This paper presents a new kind of problem in the ethics of distribution. The problem takes the form of several “calibration dilemmas,” in which intuitively reasonable aversion to small-stakes inequalities requires leading theories of distribution to recommend intuitively unreasonable aversion to large-stakes inequalities. We first lay out a series of such dilemmas for prioritarian theories. We then consider a widely endorsed family of egalitarian views and show that they are subject to even more forceful calibration dilemmas than prioritarian (...) theories. Finally, we show that our results challenge common utilitarian accounts of the badness of inequalities in resources. (shrink)

In 2012, the Geological Time Scale, which sets the temporal framework for studying the timing and tempo of all major geological, biological, and climatic events in Earth’s history, had one-quarter of its boundaries moved in a widespread revision of radiometric dates. The philosophy of metrology helps us understand this episode, and it, in turn, elucidates the notions of calibration, coherence, and consilience. I argue that coherence testing is a distinct activity preceding calibration and consilience, and I highlight the (...) value of discordant evidence and trade-offs scientists face in calibration. The iterative nature of calibration, moreover, raises the problem of legacy data. (shrink)

This paper deals with calibration in scientific practices which investigate relatively well-understood natural phenomena by means of already standardized instrumental devices. Calibration is a crucial topic, since it conditions the reliability of instrumental procedures in science. Yet although important, calibration is a relatively neglected topic. We think more attention should be devoted to calibration. The paper attempts to take a step in this direction. The aims are two-fold: (1) to characterize calibration in a relatively simple (...) kind of scientific practices; (2) to provide conceptual and taxonomic tools of broader scope that help to get a better understanding of calibration in more complex cases and other kinds of scientific practices. For this purpose, we first provide indications for why a conceptual framework is needed. Second, a bibliographic survey of works dealing with calibration is attempted. Third, we introduce different tools to enable a better understanding of calibration. Fourth, we turn to the elaboration of what we call a “simple exemplar” of calibration, illustrated through the case of the calibration of an equal-arm balance. Fifth, the tools previously introduced, and the framework of the simple exemplar, are applied to a more complex case of calibration: calibration procedures in X-ray experiments. This serves to show the work accomplished by the simple exemplar and to emphasize features of more complex cases of calibration. Eventually, we revisit and specify the nature, status, scope and value of the proposed framework. (shrink)

To several critics of the philosophical method of cases—Robert Cummins, Jonathan Weinberg and his colleagues, and Avner Baz—the fact that philosophical intuitions cannot be calibrated means that we cannot rule out the skeptical hypothesis that the outcome of our theorizing based on these intuitions is deeply distorted by our cognitive artifacts. Moreover, they take this hypothesis to license the negative conclusion that we are unable to have much of the armchair knowledge we typically attribute to ourselves when philosophizing based on (...) appeal to philosophical intuitions. This paper addresses this skeptical argument and shows how this negative conclusion can be resisted. (shrink)

We reframe the central challenge of large language model safety guardrails as a psychometric calibration problem rather than a binary policy dispute. Jamhour’s Guard Rails and Distributed Relational Cognition [1] names a genuine tension: safety measures designed to suppress manipulation, dependence, sycophancy, and unsafe advice may simultaneously erode the relational conditions that make sustained human–AI cognitive partnership possible— temporal depth, epistemic vulnerability, meta-cognitive reflection, calibrated disagreement, and long-horizon collaboration. The diagnosis is persuasive but leaves an unresolved design question: how (...) can a safety system distinguish harmful entanglement from productive cognitive coupling without retreating either to permissive romanticism or blunt paternalism? We argue that Machine Psychometrics [3] supplies the missing measurement layer. We develop three contributions. First, we reinterpret guardrails through Signal Detection Theory: under-restriction is a miss, over-restriction a false alarm, and the central design challenge is to increase discriminability rather than shift the criterion toward refusal. Second, we extend Machine Psychometrics from agent-level Mindprints to a relational measurement layer—the Coupling Profile—that characterizes temporal depth, epistemic openness, calibrated disagreement, meta-cognitive co-construction, conceptual yield, boundary clarity, and dependency pressure in human–AI partnerships. Third, we propose a Guardrail Calibration Protocol that uses adaptive probe batteries, perturbation testing, psychometric validation, longitudinal drift monitoring, and context-bounded validity envelopes to design safety regimes that preserve valuable coupling while limiting genuine risk. The framework accepts the reality of sycophancy, manipulation, over-attachment, and unsafe advice but rejects the assumption that these risks can only be addressed by flattening the relational field. It also avoids taking a premature stance on artificial consciousness: productive human–AI coupling deserves measurement and protection as a behavioral and epistemic phenomenon whether or not future artificial systems ever qualify as conscious subjects. The aim is not to weaken guardrails but to replace blunt guardrails with measured boundaries. (shrink)

