Racial capitalism depends on the reproduction of an existing racialized economic order. In this article, I argue that the disavowal of past injustice is a central way in which this reproduction is ensured and that market-based forms of knowledge production, such as for-profit predictive practices, play a crucial role in facilitating this disavowal. Recent debates about the fairness of algorithms, data justice, and predictive policing have intensified long-standing controversies, both popular and academic, about the way in which statistical and financial (...) modes of accounting and predicting articulate, represent and produce ascriptive categories of hierarchically ordered social difference, and reproduce unjust social hierarchies and inequalities. These debates have productively problematized the racial lives of seemingly apolitical predictive technologies and demanded the re-politicization of predictive practices. What has been missing from these debates so far, however, is a more explicit engagement with ways in which anti-racist movements and activists themselves have contested the entanglements of prediction and race making. I turn to a recent prominent example, namely the contestation over racial discrepancies in subprime lending to examine how fair lending activists have conceptualized and troubled the reproduction of a racialized economic order through for-profit predictive practices in the decade before the Great Financial Crisis. I situate this particular example in the broader historical and political context of politicizing prediction that first emerged with the ascendancy of a liberal, individualist-proprietary conception of risk, and the political problem space to which this has given rise. My analysis shows that actuarial conceptions of fairness continue to reverberate in anti-racist contestations of for-profit predictive practices, and that they tend to marginalize and undercut more radical strands of critique of the racialization of financial markets. Insofar as these modalities of contestation implicitly reproduce a liberal, proprietary-individualist conception of risk, I argue, they fail to effectively challenge the quasi-alchemical transformation of injustice into personal responsibility, and thus contribute to the disavowal of past injustice. (shrink)

Are predictions about how one will freely and intentionally behave in the future ever relevant to how one ought to behave? There is good reason to think they are. As imperfect agents, we have responsibilities of self-management, which seem to require that we take account of the predictable ways we're liable to go wrong. I defend this conclusion against certain objections to the effect that incorporating predictions concerning one's voluntary conduct into one's practical reasoning amounts to evading responsibility for that (...) conduct. There is, however, some truth to this sort of objection. To understand the legitimate role of self-prediction in practical reasoning, we need to distinguish instances of coping responsibly with an anticipated failure to behave as one ought, on the one hand, from mere acquiescence in one's flaws, on the other. I argue that, to draw this distinction, we must recognize certain limits on the use of self-prediction as a ground of choice. (shrink)

It is a popular view thatpractical deliberation excludes foreknowledge of one's choice. Wolfgang Spohn and Isaac Levi have argued that not even a purely probabilistic self-predictionis available to thedeliberator, if one takes subjective probabilities to be conceptually linked to betting rates. It makes no sense to have a betting rate for an option, for one's willingness to bet on the option depends on the net gain from the bet, in combination with the option's antecedent utility, rather than on the offered (...) odds. And even apart from this consideration, assigning probabilities to the options among which one is choosing is futile since such probabilities could be of no possible use in choice. The paper subjects these arguments to critical examination and suggests that, appearances notwithstanding, practical deliberation need not crowd outself-prediction. (shrink)

Rid and Wendler propose the development of a Patient Preference Predictor (PPP), an actuarial model for predicting incapacitated patient’s life-sustaining treatment preferences across a wide range of end-of-life scenarios. An actuarial approach to end-of-life decision making has enormous potential, but transferring the logic of actuarial prediction to end-of-life decision making raises several conceptual complexities and logistical problems that need further consideration. Actuarial models have proven effective in targeted prediction tasks, but no evidence supports their effectiveness in the kind of broad (...) spectrum prediction task that is the proposed goal of the PPP. We argue that a more focused approach, targeting specific medical conditions and generating treatment predictions based on the preferences of individuals with actual disease experience, is both more firmly grounded in past research and is a more prudent initial strategy for exploring the efficacy of actuarial prediction in end-of-life decision making. (shrink)

