In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting. The resulting model average estimator is proved to be asymptotically optimal. It is shown that the Kullback–Leibler model averaging estimator asymptotically minimizes the in-sample Kullback–Leibler divergence and improves the forecast accuracy of out-of-sample even under different loss functions. In simulations, we show that the KLMA (...) estimator compares favorably with smooth-AIC estimator, smooth-BIC estimator, and Mallows model averaging estimator, especially when some nonlinear noise is added to the data generation process. The empirical applications in the daily range of S&P500 and price duration of IBM show that the out-of-sample forecasting capacity of the KLMA estimator is better than that of other methods. (shrink)

I examine error diagnosis (model-model disagreement) in climate model intercomparisons including its difficulties, fruitful examples, and prospects for streamlining error diagnosis. I suggest that features of climate model intercomparisons pose a more significant challenge for error diagnosis than do features of individual model construction and complexity. Such features of intercomparisons include, e.g., the number of models involved, how models from different institutions interrelate, and what scientists know about each model. By considering numerous examples in the (...) climate modeling literature, I distill general strategies (e.g., employing physical reasoning and using dimension reduction techniques) used to diagnose model error. Based on these examples, I argue that an error repertoire could be beneficial for improving error diagnosis in climate modeling, although constructing one faces several difficulties. Finally, I suggest that the practice of error diagnosis demonstrates that scientists have a tacit-yet-working understanding of their models which has been under-appreciated by some philosophers. (shrink)

Experimental philosophy studies show that ordinary people have conflicting moral intuitions: when asked about events in a deterministic universe, respondents exhibit compatibilist intuitions about vignettes describing concrete actions, but they have incompatibilist intuitions in response to more abstract queries. Nichols and Knobe maintain that concrete compatibilist intuitions should be explained as emotion-induced performance errors in the psychological process of moral judgment. Their theory is criticized in two main ways. First, they fail to establish that the role of emotion in generating (...) compatibilist intuitions justifies the charge of performance error. Second, doubts are raised about the reliability of the psychological processes that generate incompatibilist intuitions. (shrink)

The mental model theory predicts variations in the percentage of errors in meta-propositional reasoning tasks but does not specify the nature of these errors. In the present study, we drew predictions concerning the nature of errors in a meta-propositional reasoning task by importing and elaborating the distinction between implicit and explicit models previously applied by the mental model theory to the domain of propositional reasoning. An experiment was conducted in which participants were asked to solve problems concerning the truth (...) or falsity of propositional assertions. The task was to determine the truth-status of two persons making a propositional assertion about their own truth-status and/or the truth-status of the other person. The results were consistent with the hypothesis that people may reason in ways that differ in the extent to which initially implicit models are made explicit. As such, the results corroborate a basic principle of the mental model theory: the errors that reasoners make are consistent with at least one of the mental models they construct during the process of reasoning. (shrink)

In the past dozen years a number of theoretical models of schizophrenic symptoms have been proposed, often inspired by advances in the cognitive sciences, and especially cognitive neuroscience. Perhaps the most widely cited and influential of these is the neurocognitive model proposed by Christopher Frith (1992). Frith's influence reaches into psychiatry, neuroscience, and even philosophy. The philosopher John Campbell (1999a), for example, has called Frith's model the most parsimonious explanation of how self-ascriptions of thoughts are subject to errors of (...) identification. "On reflection, it also seems that this is not just one possible theory; it is the simplest theory which has any prospect of explaining the sense of agency, and we ought to work from it, introducing complications only as necessary" (1999a, p. 612). Not everyone agrees. In their recent analysis of alien voices and inserted thoughts in schizophrenia, Stephens and Graham (2000) offer a critique of Frith. Their criticism, however, although serious, is neither deep nor extensive. They outline three points. First, Frith fails to provide an adequate account of why a subject who experiences thought insertion would misattribute that thought to some other agent. Second, Frith does not clarify the distinction between thought insertion and thought influence. And third, Frith fails to explain how a subject can claim both that he is thinking the thought and that the thought is someone else's thought (Stephens and Graham.. (shrink)

We discuss two research projects in material science in which the results cannot be stated with an estimation of the error: a spectroscopic ellipsometry study aimed at determining the orientation of DNA molecules on diamond and a scanning tunneling microscopy study of platinum-induced nanowires on germanium. To investigate the reliability of the results, we apply ideas from the philosophy of models in science. Even if the studies had reported an error value, the trustworthiness of the result would (...) not depend on that value alone. (shrink)

