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The aim of this study is to examine the public preferences for urban riparian corridors in arid ‎regions, by testing to what extent people are willing to trade-off unmaintained ecological ‎landscape for aesthetics offered by specific micro and ‎macro environmental factors. Landscape ‎design reflects ecological and aesthetic values, and trade-offs are often made ‎between the two in ‎‎practice. In arid regions, water scarcity means riparian corridors are the ‎richest landscape ‎typology and the only ‎blue-green links for hundreds of miles. Pressure from ‎urbanization and ‎lack of eco-literacy contribute to negative ‎feedback loops which present dire ‎challenges for ‎migrating avifauna and regional wildlife. Regarding natural ‎resources and ‎biodiversity, where ‎multiple deliverable ‎ecosystem services rely on the quality and health of that ‎‎ecosystem, riparian ‎systems with high biomass are more desirable. Although this can be ‎achieved with low or no ‎‎maintenance riparian buffers, these unmaintained ecological landscapes ‎play an intrinsic role in ‎sustaining the ‎global ecosystem services and are important for the survival of the inhabitants ‎‎(avifauna). Ecological ‎landscapes are often subjected to trade-offs with aesthetic ‎landscapes that ‎include micro and ‎macro environmental factors such as manicured landscapes. It is accepted that ‎‎there is a ‎preference for aesthetics in landscape design; however, it is unclear how laypeople ‎prioritize ‎aesthetics ‎over different ecological factors in landscape scenes. This study uses a ‎Discrete Choice ‎Experiment (DCE) to elicit the ‎preferences of current or pretendant residents of ‎Jeddah City, Saudi Arabia for multiple landscape scenes. The method ‎combines ecological ‎landscape characteristics adopted from ‎the QBR index that are found in the study area in Jeddah ‎and aesthetic ‎characteristics, such as micro and macro environmental factors that are commonly ‎suggested in landscape design projects adapted from relevant visual preference studies (Alsaiari, ‎‎2018; Kenwick et al., 2009; Kuper‎ ‎,‎2017; Zhao et al., 2017). ‎DCE is a widely used method to ‎reveal preferences by analyzing the trade-offs people make ‎between ‎alternatives. Participants in ‎this study were exposed to a set of designs, which included ‎various configurations of ‎aesthetic ‎and ecological elements. Participants' choices revealed the influence of their ecological and ‎‎aesthetic values. Results show that minimal design interventions would prevent trading off the ‎ecological unmaintained landscape and that there are four subgroups with distinct homogeneous ‎preferences for the attributes affecting the appeal for the urban riparian corridor in Jeddah City. ‎Finally, results show that even though there are significant differences between subgroups based ‎on preferences, the demographic information is proportionally distributed in a way the means ‎differences diminish between the subgroups. ‎Findings in this study will equip ‎decision-makers ‎with operational definitions relating to riparian ‎landscape design and a method ‎that they can use ‎to minimize losses in ecological value over aesthetic value. This study will help‎ ‎researchers and ‎landscape architects advance visual preference research further into the domain ‎of empirical ‎‎studies.‎ ‎ / Doctor of Philosophy / Landscape architecture is a profession that entails planning and design outdoor spaces, ‎‎landmarks, and ‎structures to improve the built environment and increasing the quality of ‎‎people's lives by achieving ‎environmental, social, economic, and aesthetic outcomes‎. The ‎profession often reflects ecological and ‎aesthetic values, and trade-offs are often made ‎between ‎the two in ‎practice. These ecological values ‎represent environmental characteristics that are ‎important for the survival of wildlife (protected path ‎and safe habitat) and the overall ecosystem ‎‎(every being has a role that sustain the health of the ‎environment). Culturally, human is ‎accustomed to a slick-and-clean (tamped) looking plant within urban ‎developments (i.e., ‎neighborhoods). An example of the trade-off that often happen in practice between ‎the ‎ecological and aesthetic values is replacing an ecologically unmaintained plants that play ‎important ‎ecological role (i.e., wildlife habitat) with clean tamped plants to increase the value of ‎a real estate. Due ‎to the uncertainty surrounding people's acceptance of the features of these ‎ecological unmaintained ‎plants, especially when it entails introducing ecological riparian ‎landscape attributes within ‎neighborhoods for the first time, this dissertation focuses on both ‎assessing ecological elements ‎preferences within an urban arid region in Jeddah, Saudi Arabia ‎and assessing the extent to which ‎advanced analytical methods are capable of providing a better ‎understanding of ecological riparian ‎landscape attributes preference differences among a ‎seemingly homogenous sample of participants. ‎The increasing usage of manipulated images in ‎choice tasks inspired this dissertation. The results of the ‎study demonstrate that among the ‎relatively homogenous sample of participants that was recruited, ‎four significant preference ‎patterns have emerged, which could be used to describe and predict ‎preference for ecological ‎riparian landscape attributes and choice with great accuracy. The dissertation ‎also investigates ‎policy implications that might be beneficial in creating a ‎physical environment that ‎match public ‎preferences. ‎It also offers research implications and recommendations for landscape ‎architects ‎and urban designers on how to employ visual choice experiments, which have been well-‎‎developed in other research field

