Over the past three decades, the discourse on the mechanistic approach to scientific modelling and explanation has notably sidestepped the topic of simplicity and fit within the process of model selection. This paper aims to rectify this disconnect by delving into the topic of simplicity and fit within the context of mechanistic explanations. More precisely, our primary objective is to address whether simplicity metrics hold any significance within mechanistic explanations. If they do, then our inquiry extends to the suitability of (...) the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and related criteria in determining the optimal balance of fit and simplicity of mechanistic models. As our main claims, we argue that mechanistic models inherently lend themselves to considerations of simplicity, and that the AIC and BIC and related criteria are applicable to some submodels of certain kinds of mechanistic models. However, these criteria and related criteria designed for curve fitting and causal modelling are of little help for a comparative assessment of full mechanistic models, and a fundamentally different approach is needed to make determinations of this kind. (shrink)

Theory choice can be approached in at least four ways. One of these calls for the application of decision theory, and this article endorses this approach. But applying standard forms of decision theory imposes an overly demanding standard of numeric information, supposedly satisfied by point-valued utility and probability functions. To ameliorate this difficulty, a version of decision theory that requires merely comparative utilities and plausibilities is proposed. After a brief summary of this alternative, the article illustrates how comparative decision theory (...) affords a rational reconstruction of decisions made by exemplary scientists in two cases of theory choice: Buffon’s law and the luminiferous ether. It also offers a rational reconstruction of two cases of theory diagnosis: Mendeleev’s anomalies and the Pioneer anomaly. (shrink)

We explore the evidential relationships that connect two standard claims of modern evolutionary biology. The hypothesis of common ancestry (which says that all organisms now on earth trace back to a single progenitor) and the hypothesis of natural selection (which says that natural selection has been an important influence on the traits exhibited by organisms) are logically independent; however, this leaves open whether testing one requires assumptions about the status of the other. Darwin noted that an extreme version of adaptationism (...) would undercut the possibility of making inferences about common ancestry. Here we develop a converse claim—hypotheses that assert that natural selection has been an important influence on trait values are untestable unless supplemented by suitable background assumptions. The fact of common ancestry and a claim about quantitative genetics together suffice to render such hypotheses testable. Furthermore, we see no plausible alternative to these assumptions; we hypothesize that they are necessary as well as sufficient for adaptive hypotheses to be tested. This point has important implications for biological practice, since biologists standardly assume that adaptive hypotheses predict trait associations among tip species. Another consequence is that adaptive hypotheses cannot be confirmed or disconfirmed by a trait value that is universal within a single species, if that trait value deviates even slightly from the optimum. 1 Two Darwinian hypotheses 2 Logical independence 3 How adaptive hypotheses bear on the tree of life hypothesis 4 How the tree of life hypothesis bears on adaptive hypotheses 5 What do adaptive hypotheses predict? 6 Common ancestry and quantitative genetics to the rescue 7 Conclusion. (shrink)

In this paper I compare parametric and nonparametric regression models with the help of a simulated data set. Doing so, I have two main objectives. The first one is to differentiate five concepts of simplicity and assess their respective importance. The second one is to show that the scope of the existing philosophical literature on simplicity and model selection is too narrow because it does not take the nonparametric approach into account, S112–S123, 2002; Forster and Sober in The British Journal (...) for the Philosophy of Science 45, 1–35, 1994; Forster, 2001, in Philosophy of Science 74, 588–600, 2007; Hitchcock and Sober in The British Journal for the Philosophy of Science 55, 1–34, 2004; Mikkelson in Philosophy of Science 73, 440–447, 2006; Baker 2013). More precisely, I point out that a measure of simplicity in terms of the number of adjustable parameters is inadequate to characterise nonparametric models and to compare them with parametric models. This allows me to weed out false claims about what makes a model simpler than another. Furthermore, I show that the importance of simplicity in model selection cannot be captured by the notion of parametric simplicity. ‘Simplicity’ is an umbrella term. While parametric simplicity can be ignored, there are other notions of simplicity that need to be taken into consideration when we choose a model. Such notions are not discussed in the previously mentioned literature. The latter therefore portrays an incomplete picture of why simplicity matters when we choose a model. Overall I support a pluralist view according to which we cannot give a general and interesting justification for the importance of simplicity in science. (shrink)

The main point of this paper is to underscore the link between simplicity and truth in an unsupervised machine learning context. More precisely, we argue that parametric and dimensional simplicity are not indicators of truth but the methodological principle that urges us to pay attention to such notions of simplicity is truth conducive. The truth that we are looking for are specific geometrical shapes and we know which algorithm can find which shapes provided that we pay attention to parametric and (...) dimensional simplicity. (shrink)

