Of three types of evidence available to evolution theorists – comparative, continuity, and computational – the first is largely productive rather than predictive. Although comparison between extant species or languages is possible and can be suggestive of evolutionary processes, leading to theory development, comparison with extinct species and languages seems necessary for validation. Continuity and computational evidence provide the best opportunities for supporting predictions.

Criteria of empirical significance are supposed to state conditions under which reference to an unobservable object or property is “empirically meaningful.” The intended kind of empirical meaningfulness should be necessary for admissibility into the selective contexts of scientific inquiry. I defend Justus’s recent argument that the reasons generally given for rejecting the project of defining a significance criterion are unpersuasive. However, as I show, this project remains wedded to an overly narrow conception of its subject matter. Even the most cutting (...) edge significance criteria identify empirical significance with predictive power, and thereby rule out vocabulary with legitimate scientific functions. In a nutshell, the problem is that there are terms that reduce the computational burden of extracting predictions from theory, and that may therefore be scientifically useful, but that do not add to the theory’s observational consequences, and so are ruled scientifically inadmissibility by existing significance criteria. I spell out this objection by specifying shortcut terms that are ruled inadmissible by Creath’s and Schurz’s criteria. Having objected in this way to extant criteria, and to the equation of empirical significance with predictive power in general, I discuss an approach to defining empirical significance that is capable of avoiding my objection and, more ambitiously, that may break the cycle of “punctures and patches” that has plagued the project from the beginning. I gloss Goldfarb and Ricketts’s idea of “case-by-case” delineations of empirically significant terms as the provision of special rather than general explications of the informal concept of empirical significance. (shrink)

Criteria of empirical significance are supposed to state conditions under which reference to an unobservable object or property is “empirically meaningful”. The intended kind of empirical meaningfulness should be necessary for admissibility into the selective contexts of scientific inquiry. I defend Justus’s recent argument that the reasons generally given for rejecting the project of defining a significance criterion are unpersuasive. However, as I show, this project remains wedded to an overly narrow conception of its subject matter. Even the most cutting (...) edge significance criteria identify empirical significance with predictive power, and thereby rule out vocabulary with legitimate scientific functions. In a nutshell, the problem is that there are terms that reduce the computational burden of extracting predictions from theory, and that may therefore be scientifically useful, but that do not produce any additional predictions, and so are ruled scientifically inadmissibility by existing significance criteria. I spell out this objection by specifying terms of this kind that are ruled inadmissible by Creath’s and Schurz’s criteria. Having objected in this way to extant criteria, and to the equation of empirical significance with predictive power in general, I discuss an approach to defining empirical significance that is capable of avoiding my objection and, more ambitiously, that may break the cycle of “punctures and patches” that has plagued the project from the beginning: I gloss Goldfarb and Ricketts’s idea of “case-by-case” delineations of empirically significant terms as the provision of special rather than general explications of the informal concept of empirical significance. (shrink)

Many apparently complex mechanisms in biology, especially in embryology and molecular biology, can be explained easily by reasoning at the level of the “efficient cause” of the observed phenomenology: the mechanism can then be explained by a simple geometrical argument or a variational principle, leading to the solution of an optimization problem, for example, via the co-existence of a minimization and a maximization problem . Passing from a microscopic level to the macroscopic level often involves an averaging effect that gives (...) birth to a global functional feature . We will illustrate these general principles by building in four different domains of application “a minima” models and showing the main properties of their solutions: extraction of a minimal RNA structure functioning as the first “peptidic machine,” a kind of ancestral ribosome; study of a genetic regulatory network of Drosophila centred on Engrailed gene and expressing successively two genes inside a limit cycle; study of a genetic network regulating neural activity and proliferation in mammals; and study of a simple geometric model of epiboly in zebrafish. (shrink)

