There are two main theoretical frameworks in statistical mechanics, one associated with Boltzmann and the other with Gibbs. Despite their well-known differences, there is a prevailing view that equilibrium values calculated in both frameworks coincide. We show that this is wrong. There are important cases in which the Boltzmannian and Gibbsian equilibrium concepts yield different outcomes. Furthermore, the conditions under which equilibriums exists are different for Gibbsian and Boltzmannian statistical mechanics. There are, however, special circumstances under which it is true (...) that the equilibrium values coincide. We prove a new theorem providing sufficient conditions for this to be the case. (shrink)

Gibbsian statistical mechanics (GSM) is the most widely used version of statistical mechanics among working physicists. Yet a closer look at GSM reveals that it is unclear what the theory actually says and how it bears on experimental practice. The root cause of the difficulties is the status of the Averaging Principle, the proposition that what we observe in an experiment is the ensemble average of a phase function. We review different stances toward this principle, and eventually present a coherent (...) interpretation of GSM that provides an account of the status and scope of the principle. (shrink)

Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an environment is to rely on evidence. Despite the recent increase in post-fact figures in politics, evidence-based policymaking takes centre stage in policy-setting institutions. Often, however, policy-makers face large volumes of evidence from different sources. Robustness analysis can, prima facie, handle this evidential diversity. Roughly, a hypothesis is supported by robust evidence if the different evidential sources are in agreement. In this thesis, I strengthen the (...) case for the use of robustness analysis in evidence-based policymaking by answering open research questions about this inference technique. First, I argue that existing taxonomies miss a fruitful category of robustness reasoning, that is predictive stability. Second, I claim that derivational robustness analysis – the investigation of whether the results of different models are in agreement – can yield interesting insights even if not the entire relevant model space is covered by available models or if the model results are only partially in agreement. Third, I claim that expert knowledge is necessary to address questions that arise when one applies measurement robustness analysis – the investigation into whether multiple means of measurement yield the same result. Finally, I argue that, in situations where evidence from different measurements is not in agreement, it can be advisable to no longer take all of the evidence into account. This can be done in a rationally defensible way by choosing the most adequate theory or model underlying parts of the evidence set. I discuss examples from climate, medical, and economic policy-making to establish my claims. (shrink)