality traits, environmental attitudes, car pride and perceptions of convenience and comfort. Despite the recognition of the importance of these psychological factors in better understanding travel behavior, transportation agencies have failed to integrate them into planning practice and policy debate in the quantitative way. This dissertation reflects on this failure, identifies the barriers that have contributed to it, and reviews innovations in travel behavior research which may help overcome these barriers. This dissertation proposes a structure for analyzing traveler preferences that incorporates these psychological factors into travel behavior analysis. A set of eight factors are presented as the latent elements of travel preferences to illustrate the structure, including two personality traits; three environmental attitude factors and car pride; and two perceptual factors of convenience and comfort. A MIMIC model quantifies the eight factors and examines the relationships among these factors as well as between them and socioeconomic variables. Despite the significant correlations with socioeconomic variables, personality, attitudes and perceptions prove to be characteristics of individuals that are distinct from the socioeconomics. The dissertation presents three applications that incorporate these latent factors into travel demand analysis of three critical aspects of travel behavior: car use, mode choice and car ownership. Incorporating the latent variables significantly improves the overall exploratory power of the transportation models.
(cont.) The results suggest that plausible changes in traveler preferences can have an effect on behavior in magnitude similar to the impacts that result from rising household income or increased population density. Unobserved heterogeneities exist not only for preferences with respect to observed variables such as travel time, but also for latent factors such as car pride and perception of convenience. Preference Accommodating and Preference Shaping in Transportation Planning 3 Mutual dependencies between travel preferences and behavior are identified and the direction and strength of the causal connections are modeled explicitly. Depending on the specific latent factors and aspect of travel behavior, the causal relationships could be from preferences to behavior, from behavior to preferences, or be significant in both directions concurrently These three applications also demonstrate in terms of methodology that 1) hierarchical relationships among latent factors can be simultaneously estimated with discrete choice models; 2) latent variable and latent class modeling techniques can be combined to test unobserved heterogeneities in travelers' sensitivity to latent variables; 3) causal relationships between behavior and preferences can be examined in the SEM or hybrid SEM and discrete choice model. This dissertation proposes two complementary perspectives to examine how to embed traveler preferences in the planning practice: planning as preference accommodating and planning as preference shaping.
(cont.) Combining both perspectives, this dissertation argues that by ignoring the importance of traveler preferences, not only may we make serious mistakes in the planning, modeling and appraisal processes, but we may also fail to recognize significant opportunities to mitigate or solve transportation problems by influencing and exploiting changes in people's preferences.
by Jinhua Zhao.
Ph.D.
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Vosper, Jane. "Using stated preference choice modelling to determine treatment preferences : investigating preferences for depression treatment." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.559707.

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Background & Aim The PhD aimed to quantify preferences for depression treatments using Discrete Choice Experiments (DCEs). A secondary aim was to investigate the relationship between demographic variables, psychological variables and preferences. Methods & Analysis Two DCEs were designed and administered, investigating preferences for treatment of depression by: (1) a drug and (2) a physical activity intervention. The DCE designs were informed by focus groups and qualitative interviews. A best-worst scaling DCE was used for both studies. The physical activity intervention DCE was included in the baseline questionnaire of an RCT at the University of Bristol. Demographic information and psychological measures (including BDI scores) were analysed alongside the DCE. The drug treatment DCE was distributed as a postal questionnaire to a general population sample of 5000. Psychological measures of illness and medicine beliefs as well as the BDI were included in the questionnaire. Demographic data were also collected. Quantitative data were analysed primarily using conditional logistic regression. Results Results from the Physical activity DCE (for 152 patients) indicated that on average, patients particularly valued key aspects of the intervention, such as small goals, fitting activity into daily routine and having support over the intervention itself (being one giving choice of activity rather than exercise on prescription). Results from Drug study DCE (for 425 respondents) revealed a particular desire for no side-effects: large improvements in likely effectiveness are required to compensate respondents for non-zero risks of these. Heterogeneity analysis for both studies revealed effects of a number of demographic and psychological variables on preferences for attributes of depression treatment. Discussion Results of both studies are discussed in terms of their policy relevance and also from a methodological angle. The implications the results have on the use of DCEs in health care is considered.
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Rybáková, Nina. "Mezičasová volba osob romského etnika a většinové populace." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75191.