According to standard philosophical and clinical understandings, pain is an essentially mental phenomenon (typically, a kind of conscious experience). In a challenge to this standard conception, a recent burst of empirical work in experimental philosophy, such as that by Justin Sytsma and Kevin Reuter, purports to show that people ordinarily conceive of pain as an essentially bodily phenomenon—specifically, a quality of bodily disturbance. In response to this bodily view, other recent experimental studies have provided evidence that the ordinary (‘folk’) concept (...) of pain is more complex than previously assumed: rather than tracking only bodily or only mental aspects of pain, the ordinary concept of pain can actually track either of these aspects. The polyeidic (or ‘many ideas’) analysis of the folk concept of pain, as proposed by Emma Borg et al., captures this complexity. Whereas previous empirical support for the polyeidic view has focused on the context-sensitivity of the folk concept of pain, here we discuss individual differences in people’s ‘pain priors’—namely, their standing tendencies to think of pain in relatively mind-centric or body-centric ways. We describe a preliminary empirical study and present a small number of findings, which will be explored further in future work. The results we discuss are part of a larger programme of work which seeks to integrate philosophical pain research into clinical practice. For example, we hypothesise that variations in how patients with chronic pain are thinking about pain could help predict their responses to treatment. (shrink)

Prepared for NATO conference on Kondratieff Waves and Warfare, Covilha, Portugal, Feb. 2005. Published in T. C. Devezas, ed., Kondratieff Waves, Warfare and World Security. Amsterdam : IOS, 2006. Also online here.: My work in the mid-1980s on Kondratieff waves tried to explain long waves in terms of causal relationships among six main variables : war, production, prices, innovation, investment, and real wages. I emphasized war as a central element ; saw production waves as (...) - Économie et Marxisme – (...) Nouvel article. (shrink)

In his sociological works Pareto developed a theory of cyclical social change within the general equilibrium framework. Building on an earlier propositional formalization, we translate Pareto's theory into a series of simultaneous equations and simulate the equation system. The dynamic behavior of the simulation is consistent with Pareto's predictions and demonstrates the internal logic of the theory.

Disarming the Prophets - ABSTRACT: Kinch Hoekstra takes up another question related to supposed revelatory experience, namely, the claim to prophesy, and, more particularly, why Hobbes’s concern with it grows during the decade after 1640, what varieties of it preoccupy him and what his responses are to them. Of central importance for Hobbes was his contemporaries’ concern with biblical prophecy, both radical and royalist. Trying to pluck its political sting, Hobbes argues that apocalyptic prophecy is a form of madness but (...) more broadly seeks to place the interpretation of scripture solidly within the purview of the sovereign. By blunting the belief in prophecy, Hobbes disarms the prophet. (shrink)

ObjectiveTo examine how physical and psychological childbirth experiences affect maternal perceptions and experiences of early infant behavioural style.BackgroundUnnecessary interventions may disturb the normal progression of physiological childbirth and instinctive neonatal behaviours that facilitate mother–infant bonding and breastfeeding. While little is known about how a medicalised birth may influence developing infant temperament, high impact interventions which affect neonatal crying and cortisol levels could have longer term consequences for infant behaviour and functioning.MethodsA retrospective Internet survey was designed to fully explore maternal experiences (...) of childbirth and her postnatal perceptions of infant behaviour. Data collected from 999 mother–infant dyads were analysed using Pearson’s correlations and multiple analyses of covariance, employing the Bonferroni method of correction to establish initially significant variables. Multiple linear regressions were conducted to determine major perinatal contributors to perceived early infant temperament.ResultsMultiple regression analyses on each of the eight Mother and Baby Scales outcome variables indicated that early infant behavioural style was largely predicted by subjective maternal states during and post-childbirth, postnatal depression scores, maternal personality traits and infant age. For example, infant age was the most significant predictor of Alert-Responsive infant behaviour, followed by maternal Postnatal Positive experience. In contrast, depression scores were the most significant predictor of Unsettled-Irregular infant behaviour, followed by Anxious-Afraid Birth Emotions and infant age. Mothers also perceived their infants as more Alert-Responsive and Easier overall after a Supported birth experience.ConclusionMaternal and infant outcomes were influenced by multiple physical and psychological perinatal variables. The mother’s subjective experience appeared to be of equal significance to more objective factors. Social support enhanced the mother’s childbirth experience, benefitting her perceptions of her baby’s early temperament. These findings provide further support for current World Health Organisation intrapartum guidelines on the importance of making childbirth a ‘positive experience’ for women. (shrink)