Artificial intelligence (AI) systems are quickly gaining ground in healthcare and clinical decision-making. However, it is still unclear in what way AI can or should support decision-making that is based on incapacitated patients’ values and goals of care, which often requires input from clinicians and loved ones. Although the use of algorithms to predict patients’ most likely preferred treatment has been discussed in the medical ethics literature, no example has been realised in clinical practice. This is due, arguably, to the (...) lack of a structured approach to the epistemological, ethical and pragmatic challenges arising from the design and use of such algorithms. The present paper offers a new perspective on the problem by suggesting that preference predicting AIs be viewed as sociotechnical systems with distinctive life-cycles. We explore how both known and novel challenges map onto the different stages of development, highlighting interdisciplinary strategies for their resolution. (shrink)

A self-fulfilling prophecy is, roughly, a prediction that brings about its own truth. Although true predictions are hard to fault, self-fulfilling prophecies are often regarded with suspicion. In this article, we vindicate this suspicion by explaining what self-fulfilling prophecies are and what is problematic about them, paying special attention to how their problems are exacerbated through automated prediction. Our descriptive account of self-fulfilling prophecies articulates the four elements that define them. Based on this account, we begin our critique by showing (...) that typical self-fulfilling prophecies arise due to mistakes about the relationship between a prediction and its object. Such mistakes—along with other mistakes in predicting or in the larger practical endeavor—are easily overlooked when the predictions turn out true. Thus we note that self-fulfilling prophecies prompt no error signals; truth shrouds their mistakes from humans and machines alike. Consequently, self-fulfilling prophecies create several obstacles to accountability for the outcomes they produce. We conclude our critique by showing how failures of accountability, and the associated failures to make corrections, explain the connection between self-fulfilling prophecies and feedback loops. By analyzing the complex relationships between accuracy and other evaluatively significant features of predictions, this article sheds light both on the special case of self-fulfilling prophecies and on the ethics of prediction more generally. (shrink)

The quest to foretell the future is omnipresent in human affairs. A potential solution to this epistemological conundrum has emerged through mass collaboration. Motored by the Internet, prediction markets allow a multitude of individuals to assume a stake in a security whose value is tied to a future event. The resulting prices offer a continuously updated probability estimate of the event actually taking place. This paper gives a survey of prediction markets, their history, mechanics, uses, and theoretical foundation. We also (...) review the literature surrounding their efficacy. Though there are shortcomings with prediction markets, as well as practical constraints, they hold out the prospect of improving the quality of organizational decisions and increasing the level of participation in the deliberative process. We also note that lessons can be drawn from these markets to guide the epistemological practices of disciplines and inquiries in which empirical methods are difficult to apply. (shrink)

A new theory of the mind, the predictive processing model, is ascendant in recent work in cognitive science. According to this theory, all the mind ever fundamentally does is make hypotheses about the environment, generate prediction-errors by comparing its predictions with its sensory data, and use these prediction-errors to update its representation of the world. The theory of motivation and action to which the predictive processing model is committed has been the subject of lively debate in the literature. However, the (...) upshots of the predictive processing model for the theory of practical reason have received relatively scant attention. Here I investigate how proponents of predictive processing should conceive of practical reason. The predictive processing model has it that the practical attitudes – desires and intentions etc. – reduce to certain theoretical attitudes – in particular, to beliefs and predictions. I argue that this psychological reduction precipitates a broader normative reduction, in which practical reasoning reduces to a kind of theoretical reasoning about what you will do, and in which practical normativity turns out to really be a species of epistemic normativity. In other words, that practical reason reduces to theoretical reason. If I am correct, the predictive processing model of the mind is committed to – what I am dubbing – a ‘radical cognitivism about practical reason’. (shrink)

This study focuses on the prediction of the engagement of small- and medium-sized enterprises (SMEs) in environmental management practices, based on a random sample of 689 SMEs. The study finds that several endogenous factors, including tangibility of sector, firm size, innovative orientation, family influence and perceived financial benefits from energy conservation, predict an SME’s level of engagement in selected environmental management practices. For family influence, this effect is found only in interaction with the number of owners. In addition to empirical (...) research on SMEs’ environmental behavior, this article draws on the ecological modernization literature as well as the theory of planned behavior. (shrink)

