Adrian Currie

Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion

ABSTRACTExperimentation is traditionally considered a privileged means of confirmation. However, why and how experiments form a better confirmatory source relative to other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. ‘Specimenhood’ involves possessing relevant properties given the investigative target and the hypothesis in question. Specimens are thus representative members of a class of systems, to which a hypothesis refers. It is in virtue of both control and specimenhood that experiments provide powerful confirmatory evidence. This explains the distinctive power of experiments: although modelers exert extensive control, they do not exert this control over specimens; although natural experiments utilize specimens, control is diminished.

The comparative method grants epistemic access to the biological past. Comparing lineages provides empirical traction on both hypotheses about particular lineages and models of trait evolution. Understanding this evidential role is important. Although philosophers have recently turned their attention to relations of descent, little work exists exploring the status of evidence from convergences. I argue that, where they exist, convergences play a central role in the confirmation of adaptive hypotheses. I focus on ‘analogous inferences’, show how such inferences ought to be analysed and suggest three methods for strengthening their evidential weight. 1 Introduction2 Analogous Inferences2.1 Adaptive explanations and analogies2.2 Analogous inferences2.3 Scope, grain, and specificity3 Parallel Modelling, Integrated Explanations, and Convergent Modelling3.1 Parallel modelling3.2 Integrated explanations3.1 Convergent modelling4 Conclusion

We argue that narratives are central to the success of historical reconstruction. Narrative explanation involves tracing causal trajectories across time. The construction of narrative, then, often involves postulating relatively speculative causal connections between comparatively well-established events. But speculation is not always idle or harmful: it also aids in overcoming local underdetermination by forming scaffolds from which new evidence becomes relevant. Moreover, as our understanding of the past’s causal milieus become richer, the constraints on narrative plausibility become increasingly strict: a narrative’s admissibility does not turn on mere logical consistency with background data. Finally, narrative explanation and explanation generated by simple, formal models complement one another. Where models often achieve isolation and precision at the cost of simplification and abstraction, narratives can track complex changes in a trajectory over time at the cost of simplicity and precision. In combination both allow us to understand and explain highly complex historical sequences.

To a large extent, the evidential base of claims in the philosophy of science has switched from thought experiments to case studies. We argue that abandoning thought experiments was a wrong turn, since they can effectively complement case studies. We make our argument via an analogy with the relationship between experiments and observations within science. Just as experiments and ‘natural’ observations can together evidence claims in science, each mitigating the downsides of the other, so too can thought experiments and case studies be mutually supporting. After presenting the main argument, we look at potential concerns about thought experiments, suggesting that a judiciously applied mixed-methods approach can overcome them.

The past can be a stubborn subject: it is complex, heterogeneous and opaque. To understand it, one must decide which aspects of the past to emphasise and which to minimise. Enter frameworks. Frameworks foreground certain aspects of the historical record while backgrounding others. As such, they are both necessary for, and conducive to, good history as well as good philosophy. We examine the role of frameworks in the history and philosophy of science and argue that they are necessary for both forms of enquiry. We then suggest that the right attitude towards frameworks is pluralism rather than monism: there is no single correct framework to be applied to a given scientific episode. Rather, a multitude of different frameworks are more or less appropriate given various contexts and aims. From this perspective, good frameworks generate and further, rather than frustrate, historical and philosophical enquiry. Our view sheds light on historical disagreement, and on the relationship between philosophy and history of science.

Geologists, Paleontologists and other historical scientists are frequently concerned with narrative explanations targeting single cases. I show that two distinct explanatory strategies are employed in narratives, simple and complex. A simple narrative has minimal causal detail and is embedded in a regularity, whereas a complex narrative is more detailed and not embedded. The distinction is illustrated through two case studies: the ‘snowball earth’ explanation of Neoproterozoic glaciation and recent attempts to explain gigantism in Sauropods. This distinction is revelatory of historical science. I argue that at least sometimes which strategy is appropriate is not a pragmatic issue, but turns on the nature of the target. Moreover, the distinction reveals a counterintuitive pattern of progress in some historical explanation: shifting from simple to complex. Sometimes, historical scientists rightly abandon simple, unified explanations in favour of disunified, complex narratives. Finally I compare narrative and mechanistic explanation, arguing that mechanistic approaches are inappropriate for complex narrative explanations

Thought experiments play a role in science and in some central parts of contemporary philosophy. They used to play a larger role in philosophy of science, but have been largely abandoned as part of the field’s “practice turn”. This chapter discusses possible roles for thought experimentation within a practice-oriented philosophy of science. Some of these roles are uncontroversial, such as exemplification and aiding discovery. A more controversial role is the reliance on thought experiments to justify philosophical claims. It is proposed that if we adopt an underlying empiricist view of concepts, then thought experiments can be seen as affording us contact with scientific practice, despite their seemingly a priori character. The advantages and drawbacks of thought experiments are discussed via comparison with case studies, on the one, and simulations on the other hand. The chapter closes with some remarks on how to combine thought experiments with other methods.