The EU Kids Online project aims to enhance knowledge of the experiences and practices of European children and their parents regarding online risks and safety. A crucial research effort by the EU Kids Online network has been a survey in 25 European countries which targeted approximately 1,000 children per country. This article applies a cross-cultural values filter to the data that were gathered on parental mediation and the Internet in this survey. Our intention is to test whether Geert Hofstede’s cross-national (...) research results about national cultural values also apply to the EU Kids Online data on parental mediation. This implies studying collectivism versus individualism, low versus high power distance, masculinity versus femininity and low versus high uncertainty avoidance. We test whether differences between nations on these four dimensions correlate with differences between countries in parental mediation of the Internet and we test which European countries form clusters. (shrink)

According to standard philosophical and clinical understandings, pain is an essentially mental phenomenon. In a challenge to this standard conception, a recent burst of empirical work in experimental philosophy, such as that by Justin Sytsma and Kevin Reuter, purports to show that people ordinarily conceive of pain as an essentially bodily phenomenon—specifically, a quality of bodily disturbance. In response to this bodily view, other recent experimental studies have provided evidence that the ordinary concept of pain is more complex than previously (...) assumed: rather than tracking only bodily or only mental aspects of pain, the ordinary concept of pain can actually track either of these aspects. The polyeidic analysis of the folk concept of pain, as proposed by Emma Borg et al., captures this complexity. Whereas previous empirical support for the polyeidic view has focused on the context-sensitivity of the folk concept of pain, here we discuss individual differences in people’s ‘pain priors’—namely, their standing tendencies to think of pain in relatively mind-centric or body-centric ways. We describe a preliminary empirical study and present a small number of findings, which will be explored further in future work. The results we discuss are part of a larger programme of work which seeks to integrate philosophical pain research into clinical practice. For example, we hypothesise that variations in how patients with chronic pain are thinking about pain could help predict their responses to treatment. (shrink)

Feigning symptoms distorts diagnostic evaluations. Therefore, dedicated tools known as symptom validity tests have been developed to help clinicians differentiate feigned from genuine symptom presentations. While a deviant SVT score is an indicator of a feigned symptom presentation, a non-deviant score provides support for the hypothesis that the symptom presentation is valid. Ideally, non-deviant SVT scores should temper suspicion of feigning even in cases where the patient fits the DSM’s stereotypical yet faulty profile of the “antisocial” feigner. Across three studies, (...) we tested whether non-deviant SVT scores, indeed, have this corrective effect. We gave psychology students and clinical experts a case alluding to the DSM profile of feigning. In successive steps, they received information about the case, among which non-deviant SVT outcomes. After each step, participants rated how strongly they suspected feigning and how confident they were about their judgment. Both students and experts showed suspicion rates around the midpoint of the scale and did not respond to non-deviant SVT outcomes with lowered suspicion rates. In Study 4, we educated participants about the shortcomings of the DSM’s antisocial typology of feigning and the importance of the negative predictive power of SVTs, after which they processed the case information. Judgments remained roughly similar to those in Studies 1–3. Taken together, our findings suggest that students and experts alike have difficulties understanding that non-deviant scores on SVTs reduce the probability of feigning as a correct differential diagnosis. (shrink)

The ability to predict is the most importantability of the brain. Somehow, the cortex isable to extract regularities from theenvironment and use those regularities as abasis for prediction. This is a most remarkableskill, considering that behaviourallysignificant environmental regularities are noteasy to discern: they operate not only betweenpairs of simple environmental conditions, astraditional associationism has assumed, butamong complex functions of conditions that areorders of complexity removed from raw sensoryinputs. We propose that the brain's basicmechanism for discovering such complexregularities is implemented in (...) the dendritictrees of individual pyramidal cells in thecerebral cortex. Pyramidal cells have 5–8principal dendrites, each of which is capableof learning nonlinear input-to-outputtransfer functions. We propose that eachdendrite is trained, in learning its transferfunction, by all the other principal dendritesof the same cell. These dendrites teach eachother to respond to their separate inputs with matching outputs. Exposed to differentbut related information about the sensoryenvironment, principal dendrites of the samecell tune to functions over environmentalconditions that, while different, are correlated . As a result, the cell as awhole tunes to the source of the regularitiesdiscovered by the cooperating dendrites,creating a new representation. When organizedinto feed-forward/feedback layers, pyramidalcells can build their discoveries on thediscoveries of other cells, graduallyuncovering nature's hidden order. Theresulting associative network is powerfulenough to meet a troubling traditionalobjection to associationism: that it is toosimple an architecture to implement rationalprocesses. (shrink)