Philosophers of science have often favoured reductive approaches to how-possibly explanation. This article identifies three alternative conceptions making how-possibly explanation an interesting phenomenon in its own right. The first variety approaches “how possibly X?” by showing that X is not epistemically impossible. This can sometimes be achieved by removing misunderstandings concerning the implications of one’s current belief system but involves characteristically a modification of this belief system so that acceptance of X does not result in contradiction. The second variety offers (...) a potential how-explanation of X. It is usually followed by a range of further potential how-explanations of the same phenomenon. In recent literature the factual claims implied by the second variety have been downplayed whereas the heuristic role of mapping the space of conceptual possibilities has been emphasized. I will focus especially on this truth-bracketing sense of potentiality when looking closer at the second variety in the paper. The third variety has attracted less interest. It presents a partial how-explanation of X. Typically it aims to establish the existence of a mechanism by which X could be and was generated. The third conception stands out as the natural alternative for the advocate of ontic how-possibly explanations. This article transfers Salmon’s view that explanation-concepts can be broadly divided into epistemic, modal, and ontic to the context of how-possibly explanations. Moreover, it is argued that each of the three above-mentioned varieties of how-possibly explanation occurs in science. To recognize this may be especially relevant for philosophers. We are often misled by the promises of various why-explanation accounts, and seem to have forgotten nearly everything about the diversity of how-possibly explanations. (shrink)

In the general problem of verisimilitude, we try to define the distance of a statement from a target, which is an informative truth about some domain of investigation. For example, the target can be a state description, a structure description, or a constituent of a first-order language. In the problem of legisimilitude, the target is a deterministic or universal law, which can be expressed by a nomic constituent or a quantitative function involving the operators of physical necessity and possibility. The (...) special case of legisimilitude, where the target is a probabilistic law, has been discussed by Roger Rosenkrantz and Ilkka Niiniluoto. Their basic proposal is to measure the distance between two probabilistic laws by the Kullback–Leibler notion of divergence, which is a semimetric on the space of probability measures. This idea can be applied to probabilistic laws of coexistence and laws of succession, and the examples may involve discrete or continuous state spaces. In this paper, these earlier studies are elaborated in four directions. First, even though deterministic laws are limiting cases of probabilistic laws, the target-sensitivity of truthlikeness measures implies that the legisimilitude of probabilistic laws is not easily reducible to the deterministic case. Secondly, the Jensen-Shannon divergence is applied to mixed probabilistic laws which entail some universal laws. Thirdly, a new class of distance measures between probability distributions is proposed, so that their horizontal differences are taken into account in addition to vertical ones. Fourthly, a solution is given for the epistemic problem of estimating degrees of probabilistic legisimilitude on the basis of empirical evidence. (shrink)

The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, agreeing measurements (...) of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation. (shrink)

The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, agreeing measurements (...) of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation. (shrink)

In this paper, I offer a new defense of scientific realism, tailored for the Akaikean paradigm of statistical hypothesis testing. After proposing definitions of verisimilitude and predictive success, I use computer simulations to show how the latter depends on the former, even in the kind of case featured in a recent argument for instrumentalism.

Stein has raised a fundamental problem for any attempt to characterize instrumentalism and realism as substantive alternatives. This is the distinguishability problem, which consists in the problem of developing a form of instrumentalism that is substantially different from a plausible realist alternative and the problem of showing that this form of instrumentalism does justice to actual scientific practice. Using Stein’s own discussion of Maxwell, I formulate instrumentalism and realism as a scientist’s attitudes toward models, where an attitude is understood to (...) be a complex of the scientist’s belief and intention regarding models. Developing a case study of Benzer’s modeling practice, I show that each attitude can structure inquiry differently and argue that to understand certain aspects of scientific practice, such as the practice of genetic mapping in Benzer’s work, we sometimes need to appeal to the coexistence of these attitudes. (shrink)

Why has it been so difficult to integrate paleontology and mainstream evolutionary biology? Two common answers are: (1) the two fields have fundamentally different aims, and (2) the tensions arise out of disciplinary squabbles for funding and prestige. This paper examines the role of fossil data in phylogeny reconstruction in order to assess these two explanations. I argue that while cladistics has provided a framework within which to integrate fossil character data, the stratigraphic (temporal) component of fossil data has been (...) harder to integrate. A close examination of how fossil data have been used in phylogeny reconstruction suggests that neither explanation is adequate. While some of the tensions between the fields may be intellectual turf wars, the second explanation downplays the genuine difficulty of combining the distinctive data of the two fields. Furthermore, it is simply not the case that the two fields pursue completely distinct aims. Systematists do disagree about precisely how to represent phylogeny (e.g., minimalist cladograms or trees with varying levels of detail) but given that every tree presupposes a pattern of branching (a cladogram), these aims are not completely distinct. The central problem has been developing methods that allow scientists to incorporate the distinctive bodies of data generated by these two fields. Further case studies will be required to determine if this explanation holds for other areas of interaction between paleontology and neontology. (shrink)

