Science, Logic, and Mathematics

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the current (...) literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)

Despite recent essentialist approaches to Kant’s laws of nature, it is unclear whether Kant’s critical philosophy is compatible with core tenets of essentialism. In this paper, I first reconstruct Kant’s position by identifying the key metaphysical and epistemological features of his notion of ‘nature’ or ‘essence’. Two theses about natures can be found in the literature, namely that they are noumenal in character (noumenal thesis) and that they guide scientific investigation as regulative ideas of reason (regulative thesis). I argue that (...) Kant’s notion of nature does not entail the noumenal thesis and, based on his model of causal explanation, I propose a novel, phenomenal thesis, that allows for a better understanding of the function of natures as regulative ideas. In the last part of the paper, I show that Kant’s ‘essentialism’ is a genuine form of essentialism committed to de re modality, although it differs in several respects from major contemporary essentialist accounts. I conclude by suggesting that Kant’s essentialism (if appropriately updated) can be relevant to the contemporary debate, which has so far been dominated by Humean and Aristotelian proposals. (shrink)

We combine linear temporal logic (with both past and future modalities) with a deontic version of justification logic to provide a framework for reasoning about time and epistemic and normative reasons. In addition to temporal modalities, the resulting logic contains two kinds of justification assertions: epistemic justification assertions and deontic justification assertions. The former presents justification for the agent’s knowledge and the latter gives reasons for why a proposition is obligatory. We present two kinds of semantics for the logic: one (...) based on Fitting models and the other based on neighborhood models. The use of neighborhood semantics enables us to define the dual of deontic justification assertions properly, which corresponds to a notion of permission in deontic logic. We then establish the soundness and completeness of an axiom system of the logic with respect to these semantics. Further, we formalize the Protagoras versus Euathlus paradox in this logic and present a precise analysis of the paradox, and also briefly discuss Leibniz’s solution. (shrink)

Let $\varSigma $ be a signature without $0$-ary operation symbols and $\textsf{Sl}$ the category of semilattices. Then, after defining and investigating the categories $\int ^{\textsf{Sl}}\textrm{Isys}_{\varSigma }$, of inductive systems of $\varSigma $-algebras over all semilattices, which are ordered pairs $\mathscr{A}= (\textbf{I},\mathscr{A})$ where $\textbf{I}$ is a semilattice and $\mathscr{A}$ an inductive system of $\varSigma $-algebras relative to $\textbf{I}$, and PłAlg$ (\varSigma )$, of Płonka $\varSigma $-algebras, which are ordered pairs $(\textbf{A},D)$ where $\textbf{A}$ is a $\varSigma $-algebra and $D$ a Płonka operator (...) for $\textbf{A}$, i.e. in the traditional terminology, a partition function for $\textbf{A}$, we prove the main result of the paper: There exists an adjunction, which we call the Płonka adjunction, from $\int ^{\textsf{Sl}}\textrm{Isys}_{\varSigma }$ to PłAlg$ (\varSigma )$. (shrink)

The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor (...) an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control. (shrink)

Studies of the Early Modern debate concerning absolute and relative space and motion often ignore the significance of the concept of true motion in this debate. Even philosophers who denied the existence of absolute space maintained that true motions could be distinguished from merely apparent ones. In this paper, I examine Berkeley's endorsement of this distinction and the problems it raises. First, Berkeley's endorsement raises a problem of consistency with his other philosophical commitments, namely his idealism. Second, Berkeley's endorsement raises (...) a problem of adequacy, namely whether Berkeley can provide an adequate account of what grounds the distinction between true and merely apparent motion. In this paper, I argue that sensitivity to Berkeley's distinction between what is true in the metaphysical, scientific, and vulgar domains can address both the consistency and the adequacy problems. I argue that Berkeley only accepts true motion in the scientific and vulgar domains, and not the metaphysical. There is thus no inconsistency between his endorsement of true motion in science and ordinary language, and his metaphysical idealism. Further, I suggest that sensitivity to these three domains shows that Berkeley possesses resources to give an adequate account of how true motions are discovered in natural science. (shrink)

