Despite my presence on the program, this should actually be really good.

"Scientific theory choice is guided by judgments of simplicity, a bias frequently referred to as "Ockham's Razor". But what is simplicity and how, if at all, does it help science find the truth?  Should we view simple theories as means for obtaining accurate predictions, as classical statisticians recommend?  Or should we believe the theories themselves, as Bayesian methods seem to justify?  The aim of this workshop is to re-examine the foundations of Ockham's razor, with a firm focus on the connections, if any, between simplicity and truth. "

"According to the semantic view of scientific theories, theories are classes of models. I show that this view—if taken literally—leads to absurdities. In particular, this view equates theories that are distinct, and it distinguishes theories that are equivalent. Furthermore, the semantic view lacks the resources to explicate interesting theoretical relations, such as embeddability of one theory into another. The untenability of the semantic view—as currently formulated—threatens to undermine scientific structuralism."

"It is proposed that we use the term “approximation” for inexact description of a target system and “idealization” for another system whose properties also provide an inexact description of the target system. Since systems generated by a limiting process can often have quite unexpected—even inconsistent—properties, familiar limit processes used in statistical physics can fail to provide idealizations but merely provide approximations."

"Reductionists in biology claim that all biological events can be explained in terms of genes and macromolecules alone, while antireductionists argue that some biological events must be explained at a higher level. The literature, however, does not distinguish between different kinds of molecular explanation. The goal of this article is to identify and analyze three such kinds. The analysis of molecular explanations herein carries an important philosophical implication; in shunning crude reductionism and extreme versions of holism, we can combine the insights of thoughtful reductionists with sophisticated antireductionism. When this is done, the question of explanatory reductionism becomes less substantial than often supposed."

Have I really never bookmarked this before?

"Most introductory statistics text-books are written either in a highly mathematical style for an intended readership of mathematics undergraduate students, or in a recipe-book style for an intended audience of non-mathematically inclined undergraduate or postgraduate students, typically in a single discipline; hence, "statistics for biologists", "statistics for psychologists", and so on.
An antidote to technique-oriented service courses, Statistics and Scientific Method is different. It studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. Instead, the text aims to give the reader a clear understanding of how core statistical ideas of experimental design, modelling and data analysis are integral to the scientific method.

Aimed primarily at beginning postgraduate students across a range of scientific disciplines (albeit with a bias towards the biological, environmental and health sciences), it therefore assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation.

Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to our open-source software of choice, R, whilst the book's web-page includes downloadable data and R code that enables the reader to reproduce all of the analyses in the book and, with easy modifications, to adapt the code to analyse their own data if they wish. However, the book is not intended to be a textbook on statistical computing, and all of the material in the book can be understood without using either R or any other computer software."

"The hard problem of induction is to argue without begging the question that inductive inference, applied properly in the proper circumstances, is con- ducive to truth. A recent theorem seems to show that the hard problem has a deductive solution. The theorem, provable in , states that a predictive func- tion M exists with the following property: whatever world we live in, M correctly predicts the world’s present state given its previous states at all times apart from a well-ordered subset. On the usual model of time a well-ordered subset is small relative to the set of all times. M’s existence therefore seems to provide a solution to the hard problem.
My paper argues for two conclusions. First, the theorem does not solve the hard problem of induction. More positively though, it solves a version of the problem in which the structure of time is given modulo our choice of set theory."

--- Seems prodigiously strange, on first glance. Ask the people downstairs and up the hall what they think of it?

"Nonreductive physicalists have long used multiple realizability to argue for the explanatory “autonomy” of the special sciences. Recently, in the face of the local reduction and disjunctive property responses to multiple realizability, some defenders of nonreductive physicalism have suggested that autonomy can be grounded merely in human cognitive limitations. In this article, I argue that this is mistaken. By distinguishing between two kinds of abstraction I show that the greater explanatory relevance of some special-science predicates (to certain explananda) is both nonanthropocentric and not solely based on considerations of multiple realizability."

"Machamer, Darden, and Craver’s account of the nature and role of mechanisms in the special sciences has been very influential. Unfortunately, a confusing array of ontic, epistemic, and pragmatic distinctions is required to individuate their mechanisms, mechanism schemata, and mechanism sketches. I diagnose this as a conflation of token-level causal relations with type-level relations. I propose instead that a mechanism is an abstraction that relates entity types and activity types on the model of a directed graph. Mechanisms have an ontic status distinct from the causal chains of token entities and token activities that instantiate them."

