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dc.contributor.authorMcNerney, Jamesen_US
dc.date.accessioned2015-08-05T00:54:26Z
dc.date.available2015-08-05T00:54:26Z
dc.date.issued2012
dc.date.submitted2012
dc.identifier.other(ALMA)contemp
dc.identifier.urihttps://hdl.handle.net/2144/12515
dc.descriptionThesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.en_US
dc.description.abstractUnderstanding how changing technology affects the prices of goods is a problem with both rich phenomenology and important policy consequences. Using methods from statis- tical physics, I model technology-driven price evolution. First, I examine a model for the price evolution of individual technologies. The price of a good often follows a power law equation when plotted against its cumulative production. This observation turns out to have significant consequences for technology policy aimed at mitigating climate change, where technologies are needed that achieve low carbon emissions at low cost. However, no theory adequately explains why technology prices follow power laws. To understand this behavior, I simplify an existing model that treats technologies as machines composed of interacting components. I find that the power law exponent of the price trajectory is inversely related to the number of interactions per component. I extend the model to allow for more realistic component interactions and make a testable prediction. Next, I conduct a case-study on the cost evolution of coal-fired electricity. I derive the cost in terms of various physical and economic components. The results suggest that commodities and technologies fall into distinct classes of price models, with commodities following martingales, and technologies following exponentials in time or power laws in cumulative production. I then examine the network of money flows between industries. This work is a precursor to studying the simultaneous evolution of multiple technologies. Economies resemble large machines, with different industries acting as interacting components with specialized functions. To begin studying the structure of these machines, I examine 20 economies with an emphasis on finding common features to serve as targets for statistical physics models. I find they share the same money flow and industry size distributions. I apply methods from statistical physics to show that industries cluster the same way according to industry type. Finally, I use these industry money flows to model the price evolution of many goods simultaneously, where network effects become important. I derive a prediction for which goods tend to improve most rapidly. The fastest-improving goods are those with the highest mean path lengths in the money flow network.en_US
dc.language.isoen_US
dc.publisherBoston Universityen_US
dc.titleApplications of statistical physics to technology price evolutionen_US
dc.typeThesis/Dissertationen_US
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplinePhysicsen_US
etd.degree.grantorBoston Universityen_US


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