There has been increased attention on how scientific communities should respond to spurious dissent. One proposed method is to hide such dissent by preventing its publication. To investigate this, I computationally model the epistemic effects of hiding dissenting evidence on scientific communities. I find that it is typically epistemically harmful to hide dissent, even when there exists an agent purposefully producing biased dissent. However, hiding dissent also allows for quicker correct epistemic consensus among scientists. Quicker consensus may be important when (...) policy decisions must be made quickly, such as during a pandemic, suggesting times when hiding dissent may be useful. (shrink)

This article examines how deduction preserves certainty and how much certainty it can preserve according to Descartes’s Rules for the Direction of the Mind. I argue that the certainty of a deduction is a matter of four conditions for Descartes. First, certainty depends on whether the conjunction of simple propositions is composed with necessity or contingency. Second, a deduction approaches the certainty of an intuition depending on how many “acts of conceiving” it requires and—third—the complexity or difficulty of the acts (...) of thinking, which is determined by the content of the thoughts and on external factors. Fourth, certainty depends on the intellectual aptitude of the person using the deduction. A deduction lacks certainty when it relies on memory such that it is not apprehended with immediacy. However, the mental capacity and speed of a mind can be increased by training the special mental faculties of perspicacity and discernment. Increasing one’s intellectual aptitude allows for more steps of a deduction to be inferred in fewer acts of conceiving, thereby helping preserve the certainty of a deduction. (shrink)

Language not only reflects and cocreates social universes but can also be and is performative regarding the planetary common good, e.g., through international treaties and agreements. This paper investigates the rationale and feasibility of altering the language used by Glasgow Climate Pact to a posthuman mode that addresses the issue of more-than-human inequality by becoming inclusive toward nonhuman actors, and presents a selection of edited excerpts. The main findings state that (1) the language of the Glasgow Climate Pact is inadequate (...) concerning its expected agency, (2) the reasons for this include its anthropocentrism, capitalocentrism and technocentrism, and (3) it is possible to rephrase the Glasgow Climate Pact and similar documents to address the diagnosed problems in a radically inclusive way. This paper represents a starting point for a discussion on the interrelations between climate and language and on the importance of language used by policy documents in the optics of climate action. (shrink)

The critical node detection problem is a central task in computational graph theory due to its large applicability, consisting in deleting $k$ nodes to minimize a certain graph measure. In this article, we propose a new Extremal Optimization-based approach, the Pseudo-Deterministic Noisy Extremal Optimization (PDNEO) algorithm, to solve the Critical Node Detection variant in which the pairwise connectivity is minimized. PDNEO uses an adaptive pseudo-deterministic parameter to switch between random nodes and articulation points during the search, as well as other (...) features, such as noise induction to preserve diversity, greedy search to better exploit the search space and a greater search space exploration mechanism. Numerical experiments on synthetic and real-world networks show the effectiveness of the proposed algorithm compared with existing methods. (shrink)

In the industrial context, steel is a broadly-used raw material with applications in many different fields. Due to its high impact in the activity of many industries all over the world, forecasting its price is of utmost importance for a huge amount of companies. In this work, non-linear neural models are applied for the first time to different datasets in order to validate their suitability when predicting the price of this commodity. In particular, the NAR, NIO and NARX neural network (...) models are innovatively applied for the first time to forecast the price of hot rolled steel in Spain. Besides these variety of models, different datasets consisting of a set of heterogenous variables from the last seven years and related to the price of this commodity are benchmarked and analyzed. The results showed that NARX is the best performing model when the price of raw materials used to produce steel and the stock market prices of three major global steel producing companies are employed as input to this predictive model. Consequently, this result may boost the application of Machine Learning in companies, in order to schedule the supplying operations according to the price forecasting. (shrink)