To study consciousness, scientists need to determine when participants are conscious and when they are not. They do so with consciousness detection procedures. A recurring skeptical argument against those procedures is that they cannot be calibrated: there is no way to make sure that detection outcomes are accurate. In this article, I address two main skeptical arguments purporting to show that consciousness scientists cannot calibrate detection procedures. I conclude that there is nothing wrong with calibration in consciousness science.

Calibration, the use of a surrogate signal to standardize an instrument, is an important strategy for the establishment of the validity of an experimental result. In this paper, I present several examples, typical of physics experiments, that illustrate the adequacy of the surrogate. In addition, I discuss several episodes in which the question of calibration is both difficult to answer and of paramount importance. These episodes include early attempts to detect gravity waves, the question of the existence of (...) a 17–keV neutrino, and the question of the existence of a Fifth Force in gravity. I argue that in these more complex cases, the adequacy of calibration, in an extended sense, was both considered and established. (shrink)

Variable-Value axiologies avoid Parfit’s Repugnant Conclusion while satisfying some weak instances of the Mere Addition principle. We apply calibration methods to two leading members of the family of Variable-Value views conditional upon: first, a very weak instance of Mere Addition and, second, some plausible empirical assumptions about the size and welfare of the intertemporal world population. We find that such facts calibrate these two Variable-Value views to be nearly totalist, and therefore imply conclusions that should seem repugnant to anyone (...) who opposes Total Utilitarianism only due to the Repugnant Conclusion. (shrink)

This paper assesses the comparative reliability of two belief-revision rules relevant to the epistemology of disagreement, the Equal Weight and Stay the Course rules. I use two measures of reliability for probabilistic belief-revision rules, calibration and Brier Scoring, to give a precise account of epistemic peerhood and epistemic reliability. On the calibration measure of reliability, epistemic peerhood is easy to come by, and employing the Equal Weight rule generally renders you less reliable than Staying the Course. On the (...) Brier-Score measure of reliability, epistemic peerhood is much more difficult to come by, but employing the Equal Weight rule always renders you more reliable than Staying the Course. I conclude with some normative lessons we can draw from these formal results. (shrink)

This paper establishes a calibration theorem for risk-weighted expected utility theory (REU). REU extends expected utility theory (EU) by introducing a risk function, which allows a decision-maker to weigh potential outcomes according to their respective risks when determining the instrumental value of an action. REU is intuitively appealing as it seems to resolve some of the classical challenges to EU, such as the Allais paradox and Rabin’s calibration result. Using only widely accepted and minimally controversial assumptions, I calibrate (...) REU using Allais preferences and show that accommodating Allais preferences commits REU to questionable behaviors. Thus, REU resolves the Allais paradox only at the cost of becoming susceptible to a Rabin-style challenge (being calibratable). Compared to existing calibration results against REU, my approach relies on less demanding and more realistic assumptions. (shrink)

