UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

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 given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    THE PHYSICS OF IDEAS: INFERRING THE MECHANICS OF OPINION FORMATION FROM MACROSCOPIC STATISTICAL PATTERNS
    (2016) Burghardt, Keith A.; Girvan, Michelle; Rand, William; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In a microscopic setting, humans behave in rich and unexpected ways. In a macroscopic setting, however, distinctive patterns of group behavior emerge, leading statistical physicists to search for an underlying mechanism. The aim of this dissertation is to analyze the macroscopic patterns of competing ideas in order to discern the mechanics of how group opinions form at the microscopic level. First, we explore the competition of answers in online Q&A (question and answer) boards. We find that a simple individual-level model can capture important features of user behavior, especially as the number of answers to a question grows. Our model further suggests that the wisdom of crowds may be constrained by information overload, in which users are unable to thoroughly evaluate each answer and therefore tend to use heuristics to pick what they believe is the best answer. Next, we explore models of opinion spread among voters to explain observed universal statistical patterns such as rescaled vote distributions and logarithmic vote correlations. We introduce a simple model that can explain both properties, as well as why it takes so long for large groups to reach consensus. An important feature of the model that facilitates agreement with data is that individuals become more stubborn (unwilling to change their opinion) over time. Finally, we explore potential underlying mechanisms for opinion formation in juries, by comparing data to various types of models. We find that different null hypotheses in which jurors do not interact when reaching a decision are in strong disagreement with data compared to a simple interaction model. These findings provide conceptual and mechanistic support for previous work that has found mutual influence can play a large role in group decisions. In addition, by matching our models to data, we are able to infer the time scales over which individuals change their opinions for different jury contexts. We find that these values increase as a function of the trial time, suggesting that jurors and judicial panels exhibit a kind of stubbornness similar to what we include in our model of voting behavior.
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    Modeling the Dynamics of Opinion Formation and Propagation: An Application to Market Adoption of Transportation Services
    (2007-08-29) Kozuki, Aaron T.C.Y.; Mahmassani, Hani S.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The objective of this research is to present a model that utilizes social and learning mechanisms to first explore the underlying dynamics of opinion formation and propagation, and then applies those mechanisms to an application of freight mode choice to investigate the effect that opinions have on choice set considerations, attribute perceptions, and the market adoption of a new rail freight service. Primary contributions of this research include the explicit modeling of social and learning mechanisms and their effects on opinion formation and propagation, the evolution of these opinions over time, and an exploration of the role that opinion dynamics have in choice processes. Research findings will offer insight to the process of evolving attitudes, perceptions, and opinions and the effects on individuals' judgment and decision making. It will also offer insight to the effects of attribute distortion on decision making.