Accurate measurement of livestock weight is a primary indicator in the meat industry to increase the economic gain. In lambs, the weight of a live animal is still usually estimated manually using traditional scales, resulting in a tedious process for the experienced assessor and stressful for the animal. In this paper, we propose a solution to this problem using computer vision techniques; thus, the proposed procedure estimates the weight of a lamb by analysing its zenithal image without interacting with the (...) animal, which speeds up the process and reduces weighing costs. It is based on a data-driven decision support system that uses RGB-D machine vision techniques and regression models. Unlike existing methods, it does not require walk-over-weighing platforms or special and expensive infrastructures. The proposed method includes a decision support system that automatically rejects those images that are not appropriate to estimate the lamb weight. After determining the body contour of the lamb, we compute several features that feed different regression models. Best results were achieved with Extra Tree Regression ($R^{2}$=91.94%), outperforming the existing techniques. Using only an image, the proposed approach can identify with a minimum error the optimal weight of a lamb to be slaughtered, so as to maximise the economic profit. (shrink)

El método axiomático estilo Hilbert es reconocido por la mayoría de los lógicos como un cálculo de gran dificultad, de aquí que uno de los primeros objetivos que se plantean es dar una prueba del teorema de la deducción para facilitar las derivaciones consecuentes, sin embargo, esto modifica la estructura de las derivaciones naturalizándolas. En el presente trabajo se brindarán definiciones de "axioma" y "corolario" con el fin de simplificar las derivaciones y aprehender el funcionamiento del cálculo sin modificar la (...) estructura de la prueba. Palabras clave: Método axiomático - Esquema - Axioma – Corolario – Teoría de la prueba. (shrink)

This brief elucidation of two quotes from the Genealogy will be apt for more accessible interpretation following the completion of my next paper. Stay tuned for some hard-fought philosophy.

The objective of this research is to develop accurate forecasting models for chlorophyll-α concentrations at various depths in El Mar Menor, Spain. Chlorophyll-α plays a crucial role in assessing eutrophication in this vulnerable ecosystem. To achieve this objective, various deep learning forecasting techniques, including long short-term memory, bidirectional long short-term memory and gated recurrent uni networks, were utilized. The models were designed to forecast the chlorophyll-α levels with a 2-week prediction horizon. To enhance the models’ accuracy, a sliding window method (...) combined with a blocked cross-validation procedure for time series was also applied to these techniques. Two input strategies were also tested in this approach: using only chlorophyll-α time series and incorporating exogenous variables. The proposed approach significantly improved the accuracy of the predictive models, no matter the forecasting technique employed. Results were remarkable, with |$\overline{\sigma}$| values reaching approximately 0.90 for the 0.5-m depth level and 0.80 for deeper levels. The proposed forecasting models and methodologies have great potential for predicting eutrophication episodes and acting as decision-making tools for environmental agencies. Accurate prediction of eutrophication episodes through these models could allow for proactive measures to be implemented, resulting in improved environmental management and the preservation of the ecosystem. (shrink)

The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the network. (...) Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model. (shrink)

The use of technologies like artificial intelligence can drive productivity growth, efficiency and innovation. The goal of this study is to develop an anomaly detection method for locating flaws on the surface of sandwich panels using YOLOv5. The proposed algorithm extracts information locally from an image through a prediction system that creates bounding boxes and determines whether the sandwich panel surface contains flaws. It attempts to reject or accept a product based on quality levels specified in the standard. To evaluate (...) the proposed method, a comparison was made with a sandwich panel damage detection method based on a convolutional neural network and methods based on thresholding. The findings show that the proposed method, which is based on an object detector, is more accurate than the alternatives. The characteristics of the model, which can reject or accept a product according to the standard and limit allowable manufacturing flaws to obtain a quality product, also enable this system to improve industrial standards for producing sandwich panels while increasing speed. (shrink)