In recent years, the green aviation technology draws more attention, and more hybrid power units have been applied to the aerial vehicles. To achieve the high performance and long lifetime of components during varied working conditions, the effective regulation of the energy management is necessary for the vehicles with hybrid power unit. In this paper, power prediction-based model predictive control for energy management strategy is proposed for the vehicle equipped with HPU based on turboshaft engine in (...) order to maintain proper battery’s state of charge and decrease turboshaft engine’s exhaust gas temperature. First, a modeling approach based on data-driven method is adopted to obtain the mathematical model of turboshaft engine considering time delay and inertial of states. An integrated power predictor consisting of the classification of input status and the subpredictors are developed based on the deep learning method to improve the accuracy of the prediction model of the model predictive control. Subsequently, an EMS based on MPC using the proposed power predictor is introduced to regulate the SOC of battery and the EGT of turboshaft engine. The comparison with experimental results shows the high accuracy of mathematical model of turboshaft engine. The simulation results show the effectiveness of the proposed EMS for the vehicle, and the effects of different weight coefficients of objective function on the proposed EMS are discussed. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that (...) the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the (...) dataset into multiple clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods. (shrink)

In this study, a hybrid model based on intelligent techniques is developed to predict the active power generated in a bioclimatic house by a low power wind turbine. Contrary to other researches that predict the generated power taking into account the speed and the direction of the wind, the model developed in this paper only uses the speed of the wind, measured mainly in a weather station from the government meteorological agency (MeteoGalicia). The wind speed is measured (...) at different heights, against the usual measurements in others researches, which uses the wind speed and the direction measured in a weather station on the wind turbine nacelle. The prediction is performed 30 minutes ahead, what ensures that the Building Management System knows the energy generated by the low power wind turbine 30 minutes before, and it can adapt the consumption of different equipment in the house to optimize the power use. The main objective is to allow the Building Management System to optimize the uses of energy, taking into account the predicted amount of energy that will be produced and the energy consumed in the house. The developed model uses a hybrid topology with four clusters to improve the prediction, achieving an error lower than 6.5% for Mean Absolute Error measured in a final test. To perform this test, part of the original dataset was isolated from the beginning of the training process to check the model with a dataset that is not used before, simulating the model as it is receiving new data. (shrink)

Humans have undergone a long evolutionary history of violent agonistic exchanges, which would have placed selective pressures on greater body size and the psychophysical systems that detect them. The present work showed that greater body size in humans predicted increased knockout power during combative contests (Study 1a-1b: total N = 5,866; Study 2: N = 44 openweight fights). In agonistic exchanges reflective of ancestral size asymmetries, heavier combatants were 200% more likely to win against their lighter counterparts because they (...) were 200% more likely to knock them out (Study 2). Human dominance judgments (total N = 500 MTurkers) accurately tracked the frequency with which men (N = 516) knocked out similar-sized adversaries (Study 3). Humans were able to directly perceive a man’s knockout power because they were attending to cues of a man’s body size. Human dominance judgments—which are important across numerous psychological domains, including attractiveness, leadership, and legal decision-making—accurately predict the likelihood with which a potential mate, ally, or rival can incapacitate their adversaries. (shrink)

In the present study we asked whether it is possible to decode personality traits from resting state EEG data. EEG was recorded from a large sample of subjects (n=289) who had answered questionnaires measuring personality trait scores of the 5 dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the (...) five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data. Finally, to demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or closed. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all. (shrink)

Knowledge of causal laws is expensive and hard to come by. But we work hard to get it because we believe that it will reduce contingency in planning policies and in building new technologies: knowledge of causal laws allows us to predict reliably what the outcomes will be when we manipulate the factors cited as causes in those laws. Or do they? This paper will argue that causal laws have no special role here. As economists from JS Mill to Robert (...) Lucas and David Hendry stress, along recently with philosophers like James Woodward and Sandra Mitchell, they can do the job only if they are invariant under the manipulations proposed. But then, I shall argue, anything that is invariant under the proposed manipulations will do this job equally well. There seems to be nothing special about causal-law knowledge in and of itself that makes it particularly valuable for policy and technology prediction. What seems to matter is invariance alone, not causality. But what guarantees invariance and how do we know when it will obtain? Here certain kinds of causal laws do have a special place – those underwritten either by what I have called ‘nomological machines’ or by what I have called ‘capacities’. Capacities and nomological machines have a double virtue that makes them invaluable for policy planning. First, the causal laws they give rise to will be invariant so long as they obtain; and second, they typically have visible markers we can come to recognize that tell us when they obtain. The markers for nomological machines are shakier than those for capacities, though, since capacities are often tied to markers by well-established empirical laws. Capacities have their own drawback however, which is the final topic of this paper: the causal laws that are guaranteed by a capacity connect the obtaining (or triggering) of a capacity with its exercise. But Hume argued (mistakenly I suggest) that no distinction can be made between the obtaining of a power and its exercise. (shrink)