A postcompletion error is a type of procedural error that occurs after the main goal of a task has been accomplished. There is a strong theoretical foundation accounting for postcompletion errors (Altmann & Trafton, 2002; Byrne & Bovair, 1997). This theoretical foundation has been leveraged to develop a logistic regression model of postcompletion errors based on reaction time and eye movement measures (Ratwani, McCurry, & Trafton, 2008). This study further develops and extends this predictive model by (a) validating (...) the model and the general set of predictors on a new task to test the robustness of the model, and (b) determining which specific theoretical components are most important to postcompletion error prediction. (shrink)

Insufficient stiffness of industrial robots is a significant factor which affects its positioning accuracy. To improve the positioning accuracy, a novel positioning error compensation method based on the stiffness modelling is proposed in this paper. First, the positioning errors considering the end load and gravity of industrial robots due to stiffness are analyzed. Based on the results of analysis, it is found that the positioning errors can be described by two kinds of deformation errors at joints: the axial deformation (...) error and the radial deformation error. Then, the axial deformation error is modelled by the differential relationship of kinematics equations. The model of radial deformation error is deduced through the recurrence method and rotation transformation between joints. Finally, these two models are transformed into a Cartesian coordinate system, and a positioning error compensation method based on these two models is presented. Simulations based on the finite element analysis are implemented to verify the positioning error compensation method. The results show that the suggested method can efficiently predict the positioning error according to the gravity and loads, so that the positioning accuracy of industrial robots can be improved with the proposed method. (shrink)

Systematic errors In performance are an important aspect of human behavior that have not received adequate explanation. One such systematic error is termed postcompletion error; a typical example is leaving one's card In the automatic teller after withdrawing cash. This type of error seems to occur when people have an extra step to perform in a procedure after the main goal has been satisfied. The fact that people frequently make this type of error, but do not (...) make this error every time, may best be explained by considering the working memory load at the time the step is to be performed: The error is made when the load on working memory is high, but will not be made when the load is low. A model of performance In the task was constructed using Just and Carpenter's (1992) CAPS that predicted that high working memory load should be associated with postcompletion errors. Two experiments confirmed that such errors can be produced in a laboratory as well as a naturalistic setting, and that the conditions under which the CAPS model makes the error are consistent with the conditions under which the errors occur in the laboratory. (shrink)

English grammar error correction algorithm refers to the use of computer programming technology to automatically recognize and correct the grammar errors contained in English text written by nonnative language learners. Classification model is the core of machine learning and data mining, which can be applied to extracting information from English text data and constructing a reliable grammar correction method. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of English grammar (...) error correction algorithm, elaborated the development background, current status, and future challenges of the classification model, introduced the methods and principles of feature extraction method and dynamic residual structure, constructed a basic model for English grammar error correction based on the classification model, analyzed the classification model and translation model of English grammar error correction, proposed the English grammar error correction algorithm based on the classification model, performed the analyses of the model architecture and model optimizer of the grammar error correction algorithm, and finally conducted a simulation experiment and its result analysis. The study results show that, with the continuous increase of training samples and the continuous progress of learning process, the proposed English grammar error correction algorithm based on the classification model will continue to increase its classification accuracy, further refine its recognition rules, and gradually improve correction efficiency, thereby reducing processing time, saving storage space, and streamlining processing flow. The study results of this paper provide a certain reference for the further research on English grammar error correction algorithm based on the classification model. (shrink)

This paper is based on ethnographic research of data practices in a public health project called Weather Health and Air Pollution. I examine two different kinds of practices that make air pollution data, focusing on how they relate to particular modes of sensing and articulating air pollution. I begin by describing the interstitial spaces involved in making measurements of air pollution at monitoring sites and in the running of a computer simulation. Specifically, I attend to a shared dimension of these (...) practices, the checking of a numerical reading for error. Checking a measurement for error is routine practice and a fundamental component of making data, yet these are also moments of interpretation, where the form and meaning of numbers are ambiguous. Through two case studies of modelling and monitoring data practices, I show that making a ‘good’ measurement requires developing a feeling for the instrument–air pollution interaction in terms of the intended functionality of the measurements made. These affective dimensions of practice are useful analytically, making explicit the interaction of standardised ways of knowing and embodied skill in stabilising data. I suggest that environmental data practices can be studied through researchers’ materialisation of error, which complicate normative accounts of Big Data and highlight the non-linear and entangled relations that are at work in the making of stable, accurate data. (shrink)