Two hundred and twenty-six state employees completed a structured questionnaire that investigated their ethical values and training needs. Top management were more likely to have attitudes against cronyism and giving advantage to others. Individuals higher in the organizational hierarchy, and female employees were more likely to believe that discriminatory practices were an ethical concern. In addition, employees with a larger number of clients outside of the organization were more supportive of the need to maintain strict confidentiality in business dealings. Employees'' (...) awareness and use of the organization''s code of conduct generally proved to be poor predictors of ethical values. Other analyses revealed that a variety of sociodemographic factors, job characteristics and ethical values predicted specific areas of training needs in ethics. (shrink)

In earlier work ( Cleland [2001] , [2002]), I sketched an account of the structure and justification of ‘prototypical’ historical natural science that distinguishes it from ‘classical’ experimental science. This article expands upon this work, focusing upon the close connection between explanation and justification in the historical natural sciences. I argue that confirmation and disconfirmation in these fields depends primarily upon the explanatory (versus predictive or retrodictive) success or failure of hypotheses vis-à-vis empirical evidence. The account of historical explanation that (...) I develop is a version of common cause explanation. Common cause explanation has long been vindicated by appealing to the principle of the common cause. Many philosophers of science (e.g., Sober and Tucker) find this principle problematic, however, because they believe that it is either purely methodological or strictly metaphysical. I defend a third possibility: the principle of the common cause derives its justification from a physically pervasive time asymmetry of causation (a.k.a. the asymmetry of overdetermination). I argue that explicating the principle of the common cause in terms of the asymmetry of overdetermination illuminates some otherwise puzzling features of the practices of historical natural scientists. (shrink)

Prediction markets, which trade contracts based on the results of predictions, have been remarkably successful in predicting the results of political events. A number of proposals have been made to extend prediction markets to scientific questions, and some small-scale science prediction markets have been implemented. Advocates for science prediction markets argue that they could alleviate problems in science such as bias in peer review and epistemically unjustified consensus. I argue that bias in peer review and epistemically unjustified consensuses are genuine (...) problems in science, and that current attempts to solve them fail in practical circumstances. Prediction markets do show some promise for answering scientific questions while avoiding the pitfalls of consensus and peer review. However, there are strong reasons to believe that science prediction markets will not perform nearly so well as election prediction markets, and implementing prediction markets on a large scale could have significant unintended consequences for the organization of scientific research. The promise of prediction markets to solve current problems in assessing scientific claims is therefore largely illusory. (shrink)

Recent neo-Humean theories of laws of nature have placed substantial emphasis on the characteristic epistemic roles played by laws in scientific practice. In particular, these theories seek to understand laws in terms of their optimal predictive utility to creatures in our epistemic situation. In contrast to other approaches, this view has the distinct advantage that it is able to account for a number of pervasive features possessed by putative actual laws of nature. However, it also faces some unique challenges. First, (...) since the view tries to characterize the laws in terms of their predictive utility, any respects in which putative actual laws are sub-optimally predictively useful are inherently problematic. Such "predictive infelicities" can easily be found among our best physical theories. Second, in tying the laws to our epistemic situation, neo-Humeanism raises the possibility that by changing our epistemic situation, we can change the laws themselves, though it is hard to believe that we have such influence over the laws. This paper aims to address both of these challenges, first by presenting a variety of strategies for explaining away predictive infelicities, and then by developing a version of Lewis's "rigidification" strategy to preclude the possibility of our changing the laws. (shrink)

In recent years, the biomedical field has witnessed the emergence of novel tools and modelling techniques driven by the rise of the so-called Big Data. In this paper, we address the issue of predictability in biomedical Big Data models of cancer patients, with the aim of determining the extent to which computationally driven predictions can be implemented by medical doctors in their clinical practice. We show that for a specific class of approaches, called k-Nearest Neighbour algorithms, the ability to draw (...) predictive inferences relies on a geometrical, or topological, notion of similarity encoded in a well-defined metric, which determines how close the characteristics of distinct patients are on average. We then discuss the conditions under which the relevant models can yield reliable and trustworthy predictive outcomes. (shrink)

This paper examines the practice of using predictive systems to lengthen the prison sentences of convicted persons when the systems forecast a higher likelihood of re-offense or re-arrest. There has been much critical discussion of technologies used for sentencing, including questions of bias and opacity. However, there hasn’t been a discussion of whether this use of predictive systems makes sense in the first place. We argue that it does not by showing that there is no plausible theory of punishment that (...) supports it. (shrink)

