Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item SUBJECTIVE INTEGRATION OF PROBABILISTIC INFORMATION FROM DESCRIPTION AND FROM EXPERIENCE(2009) Shlomi, Yaron; Wallsten, Thomas S.; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Subjective integration of probabilistic information obtained via description and experience underlies potentially consequential judgments and choices. However, little is known about the quality of the integration and the underlying processes. I contribute to filling this gap by investigating judgments informed by integrating probabilistic information from the two sources. Building on existing information integration frameworks (e.g., N. Anderson, 1971), I develop and subsequently test computational models that represent the integration process. Participants in three experiments estimated the percentage of red balls in a bag containing red and blue balls based on two samples drawn from the bag. They experienced one sample by observing a sequence of draws and received a description of the other sample in terms of summary statistics. Subjective integration was more sensitive to information obtained via experience than via description in a manner that depended on the extremity of the experienced sample relative to the described one. Experiment 1 showed that experience preceding description leads to integration that is less biased towards experience than the reverse presentation sequence. Following this result, Experiment 2 examined the effect of memory-retrieval demands on the quality of the integration. Specifically, we manipulated the presence or absence of description- and experience- based decision aids that eliminate the need to retrieve source-specific information. The results show that the experience aid increased the bias, while the description aid had no interpretable effect. Experiment 3 investigated the effect of the numerical format of the description (percentage vs. frequency). When description was provided in the frequency format, the judgments were unbiased and the leading model suggested that the two sources are psychologically equivalent. However, when the description was provided in the percentage format, the leading model implied a tradeoff between the two sources. Finally, participants in Experiment 3 also rated how much they trusted the source of the description. The participants' ratings were correlated with how they used the description and with the quality of their judgments. The findings have implications for interpreting the description-experience gap in risky choice, for information integration models, and for understanding the role of format on the use of information from external sources. In addition, the methods developed here can be applied broadly to study how people integrate information from different sources or in different formats.Item Cognitive and Motivational Parameters in Motivated Biases in Human Judgment(2009) Chen, Xiaoyan; Kruglanski, Arie W.; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Motivational and cognitive factors can determine the extent and direction of information processing in judgment. When biasing motives are present, information can be distorted and judgment biased. The extent of this bias can be determined by the nature of the information, the relative magnitude of competing goals, and the individual's cognitive resources. Studies 1, 2 and 3 explored the effect of resource depletion on motivated distortions in judgment. Studies 4 and 5 examined the role of relative goal magnitude (of a biasing goal vs. a specific judgment goal) in the phenomena. I departed from the assumption that human knowledge is malleable, and that its alteration in a motivationally desirable direction may vary in difficulty across instances. It was assumed that overcoming the difficulty requires cognitive and/or motivational resources hence under certain circumstances resource-depletion should diminish individuals' ability to motivationally bias judgments. I also hypothesized when information is clear-cut (rather than ambiguous) making distortion difficult, a sufficient amount of biasing motivation could overcome the "reality constraints," holding the cognitive resources constant. In my first two studies participants' resources were depleted either via complex or simple presentational format of the information given (Study 1), or via engagement in a fatiguing prior activity (Study 2). In the third study (Study 3), I measured participants' stable cognitive capacity as a proxy for their available cognitive resources. All three studies provided supportive evidence for the hypothesis that motivated distortion is resource dependent. In Study 4 I manipulated the relative goal magnitude by experimentally increasing goal importance for either an academic success goal in line with the specific judgment task or a social wellbeing goal as the biasing goal. In Study 5 I altered relative goal magnitude through enhancing either a neutral goal or health concerns as the biasing goal. In both Study 4 and 5, orthogonal to the relative goal magnitude manipulation, stimulus ambiguity was made either high or low. Findings from Study 4 and 5 supported the hypothesis that sufficient magnitude of biasing goal could overcome distortion difficulty even in highly constraining circumstances.Item Data-Informed Calibration and Aggregation of Expert Judgment in a Bayesian Framework(2009) Shirazi, Calvin Homayoon; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Historically, decision-makers have used expert opinion to supplement lack of data. Expert opinion, however, is applied with much caution. This is because judgment is subjective and contains estimation error with some degree of uncertainty. The purpose of this study is to quantify the uncertainty surrounding the unknown of interest, given an expert opinion, in order to reduce the error of the estimate. This task is carried out by data-informed calibration and aggregation of expert opinion in a Bayesian framework. Additionally, this study evaluates the impact of the number of experts on the accuracy of aggregated estimate. The objective is to determine the correlation between the number of experts and the accuracy of the combined estimate in order to recommend an expert panel size.