Older adults have been shown to avoid negative and prefer positive information to a higher extent than younger adults. This positivity bias influences their information processing as well as decision-making. We investigate age-related positivity bias in charitable giving in two studies. In Study 1 we examine motivational factors in monetary donations, while Study 2 focuses on the emotional effect of actual monetary donations. In Study 1, participants (n = 353, age range 20-74 years) were asked to rate their affect toward a person in need and then state how much money they would be willing to donate to help this person. In Study 2, participants (n = 108, age range 19-89) were asked to rate their affect toward a donation made a few days prior. Regression analysis was used to investigate whether or not the positivity bias influences the relationship between affect and donations. In Study 1, we found that older adults felt more sympathy and compassion and were less motivated by negative affect when compared to younger adults, who were motivated by both negative and positive affect. In Study 2, we found that the level of positive emotional reactions from monetary donations was higher in older participants compared to younger participants. We find support for an age-related positivity bias in charitable giving. This is true for motivation to make a future donation, as well as affective thinking about a previous donation. We conclude that older adults draw more positive affect from both the planning and outcome of monetary donations and hence benefit more from engaging in monetary charity than their younger counterparts. (authors' abstract)

Transport infrastructure constitutes one of the key factors to a country’s economic growth. Investment in new transport infrastructure might cause potential environmental impacts, and if a project has several alternative corridors open for suggestion then each alternative corridor will have a different impact on the environment. The European Commission has stated that the natural resources are important to the quality of life. Therefore, the efficient use of resources will be a key towards meeting future climate change and reduction in greenhouse gas (GHG) emissions. This implies that in an evergrowing global society the resource efficiency as well as the choice of transport infrastructure corridor becomes even more important to consider. The aim of this research project was to contribute to early transport infrastructure planning by the development of methods for and implementation of easy understandable geological criteria and models for decision support. Moreover, the intention was to assess how geological information can be developed and extracted from existing spatial data and coupled with other areas of interest, such as ecology and life cycle assessment. It has previously been established that geological information plays an important role in transport infrastructure planning, as the geological characteristics of the proposed area as well as the possibilities of material use influences the project. Therefore, in order to couple geological information for early transport infrastructure planning, four studies (Paper I-IV) were undertaken where methods were developed and tested for the inclusion of geological information. The first study (Paper I) demonstate how optional road corridors could be evaluated using geological information of soil thickness, soil type and rock outcrops, bedrock quality and slope in combination with ecological information. The second study (Paper II) shows how geological information of soil thickness and stratigraphy can be combined with life cycle assessments (LCA) to assess the corresponding greenhouse gas emission and energy use for the proposed road corridors. The difficulty of using expert knowledge for susceptibility assessment of natural hazards, i.e. flooding, landslide and debris flow, for early transport infrastructure planning was presented in the third study (Paper III). In this study the expert knowledge was used in a multi-criteria analysis where the analytic hierarchy process (AHP) was chosen as a decision rule. This decision rule was compared to the decision rule weighted linear combination (WLC) using two different schemes of weighting. In all the mentioned studies the importance of soil thickness information was highlighted. Therefore, the fourth and final study (Paper IV) presented a new methodology for modelling the soil thickness in areas where data is sparse. A simplified regolith model (SRM) was developed in order to estimate the regolith thickness, i.e. soil thickness, for previously glaciate terrain with a high frequency of rock outcrops. SRM was based on a digital elevation model (DEM) and an optimized search algorithm. The methods developed in order to couple geological information with other areas of interest is a tentative step towards an earlier geo-environmental planning process. However, the methods need to be tested in other areas with different geological conditions. The combination of geological information in GIS with MCA enabled the integration of knowledge for decision making; it also allowed influencing the importance between various aspects of geological information as well as the importance between geological information and other fields of interest, such as ecology, through the selected weighting schemes. The results showed that synergies exist between ecology and geology, where important geological considerations could also have positive effects on ecological consideration. Soil thickness was very important for GHG emission and energy whereas stratigraphical knowledge had a minor influence. When using expert knowledge the consistency in the expert judgements also needs to be considered. It was shown that experts tended to be inconsistent in their judgements, and that some consistency could be reached if the judgements were aggregated instead of used separately. The results also showed that the developed SRM had relatively accurate results for data sparse areas, and that this model could be used in several projects where the knowledge of soil thickness is important but lacking. It was concluded that geological information should be considered. By using GIS and MCA it is possible to evaluate different aspects of geological information in order to improve decision making. / Environmental assessment of road geology and ecology in a system perspective