Emotional engagement in clinical practice has been theorised as a professional virtue, a form of emotional labour and a potential pathway to compassion fatigue and burnout. Although these traditions have shown that emotional practice is shaped by relational and organisational conditions, they have less clearly specified how clinicians regulate engagement in high‐demand perioperative settings, or what organisational conditions make such regulation achievable. Drawing on emotion regulation theory, nursing scholarship on caring and emotional labour and Donabedian's structure–process–outcome framework, we introduce calibrated (...) emotional engagement as a middle‐range theoretical concept describing the dynamic, professionally regulated adjustment of emotional, attentional and relational involvement required to remain clinically responsive without becoming detached or overwhelmed. Calibrated emotional engagement is understood as a multilevel process: individually enacted, team‐mediated and organisationally conditioned. A three‐zone model, comprising under‐engagement, over‐engagement and a calibrated zone of functionally sustainable engagement, offers a way of conceptualising the range of emotional states in high‐demand perioperative settings, including the cue‐deprived character of the operating room where anaesthetised patients and sterile draping remove the relational feedback that ordinarily supports empathic practice. We further propose that organisational conditions, including staffing, team stability, autonomy and recovery structure, constitute an empathic infrastructure that shapes whether calibrated engagement is achievable in practice. The concept opens new directions for perioperative education, workforce policy and research that move beyond individual resilience towards systemic accountability for the emotional conditions of care. (shrink)

Discussions of statistical criteria for fairness commonly convey the normative significance of calibration within groups by invoking what risk scores “mean.” On the Same Meaning picture, group-calibrated scores “mean the same thing” (on average) across individuals from different groups and accordingly, guard against disparate treatment of individuals based on group membership. My contention is that calibration guarantees no such thing. Since concrete actual people belong to many groups, calibration cannot ensure the kind of consistent score interpretation that (...) the Same Meaning picture implies matters for fairness, unless calibration is met within every group to which an individual belongs. Alas only perfect predictors may meet this bar. The Same Meaning picture thus commits a reference class fallacy by inferring from calibration within some group to the “meaning” or evidential value of an individual’s score, because they are a member of that group. The reference class answer it presumes does not only lack justification; it is very likely wrong. I then show that the reference class problem besets not just calibration but other group statistical criteria that claim a close connection to fairness. Reflecting on the origins of this oversight opens a wider lens onto the predominant methodology in algorithmic fairness based on stylized cases. (shrink)

In this paper, I consider the question of whether calibration is required for modalizing mechanisms to be reliable, that is, whether it is necessary for modalizing mechanisms to be adjusted to prevent overgeneration and undergeneration of modal beliefs. I first argue that the calibration requirement affects differently what I call bootstrapping and ordinary cases. Identifying different ways in which a modalizing mechanism could be calibrated, I argue that not all of them are effective or even viable in bootstrapping (...) cases. Then, by taking a diachronic perspective, I offer a simplified account of how different calibration mechanisms can be bootstrapped, with an emphasis on the social dimension of modal judgment. (shrink)

Human adults are thought to possess two dissociable systems to represent numbers: an approximate quantity system akin to a mental number line, and a verbal system capable of representing numbers exactly. Here, we study the interface between these two systems using an estimation task. Observers were asked to estimate the approximate numerosity of dot arrays. We show that, in the absence of calibration, estimates are largely inaccurate: responses increase monotonically with numerosity, but underestimate the actual numerosity. However, insertion of (...) a few inducer trials, in which participants are explicitly (and sometimes misleadingly) told that a given display contains 30 dots, is sufficient to calibrate their estimates on the whole range of stimuli. Based on these empirical results, we develop a model of the mapping between the numerical symbols and the representations of numerosity on the number line. (shrink)

The practice of appealing to esoteric intuitions, long standard in analytic philosophy, has recently fallen on hard times. Various recent empirical results have suggested that philosophers are not currently able to distinguish good intuitions from bad. This paper evaluates one possible type of approach to this problematic methodological situation: calibration. Both critiquing and building on an argument from Robert Cummins, the paper explores what possible avenues may exist for the calibration of philosophical intuitions. It is argued that no (...) good options are currently available, but leaves open the real possibility of such a calibration in the future. (shrink)