Insight is a construct carried out into different theoretical orientations with increasing application out of the boundaries of clinical psychology. Recent studies have investigated insight also as a promising variable for organizational outcomes. Given the relevance of Insight in promoting change, this paper aimed at describing the psychometric analysis of one of the shortest, most agile, and most versatile tool for measuring some of the characteristics of insight, the Insight Orientation Scale, using Item Response Theory. To achieve this goal, we (...) applied a Mixed Rash Model to the IOS. Data from 1,445 individuals were analyzed by the means of WIN-MIRA and Multilog. Based on the likelihood statistics we assumed a three-class solution for the IOS. Results also indicated that the greater part of items had good discrimination and threshold parameters. These findings confirmed psychometric stability of the IOS highlighting its measurement precision, supporting its utility in both research and practice. (shrink)

What has science actually achieved? A theory of achievement should define what has been achieved, describe the means or methods used in science, and explain how such methods lead to such achievements. Predictive accuracy is one truth-related achievement of science, and there is an explanation of why common scientific practices tend to increase predictive accuracy. Akaike's explanation for the success of AIC is limited to interpolative predictive accuracy. But therein lies the strength of the general framework, for it also provides (...) a clear formulation of many open problems of research. (shrink)

The “No Miracle Argument” for scientific realism contends that the only plausible explanation for the predictive success of scientific theories is their truthlikeness, but doesn’t specify what ‘truthlikeness’ means. I argue that if we understand ‘truthlikeness’ in terms of Kullback-Leibler (KL) divergence, the resulting realist thesis (RKL) is a plausible explanation for science’s success. Still, RKL probably falls short of the realist’s ideal. I argue, however, that the strongest version of realism that the argument can plausibly establish is RKL. The (...) realist needs another argument for establishing a stronger realist thesis. (shrink)

Standard proposals of scientific anti-realism assume that the methodology of a scientific research program can be endorsed without accepting its metaphysical commitments. I argue that the distinction between competence, the rules governing one’s language faculty, and performance, or linguistic behavior, precludes this. Linguistic theories aim to describe competence, not performance, and so must be able to distinguish observations reflective of the former from those reflective of the latter. This classification of data makes sense only against the background of a psychologically (...) realistic view of linguistic theory. So the very methodology of the science commits one to its realistic interpretation. (shrink)

I analyze two recent parsimony arguments that have been offered to break the current impasse in the chimpanzee mindreading controversy, the ‘logical problem’ argument from Povinelli, Penn, and Vonk, and Sober's attempt to apply model selection criteria in support of the mindreading hypothesis. I argue that Sober's approach fails to adequately rebut the ‘logical problem’. However, applying model selection criteria to chimpanzees' own mental models of behavior does yield a response to the ‘logical problem’ and reveals an adaptive advantage of (...) mindreading models and a potential solution to a paradox raised by Whiten. (shrink)

Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models (...) are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms. (shrink)

Two powerful arguments have famously dominated the realism debate in philosophy of science: The No Miracles Argument (NMA) and the Pessimistic Meta-Induction (PMI). A standard response to the PMI is selective scientific realism (SSR), wherein only the working posits of a theory are considered worthy of doxastic commitment. Building on the recent debate over the NMA and the connections between the NMA and the PMI, I here consider a stronger inductive argument that poses a direct challenge for SSR: Because it (...) is sometimes exactly the working posits which contradict each other, i.e., that which is directly responsible for empirical success, SSR cannot deliver a general explanation of scientific success. (shrink)

We evaluate a common reasoning strategy used in community ecology and comparative psychology for selecting between competing hypotheses. This strategy labels one hypothesis as a “null” on the grounds of its simplicity and epistemically privileges it as accepted until rejected. We argue that this strategy is unjustified. The asymmetrical treatment of statistical null hypotheses is justified through the experimental and mathematical contexts in which they are used, but these contexts are missing in the case of the “pseudo-null hypotheses” found in (...) our case studies. Moreover, statistical nulls are often not epistemically privileged in practice over their alternatives because failing to reject the null is usually a negative result about the alternative, experimental hypothesis. Scientists should eschew the appeal to pseudo-nulls. It is a rhetorical strategy that glosses over a commitment to valuing simplicity over other epistemic virtues in the name of good scientific and statistical methodology. (shrink)

Elliott Sober is both an empiricist and an instrumentalist. His empiricism rests on a principle called actualism, whereas his instrumentalism violates this. This violation generates a tension in his work. We argue that Sober is committed to a conflicting methodological imperative because of this tension. Our argument illuminates the contemporary debate between realism and empiricism which is increasingly focused on the application of scientific inference to testing scientific theories. Sober’s position illustrates how the principle of actualism drives a wedge between (...) two conceptions of scientific inference and at the same time brings to the surface a deep conflict between empiricism and instrumentalism. (shrink)

