The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

The Explanatory Value of Abstracting Away from Idiosyncratic and Messy Detail

Some explanations are relatively abstract: they abstract away from the idiosyncratic or messy details of the case in hand. The received wisdom in philosophy is that this is a virtue for any explanation to possess. I argue that the apparent consensus on this point is illusory. When philosophers make this claim, they differ on which of four alternative varieties of abstractness they have in mind. What's more, for each variety of abstractness there are several alternative reasons to think that the variety of abstractness in question is a virtue. I identify the most promising reasons, and dismiss some others. The paper concludes by relating this discussion to the idea that explanations in biology, psychology and social science cannot be replaced by relatively micro explanations without loss of understanding.This work has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement no 284123.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s11098-015-0554-

Drift as a Force of Evolution: A Manipulationist Account

Can evolutionary theory be properly characterised as a “theory of forces”, like Newtonian mechanics? One common criticism to this claim concerns the possibility to conceive genetic drift as a causal process endowed by a specific magnitude and direction. In this article, we aim to offer an original response to this criticism by pointing out a connection between the notion of force and the notion of explanatory depth, as depicted in Hitchcock and Woodward’s manipulationist account of causal explanation. In a nutshell, our argument is that, since force-explanations can be consistently reframed as deep explanations and vice versa, and the notion of drift can be characterised in manipulationist terms as constitutively intervening in evolutionary deep explanations, then drift-explanations can be consistently reframed as force-explanations, and drift can be properly considered as a force of evolution. Insofar as similar considerations may be extended also to other evolutionary factors – chiefly selection –, our analysis offers an important support to the claim that evolutionary theory is a theory of forces.info:eu-repo/semantics/publishedVersio

Abstraction in ecology : reductionism and holism as complementary heuristics

In addition to their core explanatory and predictive assumptions, scientific models include simplifying assumptions, which function as idealizations, approximations, and abstractions. There are methods to investigate whether simplifying assumptions bias the results of models, such as robustness analyses. However, the equally important issue - the focus of this paper - has received less attention, namely, what are the methodological and epistemic strengths and limitations associated with different simplifying assumptions. I concentrate on one type of simplifying assumption, the use of mega parameters as abstractions in ecological models. First, I argue that there are two kinds of mega parameters qua abstractions, sufficient parameters and aggregative parameters, which have gone unnoticed in the literature. The two are associated with different heuristics, holism and reductionism, which many view as incompatible. Second, I will provide a different analysis of abstractions and the associated heuristics than previous authors. Reductionism and holism and the accompanying abstractions have different methodological and epistemic functions, strengths, and limitations, and the heuristics should be viewed as providing complementary research perspectives of cognitively limited beings. This is then, third, used as a premise to argue for epistemic and methodological pluralism in theoretical ecology. Finally, the presented taxonomy of abstractions is used to comment on the current debate whether mechanistic accounts of explanation are compatible with the use of abstractions. This debate has suffered from an abstract discussion of abstractions. With a better taxonomy of abstractions the debate can be resolved.Peer reviewe