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This thesis deals with the differences in time preferences, individual discount rates, among non-Romany (Czechs) and Romany ethnic group in the Czech Republic. Subjects of almost homogenous ethnic groups -- low income, low education, currently unemployed -- were subjected to experiments based on an in-depth questionnaire (Romany N = 35, N = 25 non-Romany). In addition to time preferences, their relationship with the socio-economic and demographic characteristics was inspected. The sub-tests showed a difference across ethnic groups in regard to the discounting models. Romany ethnic group is better characterized by an inconsistent model compared to the non-Romany using an more consistent model. Participants from both groups, however, appear to be very impatient, with discount rates closer to the values of individuals dependent on addictive substances. Probably because of insufficient sample size, no statistically significant correlation was found between the discount rates and socio-economic and demographic factors. Financial (il)literacy among Romany proved to be an important factor affecting the formation of savings. Finally, the recorded signal effect of expenditures on signal goods among the Romany ethnic group is briefly analyzed.
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Earthy, Philippa Jane. "Psychological context effects in preference and preference mapping." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363410.

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Kvapil, Mikuláš. "Preference šikmosti." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-198221.

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The diploma thesis is concerned with analysis of behavior of bettors on horse races. The aim is to test the hypothesis that bettors prefer skewness and they are risk averse. In the thesis is used the method of empirical data collection of horse racing in the Czech Republic and their subsequent testing using a regression model. The testing of the presented model did not confirm the hypothesis of skewness preference in the case of betting on horse races in the Czech Republic.
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Dave, Kaushali. "Preference elicitation and preference uncertainty : an application to noise valuation." Thesis, University of Leeds, 2011. http://etheses.whiterose.ac.uk/2593/.

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The valuation of environmental impacts through Choice Experiments (CE) has been increasing applied in order to estimate the cost of environmental externalities. While this valuation technique offers several advantages over other methods, a crucial problem lies in representing the attributes in a manner that can be easily understood by the respondents. Another problem associated with this valuation technique is the assumption that respondents have known and consistent preferences. This thesis relaxes the restraint by allowing respondents to indicate their level of preference certainty. The effect of different attribute representation techniques especially in context of traffic noise is also examined in relation to the level of preference certainty, while the effect of preference elicitation methods on certainty levels is also scrutinised. Several CE surveys were conducted to evaluate the impact of traffic noise under a residential setting. In order to examine the effects of attribute representation method on the respondents, two different surveys were undertaken using the location and the linguistic representation techniques. This has been carried out in conjunction with three different methods of preference elicitation: the binary choice, one stage Likert and two stage Likert methods. Thus for each of the attribute representation methods, different preference elicitation techniques have been employed. The main purpose of the analyses has been to examine the variation in error structure and the need for error flexibility due to the different preference elicitation and representation techniques. The results reveal that these components of choice design significantly affect respondents’ decision making and subsequent valuation. Moreover, different methods of representation also influence the level and cause of preference uncertainty as well the decision process.
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Cai, Changqing. "Personal preference prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ61879.pdf.

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Gustafsson, Johan E. "Preference and Choice." Doctoral thesis, KTH, Filosofi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-34690.

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Jenson, Audra Christine. "Adaptive Preference Tradeoffs." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83433.

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Consider the following scenario: A mother chooses to marry off her 10 year-old daughter, not because she doesn’t know the harmful effects of child marriage, nor because she thinks that it is good that her daughter marries when she is 10 years old. Rather, she is unable to feed her daughter and realizes that her daughter’s survival depends upon her marrying a financially stable man. This is an apparent example of what human development practitioners and political philosophers call an adaptive preference (AP): a preference, formed under oppressive circumstances, that seems to perpetuate the agent’s own oppression. Prevailing opinion is that forced tradeoffs—especially following Serene Khader’s taxonomy—, like the case presented above, are a type of AP: one in which a person makes a decision because of a limited option set. In this paper I argue that no paradigm cases of forced tradeoffs should not be classified as APs. Instead, I offer a revised definition of adaptive preferences where I argue that adaptive preferences are psychological traits that cause the agent with adaptive preferences to make irrational or uninformed decisions that perpetuate their own oppression. I defend this new definition by exploring the implications of changing the definition. In particular, forced tradeoffs involve different kinds of interventions from other kinds of adaptive preferences and including forced tradeoffs risks committing testimonial injustice against those who have limited option sets.
Master of Arts
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Chien, Yung-hsin. "Probabilistic preference modeling /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Books on the topic "Preference"

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Epstein, Larry G. Habits, interdependent preferences and time preference. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1991.

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Puterbaugh, Geoff. Born gay?: Hand preference and sex preference. Cupertino, Calif: Henderson Associates, 1986.