The program of research now known as the heuristics and biases approach began with a study of the statistical intuitions of experts, who were found to be excessively confident in the replicability of results from small samples. The persistence of such systematic errors in the intuitions of experts implied that their intuitive judgments may be governed by fundamentally different processes than the slower, more deliberate computations they had been trained to execute. The ancient idea that cognitive processes can be partitioned (...) into two main families--traditionally called intuition and reason--is now widely embraced under the general label of dual-process theories. Dual-process models come in many flavors, but all distinguish cognitive operations that are quick and associative from others that are slow and governed by rules. To represent intuitive and deliberate reasoning, we borrow the terms "system 1" and "system 2" from Stanovich and West. In the following section, we present an attribute-substitution model of heuristic judgment, which assumes that difficult questions are often answered by substituting an answer to an easier one. Following sections introduce a research design for studying attribute substitution, as well as discuss the controversy over the representativeness heuristic in the context of a dual-system view that we endorse. The final section situates representativeness within a broad family of prototype heuristics, in which properties of a prototypical exemplar dominate global judgments concerning an entire set. (shrink)

Prediction Error Minimization theory (PEM) is one of the most promising attempts to model perception in current science of mind, and it has recently been advocated by some prominent philosophers as Andy Clark and Jakob Hohwy. Briefly, PEM maintains that “the brain is an organ that on aver-age and over time continually minimizes the error between the sensory input it predicts on the basis of its model of the world and the actual sensory input” (Hohwy 2014, p. 2). (...) An interesting debate has arisen with regard to which is the more adequate epistemological interpretation of PEM. Indeed, Hohwy maintains that given that PEM supports an inferential view of perception and cognition, PEM has to be considered as conveying an internalist epistemological perspective. Contrary to this view, Clark maintains that it would be incorrect to interpret in such a way the indirectness of the link between the world and our inner model of it, and that PEM may well be combined with an externalist epistemological perspective. The aim of this paper is to assess those two opposite interpretations of PEM. Moreover, it will be suggested that Hohwy’s position may be considerably strengthened by adopting Carlo Cellucci’s view on knowledge (2013). (shrink)

A review of the literature indicates that linear models are frequently used in situations in which decisions are made on the basis of multiple codable inputs. These models are sometimes used normatively to aid the decision maker, as a contrast with the decision maker in the clinical vs statistical controversy, to represent the decision maker "paramorphically" and to "bootstrap" the decision maker by replacing him with his representation. Examination of the contexts in which linear models have been (...) successfully employed indicates that the contexts have the following structural characteristics in common: each input variable has a conditionally monotone relationship with the output; there is error of measurement; and deviations from optimal weighting do not make much practical difference. These characteristics ensure the success of linear models, which are so appropriate in such contexts that random linear models may perform quite well. 4 examples involving the prediction of such codable output variables as GPA and psychiatric diagnosis are analyzed in detail. In all 4 examples, random linear models yield predictions that are superior to those of human judges. (shrink)

The medial prefrontal cortex (mPFC) has been the subject of intense interest as a locus of cognitive control. Several computational models have been proposed to account for a range of effects, including error detection, conflict monitoring, error likelihood prediction, and numerous other effects observed with single-unit neurophysiology, fMRI, and lesion studies. Here, we review the state of computational models of cognitive control and offer a new theoretical synthesis of the mPFC as signaling response–outcome predictions. This new (...) synthesis has two interacting components. The first component learns to predict the various possible outcomes of a planned action, and the second component detects discrepancies between the actual and intended responses; the detected discrepancies in turn update the outcome predictions. This single construct is consistent with a wide array of performance monitoring effects in mPFC and suggests a unifying account of the cognitive role of medial PFC in performance monitoring. (shrink)

In light of an historical obsession with human error, Krueger & Funder (K&F) suggest that social psychologists should emphasize the strengths of social perception. In our view, however, absolute levels of accuracy (or error) in any given experiment are less important than underlying processes. We discuss the use of the process-dissociation procedure for gaining insight into the mechanisms underlying accuracy and error.