"I argue that, in causal inference from many observational studies, the piecemeal collection of data can cause underdetermination, even if arbitrarily large amounts of reliable data are available. Two theorems reveal that, for any variable set V, there are causal theories over V that can be distinguished if and only if all variables are simultaneously measured. These results entail that, a priori, one cannot know which observational studies will be most informative with respect to the true causal theory describing V. Hence, scientific institutions may need to play a larger role in coordinating differing research programs."

"I contrast our own evidence for the hypothesis of organic fossil origins with that available in previous centuries, suggesting that the most powerful contemporary evidence consists in a form of projective support whose distinctive features are not well captured by familiar hypothetico-deductive, abductive, or even more recent and more technically sophisticated (e.g., Bayesian) accounts of scientific confirmation. I suggest that such accounts either misrepresent or ignore something important about the heterogeneous ways in which scientific hypotheses can be supported by evidence, and I go on to suggest that the search for any single such account may be misguided in any case."

"The Explanation of Social Action is a sustained critique of the conventional understanding of what it means to "explain" something in the social sciences. It makes the strong argument that the traditional understanding involves asking questions that have no clear foundation and provoke an unnecessary tension between lay and expert vocabularies. Drawing on the history and philosophy of the social sciences, John Levi Martin exposes the root of the problem as an attempt to counterpose two radically different types of answers to the question of why someone did a certain thing: first person and third person responses. The tendency is epitomized by attempts to explain human action in "causal" terms. This "causality" has little to do with reality and instead involves the creation and validation of abstract statements that almost no social scientist would defend literally.
This substitution of analysts' imaginations over actors' realities results from an intellectual history wherein social scientists began to distrust the self-understanding of actors in favor of fundamentally anti-democratic epistemologies. These were rooted most defensibly in a general understanding of an epistemic hiatus in social knowledge and least defensibly in the importation of practices of truth production from the hierarchical setting of institutions for the insane. Martin, instead of assuming that there is something fundamentally arbitrary about the cognitive schemes of actors, focuses on the nature of judgment. This implies the need for a social aesthetics, an understanding of the process whereby actors intuit intersubjectively valid qualities of complex social objects. In this thought-provoking and ambitious book, John Levi Martin argues that the most promising way forward to such a science of social aesthetics will involve a rigorous field theory."

"Since World War II methods have advanced very quickly in sociology and social science, while this has not been the case with theory. In this article I suggest that one way of beginning to close the gap between the two is to focus on theorizing rather than on theory. The place where theorizing can be used in the most effective way, I suggest, is in the context of discovery. What needs to be discussed are especially ways for how to develop theory before hypotheses are formulated and tested. To be successful in this, we need to assign an independent place to theorizing and also to develop some basic rules for how to theorize. An attempt is made to formulate such rules; it is also argued that theorizing can only be successful if it is done in close unison with observation in what is called a prestudy. Theorizing has turned into a skill when it is iterative, draws on intuitive ways of thinking, and goes beyond the basic rules for theorizing."

"So, will this theorem finally end the century-old debate about the “reality” of quantum states—proving, with mathematical certitude, that the “ontic” camp was right and the “epistemic” camp was wrong?  To ask this question is to answer it.

I expect that PBR’s philosophical opponents are already hard at work on a rebuttal paper: “The quantum state can too be interpreted statistically”, or even “The quantum state must be interpreted statistically.”

I expect the rebuttal to say that, yes, obviously two people can’t rationally assign different pure states to the same physical system—but only a fool would’ve ever thought otherwise, and that’s not what anyone ever meant by calling quantum states “statistical”, and anyway it’s beside the point, since pure states are just a degenerate special case of the more fundamental mixed states.

I expect the rebuttal to prove a contrary theorem, using a definition of the word “statistical” that subtly differs from PBRs.  I expect the difference between the two definitions to get buried somewhere in the body of the paper.

I expect the rebuttal to get blogged and Slashdotted.  I expect the Slashdot entry to get hundreds of comments taking strong sides, not one of which will acknowledge that the entire dispute hinges on the two camps’ differing definitions.