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Grüne-Yanoff, Till, and Sven Ove Hansson, eds. Preference Change. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2593-7.

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Roubens, Marc, and Philippe Vincke. Preference Modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-46550-5.

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Fürnkranz, Johannes, and Eyke Hüllermeier, eds. Preference Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14125-6.

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1951-, Vincke Philippe, ed. Preference modelling. Berlin: Springer-Verlag, 1985.

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Hüllermeier, Eyke, and Johannes Fürnkranz. Preference learning. Heidelberg: Springer, 2010.

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Bradley, M. A. Sumultaneous analysis of stated preference and revealed preference information. [London]: [PTRC Education and Research Services], 1990.

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Naked racial preference. Lanham: Madison Books, 1995.

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Oregon. Dept. of Administrative Services., ed. Reciprocal preference law. Salem, OR: State of Oregon, Dept. of Administrative Services, 1995.

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Book chapters on the topic "Preference"

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Vind, Karl. "Preferences and preference functions." In Independence, Additivity, Uncertainty, 27–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24757-9_3.

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Marczak, Edward, and Greg Neagle. "Preference Manifests and “Raw” Preferences." In Enterprise Mac Managed Preferences, 149–66. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2938-4_10.

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Takemura, Kazuhisa. "Preference and Preference Structure." In Behavioral Decision Theory, 15–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5453-4_2.

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Pomerantz, Anita, and John Heritage. "Preference." In The Handbook of Conversation Analysis, 210–28. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118325001.ch11.

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Hayashi, Takashi. "Preference." In Microeconomic Theory for the Social Sciences, 33–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3541-0_3.

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Bradley, Richard. "Preference Kinematics." In Preference Change, 221–42. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2593-7_11.

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Fürnkranz, Johannes, and Eyke Hüllermeier. "Preference Learning: An Introduction." In Preference Learning, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_1.

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Kamishima, Toshihiro, and Shotaro Akaho. "Dimension Reduction for Object Ranking." In Preference Learning, 203–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_10.

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Dembczyński, Krzysztof, Wojciech Kotłowski, Roman Słowiński, and Marcin Szeląg. "Learning of Rule Ensembles for Multiple Attribute Ranking Problems." In Preference Learning, 217–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_11.

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Yaman, Fusun, Thomas J. Walsh, Michael L. Littman, and Marie desJardins. "Learning Lexicographic Preference Models." In Preference Learning, 251–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_12.

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Conference papers on the topic "Preference"

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MacDonald, Erin, Richard Gonzalez, and Panos Papalambros. "Preference Inconsistency in Multidisciplinary Design Decision Making." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35580.

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Research from behavioral psychology and experimental economics asserts that individuals construct preferences on a case-by-case basis when called to make a decision. A common, implicit assumption in engineering design is that user preferences exist a priori. Thus, preference elicitation methods used in design decision making can lead to preference inconsistencies across elicitation scenarios. This paper offers a framework for understanding preference inconsistencies, within and across individual users. We give examples of three components of this new framework: comparative, internal, and external inconsistencies across users. The examples demonstrate the impact of inconsistent preference construction on common engineering and marketing design methods, including discrete choice analysis, modeling stated vs. revealed preferences, and the Kano method and thus QFD. Exploring and explaining preference inconsistencies produces new understandings of the relationship between user and product.
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Kulok, Michael, and Kemper Lewis. "Preference Consistency in Multiattribute Decision Making." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84764.

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A number of approaches for multiattribute selection decisions exist, each with certain advantages and disadvantages. One method that has recently been developed, called the Hypothetical Equivalents and Inequivalents Method (HEIM) supports a decision maker (DM) by implicitly determining the importances a DM places on attributes using a series of simple preference statements. In this and other multiattribute selection methods, establishing consistent preferences is critical in order for a DM to be confident in their decision and its validity. In this paper a general consistency check denoted as the Preference Consistency Check (PCC) is presented that ensures a consistent preference structure for a given DM. The PCC is demonstrated as part of the HEIM method, but is generalizable to any cardinal or ordinal preference structures. These structures are important in making selection decisions in engineering design including selecting design concepts, materials, manufacturing processes, and configurations, among others. The effectiveness of the method is demonstrated and the need for consistent preferences is illustrated using a product selection case study where the decision maker expresses inconsistent preferences.
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Sanneman, Lindsay. "Preference Elicitation and Explanation in Iterative Planning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/917.

