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Academic literature on the topic 'Will and preferences'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Will and preferences"

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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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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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Kreitner, Roy. "Anti-preferences." Theoretical Inquiries in Law 22, no. 2 (July 1, 2021): 299–328. http://dx.doi.org/10.1515/til-2021-0024.

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Abstract This Article offers a critical evaluation of preference satisfaction as a frame for normative thinking. It begins with an internal critique of the way preferences work in normative economics, distinguishing among three elements: welfare; preferences; and choices. For preference satisfaction to work well, it must be able to bridge two gaps, one between choice and preferences, and another between preferences and welfare. In contexts where both those gaps are bridged, preference satisfaction offers a workable normative framework; where at least one of those gaps is unbridgeable, the framework should be treated with extreme caution if not jettisoned altogether. The Article then goes on to pursue an external critique, by asking what price we pay for using the preference satisfaction framework when it appears to perform well. The point of the critique is that even when preference satisfaction provides a good normative framework on its own terms, the framework obscures considerations that should not be ignored. By pursuing one concrete example, the Article shows how broad considerations regarding the implications of the regime of wage labor are absent from legal contemplation when labor law is imagined and shaped through the lens of preference satisfaction. The Article concludes with a speculation about how different theories of welfare might be employed in concert, rather than as alternatives. It suggests that a pluralism of theory is a way to expose the political stakes in the kinds of policy discussion where preference satisfaction is often a dominant way of thinking.
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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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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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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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Williams, Evan G. "Preferences’ Significance Does Not Depend on Their Content." Journal of Moral Philosophy 13, no. 2 (March 16, 2016): 211–34. http://dx.doi.org/10.1163/17455243-4681064.

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Moral theories which include a preference-fulfillment aspect should not restrict their concern to some subset of people’s preferences such as “now-for-now” preferences. Instead, preferences with all contents—e.g. ones which are external, diachronic, or even modal—should be taken into account. I offer a conceptualization of preferences and preference fulfillment which allows us to understand odd species of preferences, and I give a series of examples showing what it would mean to fulfill such preferences and why we ought to do so.
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Miao, Bin, and Songfa Zhong. "Comment on “Risk Preferences Are Not Time Preferences”: Separating Risk and Time Preference." American Economic Review 105, no. 7 (July 1, 2015): 2272–86. http://dx.doi.org/10.1257/aer.20131183.

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Andreoni and Sprenger (2012a,b) observe that utility functions are distinct for risk and time preferences, and show that their findings are consistent with a preference for certainty. We revisit this question in an enriched experimental setting in which subjects make intertemporal decisions under different risk conditions. The observed choice behavior supports a separation between risk attitude and intertemporal substitution rather than a preference for certainty. We further show that several models, including Epstein and Zin (1989); Chew and Epstein (1990); and Halevy (2008) exhibit such a separation and can account for the overall experimental findings. (JEL C91, D81, D91)
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Dissertations / Theses on the topic "Will and preferences"

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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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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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Clark, Michael D. "Eliciting preferences using discrete choice experiments in healthcare : willingness to pay, stakeholder preferences, and altruistic preferences." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/88792/.