A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic (...) system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example. (shrink)

: Models that fail to satisfy the Markov condition are unstable in the sense that changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. This sort of error arises exactly from the failure of non-Markovian models to track the set of causal relations upon which the values of response variables depend. The result has implications for discussions of the level of selection: under (...) certain plausible conditions the models of selection presented in such debates will not satisfy the Markov condition when fit to data from real populations. Since this is true both for group and individual level models, models of neither sort correctly represent the causal structure generating, nor correctly explain, the phenomena of interest. (shrink)

By action model, we understand any logic-based representation of effects and executability preconditions of individual actions within a certain domain. In the context of artificial intelligence, such models are necessary for planning and goal-oriented automated behaviour. Currently, action models are commonly hand-written by domain experts in advance. However, since this process is often difficult, time-consuming, and error-prone, it makes sense to let agents learn the effects and conditions of actions from their own observations. Even though the research (...) in the area of action learning, as a certain kind of inductive reasoning, is relatively young, there already exist several distinctive action learning methods. We will try to identify the collection of the most important properties of these methods, or challenges that they are trying to overcome, and briefly outline their impact on practical applications. (shrink)

Theories of language production have long been expressed as connectionist models. We outline the issues and challenges that must be addressed by connectionist models of lexical access and grammatical encoding, and review three recent models. The models illustrate the value of an interactive activation approach to lexical access in production, the need for sequential output in both phonological and grammatical encoding, and the potential for accounting for structural effects on errors and structural priming from learning.

Connectionist models offer concretemechanisms for cognitive processes. When these modelsmimic the performance of human subjects theycan offer insights into the computationswhich might underlie human cognition. We illustratethis with the performance of a recurrentconnectionist network which produces the meaningof words in response to their spellingpattern. It mimics a paradoxical pattern oferrors produced by people trying to read degradedwords. The reason why the network produces thesurprising error pattern lies in the nature ofthe attractors which it develops as it learns tomap (...) spelling patterns to semantics. The keyrole of attractor structure in the successfulsimulation suggests that the normal adult semanticreading route may involve attractor dynamics, andthus the paradoxical error pattern isexplained. (shrink)

In what follows I will discuss an example of the Duhem-Quine problem in which Pr(H A), Pr(A H), and Pr(OI +H& ?A) (where H is the hypothesis, A the auxiliary assumptions, and O the observational prediction) can be construed objectively; however, only some of those quantities are relevant to the analysis that I provide. The example involves medical diagnosis. The goal is to test the hypothesis that someone has tuberculosis; the auxiliary assumptions describe the er- ror characteristics of the test (...) procedure. Although it can make sense to talk about the objective probability that someone (randomly drawn from a given population) has tuberculosis and it also can make sense to talk about the objective probability that a test procedure has a certain set of error characteristics, neither of these quantities will enter into the analysis. The analysis proceeds entirely via likelihoods, what one needs to consider is just the probability of the observations conditional on four conjunctions of the form (?H & +A).' It is a special feature of the example that all four of these conjunctions are simple statistical hypotheses in the technical sense that each unambigu-ously confers a probability on the observations.'o After describing how the likelihood concept applies to the example concerning medical diagnosis, I will show how similar patterns can arise in the context of a second inferential framework-that of H. Akaike's criterion of model selection;" this time the example will involve phylogenetic inference. (shrink)

This paper presents a new theory of modal reasoning, i.e. reasoning about what may or may not be the case, and what must or must not be the case. It postulates that individuals construct models of the premises in which they make explicit only what is true. A conclusion is possible if it holds in at least one model, whereas it is necessary if it holds in all the models. The theory makes three predictions, which are corroborated experimentally. (...) First, conclusions correspond to the true, but not the false, components of possibilities. Second, there is a key interaction: it is easier to infer that a situation is possible as opposed to impossible, whereas it is easier to infer that a situation is not necessary as opposed to necessary. Third, individuals make systematic errors of omission and of commission. We contrast the theory with theories based on formal rules. (shrink)

