I consider recent strategies proposed by econometricians for extrapolating causal effects from experimental to target populations. I argue that these strategies fall prey to the extrapolator’s circle: they require so much knowledge about the target population that the causal effects to be extrapolated can be identified from information about the target alone. I then consider comparative process tracing as a potential remedy. Although specifically designed to evade the extrapolator’s circle, I argue that CPT is unlikely to facilitate extrapolation in (...) typical econometrics and evidence-based policy applications. To argue this, I offer a distinction between two kinds of extrapolation, attributive and predictive, the latter being prevalent in econometrics and evidence-based policy. I argue that CPT is not helpful for predictive extrapolation when using the kinds of evidence that econometricians and evidence-based policy researchers prefer. I suggest that econometricians may need to consider qualitative evidence to overcome this problem. (shrink)

In the human sciences, experimental research is used to establish causal relationships. However, the extrapolation of these results to the target population can be problematic. To facilitate extrapolation, we propose to use the statistical technique Latent Class Regression Analysis in combination with the analogical reasoning theory for extrapolation. This statistical technique can identify latent classes that differ in the effect of X on Y. In order to extrapolate by means of analogical reasoning, one can characterize the latent (...) classes by a combination of features, and then compare these features to features of the target. (shrink)

Mark Colyvan (2010) raises two problems for ‘easy road’ nominalism about mathematical objects. The first is that a theory’s mathematical commitments may run too deep to permit the extraction of nominalistic content. Taking the math out is, or could be, like taking the hobbits out of Lord of the Rings. I agree with the ‘could be’, but not (or not yet) the ‘is’. A notion of logical subtraction is developed that supports the possibility, questioned by Colyvan, of bracketing a theory’s (...) mathematical aspects to obtain, as remainder, what it says ‘mathematics aside’. The other problem concerns explanation. Several grades of mathematical involvement in physical explanation are distinguished, by analogy with Quine’s three grades of modal involvement. The first two grades plausibly obtain, but they do not require mathematical objects. The third grade is likelier to require mathematical objects. But it is not clear from Colyvan’s example that the third grade really obtains. (shrink)

Comparative process tracing is the best analysis of extrapolation inferences in the philosophical and scientific literature so far. In this essay I examine some similarities and differences between comparative process tracing and former attempts to capture the logic of extrapolation, such as the analogical approach. I show that these accounts are not different in spirit, although comparative process tracing supersedes previous proposals in terms of analytical detail. I also examine some qualms about the possibility of drawing extrapolation (...) inferences in the social sciences and conclude by suggesting that there may be cases of extrapolation without process tracing. (shrink)

In this chapter we explore the process of extrapolating causal claims from model organisms to humans in pharmacology. We describe and compare four strategies of extrapolation: enumerative induction, comparative process tracing, phylogenetic reasoning, and robustness reasoning. We argue that evidence of mechanisms plays a crucial role in several strategies for extrapolation and in the underlying logic of extrapolation: the more directly a strategy establishes mechanistic similarities between a model and humans, the more reliable the extrapolation. We (...) present case studies from the research on atherosclerosis and the development of statins, that illustrate these strategies and the role of mechanistic evidence in extrapolation. (shrink)

A particular tradition in medicine claims that a variety of evidence is helpful in determining whether an observed correlation is causal. In line with this tradition, it has been claimed that establishing a causal claim in medicine requires both probabilistic and mechanistic evidence. This claim has been put forward by Federica Russo and Jon Williamson. As a result, it is sometimes called the Russo–Williamson thesis. In support of this thesis, Russo and Williamson appeal to the practice of the International Agency (...) for Research on Cancer. However, this practice presents some problematic cases for the Russo–Williamson thesis. One response to such cases is to argue in favour of reforming these practices. In this paper, we propose an alternative response according to which such cases are in fact consistent with the Russo–Williamson thesis. This response requires maintaining that there is a role for mechanism-based extrapolation in the practice of the IARC. However, the response works only if this mechanism-based extrapolation is reliable, and some have argued against the reliability of mechanism-based extrapolation. Against this, we provide some reasons for believing that reliable mechanism-based extrapolation is going on in the practice of the IARC. The reasons are provided by appealing to the role of robustness analysis. (shrink)

Inferences like these are known as extrapolations.