There’s an important lesson here for mathematicians, theoretical computer scientists, and analytic philosophers.  You want the kind of public interest in your work that the physicists enjoy?  Then stop being so goddamned precise with words!   The taxpayers who fund us—those who pay attention at all, that is—want a riveting show, a grand Einsteinian dispute about what is or isn’t real.  Who wants some mathematical spoilsport telling them: “Look, it all depends what you mean by ‘real.’  If you mean, uniquely determined by the complete state of the universe, and if you’re only talking about pure states, then…” "

"Decoherence is widely felt to have something to do with the quantum measurement problem, but getting clear on just what is made diffcult by the fact that the "measurement problem", as traditionally presented in foundational and philosophical discussions, has become somewhat disconnected from the conceptual problems posed by real physics. This, in turn, is because quantum mechanics as discussed in textbooks and in foundational discussions has become somewhat removed from scientific practice, especially where the analysis of measurement is concerned. This paper has two goals: firstly (sections 1-2), to present an account of how quantum measurements are actually dealt with in modern physics (hint: it doesn't involve a collapse of the wavefunction) and to state the measurement problem from the perspective of that account; and secondly (sections 3-4), to clarify what role decoherence plays in modern measurement theory and what effect it has on the various strategies that have been proposed to solve the measurement problem."

"In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object." --- Isn't this just "the philosophy of 'as-if' " from around 1900?

"Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science."

"I contrast two modes of error-elimination relevant to evaluating evidence in accounts that emphasize frequentist reliability. The contrast corresponds to that between the use of of a reliable inference procedure and the critical scrutiny of a procedure with regard to its reliability, in light of what is and is not known about the setting in which the procedure is used. I propose a notion of security as a category of evidential assessment for the latter. In statistical settings, robustness theory and misspecification testing exemplify two distinct strategies for securing statistical inferences."

"Evidence claims depend on fallible assumptions. This paper discusses inferential robustness as a strategy for justifying evidence claims in spite of this fallibility. I argue that robustness can be understood as a means of establishing the partial security of evidence claims. An evidence claim is secure relative to an epistemic situation if it remains true in all scenarios that are epistemically possible relative to that epistemic situation."

"...a philosophical analysis of how, precisely, technology can be a condition for gaining scientific knowledge. ... I begin with the observation that what we know depends on what we can do. For example, in science, gaining certain knowledge depends of having certain evidence. This makes the ability to gather that evidence a necessary condition for gaining the knowledge. I’ll argue that a scientist is (under certain conditions) expected to seek evidence before making a judgment, meaning that the “epistemic possibility” of attaining scientific knowledge sometimes depends on the possibility of undertaking certain activities. In turn, the possibility of undertaking certain activities depends in part on factors like ethical constraints, economical realities, and available technology. "  Presumably not all as bleedingly obvious as this sounds.

"...how we should regard the probability distributions introduced into statistical mechanics... problematic to take them either as purely subjective credences, or as objective chances ,,, a third alternative: they are "almost objective" probabilities, or "epistemic chances". The definition of such probabilities involves an interweaving of epistemic and physical considerations, and so cannot be classified as either purely subjective or purely objective. This conception ,,, resolves some of the puzzles associated with statistical mechanical probabilities; ... how probabilistic posits introduced on the basis of incomplete knowledge can yield testable predictions ... bypasses the problem of disastrous retrodictions, that is, the fact the standard equilibrium measures yield high probability of the system being in equilibrium in the recent past, even when we know otherwise."

"We argue that there is a tension present in the modeling practice between the aim of capturing the specific mechanisms underlying the phenomena and the use of general cross-disciplinary computational templates to study them. To illuminate this tension we examine the Lotka-Volterra model, which has provided a powerful template for population biology and other areas of research. We will compare the respective approaches of Alfred Lotka and Vito Volterra. What makes this comparison especially interesting is that although they ended up presenting models that from the formal point of view looked identical – and were subsequently treated like that – they nevertheless followed different kinds of modeling strategies."

"opper famously held that the growth of scientific knowledge and the Darwinian mechanism of trial and error elimination are analogous processes. ...  But it has been ignored that the use of Popper's Darwinian analogy had changed in the course of Popper's life ...  until the 1960s, he used the Darwinian process as a model for understanding the growth of scientific knowledge, whereas from the 1960s on ... used his new insights about the growth of scientific knowledge to say something about ... Darwinian selection ... this analogy was so central for Popper's thinking that rather than giving up on it, he tried very hard to find theories of biological evolution that would make this analogy plausible... led him to make ... claims about the nature of selection ... flirt with Lamarckism. I end by outlining a biologically plausible way of maintaining this Darwinian analogy that Popper failed to consider."

"Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that “probability match” the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely."