A new theory is taking hold in neuroscience. It is the theory that the brain is essentially a hypothesis-testing mechanism, one that attempts to minimise the error of its predictions about the sensory input it receives from the world. It is an attractive theory because powerful theoretical arguments support it, and yet it is at heart stunningly simple. Jakob Hohwy explains and explores this theory from the perspective of cognitive science and philosophy. The key argument throughout The Predictive Mind (...) is that the mechanism explains the rich, deep, and multifaceted character of our conscious perception. It also gives a unified account of how perception is sculpted by attention, and how it depends on action. The mind is revealed as having a fragile and indirect relation to the world. Though we are deeply in tune with the world we are also strangely distanced from it. (shrink)

Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure model and long short-term memory network models, systematically and comprehensively measures and predicts the wind (...) class='Hi'>power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement. (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)

This study examines the varying roles of power, status, and national culture in unethical decision-making. Most research on unethical behavior in organizations is grounded in Western societies; empirical comparative studies of the antecedents of unethical behavior across nations are rare. The authors conduct this comparative study using scenario studies with four conditions in both China and Canada. The results demonstrate that power is positively related to unethical decision-making in both countries. Status has a positive effect on unethical decision-making (...) and facilitates the unethical decisions of Canadian participants who have high power but not Chinese participants who have high power. To explicate participants’ unethical decision-making rationales, the authors ask participants to justify their unethical decisions; the results reveal that Chinese participants are more likely to cite position differences, whereas Canadian participants are more likely to cite work effort and personal abilities. These findings expand theoretical research on the relationship between social hierarchy and unethical decision-making and provide practical insights on unethical behavior in organizations. (shrink)

Knowledge of causal laws is expensive and hard to come by. But we work hard to get it because we believe that it will reduce contingency in planning policies and in building new technologies: knowledge of causal laws allows us to predict reliably what the outcomes will be when we manipulate the factors cited as causes in those laws. Or do they? This paper will argue that causal laws have no special role here. As economists from JS Mill to Robert (...) Lucas and David Hendry stress, along recently with philosophers like James Woodward and Sandra Mitchell, they can do the job only if they are invariant under the manipulations proposed. But then, I shall argue, anything that is invariant under the proposed manipulations will do this job equally well. There seems to be nothing special about causal-law knowledge in and of itself that makes it particularly valuable for policy and technology prediction. What seems to matter is invariance alone, not causality. But what guarantees invariance and how do we know when it will obtain? Here certain kinds of causal laws do have a special place – those underwritten either by what I have called ‘nomological machines’ or by what I have called ‘capacities’. Capacities and nomological machines have a double virtue that makes them invaluable for policy planning. First, the causal laws they give rise to will be invariant so long as they obtain; and second, they typically have visible markers we can come to recognize that tell us when they obtain. The markers for nomological machines are shakier than those for capacities, though, since capacities are often tied to markers by well-established empirical laws. Capacities have their own drawback however, which is the final topic of this paper: the causal laws that are guaranteed by a capacity connect the obtaining (or triggering) of a capacity with its exercise. But Hume argued (mistakenly I suggest) that no distinction can be made between the obtaining of a power and its exercise. (shrink)

Although predictive power and explanatory insight are both desiderata of scientific models, these features are often in tension with each other and cannot be simultaneously maximized. In such situations, scientists may adopt what I term a ‘division of cognitive labor’ among models, using different models for the purposes of explanation and prediction, respectively, even for the exact same phenomenon being investigated. Adopting this strategy raises a number of issues, however, which have received inadequate philosophical attention. More specifically, while (...) one implication may be that it is inappropriate to judge explanatory models by the same standards of quantitative accuracy as predictive models, there still needs to be some way of either confirming or rejecting these model explanations. Here I argue that robustness analyses have a central role to play in testing highly idealized explanatory models. I illustrate these points with two examples of explanatory models from the field of geomorphology. (shrink)