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Planning for complex scenarios, particularly in which large teams of humans with distributed expertise and varying preferences share a set of resources, poses a number of challenges including integrating distributed information and accounting for context-dependent preferences and constraints. We see three key pieces to solving the problem of introducing autonomous assistance through a mixed-initiative planning system in these scenarios: preference elicitation, integrating preferences into planning, and providing tailored explanations back to the humans in the loop. The process of preference elicitation, planning, and explanation can be integrated as an iterative process by which teams can efficiently converge on the ideal schedule. Linear Temporal Logic (LTL) is a common language, readily understandable by both planners and humans, that provides a natural link between the three components of the iterative planning problem, facilitating both elicitation of expressive preferences and intelligible explanations of the system's decision-making processes. Outputs of each of the preference elicitation, planning, and explanation pieces can be expressed as LTL specifications and used as inputs to each next step in the process. We propose to explore preference elicitation, planning, and explanation using LTL specifications and the integration of these pieces into an iterative process.
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Cao, Dongxing, Karthik Ramani, Zhanjun Li, Victor Raskin, Ying Liu, and Zhanwei Li. "Developing Customer Preferences for Concept Generation by Using Engineering Ontologies." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28499.

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As customer preference is viewed as a reflection of desires for product features and even future product opportunities, it is crucial for the generation of design concepts. In this paper, a six-stage methodology is described for developing customer preference by using engineering ontology. The methodology mainly focuses upon understanding the product domain knowledge and preference concepts. A modeling process of the preference ontology for searching, indexing, and retrieving domain knowledge is described. The taxonomies of the customer preferences are elicited by classifying specific concepts. The definition of preference concepts and their ontological relationships are extracted. The objective is to allow product designers to generate customer preference ontologies for their specific products. At first, the documents or catalogs of design requirements are normalized by using ontology-based semantic representation. Design specification formalization is required for system input. A model of preference elicitation from customers is proposed based on ontology knowledge for concept generation. Secondly, the attributes of the customer preferences are classified by identifying the root concepts and developing a kind of preference taxonomy as well as their relationships to each other. They are mapped to engineering ontologies for driving high-level preference concept generation. A customer preference knowledge modeling is developed to construct a thesaurus for preference terms within the domain ontology. Finally, the evaluation and analysis are given to describe the validity of concept generation from customer preferences.
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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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Burnap, Alex, Yi Ren, Honglak Lee, Richard Gonzalez, and Panos Y. Papalambros. "Improving Preference Prediction Accuracy With Feature Learning." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35440.

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Motivated by continued interest within the design community to model design preferences, this paper investigates the question of predicting preferences with particular application to consumer purchase behavior: How can we obtain high prediction accuracy in a consumer preference model using market purchase data? To this end, we employ sparse coding and sparse restricted Boltzmann machines, recent methods from machine learning, to transform the original market data into a sparse and high-dimensional representation. We show that these ‘feature learning’ techniques, which are independent from the preference model itself (e.g., logit model), can complement existing efforts towards high-accuracy preference prediction. Using actual passenger car market data, we achieve significant improvement in prediction accuracy on a binary preference task by properly transforming the original consumer variables and passenger car variables to a sparse and high-dimensional representation.
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Grossi, Davide, Wiebe van der Hoek, and Louwe B. Kuijer. "Logics of Preference when There Is No Best." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/46.

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Well-behaved preferences (e.g., total pre-orders) are a cornerstone of several areas in artificial intelligence, from knowledge representation, where preferences typically encode likelihood comparisons, to both game and decision theories, where preferences typically encode utility comparisons. Yet weaker (e.g., cyclical) structures of comparison have proven important in a number of areas, from argumentation theory to tournaments and social choice theory. In this paper we provide logical foundations for reasoning about this type of preference structures where no obvious best elements may exist. Concretely, we compare and axiomatize a number of ways in which the concepts of maximality and optimality can be generalized in this general class of preferences. We thereby expand the scope of the long-standing tradition of the logical analysis of preference.
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Zafari, Farhad, Rasoul Rahmani, and Irene Moser. "Proposing a Highly Accurate Hybrid Component-Based Factorised Preference Model in Recommender Systems." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/185.