Master of Science / Food Science Institute / Koushik Adhikari / The market of nail polish has been booming in recent years. Research on nail polish is scarce. A sensory lexicon for nail polish has been developed at Kansas State University, but how sensory factors affect female consumers’ acceptability of nail polish has not been examined. Also, other factors, such as price and usage characteristics that could affect consumers’ acceptability, are yet to be determined. A nail polish consumer study was conducted at Kansas State University to explore several sensory and non-sensory factors that could affect female consumers’ acceptability of nail polish. Eight nail polish samples, belonging to four categories, namely, regular (REG), gel (GEL), flake (FLK) and water-based (WAT), were evaluated by each of the 98 female consumers. The questionnaire consisted of three sections – application, observation and general usage questions. Results showed that consumers rated the samples similarly in both the application and observation sections. In general, consumers preferred the REG and the GEL samples more than the FLK and the WAT samples. Among all the sensory attributes, appearance attributes were the major attributes that affected consumers’ overall acceptability, while aroma had negligible impact on acceptability. Some sensory attributes like runny, shininess, opacity, spreadability, smoothness, coverage and wet-appearance were found to drive the consumer’s overall acceptability positively, while others such as pinhole, fatty-edges, blister, brushlines, pearl-like, flake-protrusion, glittery and initial-drag impacted their liking negatively. Four clusters of consumers were identified based on the consumers’ overall liking scores for both the application and observation sections. Considering all the factors that could affect consumers’ acceptability, sensory appeal, price, and conveniences of usage were the top factors picked by consumers. Age was also a factor that affected consumers’ acceptability for some of the samples. Consumers’ overall acceptability for these studied samples could guide a beauty store or a nail salon on building their selection on nail polishes. Consumers’ acceptability on different sensory attributes could help a nail polish company modify or improve their nail polish formula. The consumer cluster information could benefit a nail polish company on marketing a specific category of product and advertising to a specific group of consumers.

Master of Science / Department of Psychology / Patrick A. Knight / A structural equations model hypothesizing that individuals' core self-evaluations would significantly predict their preferences for various pay plan characteristics (e.g., high risk, variable pay, etc.) was tested. This hypothesis, which specified that individuals with higher levels of core self-evaluations would prefer pay plans that offered greater risk and less certainty regarding the amount of pay received, was supported. Furthermore, it was also hypothesized that congruence between an individual's preferred pay plan characteristics and the actual type of pay plan that he or she receives would result in higher levels of employee job satisfaction and pay satisfaction. This hypothesis was partially supported, as the relationship between congruence and job satisfaction was significant, while the relationship between congruence and pay satisfaction was not significant. Finally, it was hypothesized that the relationship between congruence and satisfaction would be moderated by the value that the individual places on money. This hypothesis was not supported. The results of this research indicate that personality characteristics may have a significant impact on the type of pay plan that an individual will prefer to receive. Furthermore, this research provides additional support for the belief that high levels of fit between the characteristics of individuals and the characteristics of the organizations for which they work will result in higher levels of employee satisfaction. Finally, the degree of importance that an individual places on money does not appear to alter the relationship between fit and satisfaction. These results have strong implications for businesses that wish to improve employee satisfaction and reduce employee turnover, as well as for individuals who are seeking occupations for which they will best be suited.