When discussing AI alignment, we usually refer to the problem of teaching or training advanced autonomous AI systems to make decisions that are aligned with human values or preferences. Proponents of this approach believe it can be employed as means to stay in control over sophisticated intelligent systems, thus avoiding certain existential risks. We identify three general obstacles on the path to implementation of value alignment: a technological/technical obstacle, a normative obstacle, and a calibration problem. Presupposing, for the purposes (...) of this discussion, that the technical and normative problems are solved, we focus on the problem of how to calibrate a system, for a specific value, to be on a specific location within a spectrum stretching between righteous and normal or average human behavior. Calibration, or more specifically mis-calibration, also raises the issue of trustworthiness. If we cannot trust AI systems to perform tasks the way we intended, we would not use them on our roads and at our homes. In an era where we strive to construct autonomous machines endowed with common sense, reasoning abilities and a connection to the world, so they would be able to act in alignment with human values, such mis-calibrations can make the difference between trustworthy and untrustworthy systems. (shrink)

Can there be good reasons for judging one set of probabilistic assertions more reliable than a second? There are many candidates for measuring "goodness" of probabilistic forecasts. Here, I focus on one such aspirant: calibration. Calibration requires an alignment of announced probabilities and observed relative frequency, e.g., 50 percent of forecasts made with the announced probability of.5 occur, 70 percent of forecasts made with probability.7 occur, etc. To summarize the conclusions: (i) Surveys designed to display calibration curves, (...) from which a recalibration is to be calculated, are useless without due consideration for the interconnections between questions (forecasts) in the survey. (ii) Subject to feedback, calibration in the long run is otiose. It gives no ground for validating one coherent opinion over another as each coherent forecaster is (almost) sure of his own long-run calibration. (iii) Calibration in the short run is an inducement to hedge forecasts. A calibration score, in the short run, is improper. It gives the forecaster reason to feign violation of total evidence by enticing him to use the more predictable frequencies in a larger finite reference class than that directly relevant. (shrink)

Sedlakova et al. (2025) argue that conversational AI (CAI) cannot satisfy the conditions of epistemic trust in therapeutic contexts. I accept their diagnosis but press a further question: given that patients will de facto treat CAI outputs as reasons, how should patients, clinicians, and designers calibrate their epistemic responses? I argue that even where epistemic trust is inappropriate, calibrated epistemic deference remains rational. On a total evidence view, CAI outputs function as defeasible contributory reasons whose weight is proportional to demonstrated (...) reliability and context-sensitive. Against preemptionism, I show that preemptive CAI deference is distinctively objectionable in therapeutic contexts: it undermines precisely the epistemic capacities—self-knowledge, critical reflection, and reason-integration—that psychotherapy aims to develop. An open question is whether calibrated deference is dynamically stable given users' documented tendency to anthropomorphize CAI over extended interaction, which may cause deference to drift toward the quasi-trust that therapeutic contexts warrant resisting. (shrink)

Over the last twenty-five years, climate scientists working on the attribution of climate change to humans have developed increasingly sophisticated statistical models in a process that can be understood as a kind of calibration: the gradual changes to the statistical models employed in attribution studies served as iterative revisions to a measurement(-like) procedure motivated primarily by the aim of neutralizing particularly troublesome sources of error or uncertainty. This practice is in keeping with recent work on the evaluation of models (...) more generally that views models as tools for particular tasks: what drives the process is the desire for models that provide more reliable grounds for inference rather than accuracy to the underlying mechanisms of data-generation. (shrink)

We report on some recent work centered on attempts to understand when one set is more random than another. We look at various methods of calibration by initial segment complexity, such as those introduced by Solovay [125], Downey, Hirschfeldt, and Nies [39], Downey, Hirschfeldt, and LaForte [36], and Downey [31]; as well as other methods such as lowness notions of Kučera and Terwijn [71], Terwijn and Zambella [133], Nies [101, 100], and Downey, Griffiths, and Reid [34]; higher level randomness (...) notions going back to the work of Kurtz [73], Kautz [61], and Solovay [125]; and other calibrations of randomness based on definitions along the lines of Schnorr [117].These notions have complex interrelationships, and connections to classical notions from computability theory such as relative computability and enumerability. Computability figures in obvious ways in definitions of effective randomness, but there are also applications of notions related to randomness in computability theory. For instance, an exciting by-product of the program we describe is a more-or-less naturalrequirement-freesolution to Post's Problem, much along the lines of the Dekker deficiency set. (shrink)

