{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/62410"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/62410","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Framework theories in science","abstract":"This thesis consists of three papers on the nature of scientific theories and inference. In many cases, scientific theories can be regarded as organized into hierarchies, with higher levels sometimes called 'paradigms', and lower levels encoding more specific or concrete hypotheses. The first chapter uses hierarchical Bayesian models to show that the assessment of higher level theories may proceed by the same Bayesian principles as the assessment of more specific hypotheses. It thus shows how the evaluation of higher level theories can be evidence-driven, despite claims to the contrary by authors such as Kuhn. The chapter also discusses how hierarchical Bayesian models may help to resolve certain issues for Bayesian philosophy of science, particularly how to model the introduction of new theories. The second chapter discusses the relationship between Inference to the Best Explanation (IBE) and Bayesianism. Van Fraassen has raised the concern that the explanatory considerations in IBE go beyond the Bayesian formalism, making IBE incompatible with Bayesianism. The response so far has been that the explanatory considerations can be accommodated within the Bayesian formalism by stipulating that they should constrain the assignment of the probabilities. I suggest a third alternative, which is that the extra explanatory considerations have their origins in the relationship between higher and lower level theories and can be modeled in Bayesian terms without directly constraining the probabilities. The third chapter discusses an aspect of the debate over scientific realism. The No Miracles argument and the Pessimistic Induction are often seen as the primary arguments for and against scientific realism. Yet recently it has been alleged that both of these arguments commit the base-rate fallacy. I argue that both arguments can be formulated in a non-fallacious manner, so neither should be dismissed on the grounds of faulty form alone.","abstract_html":"This thesis consists of three papers on the nature of scientific theories and inference. In many cases, scientific theories can be regarded as organized into hierarchies, with higher levels sometimes called 'paradigms', and lower levels encoding more specific or concrete hypotheses. The first chapter uses hierarchical Bayesian models to show that the assessment of higher level theories may proceed by the same Bayesian principles as the assessment of more specific hypotheses. It thus shows how the evaluation of higher level theories can be evidence-driven, despite claims to the contrary by authors such as Kuhn. The chapter also discusses how hierarchical Bayesian models may help to resolve certain issues for Bayesian philosophy of science, particularly how to model the introduction of new theories. The second chapter discusses the relationship between Inference to the Best Explanation (IBE) and Bayesianism. Van Fraassen has raised the concern that the explanatory considerations in IBE go beyond the Bayesian formalism, making IBE incompatible with Bayesianism. The response so far has been that the explanatory considerations can be accommodated within the Bayesian formalism by stipulating that they should constrain the assignment of the probabilities. I suggest a third alternative, which is that the extra explanatory considerations have their origins in the relationship between higher and lower level theories and can be modeled in Bayesian terms without directly constraining the probabilities. The third chapter discusses an aspect of the debate over scientific realism. The No Miracles argument and the Pessimistic Induction are often seen as the primary arguments for and against scientific realism. Yet recently it has been alleged that both of these arguments commit the base-rate fallacy. I argue that both arguments can be formulated in a non-fallacious manner, so neither should be dismissed on the grounds of faulty form alone.","abstract_has_math":false,"creators":["Henderson, Leah"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Linguistics and Philosophy.","school":null,"contributors":[],"advisors":["Robert Stalnaker."],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-22T22:22:13Z","subjects":["Linguistics and Philosophy."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/62410","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Robert Stalnaker."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Linguistics and Philosophy."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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In many cases, scientific theories can be regarded as organized into hierarchies, with higher levels sometimes called 'paradigms', and lower levels encoding more specific or concrete hypotheses. The first chapter uses hierarchical Bayesian models to show that the assessment of higher level theories may proceed by the same Bayesian principles as the assessment of more specific hypotheses. It thus shows how the evaluation of higher level theories can be evidence-driven, despite claims to the contrary by authors such as Kuhn. The chapter also discusses how hierarchical Bayesian models may help to resolve certain issues for Bayesian philosophy of science, particularly how to model the introduction of new theories. The second chapter discusses the relationship between Inference to the Best Explanation (IBE) and Bayesianism. Van Fraassen has raised the concern that the explanatory considerations in IBE go beyond the Bayesian formalism, making IBE incompatible with Bayesianism. The response so far has been that the explanatory considerations can be accommodated within the Bayesian formalism by stipulating that they should constrain the assignment of the probabilities. I suggest a third alternative, which is that the extra explanatory considerations have their origins in the relationship between higher and lower level theories and can be modeled in Bayesian terms without directly constraining the probabilities. The third chapter discusses an aspect of the debate over scientific realism. The No Miracles argument and the Pessimistic Induction are often seen as the primary arguments for and against scientific realism. Yet recently it has been alleged that both of these arguments commit the base-rate fallacy. I argue that both arguments can be formulated in a non-fallacious manner, so neither should be dismissed on the grounds of faulty form alone."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Framework theories in science"]}]}],"canonical_facts":{"dc:contributor.advisor":["Robert Stalnaker."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Linguistics and Philosophy."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Linguistics and Philosophy."],"dc:creator":["Henderson, Leah"],"dc:date.accessioned":["2011-04-25T15:54:24Z"],"dc:date.available":["2011-04-25T15:54:24Z"],"dc:date.issued":["2010"],"dc:description":["Thesis (Ph. 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The chapter also discusses how hierarchical Bayesian models may help to resolve certain issues for Bayesian philosophy of science, particularly how to model the introduction of new theories. The second chapter discusses the relationship between Inference to the Best Explanation (IBE) and Bayesianism. Van Fraassen has raised the concern that the explanatory considerations in IBE go beyond the Bayesian formalism, making IBE incompatible with Bayesianism. The response so far has been that the explanatory considerations can be accommodated within the Bayesian formalism by stipulating that they should constrain the assignment of the probabilities. I suggest a third alternative, which is that the extra explanatory considerations have their origins in the relationship between higher and lower level theories and can be modeled in Bayesian terms without directly constraining the probabilities. The third chapter discusses an aspect of the debate over scientific realism. The No Miracles argument and the Pessimistic Induction are often seen as the primary arguments for and against scientific realism. Yet recently it has been alleged that both of these arguments commit the base-rate fallacy. I argue that both arguments can be formulated in a non-fallacious manner, so neither should be dismissed on the grounds of faulty form alone."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/62410"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Linguistics and Philosophy."],"dc:title":["Framework theories in science"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:13Z"}