The role that need plays in dealing with problems of distributive justice is examined in a series of vignette studies. Among other things, it becomes clear that impartial observers make gradual assessments of justice that depend on the extent to which the observed individuals are endowed with a good. If it is known how high their need for that good is, the assessments are made relative to this reference point. In addition, impartial decision-makers make hypothetical distribution decisions that take into (...) account need, productivity, and accountability. The need of individuals is partially compensated even if they have contributed less to the available amount than they themselves require. However, the willingness to do so decreases if they are accountable for needing more than others or for having contributed less than others. Lastly, both impartial observers and impartial decision-makers distinguish between different types of needs, the fulfillment of which is given different levels of importance. (shrink)

The inaugural title in the new, Open Access series BSPS Open, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. The fundamental burden of a theory of inductive inference is to determine which are the good inductive inferences or relations of inductive support and why it is that they are so. The traditional approach is modeled on that taken in accounts of deductive inference. It seeks universally applicable schemas or rules or a single (...) formal device, such as the probability calculus. After millennia of halting efforts, none of these approaches has been unequivocally successful and debates between approaches persist. The Material Theory of Induction identifies the source of these enduring problems in the assumption taken at the outset: that inductive inference can be accommodated by a single formal account with universal applicability. Instead, it argues that that there is no single, universally applicable formal account. Rather, each domain has an inductive logic native to it. Which that is, and its extent, is determined by the facts prevailing in that domain. Paying close attention to how inductive inference is conducted in science and copiously illustrated with real-world examples, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. (shrink)

In this paper, I argue (i) that there are certain methodological practices that are epistemically significant, and (ii) that we can test for the success of these practices empirically by examining case-studies in the history of science. Analysing a particular episode from the history of medicine, I explain how this can help us resolve specific cases of underdetermination. I conclude that, while the anti-realist is (more or less legitimately) able to construct underdetermination scenarios on a case-by-case basis, he will have (...) to abandon the strategy of using algorithms to do so, thus losing the much needed guarantee that there will always be rival cases of the required kind. (shrink)

This article argues that a successful answer to Hume's problem of induction can be developed from a sub-genre of philosophy of science known as formal learning theory. One of the central concepts of formal learning theory is logical reliability: roughly, a method is logically reliable when it is assured of eventually settling on the truth for every sequence of data that is possible given what we know. I show that the principle of induction (PI) is necessary and sufficient for logical (...) reliability in what I call simple enumerative induction. This answer to Hume's problem rests on interpreting PI as a normative claim justified by a non-empirical epistemic means-ends argument. In such an argument, a rule of inference is shown by mathematical or logical proof to promote a specified epistemic end. Since the proof concerning PI and logical reliability is not based on inductive reasoning, this argument avoids the circularity that Hume argued was inherent in any attempt to justify PI. (shrink)

Nelson Goodman's new riddle of induction forcefully illustrates a challenge that must be confronted by any adequate theory of inductive inference: provide some basis for choosing among alternative hypotheses that fit past data but make divergent predictions. One response to this challenge is to distinguish among alternatives by means of some epistemically significant characteristic beyond fit with the data. Statistical learning theory takes this approach by showing how a concept similar to Popper's notion of degrees of testability is linked to (...) minimizing expected predictive error. In contrast, formal learning theory appeals to Ockham's razor, which it justifies by reference to the goal of enhancing efficient convergence to the truth. In this essay, I show that, despite their differences, statistical and formal learning theory yield precisely the same result for a class of inductive problems that I call strongly VC ordered, of which Goodman's riddle is just one example. (shrink)