Pains are subject to obvious, well-documented, and striking top-down influences. This is in stark contrast to visual perception, where the debate over cognitive penetrability tends to revolve around fairly subtle experimental effects. Several authors have recently taken up the question of whether top-down effects on pain count as cognitive penetrability, and what that might show us about traditional debates. I review some of the known mechanisms for top-down modulation of pain, and suggest that it reveals an issue with a relatively (...) neglected part of the cognitive penetrability literature. Much of the debate inherits Pylyshyn’s stark contrast between transducers and cognition proper. His distinction grew out of his running fight with Gibson, and is far too strong to be defensible. I suggest that we might therefore view top-down influences on pain as a species of transducer calibration. This provides a novel but principled way to distinguish between several varieties of top-down effect according to their architectural features. (shrink)

The epistemic theory of causality is analogous to epistemic theories of probability. Most proponents of epistemic probability would argue that one's degrees of belief should be calibrated to chances, insofar as one has evidence of chances. The question arises as to whether causal beliefs should satisfy an analogous calibration norm. In this paper, I formulate a particular version of a norm requiring calibration to chances and argue that this norm is the most fundamental evidential norm for epistemic probability. (...) I then develop an analogous calibration norm for epistemic causality, argue that it is the *only* evidential norm required for epistemic causality, and show how an epistemic account of causality that incorporates this norm can be used to analyse objective causal relationships. (shrink)

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

: This paper explores the calibration of laboratory models in population genetics as an experimental strategy for justifying experimental results and claims based upon them following Franklin (1986, 1990) and Rudge (1996, 1998). The analysis provided undermines Coyne et al.'s (1997) critique of Wade and Goodnight's (1991) experimental study of Wright's (1931, 1932) Shifting Balance Theory. The essay concludes by further demonstrating how this analysis bears on Diamond's (1986) claims regarding the weakness of laboratory experiments as evidence, and further (...) how the calibration strategy fits within Lloyd's (1987, 1988) account of the confirmation of ecological and evolutionary models. (shrink)

The epistemic theory of causality is analogous to epistemic theories of probability. Most proponents of epistemic probability would argue that one's degrees of belief should be calibrated to chances, insofar as one has evidence of chances. The question arises as to whether causal beliefs should satisfy an analogous calibration norm. In this paper, I formulate a particular version of a norm requiring calibration to chances and argue that this norm is the most fundamental evidential norm for epistemic probability. (...) I then develop an analogous calibration norm for epistemic causality, argue that it is the *only* evidential norm required for epistemic causality, and show how an epistemic account of causality that incorporates this norm can be used to analyse objective causal relationships. (shrink)

Drawing on a passage from Ramsey’s ‘Truth and Probability’, we formulate a simple, plausible constraint on evaluating the accuracy of credences: the calibration test. We show that any additive, continuous accuracy measure that passes the calibration test will be strictly proper. Strictly proper accuracy measures are known to support the touchstone results of accuracy-first epistemology, for example, vindications of probabilism and conditionalization. We show that our use of calibration is an improvement on previous such appeals by showing (...) how it answers or sidesteps problems that have been raised for previous work in this area. (shrink)