With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering (...) on the program even with the help of computing resources. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. Three program complexity metrics are used to define three different single-objective combinatorial optimization versions of the problem, which are solved and analysed. Additionally, three multi-objective problems are defined, those considering each of the selected metrics together with the program execution time, in order to avoid strong obfuscations penalizing the performance. The goal of the defined problems is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo-assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Classical genetic algorithms (GAs) are used to solve the studied problems, namely a basic cellular GA for the single-objective problems and the popular NSGA-II for the multi-objective ones. The promising results show the potential of the proposed technique. (shrink)

The distributed temporal logic (DTL) is a logic for reasoning about temporal properties of distributed systems from the local point of view of the system’s agents, which are assumed to execute sequentially and to interact by means of synchronous event sharing. Different versions of DTL have been provided over the years for a number of different applications, reflecting different perspectives on how non-local information can be accessed by each agent. In this paper, we propose an automata-theoretic model checking algorithm for (...) DTL. To this end, we propose a notion of distributed transition system that will be used to specify the system to be verified. The properties that the system should meet are specified in DTL. In order to capture the models of these properties, we propose the notions of generalized distributed Büchi automaton and of distributed Büchi automaton. With these concepts, we are able to adapt results from automata-theoretic approaches to model checking in LTL to the distributed case. (shrink)

As the labeling costs in legal artificial intelligence tasks are expensive. Therefore, it becomes a challenge to utilize low cost to train a robust model. In this paper, we propose a LAIAugment approach, which aims to enhance the few-shot learning capability in legal artificial intelligence tasks. Specifically, we first use the self-training approach to label the amount of unlabelled data to enhance the feature learning capability of the model. Moreover, we also search for datasets that are similar to the training (...) set by improving the text similarity function. We conducted experimental analyses for three legal artificial intelligence tasks, including evidence extraction, legal element extraction, and case multi-label prediction, which composed of 3500 judgement documents. The experimental results show that the proposed LAIAugment method has an average F1-score of 72.3% on the three legal AI tasks, which is 1.93% higher than the baseline model. At the same time, it shows a huge improvement in few-shot learning. (shrink)

This paper explores the extension of formal accounts of precedential constraint to make use of a factor hierarchy with intermediate factors. A problem arises, however, because constraints expressed in terms of intermediate factors may give different outcomes from those expressed only using base level factors. We argue that constraints that use only base level factors yield the correct outcomes, but that intermediate factors play an important role in the justification and explanation of those outcomes. The discussion is illustrated with a (...) running example. (shrink)

We live in exciting times for AI and Law: technical developments are moving at a breakneck pace, and at the same time, the call for more robust AI governance and regulation grows stronger. How should we as an AI & Law community navigate these dramatic developments and claims? In this Presidential Address, I present my ideas for a way forward: researching, developing and evaluating real AI systems for the legal field with researchers from AI, Law and beyond. I will demonstrate (...) how we at the Netherlands National Police Lab AI are developing responsible AI by combining insights from different disciplines, and how this connects to the future of our field. (shrink)

We argue that the societal consequences of the scientific realism debate, in the context of science-to-public communication are often overlooked and careful theorizing about it needs further empirical groundwork. As such, we conducted a survey experiment with 130 academics (from physics, chemistry, and biology) and 137 science communicators. We provided them with an 11-item questionnaire probing their views of scientific realism and related concepts. Contra theoretical expectations, we find that (a) science communicators are generally more inclined towards scientific antirealism when (...) compared to scientists in the same academic fields, though both groups show an inclination towards realism and (b) academics who engage in more theoretical work are not less (or more) realist than experimentalists. Lastly, (c), we fail to find differences with respect to selective realism but find that science communicators are significantly less epistemically voluntarist compared to their academic counterparts. Overall, our results provide first empirical evidence on the views of scientists and science communicators on scientific realism, with some results running contra to the theoretical expectations, opening up new empirical and theoretical research directions. (shrink)