The scientific investigation of music requires contributions from a diverse array of disciplines. Given the diverse methodologies, interests and research targets of the disciplines involved, we argue that there is a plurality of legitimate research questions about music, necessitating a focus on integration. In light of this we recommend a pluralistic conception of music—that there is no unitary definition divorced from some discipline, research question or context. This has important implications for how the scientific study of music ought to proceed: we show that some definitions are complementary, that is, they reflect different research interests and ought to be retained and, where possible, integrated, while others are antagonistic, they represent real empirical disagreement about music’s nature and how to account for it. We illustrate this in discussion of two related issues: questions about the evolutionary function of music, and questions of the innateness of music. These debates have been, in light of pluralism, misconceived. We suggest that, in both cases, scientists ought to proceed by constructing integrated models which take into account the dynamic interaction between different aspects of music

Ethnographic analogy, the use of comparative data from anthropology to inform reconstructions of past human societies, has a troubled history. Archaeologists often express concern about, or outright reject, the practice—and sometimes do so in problematically general terms. This is odd, as the use of comparative data in archaeology is the same pattern of reasoning as the ‘comparative method’ in biology, which is a well-developed and robust set of inferences which play a central role in discovering the biological past. In pointing out this continuity, I argue that there is no ‘special pleading’ on the part of archaeologists in this regard: biologists must overcome analogous epistemic difficulties in their use of comparative data. I then go on to emphasize the local, empirically tractable ways in which particular ethnographic analogies may be licensed.

Can we be realists about a general category but pluralists about concepts relating to that category? I argue that paleobiological methods of delineating species are not affected by differing species concepts, and that this underwrites an argument that species concept pluralists should be species category realists. First, the criteria by which paleobiologists delineate species are ‘indifferent’ to the species category. That is, their method for identifying species applies equally to any species concept. To identify a new species, paleobiologists show that interspecies processes, such as phenotypic plasticity, sexual dimorphism, or ontogenetic diversity, are a worse explanation of the variance between specimens than intraspecies processes. As opposed to operating under a single or plurality of species concepts, then, paleobiologists use abductive inferences, which would be required regardless of any particular species concept. Second, paleobiologists are frequently interested in large-scale, long-term morphological patterns in the fossil record, and resolving the fine-grained differences which result from different species concepts is irrelevant at those scales. I argue that this claim about paleobiological practice supports what I call ‘indifference realism’ about the species category. The indifference realist argues that when legitimate investigation is indifferent to a plurality of concepts, we should be realists about the category those concepts pertain to.

Cognitive archaeologists infer from material remains to the cognitive features of past societies. We characterize cognitive archaeology in terms of trace-based reasoning, which in the case of cognitive archaeology involves inferences drawing upon background theory linking objects from the archaeological record to cognitive features. We analyse such practices, examining work on cognitive evolution, language, and musicality. We argue that the central epistemic challenge for cognitive archaeology is often not a paucity of material remains, but insufficient constraint from cognitive theories. However, we also argue that the success of cognitive archaeology does not necessarily require well-developed cognitive theories: Success might instead lead to them.

Our epistemic access to the past is infamously patchy: historical information degrades and disappears and bygone eras are often beyond the reach of repeatable experiments. However, historical scientists have been remarkably successful at uncovering and explaining the past. I argue that part of this success is explained by the exploitation of dependencies between historical events, entities, and processes. For instance, if sauropod dinosaurs were hot blooded, they must have been gluttons; the high-energy demands of endothermy restrict sauropod grazing strategies. Understanding such dependencies extends our reach into the past in spite of incomplete data. In addition, this serves as a counterexample to two accounts of method in the historical sciences. By one, historical science proceeds by identifying ‘smoking guns’: traces that discriminate between live hypotheses. By the other, historical hypotheses are supported by consilience: the convergence of independent lines of evidence. However, testing for ‘coherency’ between past hypotheses also plays a critical role in historical confirmation. Just as historical scientists exploit dependencies between past entities and present entities to infer what the past was like, they also exploit dependencies between past entities themselves. I do not suggest that archetypical historical science proceeds in this manner. Rather, the lesson I draw is that historical methodology cannot be characterized as archetypically relying on one method or another. Historical science is at base opportunistic, and is resistant to unitary analyses. 1 Introduction2 Snowballs and Explosions: The Basic Idea3 Were Sauropods Endothermic?4 Dependent Entities and Interdependent Explanations5 Smoking Guns and Consilience.