Lisciandra poses a challenge for robustness analysis as applied to economic models. She argues that substituting tractability assumptions risks altering the main mathematical structure of the model, thereby preventing the possibility of meaningfully evaluating the same model under different assumptions. In such cases RA is argued to be inapplicable. However, Lisciandra is mistaken to take the goal of RA as keeping the mathematical properties of tractability assumptions intact. Instead, RA really aims to keep the modeling component while varying the (...) corresponding mathematical formulation. Thus, her argument concerning whether the associated mathematical properties of certain assumptions can be kept intact is irrelevant to the success of RA. Furthermore, we explicate and develop Lloyd’s account of “model robustness” to provide solutions to Lisciandra’s challenges. Our solutions are, namely, error analysis and independent empirical support. We conclude that although complex economic models do face potential dangers, there are solutions and robustness analysis need not be given up.. (shrink)

Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic (...) framework like a cognitive architecture such as ACT-R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance-based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision-making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self-efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence. (shrink)

A recent article by Kuhn1 showcases the plethora of proposed solutions for the mind-body problem as it is understood in current ’consciousness science’. Perusing this article, philosophers will likely find it surprising to see the inclusion of for instance Indian idealism and Buddhist thought, but neither German, nor British or US idealists, which seems especially unbalanced when instead of them theories like Kastrup’s analytical idealism (Hegel for physicists?) or Hoffmann’s interface theory (Kant for psychologists?) are included. The listings of dualist, (...) panpsychist and further theories seems equally eclectic, with for instance Descartes no more than a side-note in a number of entries, but several listings of religious ideas of body and soul. A possible problem with such omissions is of course that not taking philosophical history more seriously comes with the peril of repeating (now) avoidable errors. Nevertheless, judging from the disclaimers, Kuhn is well aware of this and most likely sees his list as more of a ’sociology of consciousness science’, i.e. a list of people and ideas that are currently circulating in this field. -/- It can in any case be taken from Kuhn’s monumental work effort, that it is often assumed that a solution to the mind-body problem would be either (in the wider sense) materialistic (including nonreductive and quantum theories), dualistic, panpsychistic (including monist theories) or idealistic. The core problems faced are then those of emergence (of consciousness), interaction (between brain and mind), de/combination (of ‘mind dust’ or a unity into proper subjects) or emanation, where the emanation problem is the problem of explaining in detail(!) how the physical world and our scientific theories arise from a given idealistic setup. To me it seems more likely that all these views have a point, but that none manages to cover the whole picture – and that it therefore might be possible to integrate these views into ’consensus models’, if we do not look at the proposed solutions, but the initial intentions and/or intuitions for setting them up (much like as if we would mediate a social conflict). (shrink)

It has been shown in Spirtes(1995) that X and Y are d-separated given Z in a directed graph associated with a recursive or non-recursive linear model without correlated errors if and only if the model entails that ρXY.Z = 0. This result cannot be directly applied to a linear model with correlated errors, however, because the standard graphical representation of a linear model with correlated errors is not a directed graph. The main result of this paper is to show how (...) to associate a directed graph with a linear model L with correlated errors, and then use d-separation in the associated directed graph to determine whether L entails that a particular partial correlation is zero. (shrink)

If Large Language Models can make real scientific contributions, then they can genuinely use language, be systematically wrong, and be held responsible for their errors. AI models which can make scientific contributions thereby meet the criteria for scientific authorship.

Statistical model specification and validation raise crucial foundational problems whose pertinent resolution holds the key to learning from data by securing the reliability of frequentist inference. The paper questions the judiciousness of several current practices, including the theory-driven approach, and the Akaike-type model selection procedures, arguing that they often lead to unreliable inferences. This is primarily due to the fact that goodness-of-fit/prediction measures and other substantive and pragmatic criteria are of questionable value when the estimated model is statistically misspecified. Foisting (...) one's favorite model on the data often yields estimated models which are both statistically and substantively misspecified, but one has no way to delineate between the two sources of error and apportion blame. The paper argues that the error statistical approach can address this Duhemian ambiguity by distinguishing between statistical and substantive premises and viewing empirical modeling in a piecemeal way with a view to delineate the various issues more effectively. It is also argued that Hendry's general to specific procedures does a much better job in model selection than the theory-driven and the Akaike-type procedures primary because of its error statistical underpinnings. (shrink)