Nelson Goodman’s new riddle of induction forcefully illustrates a challenge that must be confronted by any adequate theory of inductive inference: provide some basis for choosing among alternative hypotheses that fit past data but make divergent predictions. One response to this challenge is to distinguish among alternatives by means of some epistemically significant characteristic beyond fit with the data. Statistical learning theory takes this approach by showing how a concept similar to Popper’s notion of degrees of testability is linked to (...) minimizing expected predictive error. In contrast, formal learning theory appeals to Ockham’s razor, which it justifies by reference to the goal of enhancing efficient convergence to the truth. In this essay, I show that, despite their differences, statistical and formal learning theory yield precisely the same result for a class of inductive problems that I call strongly VC ordered , of which Goodman’s riddle is just one example. (shrink)

Kevin Elliott and Dan McKaughan argue that, in some cases, nonepistemic values provide legitimate reasons for scientists to accept an epistemically inferior option, a claim that they support with two case studies. This essay argues that Elliott and McKaughan have not shown that their case studies are indeed ones in which an epistemically inferior option was accepted. Specifically, their interpretation of these cases depends on problematic premises that it is epistemically better to wait for a slower-but-more-reliable method than to accept (...) the result of a quicker-but-less-reliable one and that a more detailed model is epistemically preferable to a simpler one. (shrink)

It is widely thought in philosophy and elsewhere that parsimony is a theoretical virtue in that if T1 is more parsimonious than T2, then T1 is preferable to T2, other things being equal. This thesis admits of many distinct precisifications. I focus on a relatively weak precisification on which preferability is a matter of probability, and argue that it is false. This is problematic for various alternative precisifications, and even for Inference to the Best Explanation as standardly understood.

Metaphilosophy, Volume 53, Issue 2-3, Page 202-220, April 2022.

We argue that inductive analysis and operational assessment of the scientific process can be justifiably and fruitfully brought together, whereby the citation metrics used in the operational analysis can effectively track the inductive dynamics and measure the research efficiency. We specify the conditions for the use of such inductive streamlining, demonstrate it in the cases of high energy physics experimentation and phylogenetic research, and propose a test of the method’s applicability.

In guarding against inferential mistakes, experimental comparative cognition errs on the side of underattributing sophisticated cognition to animals, or what I refer to as the underattribution bias. I propose eliminating this bias by altering the method of choosing the default, or null, hypothesis. Rather than choosing the most parsimonious null hypothesis, as is current practice, I argue for choosing the best-evidenced hypothesis. Doing so at once preserves the risk-controlling structure of the current statistical paradigm and introduces a sensitivity to probability-conferring (...) empirical and theoretical information. This analysis illustrates how values like parsimony can covertly shape statistical-experimental design and inference. (shrink)

Ockham’s razor is the principle that, all other things being equal, scientists ought to prefer simpler theories. In recent years, philosophers have argued that simpler theories make better predictions, possess theoretical virtues like explanatory power, and have other pragmatic virtues like computational tractability. However, such arguments fail to explain how and why a preference for simplicity can help one find true theories in scientific inquiry, unless one already assumes that the truth is simple. One new solution to that problem is (...) the Ockham efficiency theorem, which states that scientists who heed Ockham’s razor retract their opinions less often and sooner than do their non-Ockham competitors. The theorem neglects, however, to consider competitors following random strategies and in many applications random strategies are known to achieve better worst-case loss than deterministic strategies. In this paper, we describe two ways to extend the result to a very general class of random, empirical strategies. The first extension concerns expected retractions, retraction times, and errors and the second extension concerns retractions in chance, times of retractions in chance, and chances of errors. (shrink)

Scientists often think of the world as a dynamical system, a stochastic process, or a generalization of such a system. Prominent examples of systems are the system of planets orbiting the sun or any other classical mechanical system, a hydrogen atom or any other quantum–mechanical system, and the earth’s atmosphere or any other statistical mechanical system. We introduce a general and unified framework for describing such systems and show how it can be used to examine some familiar philosophical questions, including (...) the following: how can we define nomological possibility, necessity, determinism, and indeterminism; what are symmetries and laws; what regularities must a system display to make scientific inference possible; how might principles of parsimony such as Occam’s Razor help when we make such inferences; what is the role of space and time in a system; and might they be emergent features? Our framework is intended to serve as a toolbox for the formal analysis of systems that is applicable in several areas of philosophy. (shrink)