This article addresses the gap between what works in research and what works in practice. Currently, research in evidence-based education policy and practice focuses on randomized controlled trials. These can support causal ascriptions (“It worked”) but provide little basis for local effectiveness predictions (“It will work here”), which are what matter for practice. We argue that moving from ascription to prediction by way of causal generalization (“It works”) is unrealistic and urge focusing research efforts directly on how to build local (...) effectiveness predictions. We outline various kinds of information that can improve predictions and encourage using methods better equipped for acquiring that information. We compare our proposal with others advocating a better mix of methods, like implementation science, improvement science, and practice-based evidence. (shrink)

Predictive genetic testing for susceptibility to psychiatric conditions is likely to become part of standard practice. Because the onset of most psychiatric diseases is in late adolescence or early adulthood, testing minors could lead to early identification that may prevent or delay the development of these disorders. However, due to their complex aetiology, psychiatric genetic testing does not provide the immediate medical benefits that current guidelines require for testing minors. While several authors have argued non-medical benefits may play a crucial (...) role in favour of predictive testing for other conditions, little research has explored such a role in psychiatric disorders. This paper outlines the potential non-medical benefits and harms of psychiatric genetic testing in minors in order to consider whether the non-medical benefits could ever make such testing appropriate. Five non-medical themes arise in the literature: psychological impacts, autonomy/self-determination, implications of the biomedical approach, use of financial and intellectual resources, and discrimination. Non-medical benefits were prominent in all of them, suggesting that psychiatric genetic testing in minors may be appropriate in some circumstances. Further research needs to empirically assess these potential non-medical benefits, incorporate minors in the debate, and include normative reflection to evaluate the very purposes and motivations of psychiatric genetic testing in minors. (shrink)

Professionals often believe that they must “exercise judgment” in making decisions critical to other people’s lives. The relative superiority of statistical prediction rules to intuitive judgment for combining incomparable sources of information to predict important human outcomes leads us to question this personal input belief. Some professionals hence use SPR’s to “educate” intuitive judgment, rather than replace it. In psychology in particular, such amalgamation is not justified. If a well‐validated SPR that is superior to professional judgment exists in a relevant (...) decision making context, professionals should use it, totally absenting themselves from the prediction. (shrink)

Might practical reason be a species of theoretical reason? Can we make sense of practical deliberation as a special kind of theoretical cogitation over what you will do? The prospects of such a reduction may appear dim: it seems like it is one thing to be weighing up what you should (intend to) do, in light of your various reasons for action, and quite another thing altogether to be figuring out what you should believe you will do, in light of (...) your evidence. Here I aim to show how this reduction of our powers of practical reason to a branch of theoretical reason might be defensibly pulled off. On the view to be defended, your intentions to act are really beliefs about what you are will do, practical reasoning is nothing over and above the enterprise of predicting what you are going to do in light of evidence alone, and the constitutive aim of action turns out to be the same as the constitutive object of theoretical reason – namely, to believe the truth. (shrink)

According to predictive processing, an increasingly influential paradigm in cognitive science, the function of the brain is to minimize the prediction error of its sensory input. Conceptual engineering is the practice of assessing and changing concepts or word meanings. We contribute to both strands of research by proposing the first cognitive account of conceptual engineering, using the predictive processing framework. Our model reveals a new kind of implementation problem as prediction errors are only minimized if enough agents embrace conceptual changes. (...) This problem can be overcome by emphasizing the importance ofsocial normsandconceptual pluralism. (shrink)

This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models, Chi-squared automatic interaction detector, linear regression, and neural network ). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the profitability ratios, (...) managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios. (shrink)

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with (...) the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications. (shrink)