It is a significant coincidence that social science tends to assume a universal human need for predictability, and also uses predictive power as the basic criterion of scientific truth. It is claimed here that man's need for predictability often is crossed by a need for uncertainty and chance. Thus it seems doubtful that the methodological canon of predictability can be anchored in the universal usefulness of social predictions. Some important cases of decision?making seem to be more concerned with (...) the past than with the future. The task of the social sciences cannot be completely separated from philosophical problems, since it is part of a continuous endeavour to clarify the image of man. (shrink)

Despite its explanatory clout, the theory of evolution has thus far compiled a modest record with respect to predictive power—that other major hallmark of scientific theories. This is considered by many to be an acceptable limitation of a theory that deals with events and processes that are intrinsically random. However, whether this is an inherent restriction or simply the sign of an incomplete theory is an open question. In an attempt to help answer that question, we propose a (...) classification scheme for several types of prediction that might occur with regard to evolutionary systems, then explore the nature of these predictions in a system that simulates the evolution of neural architectures. This provides a platform from which to consider the relevance of such observations for real biological systems and illuminates a variety of key issues pertaining to prediction in those environments. (shrink)

The primary objective of the present research was to explore the statistical predictive power of thinking styles in coping strategies beyond demographic factors. One hundred and forty-eight mainland postgraduate students were administered to the Thinking Styles Inventory-Revised II and the Coping Orientation to Problems Experienced Revised. Results indicated that Type I thinking styles primarily predicted adaptive coping strategies, while Type II thinking styles positively contributed to maladaptive coping strategies. Results in the present research were largely in the expected (...) directions beyond the influence of demographic factors. Furthermore, thinking styles varied as a function of age, gender, gender-role orientation, and marital status. Implications for postgraduate students, academics, university administrators, and the limitations of the research, are discussed. (shrink)

Predicting stock markets is a problem that has generated many answers. According to one group of responses, the divergence thesis, it is impossible to accomplish this since the prediction has a ‘bending effect’ that would cause the market to behave in a way that would permanently depart from what was predicted, i.e. the prediction would falsify itself. There are at least three types of impossibility: logical, theoretical and empirical. A second class of responses argues that despite the ‘bending effect’ of (...) predictions, it is still feasible to predicting stock markets. These responses, the convergence thesis, contend that we can achieve it by demonstrating that there are fixed points or that the prediction and market behavior will eventually converge. I expand this line of reasoning by showing that the performativity makes it possible certain predictions by an alignment between the ‘ontic’ and the ‘epistemic’ state of the markets. In addition, I show that performativity enables us to explain how a prediction is produced, why it works initially and then why it fails (i.e. why its predictive power is destroyed). (shrink)

This paper examines racial discrimination and algorithmic bias in predictive policing algorithms (PPAs), an emerging technology designed to predict threats and suggest solutions in law enforcement. We first describe what discrimination is in a case study of Chicago’s PPA. We then explain their causes with Broadbent’s contrastive model of causation and causal diagrams. Based on the cognitive science literature, we also explain why fairness is not an objective truth discoverable in laboratories but has context-sensitive social meanings that need to (...) be negotiated through democratic processes. With the above analysis, we next predict why some recommendations given in the bias reduction literature are not as effective as expected. Unlike the cliché highlighting equal participation for all stakeholders in predictive policing, we emphasize power structures to avoid hermeneutical lacunae. Finally, we aim to control PPA discrimination by proposing a governance solution—a framework of a social safety net. (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)

Power has been studied in various guises in both the social cognition and the reasoning literatures. In this paper, three experiments are reported in which this factor was investigated in the domain of deontic thinking. Power of source of deontic statements was varied within several scenarios, and participants judged the degree to which they thought an injunction would be carried out. In the first experiment, permission statements were used, and it was found that, as predicted, power was (...) positively related to degree of endorsement of deontic conclusions across scenarios. In the second experiment, these findings were generalised across three further deontic domains (threat, warning, and promise) and two different syntactic forms (conjunctive and disjunctive). In the third experiment, the hypothesis that power effects were mediated by subjective judgements of conditional probability was investigated and confirmed. It is argued that these results favour theories that give a general role to probabilistic factors, rather than those based on domain-specific schemas. (shrink)

