Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in (...) light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. (shrink)

We argue that concerns about double-counting—using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate—deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood approach to incremental confirmation. (...) According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. 1 Introduction2 Remarks about Models and Adequacy-for-Purpose3 Evidence for Calibration Can Also Yield Comparative Confirmation3.1 Double-counting I3.2 Double-counting II4 Climate Science Examples: Comparative Confirmation in Practice4.1 Confirmation due to better and worse best fits4.2 Confirmation due to more and less plausible forcings values5 Old Evidence6 Doubts about the Relevance of Past Data7 Non-comparative Confirmation and Catch-Alls8 Climate Science Example: Non-comparative Confirmation and Catch-Alls in Practice9 Concluding Remarks. (shrink)

: In this paper I argue that belief in the greater confirmatory value of prediction over accommodation can best be understood as a function of the practice rather than the logic of science. Attempts to account for this asymmetry within the logic of science have revealed no non-arbitrary way to address the problem of underdetermination as it applies to prediction and thus have failed to account for the preference for prediction over accommodation on logical grounds. Instead, I propose a model (...) that not only justifies and explains this preference, but allows for a richer taxonomy of the types of evidential confirmation that are employed in scientific reasoning. (shrink)

According to Hitchcock and Sober’s argument from overfitting for weak predictivism, the fact that a theory accurately predicts a portion of its data is evidence that it has been formulated by balancing simplicity and goodness-of-fit rather than overfitting data. The core argument consists of two likelihood inequalities. In this paper I show that there is a surprising accommodation-friendly implication in their argument, and contend that it is beset by a substantial difficulty, namely, there is no good reason to think that (...) their second likelihood inequality is true. (shrink)

Summary The recent turn to the ‘context of discovery’ and other ‘postmodernist’ developments in the philosophy of science have undermined the idea of a universal rationality of science. This parallels the fate of the classical dream of a logic of discovery. Still, justificational questions have remained as a distinct perspective, though comprising both consequential and generative justification — an insight delayed by certain confusions about the (original) context distinction. An examination of one particular heuristic strategy shows its local rationality; even (...) as an efficient procedure of hypothesis generation, it carries probative weight. It will be explored in which respects such a strategy can be normative or contain normative elements. (shrink)

This paper offers an answer to Glymour's ‘old evidence’ problem for Bayesian confirmation theory, and assesses some of the objections, in particular those recently aired by Chihara, that have been brought against that answer. The paper argues that these objections are easily dissolved, and goes on to show how the answer it proposes yields an intuitively satisfactory analysis of a problem recently discussed by Maher. Garber's, Niiniluoto's and others’ quite different answer to Glymour's problem is considered and rejected, and the (...) paper concludes with some brief reflections on the prediction/accommodation issue. (shrink)

Scientific hypotheses may either predict particular unknown facts or accommodate previously-known data. Although affirmed predictions are intuitively more rewarding than accommodations of established facts, opinions divide whether predictive hypotheses are also epistemically superior to accommodation hypotheses. This paper examines the contribution of predictive hypotheses to discoveries of several bio-molecular systems. Having all the necessary elements of the system known beforehand, an abstract predictive hypothesis of semiconservative mode of DNA replication was successfully affirmed. However, in defining the genetic code whose biochemical (...) basis was unclear, hypotheses were only partially effective and supplementary experimentation was required for its conclusive definition. Markedly, hypotheses were entirely inept in predicting workings of complex systems that included unknown elements. Thus, hypotheses did not predict the existence and function of mRNA, the multiple unidentified components of the protein biosynthesis machinery, or the manifold unknown constituents of the ubiquitin–proteasome system of protein breakdown. Consequently, because of their inability to envision unknown entities, predictive hypotheses did not contribute to the elucidation of cation theories remained the sole instrument to explain complex bio-molecular systems, the philosophical question of alleged advantage of predictive over accommodative hypotheses became inconsequential. (shrink)

If a machine attains consciousness, how could we find out? In this paper, I make three related claims regarding positive tests of machine consciousness. All three claims center on the idea that an AI can be constructed “ad hoc”, that is, with the purpose of satisfying a particular test of consciousness while clearly not being conscious. First, a proposed test of machine consciousness can be legitimate, even if AI can be constructed ad hoc specifically to pass this test. This is (...) underscored by the observation that many, if not all, putative tests of machine consciousness can be passed by non-conscious machines via ad hoc means. Second, we can identify ad hoc AI by taking inspiration from the notion of an ad hoc hypothesis in philosophy of science. Third, given the first and the second claim, the most reliable tests of animal consciousness turn out to be valid and useful positive tests of machine consciousness as well. If a non-ad hoc AI exhibits clusters of cognitive capacities facilitated by consciousness in humans which can be selectively switched off by masking and if it reproduces human behavior in suitably designed double dissociation tasks, we should treat the AI as conscious. (shrink)

In engineering, as in other scientific fields, researchers seek to confirm their models with real-world data. It is common practice to assess models in terms of the distance between the model outputs and the corresponding experimental observations. An important question that arises is whether the model should then be ‘tuned’, in the sense of estimating the values of free parameters to get a better fit with the data, and furthermore whether the tuned model can be confirmed with the same data (...) used to tune it. This dual use of data is often disparagingly referred to as ‘double-counting’. Here, we analyse these issues, with reference to selected research articles in engineering. Our example studies illustrate more and less controversial practices of model tuning and double-counting, both of which, we argue, can be shown to be legitimate within a Bayesian framework. The question nonetheless remains as to whether the implied scientific assumptions in each case are apt from the engineering point of view. (shrink)