Conference paper presented at the 10th International Congress of Logic, Methodology, and Philosophy of Science, Florence, Italy (19-25 August 1995). Extrapolation here refers to the act of inferring more widely from a limited range of known facts. This notion of extrapolation, especially when applied to past events, has recently been used to formulate a pragmatic definition of truth. This paper shows that this definition has serious problems. The pragmatic definition of truth has been formulated in discussions on internal (...) realism. In this paper, the basic internal realist starting point, as expounded in the works of H. Putnam and N. Jardine, will be taken to be the claim that the distinction between judgements that are true from judgements that are false is explainable from within our judgement-forming faculty — no recourse to the external world is necessary. The paper first shows how the pragmatic definition of truth involves a counterfactual statement. It then describes how the counterfactual definition is apparently justified by an inductive argument. In the final step, this justification is shown to be unsuccessful. (shrink)

According to the received view, the transportation view, animal extrapolation consists in inductive prediction of the outcome of a mechanism in a target, based on an analogical mechanism in a model. Through an analysis of the failure of preclinical studies of TGN1412, an innovative drug, to predict the tragic consequences of its first-in-man trial in 2006, the received view is challenged by a proposed view of animal extrapolation, the chimera view. According to this view, animal extrapolation is (...) based on a hypothesis about how human organisms work, supported by the amalgamation of results drawn from various experimental organisms, and only predicting the ‘predictive grid’, that is, a global framework of the effects to be expected. (shrink)

The primary purpose of the paper is to present the issue of extrapolation, which is interesting from the perspective of contemporary philosophy of science. For its proper explanation, it is crucial to distinguish it from terms of similar meaning, such as analogy, induction or statistical inference. The second goal is to indicate key differences that exist between extrapolative inference and analogical reasoning. Because of this the ascertaining of the identification of these concepts would be a mistake though, of course, (...) there is no way not to notice certain similarities. (shrink)

We advance a model of human probability judgment and apply it to the design of an extrapolation algorithm. Such an algorithm examines a person's judgment about the likelihood of various statements and is then able to predict the same person's judgments about new statements. The algorithm is tested against judgments produced by thirty undergraduates asked to assign probabilities to statements about mammals.

Extrapolating causal effects from experiments to novel populations is a common practice in evidence-based-policy, development economics and other social science areas. Drawing on experimental evidence of policy effectiveness, analysts aim to predict the effects of policies in new populations, which might differ importantly from experimental populations. Existing approaches made progress in articulating the sorts of similarities one needs to assume to enable such inferences. It is also recognized, however, that many of these assumptions will remain surrounded by significant uncertainty in (...) practice. Unfortunately, the existing literature says little on how analysts may articulate and manage these uncertainties. This paper aims to make progress on these issues. First, it considers several existing ideas that bear on issues of uncertainty, elaborates the challenges they face, and extracts some useful rationales. Second, it outlines a novel approach, called the support graph approach, that builds on these rationales and allows analysts to articulate and manage uncertainty in extrapolation in a systematic and unified way. (shrink)

Conducting research on animals is supposed to be valuable because it provides information on how human mechanisms work. But for the use of animal models to be ethically justified, it must be epistemically justified. The inference from an observation about an animal model to a conclusion about humans must be warranted for the use of animals to be moral. When researchers infer from animals to humans, it’s an extrapolation. Often non-human primates are used as animal models in laboratory behavioral (...) research. The target populations are humans and other non-human primates. I argue that the epistemology of extrapolation renders the use of non-human primates in laboratory behavioral research unreliable. If the model is relevantly similar to the target, then the experimental conditions introduce confounding variables. If the model is not relevantly similar to the target, then the observations of the model cannot be extrapolated to the target. Since using non-human primates in as animal models in laboratory behavioral research is not epistemically justified, using them as animal models in laboratory behavioral research is not ethically justified. (shrink)

In critical care medicine, teaching and mentoring practices are extremely important in regard to attracting and retaining young trainees and faculty in this important subspecialty that has a scarcity of needed personnel in the USA. To this end, we argue that Foucault’s Technologies of the Self is critical background reading when endeavoring to effect the positive transformation of faculty into effective teachers and mentors.

