{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/366768"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/366768","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Building Black Holes: Analogue experiments and analogical reasoning","abstract":"Analogue experiments investigate empirically accessible ‘source’ systems that are meant to mirror the behaviour of less accessible ‘target’ systems. This disseration aims to answer two questions about analogue experiments. First, what are they useful for? Second, how do analogical reasoning and the reasoning underlying analogue experimentation compare to other forms of inductive reasoning? The first question has been controversial ever since experiments in analogue gravity began to reveal effects which are in principle undetectable by conventional means. The second question connects analogue experimentation with the broader landscape of inductive reasoning, including the ‘problem of induction’ (e.g. Norton (2021)), analogical reasoning more broadly construed (e.g. Hesse (1966), Bartha (2010)), and simulation and modelling (e.g. Parke (2014), Morgan (2005)). Chapter 1 provides background on both analogical reasoning and analogue experimentation. Chapter 2 expands on a Bayesian framework introduced by Dardashti et al. (2019) to argue that analogue experiments can in principle provide significant confirmation for claims about their target systems, but only when supplemented with an independently plausible claim which is *significantly positively relevant* to both the source and target systems. Chapter 3 situates analogical reasoning and analogue experimentation within a new categorisation of inductive reasoning, arguing that inferences which we would typically place into the same class often provide very different kinds of inductive support for very different conclusions. Chapter 4 represents these categories as variations of Chapter 2’s Bayesian framework, lending generality to the arguments presented in Chapter 2 and providing a quantitative perspective on the arguments presented in Chapter 3. Finally, Chapter 5 uses recent developments in analogue gravity to show that analogue experiments can be useful for reasons beyond confirmation of hypotheses about their target systems: they can directly detect generalised phenomena, and they can be used as exploratory tools.","abstract_html":"Analogue experiments investigate empirically accessible ‘source’ systems that are meant to mirror the behaviour of less accessible ‘target’ systems. This disseration aims to answer two questions about analogue experiments. First, what are they useful for? Second, how do analogical reasoning and the reasoning underlying analogue experimentation compare to other forms of inductive reasoning? The first question has been controversial ever since experiments in analogue gravity began to reveal effects which are in principle undetectable by conventional means. The second question connects analogue experimentation with the broader landscape of inductive reasoning, including the ‘problem of induction’ (e.g. Norton (2021)), analogical reasoning more broadly construed (e.g. Hesse (1966), Bartha (2010)), and simulation and modelling (e.g. Parke (2014), Morgan (2005)). Chapter 1 provides background on both analogical reasoning and analogue experimentation. Chapter 2 expands on a Bayesian framework introduced by Dardashti et al. (2019) to argue that analogue experiments can in principle provide significant confirmation for claims about their target systems, but only when supplemented with an independently plausible claim which is *significantly positively relevant* to both the source and target systems. Chapter 3 situates analogical reasoning and analogue experimentation within a new categorisation of inductive reasoning, arguing that inferences which we would typically place into the same class often provide very different kinds of inductive support for very different conclusions. Chapter 4 represents these categories as variations of Chapter 2’s Bayesian framework, lending generality to the arguments presented in Chapter 2 and providing a quantitative perspective on the arguments presented in Chapter 3. Finally, Chapter 5 uses recent developments in analogue gravity to show that analogue experiments can be useful for reasons beyond confirmation of hypotheses about their target systems: they can directly detect generalised phenomena, and they can be used as exploratory tools.","abstract_has_math":false,"creators":["Field, Grace"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Chang, Hasok"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-02-27","date_published":"2023-02-27","updated_at":"2026-07-22T22:24:04Z","subjects":["Analogical reasoning","Analogue experiments","Philosophy of modelling","Philosophy of simulation"],"languages":["eng"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c32fdd5b-f1d5-4ca5-bfe4-029d075c417d/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000326297191"],"render_values":[{"text":"0000-0003-2629-7191","href":"https://orcid.org/0000-0003-2629-7191","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.107546","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chang, Hasok"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["This thesis was supported by a Harding Distinguished Postgraduate Scholarship, and by a Doctoral Fellowship from the Social Sciences and Humanities Research Council of Canada."]},{"key":"dc:creator","label":"Author","values":["Field, Grace"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000326297191"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023-02-27"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/366768"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Analogical reasoning","Analogue experiments","Philosophy of modelling","Philosophy of simulation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c32fdd5b-f1d5-4ca5-bfe4-029d075c417d/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.107546"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/6539064d-1d92-497a-8a61-385e52489615/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Analogue experiments investigate empirically accessible ‘source’ systems that are meant to mirror the behaviour of less accessible ‘target’ systems. 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(2019) to argue that analogue experiments can in principle provide significant confirmation for claims about their target systems, but only when supplemented with an independently plausible claim which is *significantly positively relevant* to both the source and target systems. Chapter 3 situates analogical reasoning and analogue experimentation within a new categorisation of inductive reasoning, arguing that inferences which we would typically place into the same class often provide very different kinds of inductive support for very different conclusions. Chapter 4 represents these categories as variations of Chapter 2’s Bayesian framework, lending generality to the arguments presented in Chapter 2 and providing a quantitative perspective on the arguments presented in Chapter 3. Finally, Chapter 5 uses recent developments in analogue gravity to show that analogue experiments can be useful for reasons beyond confirmation of hypotheses about their target systems: they can directly detect generalised phenomena, and they can be used as exploratory tools."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["843ceea8bc57601079c33dbd91e07546","87eda9de84448d1f82354d60eee3eb5f"]},{"key":"dc:title","label":"Title","values":["Building Black Holes: Analogue experiments and analogical reasoning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Chang, Hasok"],"dc:contributor.sponsor":["This thesis was supported by a Harding Distinguished Postgraduate Scholarship, and by a Doctoral Fellowship from the Social Sciences and Humanities Research Council of Canada."],"dc:creator":["Field, Grace"],"dc:creator.authoridentifier":["0000000326297191"],"dc:date.issued":["2023-02-27"],"dc:description.abstract":["Analogue experiments investigate empirically accessible ‘source’ systems that are meant to mirror the behaviour of less accessible ‘target’ systems. 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(2019) to argue that analogue experiments can in principle provide significant confirmation for claims about their target systems, but only when supplemented with an independently plausible claim which is *significantly positively relevant* to both the source and target systems. Chapter 3 situates analogical reasoning and analogue experimentation within a new categorisation of inductive reasoning, arguing that inferences which we would typically place into the same class often provide very different kinds of inductive support for very different conclusions. Chapter 4 represents these categories as variations of Chapter 2’s Bayesian framework, lending generality to the arguments presented in Chapter 2 and providing a quantitative perspective on the arguments presented in Chapter 3. 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