Nurses make mistakes in practice despite the culturally based expectation of perfection. Such a disparity between reality and expectation calls members of the profession to question the current attitudes toward mistakes in practice. Two explanatory models of the origin of mistakes are presented. The Perfectibility Model holds that any error or harm is caused by an individual practitioner's lack of knowledge or motivation. The Faulty Systems Model offers a broader explanation of human error. I conclude that a (...) Faulty Systems Model is more comprehensive and more effective for managing mistakes. Integrating the Faulty Systems Model into practice and education can result in more ethically fitting responses to errors and ultimately better outcomes for nurses, institutions and patients. (shrink)

In this paper we are going to show that error coping strategies play an essential role in linguistic pragmatics. We study the effect of noisy speaker strategies within a framework of signalling games with feedback loop. We distinguish between cases in which errors occur in message selection and cases in which they occur in signal selection. The first type of errors affects the content of an utterance, and the second type its linguistic expression. The general communication model is inspired (...) by the Shannon–Weaver communication model. We test the model by a number of benchmark examples, including examples of relevance implicatures, quantity implicatures, and presupposition accommodation. (shrink)

I argue that there are no plausible non-representational explanations of episodes of hallucination. To make the discussion more specific, I focus on visual hallucinations in Charles Bonnet syndrome. I claim that the character of such hallucinatory experiences cannot be explained away non-representationally, for they cannot be taken as simple failures of cognizing or as failures of contact with external reality—such failures being the only genuinely non-representational explanations of hallucinations and cognitive errors in general. I briefly introduce a recent computational model (...) of hallucination, which relies on generative models in the brain, and argue that the model is a prime example of a representational explanation referring to representational mechanisms. The notion of the representational mechanism is elucidated, and it is argued that hallucinations—and other kinds of representations—cannot be exorcised from the cognitive sciences. (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)

According to the discovery model in the ontology of art, the facts concerning the ontological status of artworks are mind-independent and, hence, are facts about which the folk may be substantially ignorant or in error. In recent work Amie Thomasson has claimed that the most promising solution to the ‘ qua problem’—a problem concerning how the reference of a referring-expression is fixed—requires us to give up the discovery model. I argue that this claim is false. Thomasson's solution to the (...) qua problem—a hybrid descriptive/causal theory of reference-fixing—has a superior competitor, in the form of the account of reference-fixing suggested by Gareth Evans; and Evans's theory leaves the discovery model untouched. (shrink)

This study puts forward a rounder conceptual model for interpreting short and long-term effects of choice behavior. Kahneman’s (2011) distinction between cognitive processing System 1 and System 2 reflect the more rigorous distinction between Brief and Immediate and Extended and Elaborated Relational Responding. Specifically, we provide theoretical accounts and applied examples of how nudging, or the manipulation of environmental contingencies, works on the creation and modification of relational frames. The subset denominated educational nudges, or boosts, are particularly useful towards their (...) maintenance. We define and illustrate the fundamental mediating role of verbal behavior towards formulating rules, which inform and guide choice behavior over time. Furthermore, we suggest adopting the Implicit Relations Association Procedure to inform how coherent and immediate responding to new relational frames may be, whenever choices need be made. We provide empirical accounts of consumer behavior, stereotypy and prejudices, eating behavior, and overcoming cognitive biases. The conclusions depict a promising way forward towards an improved model for interpreting and overcoming human errors thanks to changes in the contingencies of choice behavior. (shrink)

Ex ante predicted outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. But error rates have error rates. We reapply measurements of uncertainty about the estimation errors of the estimation errors of an estimation treated as branching counterfactuals. Such recursions of epistemic uncertainty have markedly different distributial properties from conventional sampling error, and lead to fatter tails in the projections than in past realizations. Counterfactuals of error rates always lead to (...) fat tails, regardless of the probability distribution used. A mere .01% branching error rate about the STD (itself an error rate), and .01% branching error rate about that error rate, etc. (recursing all the way) results in explosive (and infinite) moments higher than 1. Missing any degree of regress leads to the underestimation of small probabilities and concave payoffs (a standard example of which is Fukushima). The paper states the conditions under which higher order rates of uncertainty (expressed in spreads of counterfactuals) alters the shapes the of final distribution and shows which a priori beliefs about conterfactuals are needed to accept the reliability of conventional probabilistic methods (thin tails or mildly fat tails). (shrink)