Adrian Currie

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.

Historical sciences like paleontology and archaeology have uncovered unimagined, remarkable and mysterious worlds in the deep past. How should we understand the success of these sciences? What is the relationship between knowledge and history? In Scientific Knowledge and the Deep Past: History Matters, Adrian Currie examines recent paleontological work on the great changes that occurred during the Cretaceous period - the emergence of flowering plants, the splitting of the mega-continent Gondwana, and the eventual fall of the dinosaurs - to analyse the knowledge of historical scientists, and to reflect upon the nature of history. He argues that distinctively historical processes are 'peculiar': they have the capacity to generate their own highly specific dynamics and rules. This peculiarity, Currie argues, also explains the historian's interest in narratives and stories: the contingency, complexity and peculiarity of the past demands a narrative treatment. Overall, Currie argues that history matters for knowledge.

Here’s something I’m willing to claim we know: Homo sapiens, in particular the Polynesian settlers who first arrived in Aotearoa around the twelfth century, take the lion’s share of causal blame for the extinction of a lineage of enormous flightless birds: the moa. Stretching to three metres at their tallest, moa were a distinctive and remarkable feature of Aotearoa’s primeval forests, playing the main browser and grazer role in this unique bird-based ecosystem. Once humans turned up forests were burned, moa were stalked, and human-0introduced arrivals such as kiore had dramatic ecological repercussions. Within a few centuries, the moa were gone.

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.

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

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.

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.

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.

There has been much discussion of the moral, legal and prudential implications of geoengineering, and of governance structures for both the research and deployment of such technologies. However, insufficient attention has been paid to how such measures might affect geoengineering in terms of the incentive structures which underwrite scientific progress. There is a tension between the features that make science productive, and the need to govern geoengineering research, which has thus far gone underappreciated. I emphasize how geoengineering research requires governance which reaches beyond science’s traditional boundaries, and moreover requires knowledge which itself reaches beyond what we traditionally expect scientists to know about. How we govern emerging technologies should be sensitive to the incentive structures which drive science.

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.

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.

Many accounts of scientific modeling conceive of models as fictions: scientists interact with models in ways analogous to various aesthetic objects. Fictionalists follow most other accounts of modeling by taking them to be revelatory of the actual world in virtue of bearing some resemblance relation to a target system. While such fictionalist accounts capture crucial aspects of modelling practice, they are ill-suited to some design and engineering contexts. Here, models sometimes serve to underwrite design projects whereby real-world targets are constructed. In such circumstances, it is unclear what the model is supposed to resemble. Further, while fictionalists often require that models qua models have their content in virtue of construal or interpretation, in some engineering and design contexts success-conditions do not require such content—all that is required is that the model generates the required outputs. I take these points to motivate a view which accommodates fictionalism, but is broader. I articulate and defend an account of models as tools: specifically, material objects which are put to particular uses in particular contexts.

Many accounts of scientific modeling conceive of models as fictions: scientists interact with models in ways analogous to various aesthetic objects. Fictionalists follow most other accounts of modeling by taking them to be revelatory of the actual world in virtue of bearing some resemblance relation to a target system. While such fictionalist accounts capture crucial aspects of modelling practice, they are ill-suited to some design and engineering contexts. Here, models sometimes serve to underwrite design projects whereby real-world targets are constructed. In such circumstances, it is unclear what the model is supposed to resemble. Further, while fictionalists often require that models qua models have their content in virtue of construal or interpretation, in some engineering and design contexts success-conditions do not require such content—all that is required is that the model generates the required outputs. I take these points to motivate a view which accommodates fictionalism, but is broader. I articulate and defend an account of models as tools: specifically, material objects which are put to particular uses in particular contexts.

. 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.

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.

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.

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

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