This paper copes with a real-world classification problem related to the management of claims received in an insurance company. The way to obtain the classifier is not easy due to the high amount of missing values as well as the inherent imbalanced scenario within class labels. Once the data partition has been done, the training set is submitted to an intensive double grid search in order to obtain the most promising type of missing value imputation approach and then a step (...) ahead is done using the best method and it starts the next round of data mining strategies which now falls into data rebalancing umbrella. Again, a grid search from an undersampling and oversampling family with different settings is done taking into account only seen data. The training data obtained after the first grid search are now submitted to the second step according the second grid search in order to get the ready training set for the further classifier training. The main objective of the work is to find the best combination of data mining techniques that suits the data set with a pipeline containing two types of data preparation methods coming from different families. As an outcome, first the problem of the presence of missing values has been addressed and then the data rebalancing techniques has been applied. The study focuses on obtaining classifiers based on Bayesian and lazy approaches as well as decision trees, evaluated on metrics such as the area under the ROC curve (AUC), Cohen’s kappa, Accuracy and the F-measure, among others. The imputation by the mean the mode is preferable to the Expectation Maximization Imputation in the scenario faced in this paper taking into account that the amount of missing values is higher than a forty percent for many features. (shrink)

This paper essentially originates from the notion of a block in an orthomodular lattice. What happens to orthomodularity when orthocomplementation is weakened? We will show that, under definitely smooth conditions, a great deal of the theory of orthomodular lattices carries over naturally.

The frequency of cyberattacks has been rapidly increasing in recent times, which is a significant concern. These attacks exploit vulnerabilities present in the software components that constitute the targeted system. Consequently, the number of vulnerabilities within these software components serves as an indicator of the system’s level of security and trustworthiness. This paper compares the accuracy, trainability and stability to configuration parameters of several neural network architectures, namely Long Short-Term Memory, Multilayer Perceptron and Convolutional Neural Network. These architectures are utilized (...) for forecasting the number of software vulnerabilities within a specified timeframe for a specific software product. By evaluating these neural network models, our aim is to provide insights into their performance and effectiveness in vulnerability forecasting. (shrink)

Unfortunately, there are still cases of domestic violence or situations where it is necessary to call for help without arousing the suspicion of the aggressor. In these situations, the help signal devised by the Canadian Women’s Foundation has proven to be effective in reporting a risky situation. By displaying a sequence of hand signals, it is possible to report that help is needed. This work presents a vision-based system that detects this sequence and implements it in a social robot, so (...) that it can automatically identify unwanted situations and alert the authorities. The gesture recognition pipeline presented in this work is integrated into a cognitive architecture used to generate behaviours in robots. In this way, the robot interacts with humans and is able to detect if a person is calling for help. In that case, the robot will act accordingly without alerting the aggressor. The proposed vision system uses the MediaPipe library to detect people in an image and locate the hands, from which it extracts a set of hand landmarks that identify which gesture is being made. By analysing the sequence of detected gestures, it can identify whether a person is performing the distress hand signal with an accuracy of 96.43%. (shrink)

The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate the proposal, two (...) different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise. (shrink)

Cooperative, connected and automated mobility technologies have the potential to revolutionize transportation systems and enhance safety, efficiency and sustainability. However, the increasing reliance on digital technologies also introduces new cybersecurity risks that can compromise the safety and privacy of passengers and the integrity of transportation systems. The purpose of this article is to examine the most important threats, vulnerabilities, risks and challenges related to automated mobility and to review the status of the most promising standardization initiatives on cryptography in a (...) world where quantum computers will soon represent an actual threat. (shrink)

A sustainable supply chain management strategy reduces risks and meets environmental, economic and social objectives by integrating environmental and financial practices. In an ever-changing environment, supply chains have become vulnerable at many levels. In a global supply chain, carefully tracing a product is of great importance to avoid future problems. This paper describes a self-updating smart contract, which includes data validation, for tracing global supply chains using blockchains. Our proposal uses a machine learning model to detect anomalies on traceable data, (...) which helps supply chain operators detect anomalous behavior at any point in the chain in real time. Hyperledger Caliper has been used to evaluate our proposal, and obtained a combined average throughput of 184 transactions per second and an average latency of 0.41 seconds, ensuring that our proposal does not negatively impact supply chain processes while improving supply chain management through data anomaly detection. (shrink)