"... we have good models for material behaviors at small and large scales ...  hard to relate these ... models to one another. Macroscale models represent the integrated effects of very subtle factors that are practically invisible at the smallest, atomic, scales. ... notoriously difficult to model realistic materials with a simple bottom-up-from-the-atoms strategy.... forced physicists interested in overall macro-behavior of materials toward completely top-down modeling strategies familiar from traditional continuum mechanics. ...  whether we can exploit our rather rich knowledge of intermediate micro- (or meso-) scale behaviors in a manner that would allow us to bridge between these two dominant methodologies. Macroscopic scale behaviors often fall into large common classes of behaviors such as the class of isotropic elastic solids, characterized by two ... elastic coefficients. Can we employ knowledge of lower scale behaviors to ... determine the coefficients ... ?"

"It is widely believed that one should not become more confident that _all swans are white and all lions are brave_ simply by observing white swans. Irrelevant conjunction or "tacking" of a theory onto another is often thought problematic for Bayesianism, especially given the ratio measure of confirmation considered here... Using the ratio measure, the irrelevant conjunction is confirmed to the same degree as the relevant conjunct, which... is ideal: the irrelevant conjunct is irrelevant. Because the past's really having been as it now appears to have been is an irrelevant conjunct, present evidence confirms theories about past events only insofar as irrelevant conjunctions are confirmed. Hence the ideal of not confirming irrelevant conjunctions would imply that historical claims are not confirmed. ..."

"How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses recent arguments for dropping the requirement of regularity from the definition of a mechanism. I provide an expanded taxonomy of kinds of regularity mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular operation within a mechanism, and highlights the role that various kinds of regularity play in scientific explanation. I defend the broadened regularity requirement in terms of regularity’s role in individuating mechanisms against a background of other causal processes, and by prioritizing mechanisms’ ability to serve as a model of scientific explanation, rather than merely as a metaphysical account of causation. It is because mechanisms are regular, in the expanded sense described here, that they are capable of supporting the kinds of generalizations that figure prominently in scientific explanations."

"In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. ... another, radically different kind of complex system ... that many scientists regard as the only genuine kind of complex system ... highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical patterns ... arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics ... calls for an explanation in terms of underlying generating mechanisms. ... dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms..."

"Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process."

I have liked DeLanda's recent books, though I still find _War in the Age of Intelligent Machines_ bad, and don't get what he sees in Deleuze.  I look forward to this one with interest.

Would it be a good deed or a bad one if I sent this post to graduate students preparing for their exams?
"I wonder if a subconscious trauma from these exams is the reason I keep writing this blog. "

I should probably submit something, shouldn't I?

I like the parts about how it's more important to have a sound epistemology about _revising_ our beliefs (i.e., changing our minds), than about warranting our beliefs at any one time.

"Computer simulation was first pioneered as a scientific tool in meteorology and nuclear physics in the period following World War II, but it has grown rapidly to become indispensible in a wide variety of scientific disciplines, including astrophysics, high-energy physics, climate science, engineering, ecology, and economics. Digital computer simulation helps study phenomena of great complexity, but how much do we know about the limits and possibilities of this new scientific practice? How do simulations compare to traditional experiments? And are they reliable? Eric Winsberg seeks to answer these questions in Science in the Age of Computer Simulation."

"This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, the relevant error probabilities are replaced with the false positive/negative rates that constitute deductive calculations based on known probabilities among events. As a result, the ampliative dimension of frequentist induction—learning from data about the underlying data-generating mechanism—is missing."

Bwahahahaha!

"Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume’s problem of the justification of induction."

I like this.

"Recently some results have been presented which show that certain kinds of deterministic descriptions and indeterministic descriptions are observationally equivalent (Werndl 2009a, 2010). ... discuss the philosophical comments made by mathematicians about observational equivalence, in particular Ornstein and Weiss (1991). Their comments are vague, and I will argue that, according to a reasonable interpretation, they are misguided. Second, the results on observational equivalence raise the question of whether the deterministic or indeterministic description is preferable relative to all evidence [or it's underdetermined].... criticize Winnie's (1998) argument that, by appealing to different observations, one finds that the deterministic description is preferable. ... if the concern is a strong kind of underdetermination, the argument delivers the desired conclusion but this conclusion is trivial; and for other kinds of underdetermination ... the argument fails."