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Chapter 1 of the thesis is divided into 5 sections. Section A begins by defining a Discrete Choice Experiment (DCE), and outlines the key stages involved in conducting a DCE. Sections B and C outline theories underpinning DCE analysis. Section B outlines the characteristics theory of demand, whilst section C, explains random utility theory (RUT), compensating variation (CV), marginal willingness to pay (MWTP), and willingness to pay (WTP) analysis. Section D of the thesis provides a review of the DCE literature. Section E outlines the research questions addressed in the thesis including calculating WTP and hypothetical bias; the description of the cost attribute; preference heterogeneity; and altruism. Chapter 2 shows how DCEs can be used to calculate WTP, using a DCE relating to Deep Vein Thrombosis (DVT). Chapter 3 uses data from a DCE applied to Menstrual disorder and Gynaecology patients. It evaluates an experimental method I developed to establish whether respondents might fail to factor in the monetary attribute into their DCE decision making, leading to hypothetical bias. Chapter 4 applies essentially the same DCE design but only analyses data from Gynaecology patients. Chapters 4-8 all use data obtained from a DCE relating to preferences for different allocation criteria for allocating kidneys for transplantation. Chapters 5 and 6 look at preference heterogeneity which is observable using interaction dummy variables (the issue of unobserved preference heterogeneity is considered in chapter 7). Chapter 5 establishes how marginal rates of substitution (MRS) differ between different respondent groups including renal patients, healthcare professionals, live donors / relatives of deceased donors, carers, and ethnic minority versus non-ethnic minority patients. Chapter 6 establishes how MRS differs between non-white ethnic minority patients versus other patients; South Asian patients versus other patients; and according to respondent gender. Chapter 7 of the thesis compares results from models which do not cater for unobserved preference heterogeneity, with results from models which do. Initially 2 basic models which do not cater for preference heterogeneity at all (because they do not include dummy variables) are applied including random effects logit and conditional logit. Then models catering for unobserved preference heterogeneity including Mixed Logit and a Latent Class Model (LCM) are used. Finally there is an analysis involving the application of conditional logit with interaction dummy variables. Chapter 8 of the thesis explores how preferences might differ according to how altruistic respondents are. It establishes how respondent preferences differ according to respondent self-disclosed perspective when answering DCEs. In other words whether they claimed to answer the DCE in terms of what would be best for me; what would be best for me and others; or what is best for others. Finally chapter 9 involves a discussion of the findings emerging from the thesis, and draws conclusions about the merits of material contained in it.
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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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Harris, Alexander Nicholas Edward. "Preferences and cooperation." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/287933.

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Chapter 1: Evolution of reciprocator preferences when agents can pay for information. A benchmark result in the evolutionary games literature is that a preference for reciprocity will evolve if preferences are observable (at zero cost), since reciprocators can cooperate with each other rather than with materialists, thereby achieving a fitness advantage. I investigate how a preference for reciprocity evolves if individuals can observe an opponent's preferences only by bearing a fitness cost. My main result applies when observing an opponent's type is cheap, but cooperating only gives a modest fitness advantage or the preference for reciprocity is intense. In this case, a preference for reciprocity cannot evolve from a small starting share in the mix of preferences, even if discovering an opponent's preferences is arbitrarily cheap. This is in sharp contrast to the benchmark result. Chapter 2: A theory of conditional cooperation on networks (with Julien Gagnon) Chapter 2 is a study of reciprocity on social networks. We model a group of connected agents who play a one-shot public good game. Some players are materialists and others are reciprocators. We characterise the maximal Nash equilibrium (ME) of this game for any network and a broad class of reciprocal preferences. At the ME, a novel concept, the q-linked set, fully determines the set of players who contribute. We show that influential players are those connected to players who are sufficiently interconnected, but not too much. Finally, we study the decision of a planner faced with an uncertain type profile who designs the network to maximise expected contributions. The ex ante optimal network comprises isolated cliques of degree k*, with k* decreasing with the incidence of materialists. We discuss an important application of our results: the workplace. Chapter 3: Ideological games Chapter 3 is a theory of ideology. I define a preference type to be a set of first-order preferences over the outcomes of a `game of life', together with a set of (`meta-') preferences over all players' first-order preferences. Players can influence each other's preferences via costly investment: if player A invests and B does not, B's preferences becomes those of A. Players may invest for instrumental reasons (i.e. to achieve better outcomes in the game of life) or `ideological' reasons (i.e. they want their opponents to have the same preferences they do). I characterise `strongly ideological', `weakly ideological' and `pragmatic' types. Weakly ideological types wish to preserve their own type, as do strongly ideological types, who also seek to convert others. A pragmatic player, in contrast, is willing to have her type changed if her new type would prefer the resulting equilibrium of the game of life to the status quo. I show that if two players of different ideological types meet, there is an equilibrium investment profile with lower aggregate welfare than the no-invest profile. If at least one type is strongly ideological, there is a unique such equilibrium. Finally, a `perfectly ideological' type is a strongly ideological type which, if held by all players, results in the best outcome of the game of life as judged by that type. If a perfectly ideological player plays a pragmatic player, aggregate welfare is always greater than in the no-invest profile.
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Boxer, Christie Marie Fitzgerald. "Predicting Spouse Preferences." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3266.