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Preferences are fundamental to decision making and play an important role in artificial intelligence. Our research focuses on three group of problems based on the preference formalism Answer Set Optimization (ASO): preference aggregation problems such as computing optimal (near optimal) solutions, strategic behaviors in preference representation, and learning ranks (weights) for preferences. In the first group of problems, of interest are optimal outcomes, that is, outcomes that are optimal with respect to the preorder defined by the preference rules. In this work, we consider computational problems concerning optimal outcomes. We propose, implement and study methods to compute an optimal outcome; to compute another optimal outcome once the first one is found; to compute an optimal outcome that is similar to (or, dissimilar from) a given candidate outcome; and to compute a set of optimal answer sets each significantly different from the others. For the decision version of several of these problems we establish their computational complexity. For the second topic, the strategic behaviors such as manipulation and bribery have received much attention from the social choice community. We study these concepts for preference formalisms that identify a set of optimal outcomes rather than a single winning outcome, the case common to social choice. Such preference formalisms are of interest in the context of combinatorial domains, where preference representations are only approximations to true preferences, and seeking a single optimal outcome runs a risk of missing the one which is optimal with respect to the actual preferences. In this work, we assume that preferences may be ranked (differ in importance), and we use the Pareto principle adjusted to the case of ranked preferences as the preference aggregation rule. For two important classes of preferences, representing the extreme ends of the spectrum, we provide characterizations of situations when manipulation and bribery is possible, and establish the complexity of the problem to decide that. Finally, we study the problem of learning the importance of individual preferences in preference profiles aggregated by the ranked Pareto rule or positional scoring rules. We provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decided all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples is NP-hard. We obtain similar results for the case of weighted profiles.

Lighting accounts for nearly 20% of overall U.S. electricity consumption, 14% of U.S. residential electricity consumption, and 6% of total U.S. carbon dioxide equivalent (CO2e) emissions. A transition to alternative energy-efficient technologies could reduce this energy consumption considerably. We studied three questions related to energy efficiency lighting choices and consequences, which are: • Question 1: How large is the system-wide effect of a residential lighting retrofit with more efficient lighting technologies? • Question 2: Based on stated preference (SP) data, which factors influence consumer choices for general service light bulbs? What is the effect of the new lighting efficiency label mandated by the Federal Trade Commission? • Question 3: What can we learn about market trends and consumer choices from consumer panel data (i.e. revealed preference (RP) data) for general service light bulbs between 2004 and 2009? How can we compare the findings from SP and RP data, and which findings are robust across the two? In Chapter 2, we focus on the issue of lighting heat replacement effects. The issue is as follows: lighting efficiency goals have been emphasized in various U.S. energy efficiency policies. However, incandescent bulbs release up to 95% of input energy as heat, and it has been argued that replacing them with more efficient alternatives has a side effect in the overall building energy consumption: it increases the heating service that needs to be provided by the heating systems and decreases the cooling service that needs to be provided by the cooling systems. We investigate the net energy consumption, CO2e emissions, and saving in energy bills for single family detached houses across the U.S. as one moves towards more efficient lighting systems. In some regions, these heating and cooling effects from more efficient lighting can undermine up to 40% of originally intended primary energy savings, erode anticipated carbon savings completely, and lead to 30% less household monetary savings than intended. However, this overall effect is at most one percent of total emissions or energy consumption by a house. The size of the effect depends on various regional factors such as climate, electricity fuel mix, differences in emission factors of main energy sources used for heating and cooling, and electricity prices. Other tested factors such as building orientation, insulation level, occupancy scenario, or day length do not significantly affect the results. Then, in Chapter 3, we focus on factors that drive consumer choices for light bulbs. We collected stated preference data from a choice-based conjoint field experiment with 183 participants. We estimate discrete choice models from the data and find that politically liberal consumers have a stronger preference for compact fluorescent lighting technology and for low energy consumption. Greater willingness-to-pay for lower energy consumption and longer life is observed in conditions where estimated operating cost information was provided. Providing estimated annual cost information to consumers reduces their implicit discount rate by a factor of five, lowering barriers to adoption of energy efficient alternatives with higher up-front costs; however, even with cost information provided, consumers continue to use implicit discount rates of around 100%, which is larger than that estimated for other energy technologies. Finally, we complemented the stated preference study with a revealed preference study. This is because stated preference data alone have limitations in explaining consumer choices, as purchases are affected by many other factors that are outside of the experimenter control. We investigate consumer preferences for lighting technology based on revealed preference data between 2004 and 2009. We assess the trends in lighting sales for different lighting technologies across the country, and by store type. We find that, across the period between 2004 and 2009, sales of all general service light bulbs are almost monotonically decreasing, while CFL sales peaked in 2007. Thanks to increasing adoption of CFLs during the period, newly purchased light bulbs contributed to lowering carbon emissions and electricity consumption, while not sacrificing total produced lumens as much. We study consumer preferences for real light bulbs by estimating choice models, from which we estimate willingness-to-pay (WTP) for light bulb attributes (watt and type) and implicit discount rates (IDR) consumers adopt for their purchases. We find that the campaign for efficient bulbs in Wal-Mart in 2007 is potentially related to the peak in CFL adoption in 2007 in addition to the effects of the EISA or other factors/programs around the same period. Consumers are willing to pay, $1.84 more for a change from an incandescent bulb to a CFL and -$0.06 for 10W increase, the values which also include willingness-to-pays for corresponding changes in unobserved variables such as life and color. IDRs for four representative states range between around 230% and 330%, which is in a similar range we estimate from the choice experiment. Overall, even with energy efficiency labels, nationwide promotion of CFLs by retailers, or better availability of CFLs in the transforming residential lighting market, we see the barriers to energy efficient residential lighting are still persistent, which are reflected in high implicit discount rates observed from the models. While we can expect the EISA to be effective in lowering the barriers through regulation, it alone will not close energy efficiency gap in the residential lighting sector.