Epistemology orthodoxy is a purist one in the sense that it separates out the epistemic from the practical. What counts as evidence is independent of what we care about. Which beliefs count as justified and which count as knowledge are independent of our practical concerns. In recent years, many epistemologists have abandoned such purist views and embraced varying degrees of pragmatic encroachment on the epistemic. I survey a variety of these views and explore the main arguments that proponents of pragmatic (...) encroachment have offered in addition to the main criticisms of the pragmatic approach. (shrink)

The thesis of this paper is that we can justify induction deductively relative to one end, and deduction inductively relative to a different end. I will begin by presenting a contemporary variant of Hume ’s argument for the thesis that we cannot justify the principle of induction. Then I will criticize the responses the resulting problem of induction has received by Carnap and Goodman, as well as praise Reichenbach ’s approach. Some of these authors compare induction to deduction. Haack compares (...) deduction to induction, and I will critically discuss her argument for the thesis that we cannot justify the principles of deduction next. In concluding I will defend the thesis that we can justify induction deductively relative to one end, and deduction inductively relative to a different end, and that we can do so in a non-circular way. Along the way I will show how we can understand deductive and inductive logic as normative theories, and I will briefly sketch an argument to the effect that there are only hypothetical, but no categorical imperatives. (shrink)

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti...

‘Simplicity’ comes up in different senses in scientific methodology. The simplicity criterion at issue here is relied on in all inductive inference, it’s argued. Therefore, it cannot be inductively...

Synchronic norms of theory choice, a traditional concern in scientific methodology, restrict the theories one can choose in light of given information. Diachronic norms of theory change, as studied in belief revision, restrict how one should change one’s current beliefs in light of new information. Learning norms concern how best to arrive at true beliefs. In this paper, we undertake to forge some rigorous logical relations between the three topics. Concerning, we explicate inductive truth conduciveness in terms of optimally direct (...) convergence to the truth, where optimal directness is explicated in terms of reversals and cycles of opinion prior to convergence. Concerning, we explicate Ockham’s razor and related principles of choice in terms of the information topology of the empirical problem context and show that the principles are necessary for reversal or cycle optimal convergence to the truth. Concerning, we weaken the standard principles of agm belief revision theory in intuitive ways that are also necessary for reversal or cycle optimal convergence. Then we show that some of our weakened principles of change entail corresponding principles of choice, completing the triangle of relations between,, and. (shrink)

Synchronic norms of theory choice, a traditional concern in scientific methodology, restrict the theories one can choose in light of given information. Diachronic norms of theory change, as studied in belief revision, restrict how one should change one’s current beliefs in light of new information. Learning norms concern how best to arrive at true beliefs. In this paper, we undertake to forge some rigorous logical relations between the three topics. Concerning, we explicate inductive truth conduciveness in terms of optimally direct (...) convergence to the truth, where optimal directness is explicated in terms of reversals and cycles of opinion prior to convergence. Concerning, we explicate Ockham’s razor and related principles of choice in terms of the information topology of the empirical problem context and show that the principles are necessary for reversal or cycle optimal convergence to the truth. Concerning, we weaken the standard principles of agm belief revision theory in intuitive ways that are also necessary for reversal or cycle optimal convergence. Then we show that some of our weakened principles of change entail corresponding principles of choice, completing the triangle of relations between,, and. (shrink)

A longstanding philosophical premise perceives simplicity as a desirable attribute of scientific theories. One of several raised justifications for this notion is that simple theories are more likely to indicate the true makeup of natural systems. Qualitatively parsimonious hypotheses and theories keep to a minimum the number of different postulated entities within a system. Formulation of such ontologically simple working hypotheses proved to be useful in the experimental probing of narrowly defined bio systems. It is less certain, however, whether qualitatively (...) parsimonious theories are effective indicators of the true nature of complex biological systems. This paper assesses the success of ontologically simple theories in envisaging the makeup of three complex systems in bacteriology, immunology, and molecular biology. Evidence shows that parsimonious theories completely misconstrued the actual ontologically complex constitutions of the three examined systems. Since evolution and selective pressures typically produce ontologically intricate rather than simple bio systems, qualitatively parsimonious theories are mostly inapt indicators of the true nature of complex biological systems. (shrink)