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I test canonical theories in the preference literature - evolutionary psychology, social role theory, and social exchange theory - using group mean comparisons to replicate basic sex differences in spouse preferences. I find that, consistent with past studies, males prefer attractiveness and females prefer resources in potential partners, and in general, we prefer partners who are similar, rather than different, to us. I also find that males who anticipate enacting the "traditional" male role of "provider" within their marriage tend to prefer spouses who would fulfill the caregiver role, compared to males who do not anticipate such "traditional" gender divisions within the family. Interestingly, females who anticipate the "traditional" caregiving role do not in turn prefer spouses who fulfill the "provider" role; they instead prefer a spouse who is family-oriented, as they themselves are. I further test four new theoretical derivations and methodological assessment techniques. First, I expand the test of social exchange theory to include a wide array of personality characteristics and find similarity between how respondents see themselves and the types of characteristics they prefer in a spouse. Second, I include an assessment of gender endorsement - how respondents see themselves in terms of characteristics we commonly associate with masculinity and femininity. Interestingly, I don't find the predicted complimentarity - that highly masculine individuals prefer highly feminine spouses and vice versa. I find instead strong homogamy effects, such that respondents with masculine self-perceptions prefer spouses who also embody those masculine traits, and respondents with feminine self-perceptions prefer spouses who also identify with feminine traits. Third, my data includes a wider age range of unmarried respondents than nearly all other preference studies, so I am able to test preference differences by age. I find that older unmarried adults are generally less "particular" in their preferences, compared to those unmarried adults still in college. Despite my predictions that age would be positively related to the desire for spouse characteristics associated with "growing up," essentially, age appears to be negatively related or unrelated to most spouse preferences. Fourth, I include factor analysis techniques that both replicate a past research study (which was pioneering for the field), and broach the possibility for latent variable assessment using a wider array of preference dimensions than have been previously considered. I find evidence of several underlying preference constructs which could, and should, be taken into account when conducting future preference studies.
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Serra, Jaime, Antónia Correia, and Paulo M. M. Rodrigues. "Yielding Tourists’ Preferences." Bachelor's thesis, Emerald Group Publishing Limited, 2015. http://hdl.handle.net/10174/16858.

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This chapter uses stated tourist preferences as a proxy of visitor yield measures, in order to analyse and understand the yield potential of different markets’ preferences. A literature review revealed that there is much progress to be made in terms of discussion, consensus and stability of methodology for the measurement of visitor yield. The aim of the visitor yield analysis, in the current chapter, is also to bring another dimension into yield analysis and discussion, contributing with a new form of measuring yield potential. Since the objective is to identify yield patterns based on tourist preferences over a period of time, dynamics may be captured from the fluctuation patterns, or expressed as volatility of visitor yield and length of stay throughout the years. Destination management organisations and tourist companies may potentially adopt this visitor yield matrix in order to support future strategic decisions.
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Shay, Nathan Michael. "Investigating Real-Time Employer-Based Ridesharing Preferences Based on Stated Preference Survey Data." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471587439.

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Skedgel, Chris D. "Estimating societal preferences for the allocation of healthcare resources using stated preference methods." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/6307/.