Big Data are increasingly used in machine learning in order to create predictive models. How are predictive practices that use such models to be situated? In the field of surveillance studies many of its practitioners assert that “governance by discipline” has given way to “governance by risk”. The individual is dissolved into his/her constituent data and no longer addressed. I argue that, on the contrary, in most of the contexts where predictive modelling is used, it constitutes Foucauldian discipline. Compliance to (...) a norm occupies centre stage; suspected deviants are subjected to close attention—as the precursor of possible sanctions. The predictive modelling involved uses personal data from both the focal institution and elsewhere. As a result, the individual re-emerges as the focus of scrutiny. Subsequently, small excursions into Foucauldian texts discuss his discourses on the creation of the “delinquent”, and on the governmental approach to smallpox epidemics. It is shown that his insights only mildly resemble prediction as based on machine learning; several conceptual steps had to be taken for modern machine learning to evolve. Finally, the options available to those subjected to predictive disciplining are discussed: to what extent can they comply, question, or resist? Through a discussion of the concepts of transparency and “gaming the system” I conclude that our predicament is gloomy, in a Kafkaesque fashion. (shrink)

An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called (...) ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios. (shrink)

Predictive Sentencing addresses the role of risk assessment in contemporary sentencing practices. Predictive sentencing has become so deeply ingrained in Western criminal justice decision-making that despite early ethical discussions about selective incapacitation, it currently attracts little critique. Nor has it been subjected to a thorough normative and empirical scrutiny. This is problematic since much current policy and practice concerning risk predictions is inconsistent with mainstream theories of punishment. Moreover, predictive sentencing exacerbates discrimination and disparity in sentencing. Although structured risk assessments (...) may have replaced 'gut feelings', and have now been systematically implemented in Western justice systems, the fundamental issues and questions that surround the use of risk assessment instruments at sentencing remain unresolved. This volume critically evaluates these issues and will be of great interest to scholars of criminal justice and criminology. (shrink)

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

The occurrence of earthquakes is usually ignored or discounted as an environmental issue, but the environmental relevance of the science of earthquake prediction is demonstrable. The social consequences of such predictions, when they are accurate, and even (once a general pattern of accuracy has been achieved) when they fail, have implications of such varied environmental issues as land-use control, building codes, social and economic costs (for predictions made when no earthquake occurs or for failures to predict earthquakes which do occur). (...) Lay members of the public are more directly involved in programs of earthquake prediction than in almost all other instances of scientific prediction, if only because the scientific findings require public participation in order to have any effect at all. Attention must be paid, accordingly, to the effect of specific public and social values on the practice of earthquake prediction-ranging from such broadly based ones as conceptions of the general relation between man and nature to narrower ones like the cost-benefit analysis of a program of earthquake prediction itself. Because of the close connection between the efficacy of earthquake prediction and public attitudes, moreover, certain questions concerning the social character of “normal” science and the deprofessionalization of scientific institutions are highlighted in this context. (shrink)

In this paper I argue that, by combining eliminativist and fictionalist approaches toward the sub-personal representational posits of predictive processing, we arrive at an empirically robust and yet metaphysically innocuous cognitive scientific framework. I begin the paper by providing a non-representational account of the five key posits of predictive processing. Then, I motivate a fictionalist approach toward the remaining indispensable representational posits of predictive processing, and explain how representation can play an epistemologically indispensable role within predictive processing explanations without thereby (...) requiring that representation metaphysically exists. Finally, I outline four consequences of accepting this approach and explain why they are beneficial: we arrive at a victory for metaphysical eliminativism in the ‘representation wars’; my account fits with extant empirical practice; my account provides guidance for future research; and, my account provides the beginnings of a response to Mark Sprevak’s IBE problem for fictionalist approaches toward sub-personal representation. (shrink)

The considerations set out in the paper are intended to suggest that in practical contexts predictive power does not play the outstanding roles sometimes accredited to it in an epistemic framework. Rather, predictive power is part of a network of other merits and achievements. Predictive power needs to be judged differently according to the specific conditions that apply. First, predictions need to be part of an explanatory framework if they are supposed to guide actions reliably. Second, in scientific expertise, the (...) demand for accurate predictions is replaced with the objective of specifying a robust corridor of estimates. Finally, it is highly uncertain to predict the success of research projects. The overall purpose of the paper is to enlarge the debate about predictions by addressing specifically the roles of predictions in application-oriented research. (shrink)