This thesis has two goals. Firstly, we consider the problem of model selection for the purposes of prediction. In modern science predictive mathematical models are ubiquitous and can be found in such diverse fields as weather forecasting, economics, ecology, mathematical psychology, sociology, etc. It is often the case that for a given domain of inquiry there are several plausible models, and the issue then is how to discriminate between them – this is the problem of model selection. We consider approaches (...) to model selection that are used in classical [also known as frequentist] statistics, and fashionable in recent years methods of Akaike Information Criterion [AIC] and Bayes Information Criterion [BIC], the latter being a part of a broader Bayesian approach. We show the connection between AIC and BIC, and provide comparison of performance of these methods. Secondly, we consider some philosophical arguments that arise within the setting of the model selection approaches investigated in the first part. These arguments aim to provide counterexamples to the epistemic thesis of scientific realism, viz., that predictively successful scientific theories are approximately true, and to the idea that truth and predictive accuracy go together. We argue for the following claims: 1) that none of the criticisms brought forward in the philosophical literature against the AIC methodology are devastating, and AIC remains a viable method of model selection; 2) that the BIC methodology likewise survives the numerous criticisms; 3) that the counterexamples to scientific realism that ostensibly arise within the framework of model selection are flawed; 4) that in general the model selection methods discussed in this thesis are neutral with regards to the issue of scientific realism; 5) that a plurality of methodologies should be applied to the problem of model selection with full awareness of the foundational issues that each of these methodologies has. (shrink)

This paper challenges the appeal to theory virtues in theory choice as well as the appeal to the intellectual and moral virtues of an agent as determining unique choices between empirically equivalent theories. After arguing that theoretical virtues do not determine the choice of one theory at the expense of another theory, I argue that nor does the appeal to intellectual and moral virtues single out one agent, who defends a particular theory, and exclude another agent defending an alternative theory. (...) I analyse Duhem’s concept of good sense and its recent interpretation in terms of virtue epistemology. I argue that the virtue epistemological interpretation does not show how good sense leads to conclusive choices and scientific progress. (shrink)

In my dissertation I define historical disciplines as disciplines that aim to give a historical interpretation of the evidence. Phylogenetic systematics is a historical discipline and therefore in my definition phylogenies should be thought of as historical interpretations of relationships between taxa.

In this dissertation I consider the problem of external world skepticism and attempts at providing an argument to the best explanation against it. In chapter one I consider several different ways of formulating the crucial skeptical argument, settling on an argument that centers on the question of whether we're justified in believing propositions about the external world. I then consider and reject several options for getting around this issue which I take to be inadequate. I finally conclude that the best (...) option available to us at the moment is to argue that the antiskeptical view is the best explanation of our ordinary experiences In chapter two I argue that, if we hope to ground what counts as defending antiskepticism in common sense, there is an argument against the possibility of ever knowing one has succeeded in defending antiskepticism. After showing that common sense is no place to look in setting a goal for our antiskeptical project, I present the view that what will be crucial to settling on our antiskeptical goal is coming to a successful analysis of the nature of physical objects. I suggest some minimal criteria that must be met by a view in order to be antiskeptical based on our intuitions about core skeptical cases, but acknowledge that a fully successful response to external world skepticism will require the antiskeptic to engage in some much more difficult analysis. In chapter three I consider various views of the nature of explanation and conclude, tentatively, that explanation as it interests the antiskeptic is fundamentally causal. In chapter four I consider and reject some of the core views on which best explanation facts are so fundamental that a project of attempting to vindicate probabilistically the virtues which make explanations epistemically good. In this chapter I show that views which analyze justification in terms of best explanation factors fail. In chapter five I attempt to vindicate the various explanatory virtues probabilistically. In doing so I attempt to express or translate the various explanatory virtues in terms of probabilities in order to show that having those virtues makes a view at least prima facie more probable. In chapters six and seven I explain and evaluate the various arguments to the best explanation against skepticism present in current philosophical literature. I attempt to show that extant arguments fail to appreciate the virtues possessed by classical skeptical scenarios. In chapter eight I briefly consider some options that may be open to the antiskeptic moving forward. All routes forward contain considerable obstacles, but there are some fruitful areas of research to pursue. (shrink)

Machine learning is a scientific discipline that can be divided into two main branches: supervised machine learning and unsupervised machine learning. In this paper, we aim to show just how simplicity matters in unsupervised contexts. This is important because unsupervised machine learning algorithms have barely received any attention in philosophy. Yet, there is a direct link between simplicity and truth in unsupervised contexts that we do not find in their supervised counterparts. This has thus far evaded philosophical discussions on simplicity.