Artificial intelligence (AI) is increasingly integrated into healthcare systems, offering advances in diagnosis, treatment, and operational efficiency. However, the cultural assumptions embedded in AI training data often misalign with the sociocultural realities of diverse patient populations—a phenomenon we term the Cultural Calibration Gap (CCG). This conceptual paper defines the CCG as a structural and epistemic deficit, distinct from algorithmic bias or dataset shift, that undermines clinical validity, patient trust, and health equity. We analyze how culturally uncalibrated AI systems exacerbate (...) disparities in diagnosis and care, compromise informed consent, and erode trust, particularly in communities with collective, spiritual, or traditional health frameworks. To address the CCG, we propose a three-pronged strategy: epistemic inclusion of diverse knowledge systems and data sources, participatory design grounded in community-based research, and regulatory reform mandating cultural responsiveness in model validation and oversight. We argue that tackling the CCG is not merely an ethical imperative but a scientific necessity, aligning healthcare AI with the evolving vision of human-centric, adaptive Healthcare 5.0. Culturally calibrated AI systems promise not only improved precision and trustworthiness but also more equitable and contextually meaningful care across global health landscapes. (shrink)

Comparative valuation of different policy interventions often requires interpersonal comparability of benefit. In the field of health economics, the metric commonly used for such comparison, quality adjusted life years (QALYs) gained, has been criticized for failing to respect the equality of all persons’ intrinsic worth, including particularly those with disabilities. A methodology is proposed that interprets ‘full quality of life’ as the best health prospect that is achievable for the particular individual within the relevant budget constraint. This calibration is (...) challenging both conceptually and operationally as it shifts dramatically when technology or budget developments alter what can be achieved for incapacitated individuals. The proposal nevertheless ensures that the maximal achievable satisfaction of one person’s preferences can carry no more intrinsic value than that of another. This approach, which can be applied to other domains of social valuation, thus prevents implicit discrimination against the elderly and those with irremediable incapacities. (shrink)

Prediction is more than testing established theory by examining whether the prediction matches the data. To show this, I examine the practices of a community of scientists, known as threaders, who are attempting to predict the final, folded structure of a protein from its primary structure, i.e., its amino acid sequence. These scientists employ a careful and deliberate methodology of prediction. A key feature of the methodology is calibration. They calibrate in order to construct better models. The construction leads (...) to knowledge of how to construct or build an object. Thus, prediction serves a cognitive goal of model construction and not just model or theory testing. The kind of knowledge that results is relevantly different than theoretical knowledge. (shrink)

Schervish (1985b) showed that every forecasting system is noncalibrated for uncountably many data sequences that it might see. This result is strengthened here: from a topological point of view, failure of calibration is typical and calibration rare. Meanwhile, Bayesian forecasters are certain that they are calibrated---this invites worries about the connection between Bayesianism and rationality.

Study is recently re-invoked as an alternative educational formation to disrupt the learning trap and trope. This paper calibrates study and learning as two hermeneutic principles and correlates them with seeing, hearing, and observing as three onto-epistemic modes that respectively underpin Greco-Christian, Rabbinic, and ancient Chinese exegetical traditions. Linking study and learning with the hermeneutic issues of language, text, meaning, and reality, my calibration unfolds in four steps. First, I introduce an epistemic aporia encountered in interpreting some Chinese educational (...) “wind” texts, exposing our naturalized reasoning of learning along a representational enclosure. Second, turning to Susan Handelman’s writing, I trace this learning-as-representation enclosure as being conditioned upon the Greco-Christian exegetical mode of seeing, meanwhile correlating study back with the Rabbinic hearing hermeneutic. Third, I move on to explicate an onto-cosmological Yijing observing, proffering a study hermeneutic as a movement of observing, following, and attuning to wendao, literally put, “a crisscrossing pattern that turns with dao.” Finally, I re-observe and study the crisscrossing Chinese educational “wind” texts, evoking a Chinese “wind-teaching” sensibility so far rarely discerned through representational thinking and learning. (shrink)

I argue that dimensional analysis provides an answer to a skeptical challenge to the theory of model mediated measurement. The problem arises when considering the task of calibrating a novel measurement procedure, with greater range, to the results of a prior measurement procedure. The skeptical worry is that the agreement of the novel and prior measurement procedures in their shared range may only be apparent due to the emergence of systematic error in the exclusive range of the novel measurement procedure. (...) Alternatively: what if the two measurement procedures are not in fact measuring the same quantity? The theory of model mediated measurement can only say that we _assume_ that there is a common quantity. In contrast, I show that the satisfaction of dimensional homogeneity across the metrological extension is independent evidence for the so-called assumption. This is illustrated by the use of dimensional analysis in high pressure experiments. This results in an extension of the theory of model mediated measurement, in which a common quantity in metrological extension is no longer assumed, but hypothesized. (shrink)

This note responds to some criticisms of my recent book In Defence of Objective Bayesianism that were provided by Gregory Wheeler in his ‘Objective Bayesian Calibration and the Problem of Non-convex Evidence’.