It is possible to predict future life forms? In this paper it is argued that the answer to this question may well be positive. As a basis for predictions a rationale is used that is derived from historical data, e.g. from a hierarchical classification that ranks all building block systems, that have evolved so far. This classification is based on specific emergent properties that allow stepwise transitions, from low level building blocks to higher level ones. This paper shows how this (...) hierarchy can be used for predicting future life forms.The extrapolations suggest several future neural network organisms. Major aspects of the structures of these organisms are predicted. The results can be considered of fundamental importance for several reasons. Firstly, assuming that the operator hierarchy is a proper basis for predictions, the result yields insight into the structure of future organisms. Secondly, the predictions are not extrapolations of presently observed trends, but are fully integrated with all historical system transitions in evolution. Thirdly, the extrapolations suggest the structures of intelligences that, one day, will possess more powerful brains than human beings. (shrink)

It is possible to predict future life forms? In this paper it is argued that the answer to this question may well be positive. As a basis for predictions a rationale is used that is derived from historical data, e.g. from a hierarchical classification that ranks all building block systems, that have evolved so far. This classification is based on specific emergent properties that allow stepwise transitions, from low level building blocks to higher level ones. This paper shows how this (...) hierarchy can be used for predicting future life forms.The extrapolations suggest several future neural network organisms. Major aspects of the structures of these organisms are predicted. The results can be considered of fundamental importance for several reasons. Firstly, assuming that the operator hierarchy is a proper basis for predictions, the result yields insight into the structure of future organisms. Secondly, the predictions are not extrapolations of presently observed trends, but are fully integrated with all historical system transitions in evolution. Thirdly, the extrapolations suggest the structures of intelligences that, one day, will possess more powerful brains than human beings.This study ends with a discussion of possibilities for falsification of the present theory, the implications of the present predictions in relation to recent developments in artificial intelligence and the philosophical implications of the role of humanity in evolution with regard to the creation of future neural network organisms. (shrink)

(Chapter 5 of Across the Boundaries, forthcoming, from Oxford University Press) This chapter argues that previous accounts of extrapolation, either by reference to capacities or mechanisms, do not adequately address the challenges confronting extrapolation. It then begins the account of how the mechanisms-approach can be developed so as to do better. The central concept in this account is what I term comparative process tracing.

This essay examines the relationship between the precautionary principle and uncertainty factors used by toxicologists to estimate acceptable exposure levels for toxic chemicals from animal experiments. It shows that the adoption of uncertainty factors in the United States in the 1950s can be understood by reference to the precautionary principle, but not by cost-benefit analysis because of a lack of relevant quantitative data at that time. In addition, it argues that uncertainty factors continue to be relevant to efforts to implement (...) the precautionary principle and that the precautionary principle should not be restricted to cases involving unquantifiable hazards. (shrink)

Extending lifelines still further, beyond determinism leads to doubt about females' ability to select mates with “good genes,” and to a scenario for troop selection and to a link with questions of scale in ecology.

Accurate perception of moving objects would be useful; accurate visually guided action is crucial. Visual motion across the scene influences perceived object location and the trajectory of reaching movements to objects. In this commentary, I propose that the visual system assigns the position of any object based on the predominant motion present in the scene, and that this is used to guide reaching movements to compensate for delays in visuomotor processing.

Extrapolation from a well-understood base population to a less-understood target population can fail if the base and target populations are not sufficiently similar. Differences between laboratory mice and humans, for example, can hinder extrapolation in medical research. Mice that carry a partial or complete human physiological system, known as humanized mice, are supposed to make extrapolation more reliable by simulating a variety of human diseases. But what justifies our belief that these mice are similar enough to their (...) human counterparts to simulate human disease? I argue that, unless three requirements are met in the process of humanizing mice, very little does. My requirements are not meant to provide necessary and sufficient conditions that guarantee a particular outcome. Instead, they serve as a heuristic for guiding scientific judgments involving extrapolation. In developing each requirement, I engage with philosophical issues concerning the nature of model-based science and the mechanistic approach (and its limits) to making generalizations in the life sciences. (shrink)

Evidence for motion extrapolation at motion offset is scarce. In contrast, there is abundant evidence that subjects mentally extrapolate the future trajectory of weak motion signals at motion offset. Further, pointing movements overshoot at motion offset. We believe that mental and sensorimotor extrapolation is sufficient to solve the problem of perceptual latencies. Both present the advantage of being much more flexible than motion extrapolation.

Extrapolating causal effects is becoming an increasingly important kind of inference in Evidence-Based Policy, development economics, and microeconometrics more generally. While several strategies have been proposed to aid with extrapolation, the existing methodological literature has left our understanding of what extrapolation consists of and what constitutes successful extrapolation underdeveloped. This paper addresses this lack in understanding by offering a novel account of successful extrapolation. Building on existing contributions pertaining to the challenges involved in extrapolation, this (...) more nuanced and comprehensive account seeks to provide tools that facilitate the scrutiny of specific extrapolative inferences and general strategies for extrapolation. Offering such resources is important especially in view of the increasing amounts of real-world decision-making in policy, development, and beyond that involve extrapolation. (shrink)

