Experiments, Simulations, and Epistemic Privilege

Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: First, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this paper I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, per se. To the extent that either methodology puts researchers in a privileged epistemic position, this is context-sensitive

Microbes, mathematics, and models

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry

Microbes, mathematics, and models

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry

Characterizing life: four dimensions and their relevance to origin of life research

The question ‘What is life?’ has been debated since antiquity, and continues to confound scientists and philosophers today. There are over 100 proposed answers to that question in the literature. Some authors continue to propose new answers, and others argue about whether or not we should just give up. Following several recent contributions to the latter ‘meta-debate’ about life, this chapter suggests a pluralist approach to characterizing life: multiple characterizations of life can co-exist, for different but often complementary purposes. After discussing the relevance of characterizing life for origin of life research, this chapter offers a new way to think about the landscape of characterizing life in terms of four conceptual dimensions: (1) treating life as an all-or-nothing phenomenon or as a matter of degree, and characterizing life (2) materially or functionally, (3) at the individual or community level, and (4) minimally or inclusively. Depending on which agenda is at stake within origin of life research—for example, explaining the actual origin of life on Earth, versus explaining how life could, in principle, emerge anywhere at all—the sorts of features we might want in a characterization of life can vary along these four dimensions

How Causal are Microbiomes? A Comparison with the Helicobacter pylori Explanation of Ulcers

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made

Against Defending Science: Asking Better Questions About Indigenous Knowledge and Science

This paper addresses problems with a defensive turn in discussions of science and Indigenous ways of knowing, being and doing. Philosophers and practitioners of science have focused recent discussions on coarse-grained questions of demarcation, epistemic parity and identity—asking questions such as “Is Indigenous knowledge science?” Using representative examples from Aotearoa New Zealand, we expose rampant ambiguities in these arguments, and show that this combative framing can overlook what is actually at stake. We provide a framework for analyzing these problems and suggest better ways forward

Going big by going small: trade-offs in microbiome explanations of cancer

Microbial factors have been implicated in cancer risk, disease progression, treatment and prevention. The key word, however, is “implicated.” Our aim in this paper is to map out some of the tensions between competing methods, goals, and standards of evidence in cancer research with respect to the causal role of microbial factors. We discuss an array of pragmatic and epistemic trade-offs in this research area: prioritizing coarse-grained versus fine-grained explanations of the roles of microbiota in cancer; explaining general versus specific cancer targets; studying model organisms versus human patients; and understanding and explaining cancer versus developing diagnostic tools and treatments. In light of these trade-offs and the distinctive complexity and heterogeneity on both sides of the microbiome-cancer relationship, we suggest that it would be more productive and intellectually honest to frame much of this work, at least currently, in terms of generating causal hypotheses to investigate further. Claims of established causal connections between the microbiome and cancer are in many cases overstated. We also discuss the value of “black boxing” microbial causal variables in this research context and draw some general cautionary lessons for ongoing discussions of microbiomes and cancer

How Causal are Microbiomes? A Comparison with the Helicobacter pylori Explanation of Ulcers

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made