639 research outputs found
Two Sides of Modus Ponens
McGee (1985) argues that it is sometimes reasonable to accept both x and x->(y->z) without accepting y->z, and that modus ponens is therefore invalid for natural language indicative conditionals. Here, we examine McGee's counterexamples from a Bayesian perspective. We argue that the counterexamples are genuine insofar as the joint acceptance of x and x->(y->z) at time t does not generally imply constraints on the acceptability of y->z at t, but we use the distance-based approach to Bayesian learning to show that applications of modus ponens are nevertheless guaranteed to be successful in an important sense. Roughly, if an agent becomes convinced of the premises of a modus ponens argument, then she should likewise become convinced of the argument's conclusion. Thus we take McGee's counterexamples to disentangle and reveal two distinct ways in which arguments can convince. Any general theory of argumentation must take stock of both
Two Sides of Modus Ponens
McGee (1985) argues that it is sometimes reasonable to accept both x and x->(y->z) without accepting y->z, and that modus ponens is therefore invalid for natural language indicative conditionals. Here, we examine McGee's counterexamples from a Bayesian perspective. We argue that the counterexamples are genuine insofar as the joint acceptance of x and x->(y->z) at time t does not generally imply constraints on the acceptability of y->z at t, but we use the distance-based approach to Bayesian learning to show that applications of modus ponens are nevertheless guaranteed to be successful in an important sense. Roughly, if an agent becomes convinced of the premises of a modus ponens argument, then she should likewise become convinced of the argument's conclusion. Thus we take McGee's counterexamples to disentangle and reveal two distinct ways in which arguments can convince. Any general theory of argumentation must take stock of both
The Similarity of Causal Structure
Does y obtain under the counterfactual supposition that x? The answer to this question is famously thought to depend on whether y obtains in the most similar world(s) in which x obtains. What this notion of âsimilarityâ consists in is controversial, but in recent years, graphical causal models have proved incredibly useful in getting a handle on considerations of similarity between worlds. One limitation of the resulting conception of similarity is that it says nothing about what would obtain were the causal structure to be different from what it actually is, or from what we believe it to be. In this paper, we explore the possibility of using graphical causal models to resolve counterfactual queries about causal structure by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which a graph G* can be more similar to a graph G than a graph G**, we introduce multiple similarity metrics, as well as multiple ways to prioritize the various metrics when settling counterfactual queries about causal structure
The Similarity of Causal Structure
Does y obtain under the counterfactual supposition that x? The answer to this question is famously thought to depend on whether y obtains in the most similar world(s) in which x obtains. What this notion of âsimilarityâ consists in is controversial, but in recent years, graphical causal models have proved incredibly useful in getting a handle on considerations of similarity between worlds. One limitation of the resulting conception of similarity is that it says nothing about what would obtain were the causal structure to be different from what it actually is, or from what we believe it to be. In this paper, we explore the possibility of using graphical causal models to resolve counterfactual queries about causal structure by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which a graph G* can be more similar to a graph G than a graph G**, we introduce multiple similarity metrics, as well as multiple ways to prioritize the various metrics when settling counterfactual queries about causal structure
Do Energy Efficiency Improvements Reduce Energy Use? Empirical Evidence on the Economy-Wide Rebound Effect in Europe and the United States
Improving energy efficiency is often considered to be one of the keys to reducing greenhouse gas emissions. However, efficiency gains also reduce the cost of energy services and may even reduce the price of energy, resulting in energy use rebounding and potential energy use savings being eaten up. There is only limited empirical research quantifying the economy-wide rebound effect that takes the dynamic economic responses to energy efficiency improvements into account. We use a Structural Factor-Augmented Vector Autoregressive model (S-FAVAR) that allows us to track how energy use changes in response to an energy efficiency improvement while accounting for a vast range of potential confounders. Our findings point to economy-wide rebound effects of 78% to 101% after two years in France, Germany, Italy, the U.K., and the U.S. These findings imply that energy efficiency innovations alone may be of limited help in reducing future energy use and emphasize the importance of tackling carbon emissions directly
Topographic analysis of individual activation patterns in medial frontal cortex in schizophrenia
Individual variability in the location of neural activations poses a unique problem for neuroimaging studies employing group averaging techniques to investigate the neural bases of cognitive and emotional functions. This may be especially challenging for studies examining patient groups, which often have limited sample sizes and increased intersubject variability. In particular, medial frontal cortex (MFC) dysfunction is thought to underlie performance monitoring dysfunction among patients with schizophrenia, yet previous studies using group averaging to compare schizophrenic patients to controls have yielded conflicting results. To examine individual activations in MFC associated with two aspects of performance monitoring, interference and error processing, functional magnetic resonance imaging data were acquired while 17 patients with schizophrenia and 21 healthy controls (HCs) performed an event-related version of the multisource interference task. Comparisons of averaged data revealed few differences between the groups. By contrast, topographic analysis of individual activations for errors showed that control subjects exhibited activations spanning across both posterior and anterior regions of MFC while patients primarily activated posterior MFC, possibly reflecting an impaired emotional response to errors in schizophrenia. This discrepancy between topographic and group-averaged results may be due to the significant dispersion among individual activations, particularly in HCs, highlighting the importance of considering intersubject variability when interpreting the medial frontal response to error commission. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63054/1/20657_ftp.pd
Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models
The academic system incentivizes p-hacking, where researchers select estimates and statistics with statistically significant p-values for publication. We analyze the complete process of Granger causality testing including p-hacking using Monte Carlo simulations. If the degrees of freedom of the underlying vector autoregressive model are small to moderate, information criteria tend to overfit the lag length and overfitted vector autoregressive models tend to result in false-positive findings of Granger causality. Researchers may p-hack Granger causality tests by estimating multiple vector autoregressive models with different lag lengths and then selecting only those models that reject the null of Granger non-causality for presentation in the final publication. We show that overfitted lag lengths and the corresponding false-positive findings of Granger causality can frequently occur in research designs that are prevalent in empirical macroeconomics. We demonstrate that meta-regression models can control for spuriously significant Granger causality tests due to overfitted lag lengths. Finally, we find evidence that false-positive findings of Granger causality may be prevalent in the large literature that tests for Granger causality between energy use and economic output, while we do not find evidence for a genuine relation between these variables as tested in the literature
Interregional compensatory mechanisms of motor functioning in progressing preclinical neurodegeneration.
Understanding brain reserve in preclinical stages of neurodegenerative disorders allows determination of which brain regions contribute to normal functioning despite accelerated neuronal loss. Besides the recruitment of additional regions, a reorganisation and shift of relevance between normally engaged regions are a suggested key mechanism. Thus, network analysis methods seem critical for investigation of changes in directed causal interactions between such candidate brain regions. To identify core compensatory regions, fifteen preclinical patients carrying the genetic mutation leading to Huntington's disease and twelve controls underwent fMRI scanning. They accomplished an auditory paced finger sequence tapping task, which challenged cognitive as well as executive aspects of motor functioning by varying speed and complexity of movements. To investigate causal interactions among brain regions a single Dynamic Causal Model (DCM) was constructed and fitted to the data from each subject. The DCM parameters were analysed using statistical methods to assess group differences in connectivity, and the relationship between connectivity patterns and predicted years to clinical onset was assessed in gene carriers. In preclinical patients, we found indications for neural reserve mechanisms predominantly driven by bilateral dorsal premotor cortex, which increasingly activated superior parietal cortices the closer individuals were to estimated clinical onset. This compensatory mechanism was restricted to complex movements characterised by high cognitive demand. Additionally, we identified task-induced connectivity changes in both groups of subjects towards pre- and caudal supplementary motor areas, which were linked to either faster or more complex task conditions. Interestingly, coupling of dorsal premotor cortex and supplementary motor area was more negative in controls compared to gene mutation carriers. Furthermore, changes in the connectivity pattern of gene carriers allowed prediction of the years to estimated disease onset in individuals. Our study characterises the connectivity pattern of core cortical regions maintaining motor function in relation to varying task demand. We identified connections of bilateral dorsal premotor cortex as critical for compensation as well as task-dependent recruitment of pre- and caudal supplementary motor area. The latter finding nicely mirrors a previously published general linear model-based analysis of the same data. Such knowledge about disease specific inter-regional effective connectivity may help identify foci for interventions based on transcranial magnetic stimulation designed to stimulate functioning and also to predict their impact on other regions in motor-associated networks
Is There Really Granger Causality Between Energy Use and Output?
We carry out a meta-analysis of the very large literature on Granger causality tests between energy
use and economic output to determine if there is a genuine effect in this literature or whether the
large number of apparently significant results is due to publication and misspecification bias. Our
model extends the standard meta-regression model for detecting genuine effects using the statistical
power trace in the presence of publication biases by controlling for the tendency to over-fit vector
auto regression models in small samples. These over-fitted models have inflated type 1 errors. We find that models that include energy prices as a control variable find a genuine effect from output to energy use in the long-run. A genuine causal effect also seems apparent from energy to output when employment is controlled for and the Johansen procedure is used
Electronic conduction in multi-walled carbon nanotubes: Role of intershell coupling and incommensurability
Geometry incommensurability between weakly coupled shells in multi-walled
carbon nanotubes is shown to be the origin of unconventional electronic
conduction mechanism, power-law scaling of the conductance, and remarkable
magnetotransport and low temperature dependent conductivity when the dephasing
mechanism is dominated by weak electron-electron coupling
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