It is well-established that, when confronted with a decision to be taken under risk, individuals use reference payoff levels as important inputs. The purpose of this paper is to study which reference points characterize decisions in a setting in which there are several plausible reference levels of payoff. We report an experiment, in which we investigate which of four potential reference points: (1) a population average payoff level, (2) the announced expected payoff of peers in a similar decision situation, (3) a historical average level of earnings that others have received in the same task, and (4) an announced anticipated individual payoff level, best describes decisions in a decontextualized risky decision making task. We find heterogeneity among individuals in the reference points they employ. The population average payoff level is the modal reference point, followed by experimenter's stated expectation of a participant's individual earnings, followed in turn by the average earnings of other participants in previous sessions of the same experiment. A sizeable share of individuals show multiple reference points simultaneously. The reference point that best fits the choices of the individual is not affected by a shock to her income.

This thesis considered the potential benefits of employing linear programming in cheese manufacturing plants as a decision tool for management. Its potential has been enhanced by the recent approval of acid orange 12 as a chemical for testing the percent protein in milk; therefore, a practical test is now available for monitoring protein as well as milk fat in milk manufacturing and fluid milk plants. Seven models, each one differing only in the milk fat and protein percentages or means of standardizing the cheese milk, were manipulated individually and simultaneously to test the managerial benefits of linear programming under various plant and market conditions. Each model consisted of five cheese activities or variables, two butter activities, three powder activities, and a selling activity for each product produced. The maximum price that could be paid the farm producer per hundred-weight of milk and the minimum wholesale rice per pound of manufactured product, to cover variable costs were determined for each variety of cheese and composition of milk. There was a definite interaction between each of the activities. This caused the cost to produce a Pound of cheese to vary according to the alternative uses for milk, cream, skim milk, and whey. When the simulated plant was being utilized at or near full capacity and the cheese milk was standardized with non fat dry milk powder, total cheese yield increased as did total profits. When the plant was not being utilized to full capacity, profits were higher by not standardizing.

Making good decisions and adapting flexibly to environmental change are critical to the survival of animals. In this thesis, I investigated neural mechanisms underlying suboptimal decision making in humans and underlying behavioural adaptation in monkeys with the use of functional magnetic resonance imaging (fMRI) in both species. In recent decades, in the neuroscience of decision making, there has been a prominent focus on binary decisions. Whether the presence of an additional third option could have an impact on behaviour and neural signals has been largely overlooked. I designed an experiment in which decisions were made between two options in the presence of a third option. A biophysical model simulation made surprising predictions that more suboptimal decisions were made in the presence of a very poor third alternative. Subsequent human behavioural testing showed consistent results with these predictions. In the ventromedial prefrontal cortex (vmPFC), I found that a value comparison signal that is critical for decision making became weaker in the presence of a poor value third option. The effect contrasts with another prominent potential mechanism during multi-alternative decision making – divisive normalization – the signatures of which were observed in the posterior parietal cortex. It has long been thought that the orbitofrontal cortex (OFC) and amygdala mediate reward-guided behavioural adaptation. However, this viewpoint has been recently challenged. I recorded whole brain activity in macaques using fMRI while they performed an object discrimination reversal task over multiple testing sessions. I identified a lateral OFC (lOFC) region in which activity predicted adaptive win-stay/lose-shift behaviour. In contrast, anterior cingulate cortex (ACC) activity predicted future exploratory decisions regardless of reward outcome. Amygdala and lOFC activity was more strongly coupled for adaptive choice shifting and decoupled for task irrelevant reward memory. Day-to-day fluctuations in signals and signal coupling were correlated with day-to-day fluctuations in performance. These data demonstrate OFC, ACC, and amygdala each make unique contributions to flexible behaviour and credit assignment.

My overarching modeling goal for my dissertation is to maximize generalization â some function of data and knowledge â from one sample, with its observations drawn independently from the distribution D, to another sample drawn from D, while also obtaining interpretable insights from the models. The processes of collecting relevant data and generating features from the raw data impart substantive knowledge into predictive models (and the model representation and optimization algorithms applied to those features contain methodological knowledge). I combine this knowledge with the data to train predictive models to deliver generalizability, and then investigate the implications of those models with simulations systematically exploring parameter spaces. The exploration of parameter space provides insights about the relationships between key variables. Chapter 2 describes a method to generate descriptive models of strategic decision-making. I use an efficient representation of repeated game strategies with state matrices and a genetic algorithm-based estimation process to learn these models from data. This combination of representation and optimization is effective for modeling decision-making with experimental game data and observational international relations data. Chapter 3 demonstrates that models can deliver high levels of generalizability with accurate out-of-sample predictions and interpretable scores of variable importance that can guide future behavioral research. I combine behavioral-game-theory-inspired feature design with data to train predictive models to deliver generalizability, and then investigate interactive implications of those models with optimization and sensitivity analyses. Chapter 4 presents a computational model as a test-bed for designing climate prediction markets. I simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and the most plausible hypothesis advanced by prominent skeptics. Then I conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect tradersâ beliefs about the cause of global climate change. Market participation causes most traders to converge quickly toward believing the âtrueâ climate model.