"We present an information theoretic account of models as scientific representations, where scientific models are understood as information carrying artifacts. We propose that the semantics of models should be based on this information coupling of the model to the world. The information theoretic account presents a way of avoiding the need to refer to agents' intentions as constitutive of the semantics of scientific representations, and it provides a naturalistic account of model semantics, which can deal with the problems of asymmetry, relevance and circularity that afflict other currently popular naturalistic proposals."

"n the first part of this article we will formulate postulates, which must be satisfied by a reasonable concept of emergence. The postulates will articulate conditions of adequacy for an appropriate explication of the concept of emergence. These conditions of adequacy are based primarily upon the philosophical and scientific history of the concept of emergence, in which the intended role of the concept is expressed. In the second part we will discuss and evaluate some candidates for the concept of emergence in light of these conditions of adequacy."

"Physicists regularly invoke universal laws, such as those of motion and electromagnetism, to explain events. Biological and medical scientists have no such laws. How then do they acquire a reliable body of knowledge about biological organisms and human disease? One way is by repeatedly returning to, manipulating, observing, interpreting, and reinterpreting certain subjects—such as flies, mice, worms, or microbes—or, as they are known in biology, “model systems.” Across the natural and social sciences, other disciplinary fields have developed canonical examples that have played a role comparable to that of biology’s model systems, serving not only as points of reference and illustrations of general principles or values but also as sites of continued investigation and reinterpretation..."

Review of Fodor & Piatelli-Palmarini.

"Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us in generating new predictions. This view of explanation and prediction clarifies what makes an explanation scientific and why inference to the best explanation makes sense in science."

"The world is complex, but acknowledging its complexity requires an appreciation for the many roles context plays in shaping natural phenomena.... deference to reductive explanations founded on simple universal laws, linear causal models, and predict-and-act strategies fails to accommodate the kinds of knowledge that many contemporary sciences [provide] about the world. ... new understanding that represents the rich, variegated, interdependent fabric of many levels and kinds of explanation that ... ground effective prediction and action. ... draws from diverse fields including psychiatry, social insect biology, and studies of climate change to defend ... a theory of scientific practices that makes sense of how [sciences] represent multi-level, multi-component, dynamic structures ... must revise how we conceptualize the world, how we investigate the world, and how we act in the world. ... the very idea of what should count as legitimate science itself should change."

Warning: it turns out that his case study for his approach is the development of the incest taboo, and he's pretty free in quoting the clinical literature about how exactly the taboo gets broken. This actually has considerable redeeming intellectual value, but is still not for the squeamish and/or victimized.

It'd be more impressive if he'd published this in a sociology-of-science journal, rather than _Philosophy of Science_...

A surprisingly weak paper, along the lines of "hey! did you realize that you can use the prior distribution to penalize things other than not fitting the data?", but I should re-read. Plus: this only makes sense if everyone always had both the old and the new paradigms in the support of their priors. ("Surprising", because Salmon was very good.)

"probabilities as deriving from ranges in suitably structured initial state spaces. Roughly, the probability of an event is the proportion of initial states that lead to this event in the space of all possible initial states, provided that this proportion is approximately the same in any not too small interval of the initial state space. This idea can also be expressed by saying that in the types of situations that give rise to probabilistic phenomena we may expect to find an initial state space such that any "reasonable" density function over this space leads to the same probabilities for the possible outcomes"

"A law about frequencies would be a law of nature that imposes a constraint on one or more (actual, global) frequencies. On any of the leading philosophical approaches to laws of nature, there could be laws about frequencies. Hypotheses that posit laws about frequencies turn out to behave very similarly to hypotheses that posit corresponding laws about probabilities or chances -- they make the same predictions, provide similar explanations, and are confirmed or disconfirmed by empirical evidence in the same ways. This makes it interesting to consider the possibility of interpreting probabilistic laws from scientific theories as laws about frequencies. This is surprising proposal, but I argue that the resulting view (which I call 'nomic frequentism') is able to overcome all of the standard objections to frequentist interpretation of objective probabilities."

"The guiding question of this paper is: how similar are deterministic descriptions and indeterministic descriptions from a predictive viewpoint? The deterministic and indeterministic descriptions of concern in this paper are measure-theoretic deterministic systems and stochastic processes, respectively. I will explain intuitively some mathematical results which show that measure-theoretic deterministic systems and stochastic processes give more often the same predictions than one might perhaps have expected, and hence that from a predictive viewpoint these descriptions are quite similar." This needs saying?!?