We argue for conceptual pluralism about music. In our view, there is no right answer to the question ‘What is music?’ divorced from some context or interest. Instead, there are several, non-equivalent music concepts suited to different interests – from within some tradition or practice, or by way of some research question or field of inquiry. We argue that unitary definitions of music are problematic, that the role music concepts play in various research questions should motivate conceptual pluralism about music, and that taking music pluralism seriously grounds a fruitful research programme in aesthetics. We suspect that pluralism about music is a good test case for the utility of pluralism in aesthetics more generally, and we present it as such.

Historical scientists frequently face incomplete data, and lack direct experimental access to their targets. This has led some philosophers and scientists to be pessimistic about the epistemic potential of the historical sciences. And yet, historical science often produces plausible, sophisticated hypotheses. I explain this capacity to generate knowledge in the face of apparent evidential scarcity by examining recent work on Thylacoleo carnifex, the ‘marsupial lion’. Here, we see two important methodological features. First, historical scientists are methodological omnivores, that is, they construct purpose-built epistemic tools tailored to generate evidence about highly specific targets. This allows them to produce multiple streams of independent evidence and thus maximize their epistemic reach. Second, investigative scaffolding: research proceeds in a piece-meal fashion, information only gaining evidential relevance once certain hypotheses are well supported. I illustrate scaffolding in a discussion of the nature of functional ascription in paleobiology. Frequently, different senses of ‘function’ are not discriminated during paleobiological investigation—something which can mar adaptationist investigations of extant organisms. However, I argue that, due to scaffolding, conflating senses of ‘function’ can be the right thing to do. Coarse-grained functional hypotheses are required before it is clear what evidence could discriminate between more fine-grained ones. I draw on omnivory and scaffolding to argue that pessimists make a bad empirical bet. It is a bad idea to bet against the epistemic fortunes of such opportunistic and resourceful scientists, especially when we have reason to think we will systematically underestimate the amount of evidence ultimately available to them

Both paleobiology and investigations of ‘major evolutionary transitions’ are intimately concerned with the macroevolutionary shape of life. It is surprising, then, how little studies of major transitions are informed by paleontological perspectives and. I argue that this disconnect is partially justified because paleobiological investigation is typically ‘phenomena-led’, while investigations of major transitions are ‘theory-led’. The distinction turns on evidential relevance: in the former case, evidence is relevant in virtue of its relationship to some phenomena or hypotheses concerning those phenomena; in the latter, evidence is relevant in virtue of providing insights into, or tests of, an abstract body of theory. Because paleobiological data is by-and-large irrelevant to the theory which underwrites the traditional conception of major transitions, it is of limited use to that research program. I suggest that although the traditional conception of major transitions is neither ad-hoc or problematically incomplete, its promise of providing unificatory explanations of the transitions is unlikely to be kept. Further, examining paleobiological investigations of mass extinctions and organogenesis, I further argue that whether or not transitions in paleobiology count as ‘major’ turns on how we conceive of major transitions ; although major transitions potentially have a unified theoretical basis, recent developments suggest that investigations are becoming increasingly phenomena-led; adopting phenomena-led investigations maximizes the evidence available to paleobiologists.

Angela Potochnik’s Idealization and the Aims of Science defends an ambitious and systematic account of scientific knowledge: ultimately science pursues human understanding rather than truth. Potochnik argues that idealization is rampant and unchecked in science. Further, given that idealizations involve departures from truth, this suggests science is not primarily about truth. I explore the relationship between truths about causal patterns and scientific understanding in light of this, and suggest that Potochnik underestimates the importance and power of highly particular narrative explanations.

Pluralism about scientific method is more-or-less accepted, but the consequences have yet to be drawn out. Scientists adopt different methods in response to different epistemic situations: depending on the system they are interested in, the resources at their disposal, and so forth. If it is right that different methods are appropriate in different situations, then mismatches between methods and situations are possible. This is most likely to occur due to method bias: when we prefer a particular kind of method, despite that method clashing with evidential context or our aims. To explore these ideas, we sketch a kind of method pluralism which turns on two properties of evidence, before using agent-based models to examine the relationship between methods, epistemic situations, and bias. Based on our results, we suggest that although method bias can undermine the efficiency of a scientific community, it can also be productive through preserving a diversity of evidence. We consider circumstances where method bias could be particularly egregious, and those where it is a potential virtue, and argue that consideration of method bias reveals that community standards deserve a central place in the epistemology of science.