Recent discussion of the calibration of probability assessments is related to the earlier influential attitudes of Fréchet. The limiting frequency criterion of good calibration is criticised as being of no relevance to the evaluation of the probability of any event. An operational definition of good calibration is proposed which treats calibration properties as characteristics of the assessor's entire body of opinion, not of opinion about some particular event or events. In these terms a result is shown (...) which says that every coherent opinion distribution is well-calibrated. Scoring rule representations as calibration plus refinement scores are discussed. It is proposed that a subjectivist could well attribute particular bad experiences to 'luck', and must balance this sense of luck with a sense of the inadequacy of his/her own knowledge when evaluating previous knowledge and experience. (shrink)

This chapter examines the role of parameterParametercalibrationCalibration in the confirmation and validation of complex computer simulation models. I examine the question to what extent calibration data can confirm or validate the calibrated model, focusing in particular on Bayesian approaches to confirmation. I distinguish several different Bayesian approaches to confirmation and argue that complex simulation models exhibit a predictivist effect: Complex computer simulation models constitute a case in which predictive success, as opposed to the mere accommodation of evidence, provides a (...) more stringent test of the model. DataData used in tuning do not validate or confirm a model to the same extent as data successfully predicted by the model do. (shrink)

We propose a new account of calibration according to which calibrating a technique shows that the technique does what it is supposed to do. To motivate our account, we examine an early twentieth-century debate about chlorophyll chemistry and Mikhail Tswett’s use of chromatographic adsorption analysis to study it. We argue that Tswett’s experiments established that his technique was reliable in the special case of chlorophyll without relying on either a theory or a standard calibration experiment. We suggest that (...) Tswett broke the experimenters’ regress by appealing to material facts in the common ground for chemists at the time. (shrink)

Based on an original ethnographic study of a translational cancer research institute in the United States, we propose calibration as a process that makes interdisciplinary collaboration without consensus possible. Calibration refers to ongoing, day-to-day negotiation and alignment of personal identities, disciplinary commitments, and research group customs that occur during face-to-face group deliberations around everyday research concerns. Calibration provides a mechanism that explains how collaboration without consensus is possible. Crucially, it does not presuppose that interdisciplinary collaboration either indicates (...) or causes the blurring or softening of disciplinary boundaries. We argue that at the microsociological level it may be disciplinary robustness, rather than synthesis, that contributes to interdisciplinary innovation. (shrink)

Variable-Value axiologies avoid Parfit's Repugnant Conclusion while satisfying some weak instances of the Mere Addition principle. We apply calibration methods to two leading members of the family of Variable-Value views conditional upon: first, a very weak instance of Mere Addition and, second, some plausible empirical assumptions about the size and welfare of the intertemporal world population. We find that such facts calibrate these two Variable-Value views to be nearly totalist, and therefore imply conclusions that should seem repugnant to anyone (...) who opposes Total Utilitarianism only due to the Repugnant Conclusion. (shrink)

Systematic reviews call for improved strategies of evaluating and communicating the degree of certainty and quality of a given body of scientific information. Drawing on environmental assessment projects, the paper argues, that building, promoting and maintaining trust in scientific methodology and outcomes, first of all, is a necessary prerequisite of scientific practice itself. Especially, as the paper shall illustrate, when the systematic review process expands across multiple disciplines, and brings authors from a broad range of research traditions to the table. (...) In 2010 the Intergovernmental Panel on Climate Change (IPCC), the international body for assessing the science related to climate change, introduced a revised process of evaluating the degree of certainty in key findings. Drawing on the growing attention towards the reporting of confidence levels, the paper explores recent frameworks in large-scale assessment projects and their merit in calibrating trust in the sciences. (shrink)

