University of Cambridge
Building Black Holes: Analogue experiments and analogical reasoning
Abstract
dc:description.abstractAnalogue 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.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Philosophy (PhD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Field, Grace
- Advisor dc:contributor.advisor
-
- Chang, Hasok
Subjects
dc:subject × 4Rights
dc:rightsIdentifiers
dc:identifier.*- Author Identifier
- 0000-0003-2629-7191
- OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/366768