Internal misconduct is a universal problem in prisons and affects the maintenance of social order. Consequently, correctional institutions often develop rehabilitation programs to reduce the likelihood of inmates committing internal offenses and criminal recidivism after release. Therefore, it is necessary to identify the profile of each offender, both for the appropriate indication of a rehabilitation program and the level of internal security to which he must be submitted. In this context, this work aims to discover the most significant characteristics in (...) predicting inmate misconduct from ML methods and the SHAP approach. A database produced in 2004 through the Survey of Inmates in State and Federal Correctional Facilities in the United States of America was used, which provides nationally representative data on prisoners from state and federal facilities. The predictive model based on Random Forest performed the best, thus, we applied the SHAP to it. Overall, the results showed that features related to victimization, type of crime committed, age and age at first arrest, history of association with criminal groups, education, and drug and alcohol use are most relevant in predicting internal misconduct. Thus, it is expected to contribute to the prior classification of an inmate on time, to use programs and practices that aim to improve the lives of offenders, their reintegration into society, and consequently, the reduction of criminal recidivism. (shrink)

This essay argues that prediction markets, as one approach for aggregating dispersed private information, may not only be praised for their epistemic accuracy. They also feature characteristics that are morally desirable from the point of view of epistemic justice. Notably, they are a promising approach when we are trying to address testimonial injustice. The impersonality of market transactions effectively tackles the issue of identity prejudice, which underlies many forms of testimonial injustice. This is not to say that prediction markets do (...) not pose challenges for epistemic justice of their own like the risk of excluding knowers based on ability to pay. Or, in light of identity prejudice’s systematicity, the risk of discouraging some knowers because of differences in education or social background that lead to a certain unease when faced with prediction markets. The essay discusses these challenges, concedes limitations but also offers countermeasures to certain worries – e.g. hosting prediction markets on a play-money basis. Throughout, the essay addresses the question of the potential scope of using prediction markets for epistemic purposes, notably by means of the practical example of a prediction market for monetary policy. (shrink)

Abstractabstract:Expectations about future events underlie practically every decision we make, including those in medical research. This paper reviews five studies undertaken to assess how well medical experts could predict the outcomes of clinical trials. It explains why expert trial forecasting was the focus of study and argues that forecasting skill affords insights into the quality of expert judgment and might be harnessed to improve decision-making in care, policy, and research. The paper also addresses potential criticisms of the research agenda and (...) summarizes key findings from the five studies of trial forecasting. Together, the studies suggest that trials frequently deliver surprising results to expert communities and that individual experts are often uninformative when it comes to forecasting trial outcome and recruitment. However, the findings also suggest that expert forecasts often contain a "signal" about whether a trial will be positive, especially when forecasts are aggregated. The paper concludes with needs for further research and tentative policy recommendations. (shrink)

Shared activity is often simply willed into existence by individuals. This poses a problem. Philosophical reflection suggests that shared activity involves a distinctive, interlocking structure of intentions. But it is not obvious how one can form the intention necessary for shared activity without settling what fellow participants will do and thereby compromising their agency and autonomy. One response to this problem suggests that an individual can have the requisite intention if she makes the appropriate predictions about fellow participants. I argue (...) that implementing this predictive strategy risks derailing practical reasoning and preventing one from forming the intention. My alternative proposal for reconciling shared activity with autonomy appeals to the idea of acting directly on another's intention. In particular, I appeal to the entitlement one sometimes has to another's practical judgment, and the corresponding authority the other sometimes has to settle what one is to do. (shrink)

Develops a general theory of prediction that encompasses its fundamental principles, methodology, and practice and gives an overview of its promises and problems.

Recent meta-studies have shown that there are no known risk factors for suicidal behavior that could be used for behavior prediction, rendering the classic understanding of high and low risk for suicide questionable. The aim of this manuscript is to propose a new understanding of suicide risk that does not depend on predictive powers. In contrast, the developed Normic account of suicide risk, which is based on a possible world framework rather than probability calculations, suggests that the normalcy of an (...) event determines whether the risk for the event’s occurrence is high or low. I suggest that this normalcy stems from the norms of practical rationality for agents who consider self-killing. The more difficult it is to make sense of possible intentional self-killing, the more abnormal the event is, which renders the event less risky. Consequently, from the Normic account of suicide risk, an individual who has weak or no life-sustaining reasons is at high risk for suicide. In conclusion, although the prediction of suicide is beyond the current scope of clinical psychology, there are still reasonable options for understanding what distinguishes individuals at high risk from those at low risk for suicide. (shrink)

Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role of orthodox (...) economic theory. Second, a conditional predictivism: if we are not committed to orthodox economic theory, then we should demand prediction rather than accommodation – against most current practice. (shrink)

A common type of inductive problem is to predict the nature of an unobserved finite sample of a given population on the basis of an observed finite sample of the same population. More precisely, given a class of events A, we examine a sample Sn having n members, of which mi belong to the class Bi. On the basis of our knowledge that mi/n of Sn have been Bi, we attempt to predict the ratio of members of Bi to members (...) of A in a sample Sr containing r unobserved members of A. This type of inference has been called “predictive inference” by Carnap. It is not necessary to argue that all inductive problems reduce to this form; we merely observe that such problems frequently arise, and that they have practical and theoretical importance. In The Short Run I suggested that this kind of problem could be handled by first determining the long run probability P of Bi, relative to A of Bi, to A in the observed sample Sn) and then predicting that the relative frequency of Bi within the unobserved sample Sr will approximate P sufficiently for practical purposes. The foregoing procedure requires a rule of each of two types: a rule for the ascertainment of the values of long run probabilities, and a rule for the application of knowledge of long run probabilities to predictions in the short run. The Short Run was not concerned with the nature and justification of rules of the first type, but rather sought to justify a rule of the second type on the assumption that we already possess knowledge of long run probabilities. (shrink)

The distinction between explanation and prediction has received much attention in recent literature, but the equally important distinction between explanation and description (or between prediction and description) remains blurred. This latter distinction is particularly important in the social sciences, where probabilistic models (or theories) often play dual roles as explanatory and descriptive devices. The distinction between explanation (or prediction) and description is explicated in the present paper in terms of information theory. The explanatory (or predictive) power of a probabilistic model (...) is identified with information taken from (or transmitted by) the environment (e.g., the independent, experimentally manipulated variables), while the descriptive power of a model reflects additional information taken from (or transmitted by) the data. Although information is usually transmitted by the data in the process of estimating parameters, it turns out that the number of free parameters is not a reliable index of transmitted information. Thus, the common practice of treating parameters as degrees-of-freedom in testing probabilistic models is questionable. Finally, this information-theoretic analysis of explanation, prediction, and description suggests ways of resolving some recent controversies surrounding the pragmatic aspects of explanation and the so-called structural identity thesis. (shrink)

Despite the popularity of the employment interview in the employee selection literature and organizational talent selection process, few have examined the comments interviewers give after each interview. This study investigated the predictability of the match between interviewer post-interview notes and radar charts from job analysis on the candidate’s later career performance using text mining techniques and data from one of the largest internet-based technology companies in China. A large sample of 7,650 interview candidates who passed the interviews and joined the (...) company was obtained to show that the number of job-related capabilities interviewers mentioned in their notes was positively related to candidate’s job performance, the number of promotions, and negatively related to turnover. Moreover, the dimensions of the radar chart from job analysis covered in the interview moderated the predictability of interview post-interview notes. Our results indicated that a smaller number of radar chart dimensions by which interviewers assessed the candidates in the interview positively moderated candidates’ promotion for product development jobs, and negatively moderated turnover for technical jobs. The implications of these results for structured interview research in both theory and practice are discussed. (shrink)

In this paper we present a discussion of the basic aspects of the well-known problem of prediction and inference in physics, with specific attention to the role of models, the use of data and the application of recent developments in artificial intelligence. By focussing in the time evolution of dynamic system, it is shown that main difficulties in predictions arise due to the presence of few factors as: the occurrence of chaotic dynamics, the existence of many variables with very different (...) characteristic time-scales and the lack of an accurate understanding of the underlying physical phenomena. It is shown that a crucial role is assigned to the preliminary identification of the proper variables, their selection and the identification of an appropriate level of description (coarse-graining procedure). Moreover, it is discussed the relevance, even in modern practical issues, of old well-known fundamental results, like the Poincaré recurrence theorem, the Kac’s lemma and the Richard’s paradox. (shrink)