The purpose of this investigation was to determine if a relationship exists between the actual standard used by judges during a tournament and the theory taught in collegiate debate courses.

Research has suggested that memories for difficult or painful experiences seem related to a combination of the worst and most recent moments. This peak-end theory was tested in relation to an exercise task (eccentric quadriceps using a BIODEX machine) as well as a cognitive task (standardized quantitative test questions). For each type of task there were two trials: short and happy endings. The happy endings trial included the same task as the short trial with an additional 25% duration at a lesser intensity (80% of short task intensity). A 2 (task type) by 2 (trial type) repeated measures design was used. Participants made global ratings of difficulty immediately after each component, thus generating four ratings, and later indicated their preferences for hypothetical future trials. Results indicated support for the theory that the shorter trials are evaluated as more difficult, with the cognitive task being evaluated as more difficult overall than the exercise task. Preference scores, however, revealed a preference only for the happy endings cognitive task, with no preference indicated on the exercise task. Results confirm previous research in suggesting differences between judgements of tasks and future choices. However, confounds complicated interpretations, especially for the cognitive task. The most conservative interpretation of data suggests that in circumstances where "more is better," happy endings will result in more work with no higher level of discomfort. Implications for future research and applications of the theory are discussed.

This study extends a research stream calling for further research regarding pricing and accounting feedback. Marketing executives rely heavily on accounting information for pricing decisions, yet criticize accounting feedback usefulness. To address this criticism, this research integrates the cognitive psychology and accounting literature addressing feedback effectiveness with pricing research in the marketing discipline. The research extends the scope of previous accounting feedback studies by using a control group and comparing two proxies of subject task knowledge; years of pricing experience and a measure of the cognitive structure of pricing knowledge. In addition, this research manipulates task complexity by using two different accounting systems. These systems vary in the number of cost pools used in allocating overhead, resulting in differentially projected cost and profit information. A total of 60 subjects participated in a computer laboratory experiment. These subjects were non-accountants with varying amounts of pricing knowledge. Subjects were randomly assigned to six experimental groups which varied by feedback type (no accounting feedback, outcome feedback only, or a combination of outcome and task properties feedback) and task complexity (high or low number of overhead cost pools). The subjects attempted to (1) maximize profits for a product during 15 rounds of pricing decisions, and (2) accurately estimate their profit for each round. The experimental results indicate no difference in performance between the three feedback types examined. However, increases in both subjects' pricing knowledge and the number of cost pools do influence feedback effectiveness. This study suggests that the amount of the users' task knowledge may influence the effectiveness of current accounting reports. In addition, increasing the number of cost pools in accounting systems may be beneficial for all users.

The purpose of this research was to conduct an exploratory study of the information processing utilized by incubator-housed manufacturers during channel member selection. The study included the evaluation of the decision models used by the manufacturers as well as criteria used in the selection process. The study was specifically designed to achieve the following objectives. First, the research was to identify the evaluation modes used by the manufacturers as either compensatory or noncompensatory. Second, the study was to evaluate the effect of the task on the selection of the evaluation model(s) used during the channel member selection process. Third, the study was to evaluate the effect of the selected decision strategy on the amount of information used during the decision process. Finally, the study was to identify and examine the importance of the criteria used by the manufacturers in the selection process. The methodology in this study consisted of primary research using protocol analysis as the main data gathering technique. A ranking instrument was also mailed to the respondents prior to the protocol session. The population for the study was identified as all manufacturers located in publicly-sponsored business incubators. A total of 235 incubators were in existence with approximately 47 percent of them being publicly-sponsored. Approximately 42 percent of the incubators house at least one manufacturing firm. It was estimated that there were approximately 46 manufacturing firms located in public incubators. A sample of six was used in this study. The statistical analysis included frequencies, cross tabulations, correlations, paired comparisons, and measures of association. The findings of this study suggest that the incubator-housed manufacturers' choice of evaluation models was not affected by the task nor did the selected strategy influence the amount of information used by the manufacturers. The findings indicate a need for further research to evaluate the relationships brought forth in this study. Based on the conclusions and findings of this study, recommendations for further research were given.