This paper discusses Kevin Kelly’s recent attempt to justify Ockham’s Razor in terms of truth-finding efficiency. It is argued that Kelly’s justification fails to warrant confidence in the empirical content of theories recommended by Ockham’s Razor. This is a significant problem if, as Kelly and many others believe, considerations of simplicity play a pervasive role in scientific reasoning, underlying even our best tested theories, for the proposal will fail to warrant the use of these theories in practical prediction.

A potential explanation of a fact is a hypothesis such that, if it were true, it would explain the fact in question. Let’s suppose that we become aware of a fact and some potential explanations thereof. Let’s also suppose that we would like to believe the truth. Given this aim, we can ask two questions. First, is it likely that one of these hypotheses is true? Second, given an affirmative answer to the first question, which one is it likely to (...) be? Inference to the best explanation (IBE) offers answers to both questions. To the first, it says ‘Yes’—assuming that at least one of the hypotheses would, if true, provide a satisfactory explanation of the fact under consideration. To the second, it says that the hypothesis most likely to be true is the one that scores best on the explanatory virtues: conservatism, modesty, simplicity, generality, and predictive power. Many philosophers have argued against IBE’s answer to the first question. I am interested in an objection to its answer to the second. Many philosophers seem to think that it is unsustainable: they seem to think that even if we assume that one of the competing hypotheses is true, we should not think that IBE will help us to identify it. Or, more carefully, if these philosophers are doing what they appear to be doing—namely, offering critiques of IBE that don’t depend on assumptions about the field of competing hypotheses—then their claim is that IBE will not help us to identify the truth. I believe that this is mistaken: the argument for believing it assumes a model of IBE that we have no reason to accept. (shrink)

Is formal simplicity a guide to learning in humans, as simplicity is said to be a guide to the acceptability of theories in science? Does simplicity determine the difficulty of various learning tasks? I argue that, similarly to how scientists sometimes preferred complex theories when this facilitated calculations, results from perception, learning and reasoning suggest that formal complexity is generally unrelated to what is easy to learn and process by humans, and depends on assumptions about available representational and processing primitives. (...) “Simpler” hypotheses are preferred only when they are also easier to process. Historically, “simpler”, easier‐to‐process, scientific theories might also be preferred if they are transmitted preferentially. Empirically viable complexity measures should build on the representational and processing primitives of actual learners, even if explanations of their behaviour become formally more complex. (shrink)

Many scholars claim that a parsimony principle has ontological implications. The most common such claim is that a parsimony principle entails that the “world” is simple. This ontological claim appears to often be coupled with the assumption that a parsimony principle would be corroborated if the “world” were simple. I clarify these claims, describe some minimal features of simplicity, and then show that both these claims are either false or they depend upon an implausible notion of simplicity. In their stead, (...) I propose a minimal ontological claim: a parsimony principle entails a minimal realism about the existence of objects and laws, in order to allow that the descriptions of the relevant phenomena contain patterns. (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)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in social epistemology (...) of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