The backbone of our industrialised society and economy is electricity. To avoid a catastrophic situation, a plan for how to act during a power shortage is crucial. Previous research shows that decision models provide support to decision makers providing efficient energy rationing during power shortages in the Netherlands, United States and Canada. The existing research needs to be expanded with a group decision model to enable group decisions. This study is conducted with a case study approach where the Swedish emergency preparedness plan in case of power shortage, named Styrel, is explored and used to evaluate properties of a proposed group decision model. The study consist of a qualitative phase and a quantitative phase including a Monte Carlo simulation of group decisions in Styrel evaluated with correlation analysis. The qualitative results show that participants in Styrel experience the group decisions as time-consuming and unstructured. The current decision support is not used in neither of the two counties included in the study, with the motivation that the preferences provided by the decision support are misleading. The proposed group decision model include a measurable value function assigning values to priority classes for electricity users, an additive model to represent preferences of individual decision makers and an additive group decision model to aggregate preferences of several individual decision makers into a group decision. The conducted simulation indicate that the proposed group decision model evaluated in Styrel is sensitive to significant changes and more robust to moderate changes in preference differences between priority classes.

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data.

In naturally ventilated buildings, occupants play a key role in the performance and energy efficiency of the building operation, mainly through the opening and closing of windows. To include the effects of building occupants within building performance simulation, several useful models describing building occupants and their window opening/closing behaviour have been generated in the past 20 years. However, in these models, the occupants are classified based on the whole population or on sub-groups within a building, whilst the behavioural difference between individuals is commonly ignored. This research project addresses this latter issue by evaluating the importance of the modelling and prediction of occupants window behaviour individually, rather than putting them into a larger population group. The analysis is based on field-measured data collected from a case study building containing a number of single-occupied cellular offices. The study focuses on the final position of windows at the end of the working day. In the survey, 36 offices and their occupants were monitored, with respect to the occupants presence and window use behaviour, in three main periods of a year: summer, winter and transitional. From the behaviour analysis, several non-environmental factors, namely, season, floor level, gender and personal preference, are identified to have a statistically significant effect on the end-of-day window position in the building examined. Using these factors, occupants window behaviour is modelled by three different classification methods of building occupants, namely, whole population, sub-groups and personal preference. The preference-based model is found to perform much better predictive ability on window state when compared with those developed based on whole population and sub-groups. When used in a realistic building simulation problem, the preference-based prediction of window behaviour can reflect well the different energy performance among individual rooms, caused by different window use patterns. This cannot be demonstrated by the other two models. The findings from this research project will help both building designers and building managers to obtain a more accurate prediction of building performance and a better understanding of what is happening in actual buildings. Additionally, if the habits and behavioural preferences of occupants are well understood, this knowledge can be potentially used to increase the efficiency of building operation, by either relocating occupants within the building or by educating them to be more energy efficient.