This is a paper about model-building and overfitting in normative ethics. Overfitting is recognized as a methodological error in modeling in the philosophy of science and scientific practice, but this concern has not been brought to bear on the practice of normative ethics. I first argue that moral inquiry shares similarities with scientific inquiry in that both may productively rely on model-building, and, as such, overfitting worries should apply to both fields. I then offer a diagnosis of (...) the problems of overfitting in moral inquiry and explain how our current practice seems worryingly susceptible to such problems. I conclude by giving suggestions for how we might avoid overfitting when doing normative ethics. (shrink)

A hyperintensional epistemic logic would take the contents which can be known or believed as more fine-grained than sets of possible worlds. I consider one objection to the idea: Williamson’s Objection from Overfitting. I propose a hyperintensional account of propositions as sets of worlds enriched with topics: what those propositions, and so the attitudes having them as contents, are about. I show that the account captures the conditions under which sentences express the same content; that it can be pervasively (...) applied in formal and mainstream epistemology; and that it is left unscathed by the objection. (shrink)

In the present study, we tested whether civic identity consisting of moral and political identity via the bifactor model of civic identity with the Stanford Civic Purpose dataset. Previous research in youth development proposed that civic identity consists of two closely related identity constructs, i.e., moral and political identity. Given the bifactor model in factor analysis assumes the presence of both the general and specific factors, we hypothesized that the bifactor model would better fit the data than conventional alternative models. (...) Thus, we compared the candidate models, including the bifactor model, with confirmatory factor analysis. To address the concern about the potential overfitting of the bifactor model, we additionally conducted cross-validation and measurement invariance tests across different gender groups and time points. The analysis results supported our hypothesis that the bifactor model better fitted the data. Both the cross-validation and measurement invariance test results suggested that the overfitting issue was not a significant concern. In the present study, we empirically demonstrated that the bifactor model of civic identity consisting of moral and political identity, which was supposed to be more consistent with the theoretical and conceptual framework of civic identity, better fitted the data than conventional alternative models. (shrink)

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, the hierarchical PP (...) framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

The developing body of empirical work on the "Gettier effect" indicates that, in general, the presence of a Gettier-type structure in a case makes participants less likely to attribute knowledge in that case. But is that a sufficient reason to diverge from a JTB theory of knowledge? I argue that considerations of good model selection, and worries about noise and overfitting, should lead us to consider that a live, open question. The Gettier effect is perhaps so transient, and so (...) sensitive to other, epistemologically-inappropriate factors, that it raises the question of whether it ought to be counted as something to include in our theories -- or as a piece of noise to be excluded from them. (shrink)

Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset (...) size, and class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

The VIA Classification of Strengths and Virtues is the most commonly used model of positive personality. In this study, we used two methods of model modification to develop models for two measures of the character strengths, the VIA Inventory of Strengths-Revised and the Global Assessment of Character Strengths. The first method consisted of freeing residual covariances based on modification indices until good fit was achieved. The second was residual network modeling (RNM), which frees residual partial correlations while minimizing a function (...) that penalizes more complex models. Models based on both strategies were developed for the two questionnaires. The resulting structural models were then applied to four other samples. Though both modification procedures achieved good fit in the sample used to develop the models, only RNM resulted in adequate model fit for both measures in all cross-validation samples. This finding suggests RNM is more robust against overfitting than traditional practices. Moreover, the result supports the validity of the three-factor model of character strengths with replicability. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network with maximum (...) of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network with maximum (...) of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Abstract: Classifying sea animals is an important problem in marine biology and ecology as it enables the accurate identification and monitoring of species populations, which is crucial for understanding and protecting marine ecosystems. This paper addresses the problem of classifying 19 different sea animals using convolutional neural networks (CNNs). The proposed solution is to use a pretrained MobileNetV2 model, which is a lightweight and efficient CNN architecture, and fine-tune it on a dataset of sea animals. The results of the study (...) show that the fine-tuned MobileNetV2 model is able to achieve an accuracy of 85% on the classification task, which is competitive with the state-of-the-art methods. Additionally, the study also found that data augmentation and regularization techniques such as dropout were important for preventing overfitting and improving the performance of the model. This study demonstrates the potential of using pretrained models and efficient CNN architectures for classifying sea animals, and it provides insights into the challenges and best practices for solving this important problem in marine biology and ecology. (shrink)