Philosophical theories of evidence have been on offer, but they are mostly evaluated in terms of all-or-none desiderata — if they fail to meet one of the desiderata, they are not a satisfactory theory. In this thesis, I aim to accomplish three missions. Firstly, I construct a new way of evaluating theories of evidence, which I call a tool-based view. Secondly, I analyse the nature of what I will call the various relevance-mediating vehicles that each theory of evidence employs. Thirdly, (...) I articulate the comparative core of evidential reasoning in the historical sciences, one which is overlooked in major theories of evidence. On the first mission, I endorse a meta-thesis of pluralism on theories of evidence, namely a tool-based view. I regard a theory of evidence as a purpose-specific and setting-sensitive tool which has its own strengths, difficulties and limitations. Among the major theories of evidence I have reviewed, I focus on Achinstein’s explanationist theory, Cartwright’s argument theory and Reiss’s inferentialist account, scrutinising and evaluating them against the purposes they set out and the scope of their applications. On the second mission, I note that there is no such thing as intrinsically ‘being evidence’. Rather, I hold that relevance-mediating vehicles configure data, materials or claims in such ways that some of them are labelled evidence. I identify the relevance-mediating vehicles that the theories of evidence employ. On the final mission, I argue that the likelihoodist account is an appropriate tool for explaining the evidential reasoning in poorly specified settings where likelihoods can be only imprecisely compared. Such settings, I believe, are typical in the historical sciences. Using the reconstruction of proto-sounds in historical linguistics as a case study, I formalise the rationale behind it by means of the law of likelihood. (shrink)

Accounts of modal knowledge are many and varied. How should we choose between them? I propose that we employ inference to the best explanation, and I suggest that there are three desiderata that we should use to rank hypotheses: conservatism, simplicity, and the ability to handle disagreement. After examining these desiderata, I contend that they can’t be used to justify belief in the modal epistemology that fares best, but that they can justify our accepting it in an epistemically significant sense. (...) Finally, I outline a modal epistemology that seems to do well by the standards I’ve recommended. (shrink)

Pettit (2006) argues that deferring to majority testimony is not generally rational: it may lead to inconsistent beliefs. He suggests that “another ... approach will do better”: deferring to supermajority testimony. But this approach may also lead to inconsistencies. In this paper, I describe conditions under which deference to supermajority testimony ensures consistency, and conditions under which it does not. I also introduce the concept of “consistency of degree k”, which is weaker than full consistency by ruling out only “blatant” (...) inconsistencies in an agent’s beliefs while permitting less blatant ones, and show that, while super-majoritarian deference often fails to ensure full consistency, it is a route to consistency in this weaker sense. (shrink)

Here is a challenge to IBE's epistemic credentials. If a reason is an epistemic reason for a subject to believe a proposition, then that reason increases the likelihood of the proposition's truth. But IBE relies on considerations like simplicity which do not increase the likelihood that hypotheses are truth. So, the reasons given by inference to the best explanation are not epistemic reasons. I contend that this argument fails, but not because, e.g., simplicity is truth-conducive. Rather, I show that the (...) argument relies on a version of epistemic value monism, which even the argument's proponents should reject. (shrink)

My talk will be guided by the idea that there are some familiar scientific practices that are epistemically significant. I will argue that we can test for the success of these practices empirically by examining cases in the history of science. Specifically, I will reconstruct one particular episode in the history of medicine – John Snow's reasoning concerning the infectiousness of cholera – and offer this case as a concrete example of the sort of empirical research that needs to be (...) done in order to discover what kinds of methodological practices and rules are actually of epistemic interest. Analysing this case, I will explain how it (and other cases like it) can help us resolve specific cases of underdetermination. After exploring some possible anti-realist responses to this argument, I will conclude that, while the anti-realist is (more or less legitimately) able to construct underdetermination scenarios on a case-by-case basis, he will likely have to abandon the strategy of using algorithms to do so, thus losing the much needed guarantee that there will always be rival cases of the required kind. (shrink)

Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method from non-experimental data is subject to reversal as the sample size increases any finite number of times. That result, called the causal flipping theorem, extends prior results to the effect that causal discovery (...) cannot be reliable on a given sample size. We argue that since repeated flipping of causal conclusions is unavoidable in principle for consistent methods, the best possible discovery methods are consistent methods that retract their earlier conclusions no more than necessary. A series of simulations of various methods across a wide range of sample sizes illustrates concretely both the theorem and the principle of comparing methods in terms of retractions. (shrink)