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Most governments in the world provide some publicly funded healthcare to their citizens, but given the scarcity of resources relative to potential demand, some form of rationing or priority setting is required, and some patients must be denied effective treatment. The thesis took the position that an explicit approach based on maximising the value that society derives from healthcare is the preferred way to address this rationing problem. Conventional health economic practice proposes that value should be equated with quality-adjusted life years (QALYs), leading to a policy of QALY maximisation, but, it is argued, not necessarily value maximisation. A more inclusive approach to defining value, based on societal preferences, may maximise overall well-being and be associated with greater trust and legitimacy in the priority setting process. The thesis identified patient and program characteristics that appeared to have empirical evidence of public support as well as a defensible ethical justification in determining the strength of a patient’s claim to societal healthcare resources. The relative strength of preferences for these characteristics, or the equity-efficiency trade-off, was estimated using stated preference methods. Two different methods, discrete choice experiments and constant-sum paired comparisons, were used and the response behaviours of the two elicitations were compared to identify a preferred method for eliciting societal preferences in the context of healthcare. Both methods found a statistically significant equity-efficiency trade-off in an age and sex representative sample of the Canadian public as well as a convenience sample of decision-making agents. This suggested that society would be willing to sacrifice some degree of efficiency in maximising individual life year gains in order to prioritise other characteristics consistent with the promotion of equity or distributive justice in the allocation of healthcare resources. However, differences between the results of the two elicitation methods suggested some systematic procedural variance.
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Freeman, Shannon. "Developing preferences for low-preference age-appropriate leisure activities in adults with developmental disabilities /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1079659771&sid=19&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Books on the topic "Will and preferences"

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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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Fehige, Christoph, and Ulla Wessels, eds. Preferences. Berlin, New York: DE GRUYTER, 1998. http://dx.doi.org/10.1515/9783110804294.

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Loewenstein, George. Preferences. Oxford: Oxford University Press, 2002.

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Duggan, Anthony. Voidable preferences. Toronto: Faculty of Law, University of Toronto, 2006.

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Yip, Ngai Ming. Tenure preferences. York: University of York, Centre for Housing Policy, 1993.

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Farmer, Roger E. A. R.I.N.C.E. preferences. Cambridge: University of Cambridge Department of Applied Economics, 1988.

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Broome, John. Extended preferences. Bristol: Bristol University, Department of Economics, 1992.

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Dean, Judith Myrle. Quantifying the value of U.S. tariff preferences for developing countries. Washington, D.C: World Bank, 2006.

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Sunstein, Cass R. Preferences and politics. [Toronto, Ont.]: Faculty of Law, University of Toronto, 1990.

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Kable, Jim. People, preferences & performance. Brisbane: J. Wiley, 1988.

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Book chapters on the topic "Will and preferences"

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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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von Wright, Georg Henrik. "Preferences." In The New Palgrave Dictionary of Economics, 1–6. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_1335-1.

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Jureta, Ivan. "Preferences." In The Design of Requirements Modelling Languages, 239–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18821-8_14.

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Mochrie, Robert. "Preferences." In Intermediate Microeconomics, 56–75. London: Macmillan Education UK, 2016. http://dx.doi.org/10.1007/978-1-137-09166-6_4.

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von Wright, Georg Henrik. "Preferences." In The New Palgrave Dictionary of Economics, 10639–44. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_1335.

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von Wright, Georg Henrik. "Preferences." In Utility and Probability, 149–56. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20568-4_22.

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Steppan, Bernhard. "Preferences." In Eclipse Rich Clients und Plug-ins, 291–99. München: Carl Hanser Verlag GmbH & Co. KG, 2015. http://dx.doi.org/10.3139/9783446433168.016.

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Steppan, Bernhard. "Preferences." In Eclipse Rich Clients und Plug-ins, 411–15. München: Carl Hanser Verlag GmbH & Co. KG, 2015. http://dx.doi.org/10.3139/9783446433168.028.

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Seip, Knut Lehre, and Fred Wenstop. "Preferences." In A Primer on Environmental Decision-Making, 109–22. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-5067-1_7.

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Conference papers on the topic "Will and preferences"

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Honda, Tomonori, and Erik K. Antonsson. "Preferences and Correlated Uncertainties in Engineering Design." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dtm-48675.