Proponents of evidence-based medicine and some philosophers of science seem to agree that knowledge of mechanisms can help solve the problem of applying results of controlled studies to target populations (‘the problem of extrapolation’). We describe the problem of extrapolation, characterize mechanisms, and outline how mechanistic knowledge might be used to solve the problem. Our main thesis is that there are four often overlooked problems with using mechanistic knowledge to solve the problem of extrapolation. First, our understanding (...) of mechanisms is often (and arguably, likely to remain) incomplete. Secondly, knowledge of mechanisms is not always applicable outside the tightly controlled laboratory conditions in which it is gained. Thirdly, mechanisms can behave paradoxically. Fourthly, as Daniel Steel points out, using mechanistic knowledge faces the problem of the ‘extrapolator’s circle’. At the same time, when the problems with mechanistic knowledge have been addressed, such knowledge can and should be used to mitigate (nothing can entirely solve) the problem of extrapolation. (shrink)

ABSTRACT This article contributes to a better understanding of dystopia’s practical aims by offering a critical defense of what Gregory Claeys calls the “Atwood Principle.” Derived from the writings of Canadian author Margaret Atwood, it establishes a yardstick for separating speculative fiction from science fiction. I argue that, rather than elevating it to the status of a genre definer, the Atwood Principle should be vindicated in terms of a heuristic device for contextually identifying the central mechanism underpinning dystopias: warning through (...) extrapolation. The real challenge, then, is how to make sense of the complex functioning of extrapolation. Instead of viewing it in mechanistic terms, my suggestion is to envisage extrapolation as a dynamic process involving both realism and estrangement. I illustrate this through a contrast between two kinds of stories about the current climate emergency: cautionary and post-cautionary tales of the Anthropocene. (shrink)

We present a novel technique for speeding up the computation of PageRank, a hyperlink-based estimate of the “importance” of Web pages, based on the ideas presented in [7]. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Power Extrapolation, accelerates the convergence of the Power Method by subtracting off the error along several nonprincipal eigenvectors from the (...) current iterate of the Power Method, making use of known nonprincipal eigenvalues of the Web hyperlink matrix. Empirically, we show that using Power Extrapolation speeds up PageRank computation by 30% on a Web graph of 80 million nodes in realistic scenarios over the standard power method, in a way that is simple to understand and implement. (shrink)

Method by subtracting off the error along several nonprincipal eigenvectors from the current iterate of the Power Method, making use of known nonprincipal eigenvalues of the Web hyperlink matrix. Empirically, we show that using Power Extrapolation speeds up PageRank computation by 30% on a Web graph of 80 million nodes in realistic scenarios over the standard power method, in a way that is simple to understand and implement.

Zusammenfassung Unser Wissen von Zukunft beruht weder auf Erfahrung noch auf Extrapolation aus Erfahrung, sondern ist apriorisch. Da es also apriorisches Wissen von Zeit gibt, kann eine apriorische Lösung des Induktionsproblems sinnvoll versucht werden.

Unser Wissen von Zukunft beruht weder auf Erfahrung noch auf Extrapolation aus Erfahrung, sondern ist apriorisch. Da es also apriorisches Wissen von Zeit gibt, kann eine apriorische Lösung des Induktionsproblems sinnvoll versucht werden.

In this article the author examines the problem of mental experiment extrapolation to musical art. Thus an attempt was made to determine the community of mechanisms of thought process in the scientific cognition and in the artistic creation. Author talks about peculiarities of music mental experiment and emphasizes its basic functions in musical thought. Therefore, the mental experiment in the sphere of aesthetic activity has its own specific character, whichis not identical with the notion of the mental experiment in (...) the scientific sphere and scientific experience. It shows the greatest importance of rethought the category of “mental experiment” in the musical creation sphere. The study of the mental experiment in the sphere of music, as one of the forms of non-verbal treatment, storage, creation and transfer of information, is of great value, meets the self-reflectiveness necessities of life of individual or the whole society. (shrink)

Volume 20, Issue 4, May 2020, Page 114-116.

Simple extrapolation is the orthodox approach to extrapolating from clinical trials in evidence-based medicine: extrapolate the relative effect size from the trial unless there is a compelling reason not to do so. I argue that this method relies on a myth and a fallacy. The myth of simple extrapolation is the idea that the relative risk is a ‘golden ratio’ that is usually transportable due to some special mathematical or theoretical property. The fallacy of simple extrapolation is (...) an unjustified argument from ignorance: we conclude that the relative effect size is transportable in the absence of evidence to the contrary. In short, simple extrapolation is a deeply problematic solution to the problem of extrapolation. (shrink)