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is (...) linearized for the convenience of the following identification. The singular value decomposition is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter and regularized particle filter hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved. (shrink)

Abner Shimony (1988) argues that degrees of belief satisfy the axioms of probability because their epistemic goal is to match estimates of objective probabilities. Because the estimates obey the axioms of probability, degrees of belief must also obey them to reach their epistemic goal. This calibration argument meets some objections, but with a few revisions it can surmount those objections. It offers a good alternative to the Dutch book argument for compliance with the probability axioms. The defense of Shimony's (...) calibration argument examines rational pursuit of an epistemic goal, introduces strength of evidence and its measurement, and distinguishes epistemic goals and functions. (shrink)

The assessment of models in an experiment depends on their material nature and their function in the experiment. Models that are used to make the phenomenon under investigation visible - sensors - are assessed by calibration. However, calibration strategies assume material intervention. The experiment discssed in this paper is an experiment in economics to measure the influence of technology shocks on business cycles. It uses immaterial, mathematical instruments. It appears that calibration did not work for these kinds (...) of models, it did not provide reliable evidence for the facts of the business cycle. (shrink)

Jablonka & Lamb (J&L) have presented a number of different possible mechanisms for finessing design. The extra-genetic nature of these mechanisms has led them to challenge orthodox neo-Darwinian views. However, these mechanisms are for calibration and have been designed by natural selection. As such, they add detail to our knowledge, but neo-Darwinism is sufficiently resourced to account for them.

We introduce and study the D-model, which reflects the simplest situation in which one wants to calibrate an observable. We discuss the question of representing the statistics of the D-model in the context of an effect algebra.

Our (full) beliefs can have, or fail to have, a significant epistemic virtue: they can be true. What about our partial beliefs — that is, credences or subjective probabilities? Is there an epistemic virtue that credences can have or fail to have, whose nature or role with respect to credences is analogous to the role that truth has with respect to full beliefs? (This virtue can be called 'vindication', since it is natural to say that a belief that is revealed (...) to be correct has been vindicated.) Van Fraassen argued in the 1980s that there is indeed such an analog virtue, and he claimed that it is calibration: our credences should match, or be in tune with, some appropriately chosen frequencies of events. For example, if your credence in the proposition 'It will rain today' is 0.3 for any day that starts out like today did, and in fact it does rain on approximately 30% of such days, then your credence level is well-calibrated, and is vindicated by the actual frequency of rain. In an unpublished paper Alan Hájek rejects this calibrationist answer, and proposes instead that having credences in agreement with the relevant objective chances is what it is to have vindicated credences, in the relevant sense analogous to truth for full beliefs. Although there are problems that afflict both van Fraassen's and Hájek's proposals, I will argue that Hájek and the calibrationist are both largely right — although the calibrationist answer is at bottom more right. In section 4 I will propose an account of being in tune with the world that divides types of propositions into a few different classes; for one of those classes, Hájek's answer is exactly right, while for the others the calibrationist account (when correctly spelled out) gives the right answer. (shrink)

The signals in numerous complex systems of engineering can be regarded as nonlinear parameter trend with noise which is identically distributed random signals or deterministic stationary chaotic signals. The commonly used methods for parameter estimation of nonlinear trend in signals are mainly based on least squares. It can cause inaccurate estimation results when the noise is complex. This paper proposes a calibration method for this issue in the case of single parameter via nonstationarity measure from the perspective of the (...) stationarity of residual sequence. Some numerical studies are conducted for validation. Results of numerical studies show that the proposed calibration method performs well for various models with different noise strengths and types and can significantly improve the accuracy of initial estimates obtained by least squares method. This is the first time that the nonstationarity measure is applied to the parameter calibration. All these results will be a guide for future studies of other parameter calibrations. (shrink)

We argue that concerns about double-counting -- using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate --deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood (...) approach to incremental confirmation. According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. (shrink)