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The Method of Imprecision (MOI) is a multi-objective design method that maximizes the overall degree of both design and performance preferences. Sets of design variables are iteratively selected, and the corresponding performances are approximately computed. The designer’s judgment (expressed as preferences) are combined (aggregated) with the customer’s preferences, to determine the overall preference for sets of points in the design space. In addition to degrees of preference for values of the design and performance variables, engineering design problems also typically include uncertainties caused by uncontrolled variations, for example, measuring and fabrication limitations. This paper illustrates the computation of expected preference for cases where the uncertainties are uncorrelated, and also where the uncertainties are correlated. The result is a “best” set of design variable values for engineering problems, where the overall aggregated preference is maximized. As is illustrated by the examples shown here, where both preferences and uncontrolled variations are present, the presence of uncertainties can have an important effect on the choice of the overall best set of design variable values.
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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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Seimetz, Valentin, Rebecca Eifler, and Jörg Hoffmann. "Learning Temporal Plan Preferences from Examples: An Empirical Study." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/572.

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Temporal plan preferences are natural and important in a variety of applications. Yet users often find it difficult to formalize their preferences. Here we explore the possibility to learn preferences from example plans. Focusing on one preference at a time, the user is asked to annotate examples as good/bad. We leverage prior work on LTL formula learning to extract a preference from these examples. We conduct an empirical study of this approach in an oversubscription planning context, using hidden target formulas to emulate the user preferences. We explore four different methods for generating example plans, and evaluate performance as a function of domain and formula size. Overall, we find that reasonable-size target formulas can often be learned effectively.
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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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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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Delic, Amra, Francesco Ricci, and Julia Neidhardt. "Preference Networks and Non-Linear Preferences in Group Recommendations." In WI '19: IEEE/WIC/ACM International Conference on Web Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3350546.3352556.

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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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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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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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Haid, Charlotte, Sebastian Stohrer, Charlotte Unruh, Tim Büthe, and Johannes Fottner. "Accommodating Employee Preferences in Algorithmic Worker-Workplace Allocation." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002235.

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Since many processes in logistics are difficult to automate, employees will continue to be a crucial part of the logistics ecosystem. Allocating employees to tasks on the shop floor continues to be essential, but should focus more on personal preferences. Traditional allocation systems have hardly taken employee preferences into consideration. We ensure that workers can specify their preferences in more detail, and enable best-fit allocation of workers and tasks. To gather information about employee preferences, we designed a survey that can be completed quickly and allows us to get information about employee preferences. We have developed a solution for our preference-based scheduling, namely a hybrid AI algorithm. The solution is discussed for our use case: matching employees to workplaces in logistics. With this work we contribute to a transparent consideration of preferences in scheduling and show details of the algorithm. We aim to extend research in this area with our open source code on github.
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Reports on the topic "Will and preferences"

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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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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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Mollerstrom, Johanna, Avner Strulov-Shlain, and Dmitry Taubinsky. Preferences for Giving Versus Preferences for Redistribution. Cambridge, MA: National Bureau of Economic Research, October 2021. http://dx.doi.org/10.3386/w29375.

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Cohen, Jonathan, Keith Marzilli Ericson, David Laibson, and John Myles White. Measuring Time Preferences. Cambridge, MA: National Bureau of Economic Research, July 2016. http://dx.doi.org/10.3386/w22455.

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Alesina, Alberto, and Paola Giuliano. Preferences for Redistribution. Cambridge, MA: National Bureau of Economic Research, March 2009. http://dx.doi.org/10.3386/w14825.

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Backus, David, Bryan Routledge, and Stanley Zin. Exotic Preferences for Macroeconomists. Cambridge, MA: National Bureau of Economic Research, June 2004. http://dx.doi.org/10.3386/w10597.

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Canavan, G. H. Sensitivity to attack preferences. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/501500.

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Jenter, Dirk, and Katharina Lewellen. CEO Preferences and Acquisitions. Cambridge, MA: National Bureau of Economic Research, December 2011. http://dx.doi.org/10.3386/w17663.

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Beshears, John, James Choi, David Laibson, and Brigitte Madrian. How are Preferences Revealed? Cambridge, MA: National Bureau of Economic Research, May 2008. http://dx.doi.org/10.3386/w13976.

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Andreoni, James, Michael Kuhn, and Charles Sprenger. On Measuring Time Preferences. Cambridge, MA: National Bureau of Economic Research, August 2013. http://dx.doi.org/10.3386/w19392.

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