Pedagogical situations require white lies: in teaching philosophy we make decisions about what to omit, what to emphasise, and what to distort. This article considers when it is permissible to distort the historical record, arguing for a tempered respect for the historical facts. It focuses on the rationalist/empiricist distinction, which still frames most undergraduate early modern courses despite failing to capture the intellectual history of that period. It draws an analogy with Michael Strevens's view on idealisation in causal explanation to distinguish between myths and caricatures. Myths are distortions of the historical record that undermine students' understanding of the past, despite having other pedagogical benefits. Caricatures are distortions that either increase or are indifferent to understanding of the past. Mythmaking, the article argues, is unjustified

. Philosophers and scientists often cite ontic factors when explaining the methods and success of scientific inquiry. That is, the adoption of a method or approach is explained in reference to the kind of system in which the scientist is interested: these are explanations of why scientists do what they do, that appeal to properties of their target systems. We present a framework for understanding such “Opticks to his Principia. Newton’s optical work is largely experiment-driven, while the Principia is primarily mathematical, so usually, each work is taken to exemplify a different kind of science. However, Newton himself often presented them in terms of a largely consistent method. We use our framework to articulate an original and plausible position: that the differences between the Opticks and the Principia are due to the kinds of systems targeted. That is, we provide an ontic-driven explanation of methodological differences. We suspect that ontic factors should have a more prominent role in historical explanations of scientific method and development.

Paleobiologists often provide simple narratives to explain complex, contingent episodes. These narratives are sometimes ‘one-shot hypotheses’ which are treated as being mutually exclusive with other possible explanations of the target episode, and are thus extended to accommodate as much about the episode as possible. I argue that a provisional preference for such hypotheses provides two kinds of productive scaffolding. First, they generate ‘hypothetical difference-makers’: one-shot hypotheses highlight and isolate empirically tractable dependencies between variables. Second, investigations of hypothetical difference-makers provision explanatory resources, the ‘raw materials’ for constructing more complex—and likely more adequate—explanations. Provisional preferences for simple, one-shot hypotheses in historical science, then, is defeasibly justified on indirect—strategic—grounds. My argument is made in reference to recent developments regarding the K–Pg extinction.

I develop an account of homology and homoplasy drawing on their use in biological inference and explanation. Biologists call on homology and homoplasy to infer character states, support adaptationist explanations, identify evolutionary novelties and hypothesize phylogenetic relationships. In these contexts, the concepts must be understood phylogenetically and kept separate: as they play divergent roles, overlap between the two ought to be avoided. I use these considerations to criticize an otherwise attractive view defended by Gould, Hall, and Ramsey & Peterson. By this view, homology and homoplasy can only be delineated qua some level of description, and some homoplasies (parallelisms) are counted as homologous. I develop an account which retains the first, but rejects the second, aspect of that view. I then characterize parallelisms and convergences in terms of their causal role. By the Strict Continuity account, homology and homoplasy are defined phylogenetically and without overlaps, meeting my restriction. Convergence and parallelisms are defined as two types of homoplasy: convergent homoplasies are largely constrained by external factors, while parallelisms are due to internal constraints

I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when results can conflict with epistemic expectations, those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, involves articulating and bringing together different parts of our knowledge. Both experiments and simulations, then, can surprise.

It is often thought that the vindication of experimental work lies in its capacity to be revelatory of natural systems. I challenge this idea by examining laboratory experiments in ecology. A central task of community ecology involves combining mathematical models and observational data to identify trophic interactions in natural systems. But many ecologists are also lab scientists: constructing microcosm or ‘bottle’ experiments, physically realizing the idealized circumstances described in mathematical models. What vindicates such ecological experiments? I argue that ‘extrapolationism’, the view that ecological lab work is valuable because it generates truths about natural systems, does not exhaust the epistemic value of such practices. Instead, bottle experiments also generate ‘understanding’ of both ecological dynamics and empirical tools. Some lab work, then, aids theoretical understanding, as well as targeting hypotheses about nature. 1Introduction2Trophic Interactions and Observational Techniques3Cryptic Dynamics in Bottle Experiments4Extrapolationism 4.1Ecological possibility and actuality4.2Ecological heterogeneity5Understanding 5.1The epistemic good of understanding5.2Bottle experiments as understanding-generators5.3How understanding travels6Conclusion

‘Gouldian arguments’ appeal to the contingency of a scientific domain to establish that domain’s autonomy from some body of theory. For instance, pointing to evolutionary contingency, Stephen Jay Gould suggested that natural selection alone is insufficient to explain life on the macroevolutionary scale. In analysing contingency, philosophers have provided source-independent accounts, understanding how events and processes structure history without attending to the nature of those events and processes. But Gouldian Arguments require source-dependent notions of contingency. An account of contingency is source-dependent when it is indexed to some pattern and some process. Positions like Gould’s do not turn on the mere fact of life’s contingency—that life’s shape could have been different due to its sensitivity to initial conditions, path-dependence or stochasticity. Rather, Gouldian arguments require that the contingency is due to particular kinds of processes: in this case, those which microevolutionary theory cannot account for. This source-dependent perspective clarifies both debates about the nature and importance of contingency, and empirical routes for testing Gould’s thesis.