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Recommender systems play an important role in today's electronic markets due to the large benefits they bring by helping businesses understand their customers' needs and preferences. The major preference components modelled by current recommender systems include user and item biases, feature value preferences, conditional dependencies, temporal preference drifts, and social influence on preferences. In this paper, we introduce a new hybrid latent factor model that achieves great accuracy by integrating all these preference components in a unified model efficiently. The proposed model employs gradient descent to optimise the model parameters, and an evolutionary algorithm to optimise the hyper-parameters and gradient descent learning rates. Using two popular datasets, we investigate the interaction effects of the preference components with each other.We conclude that depending on the dataset, different interactions exist between the preference components. Therefore, understanding these interaction effects is crucial in designing an accurate preference model in every preference dataset and domain.Our results show that on both datasets, different combinations of components result in different accuracies of recommendation, suggesting that some parts of the model interact strongly. Moreover, these effects are highly dataset-dependent, suggesting the need for exploring these effects before choosing the appropriate combination of components.
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Zhang, Lu, Zhu Sun, Ziqing Wu, Jie Zhang, Yew Soon Ong, and Xinghua Qu. "Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/521.

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Existing studies on next point-of-interest (POI) recommendation mainly attempt to learn user preference from the past and current sequential behaviors. They, however, completely ignore the impact of future behaviors on the decision-making, thus hindering the quality of user preference learning. Intuitively, users' next POI visits may also be affected by their multi-step future behaviors, as users may often have activity planning in mind. To fill this gap, we propose a novel Context-aware Future Preference inference Recommender (CFPRec) to help infer user future preference in a self-ensembling manner. In particular, it delicately derives multi-step future preferences from the learned past preference thanks to the periodic property of users' daily check-ins, so as to implicitly mimic user’s activity planning before her next visit. The inferred future preferences are then seamlessly integrated with the current preference for more expressive user preference learning. Extensive experiments on three datasets demonstrate the superiority of CFPRec against state-of-the-arts.
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Li, Minyi, and Borhan Kazimipour. "An Efficient Algorithm To Compute Distance Between Lexicographic Preference Trees." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/262.

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Very often, we have to look into multiple agents' preferences, and compare or aggregate them. In this paper, we consider the well-known model, namely, lexicographic preference trees (LP-trees), for representing agents' preferences in combinatorial domains. We tackle the problem of calculating the dissimilarity/distance between agents' LP-trees. We propose an algorithm LpDis to compute the number of disagreed pairwise preferences between agents by traversing their LP-trees. The proposed algorithm is computationally efficient and allows agents to have different attribute importance structures and preference dependencies.
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Reports on the topic "Preference"

1

Crawford, Ian, Richard Blundell, Abi Adams, and Martin Browning. Prices versus preferences: taste change and revealed preference. IFS, March 2015. http://dx.doi.org/10.1920/wp.ifs.2015.1511.

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Andreoni, James, and Charles Sprenger. Risk Preferences Are Not Time Preferences: Discounted Expected Utility with a Disproportionate Preference for Certainty. Cambridge, MA: National Bureau of Economic Research, September 2010. http://dx.doi.org/10.3386/w16348.

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Nottingham, M. The "safe" HTTP Preference. RFC Editor, December 2019. http://dx.doi.org/10.17487/rfc8674.

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Gao, Zhenyu, Yan Luo, Shu Tian, and Hao Yang. Green Preference, Green Investment. Asian Development Bank, April 2024. http://dx.doi.org/10.22617/wps240238-2.

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This paper examines whether individual investors’ green preference will be reflected in their investment decisions. It provides compelling evidence that individuals with stronger green preference invest more in green mutual funds, influenced by concerns over the physical and regulatory risks of climate change. It suggests that this behavior is not driven by financial incentives as preference-related investments may not always lead to financial gains from trading.
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Sorkin, Isaac. Ranking Firms Using Revealed Preference. Cambridge, MA: National Bureau of Economic Research, October 2017. http://dx.doi.org/10.3386/w23938.

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Amstutz, Richard D., Timothy R. Williams, and Michael C. Chisick. Dental Continuing Education Preference Survey. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada253812.

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Coles, Peter, Alexey Kushnir, and Muriel Niederle. Preference Signaling in Matching Markets. Cambridge, MA: National Bureau of Economic Research, July 2010. http://dx.doi.org/10.3386/w16185.

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Adams, Abi. Mutually consistent revealed preference bounds. Institute for Fiscal Studies, August 2015. http://dx.doi.org/10.1920/wp.ifs.2015.1520.

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Thoreson, Dale, Leo L. Timms, and Don Lay. Dairy Free Stall Preference Field Study. Ames (Iowa): Iowa State University, January 2006. http://dx.doi.org/10.31274/ans_air-180814-84.

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Goldin, Jacob, and Daniel Reck. Revealed Preference Analysis with Framing Effects. Cambridge, MA: National Bureau of Economic Research, October 2018. http://dx.doi.org/10.3386/w25139.

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