In the absence of a fundamental theory that precisely predicts values for observable parameters, anthropic reasoning attempts to constrain probability distributions over those parameters in order to facilitate the extraction of testable predictions. The utility of this approach has been vigorously debated of late, particularly in light of theories that claim we live in a multiverse, where parameters may take differing values in regions lying outside our observable horizon. Within this cosmological framework, we investigate the efficacy of top-down anthropic reasoning (...) based on the weak anthropic principle. We argue contrary to recent claims that it is not clear one can either dispense with notions of typicality altogether or presume typicality, in comparing resulting probability distributions with observations. We show in a concrete, top-down setting related to dark matter, that assumptions about typicality can dramatically affect predictions, thereby providing a guide to how errors in reasoning regarding typicality translate to errors in the assessment of predictive power. We conjecture that this dependence on typicality is an integral feature of anthropic reasoning in broader cosmological contexts, and argue in favour of the explicit inclusion of measures of typicality in schemes invoking anthropic reasoning, with a view to extracting predictions from multiverse scenarios. (shrink)

The main focus in the paper is the connection between trust and peace which makes predictability as a necessary condition of the normalcy of life possible, especially collective and communal life. Peace is defined as a specific articulation of the distribution of (political) power within a society. Peace defined in such a way requires a set of rules (norms, or laws) needed for the stability of the established social state of affairs. The main purpose of those norms, laws, is (...) to provide predictability without which stability of such a state of affairs is not possible. The role of trust in this scheme is crucial. However, trust precedes the peace (defined as a set of accepted set of laws), and cannot be obtained by enforcing such normative instruments as laws. Trust is so a component of freedom, the same which contains distrust as its possible component. In this sense peace should be taken as the power of control, first the control over ourselves, the power which makes status and existence of trust possible and feasible. (shrink)

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)

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue (...) that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

There are widespread calls for school education to put more emphasis on developing students’ appreciation of the power and limitations of science. Without effective teaching, there is a risk that sensationalist media claims will unduly influence students’ perceptions of the power of science to already explain and predict aspects of our daily lives. Secondly, schools have a role in preparing students for a future in which they are likely to work and play alongside increasingly humanlike machines. The study (...) reported here assessed the feasibility of a survey to discover students’ stances on the predictive and explanatory power of science in relation to personality, behaviour and the mind The study forms part of a larger project that seeks to identify ways that schools can develop students’ epistemic insight when they consider big questions about the nature of reality and human personhood. Beginning with a broad conceptualisation of personhood designed to pick up on questions in the science-religion dialogue, we drew on interviews and focus groups with students in upper secondary school to formulate a set of statements that seemed to be effective in stimulating discussion about the power and limitations of science. The questionnaire was administered to 311 secondary students. Students’ responses indicate that they were engaged by the theme and that they were generally not working with a secure overarching scientistic or nonscientistic framework. When we grouped students according to how they responded to the narrower theme of personality and behaviour, one in five of the cohort was labelled as strongly scientistic. We also found that in their comments at different points in the survey, the majority of students expressed ideas and everyday phrases associated with scientism. The article concludes with implications for future research and further recommendations for teachers. (shrink)

While explaining a large proportion of any variance, accounts of the speed and accuracy of targetting movements use techniques (e.g., log transforms) that typically reduce variability before ''explaining'' the data. Therefore the predictive power of such accounts are important. We consider whether Plamondon's model can account for kinematics of targetting movements of clinical populations.

In this paper, an active frequency control strategy of wind turbines based on model predictive control is proposed by using the power margin of wind turbines operating in load shedding mode. The frequency response model of the microgrid system with the load shedding of the wind turbines is used to predict the output power and system frequency deviation of the wind turbine. According to the prediction information, the output power control signal of the model predictive (...) controller in the wind turbine can be optimized. On this basis, a wind turbine active participation frequency control strategy based on model predictive control is designed by rolling prediction and optimization. The wind turbine power control signal after the strategy is used to adjust the output power of the wind turbine and balance the change of the active power of the system to reduce the frequency deviation. (shrink)