The Next Release Problem (NRP) is a combinatorial optimization problem that aims to find a subset of software requirements to be delivered in the next software release, which maximize the satisfaction of a list of clients and minimize the effort required by developers to implement them. Previous studies have applied various metaheuristics, mostly genetic algorithms. Estimation of Distribution Algorithms (EDA), based on probabilistic modelling, have been proved to obtain good results in problems where genetic algorithms struggle. In this paper we (...) propose to adapt three EDAs to tackle the multi-objective NRP in a fast and effective way. Results show that EDAs can be applicable to solve the NRP with rather good quality of solutions. Furthermore, we prove that their execution time can be significantly reduced using a per-iteration solution subset selection method while maintaining the overall quality of the solutions obtained, and they perform the best when limiting the search time as in an interactive tool that requires fast responsiveness. The experimental framework, code and datasets have been made public in a code repository. (shrink)

The problem of explaining color perception has fascinated painters, philosophers and scientists throughout the history. In many cases, the ideas and discoveries about color perception in one of these categories influenced the others, thus resulting in one of the most remarkable cross-fertilization of human thought. At the end of the nineteenth century, two models stood out as the most convincing ones: Young-Helmholtz’s trichromacy on one side, and Hering’s opponency on the other side. The former was mainly supported by painters and (...) scientists, although with some noticeable exceptions as, e.g., Otto Runge, while the majority of philosophers supported the latter. These two apparently incompatible models were proven to be two complementary parts of the hugely complex chain of events which leads to human color perception. Recently, a rigorous mathematical theory able to incorporate both trichromacy and opponency has been developed thanks to the use of the language and tools of quantum information. In this paper, we discuss the placement of this model within the philosophical theories about color. (shrink)

This paper tackles the following puzzle. On the one hand, a growing body of literature suggests that most of our current political disagreements are pernicious and difficult to resolve because they are instances of deep disagreement. On the other hand, there does not seem to be anything inherent to a deep disagreement that necessarily makes it pernicious nor irresolvable. To address this issue, I distinguish two senses in which a disagreement can be deemed “deep”, and show that they are conflated (...) in the literature. In order to separate them, I refer to these two senses as deep disagreement and deep conflict. Contrary to recent literature, I argue that most of our current pernicious political disagreements are so not because they are instances of deep disagreement, but of deep conflict. This way, the problem disappears. (shrink)

Reductionists about testimony think that testimony is never a basic source of justification. By contrast, anti-reductionists claim that, at least in some paradigmatic cases, testimony is a basic and independent source of justification. In support of their position, anti-reductionists usually claim that paradigmatic testimony-based beliefs are non-inferential in that recipients of testimony usually don’t reason their way from the fact that they were told that p to the belief that p—they simply come to believe that p. In this paper I (...) explore in detail the idea that paradigmatic testimony-based beliefs are non-inferentially justified and conclude that it is grounded on an overly simplistic characterization of inferential relations. Then, and taking my cue from Malmgren’s (2018) rich proposal about the varieties of inferential relations, I defend the view that paradigmatic testimony-based beliefs are inferentially justified after all. (shrink)

The use of dispositions has been put into question many times in the philosophical literature, especially with regards to how dispositional attributions can be justified. Yet, dispositions are an important part not only of our everyday talk but also of our scientific practices. In this paper, we develop an argument that infers the epistemic justification of dispositional talk from itsindispensability for carrying out basic epistemological projects, and we apply it to the use of dispositions in evolutionary biology. For doing this, (...) we first advocate for a function-based strategy for the epistemic justification of dispositional attributions. We next review the functional role of some key dispositional notions in evolutionary biology, such as fitness and evolvability. Then, we show that alternative non-dispositional substitutes of these dispositions fail to fulfill their roles to the same degree. We conclude that the use of dispositions is justified in evolutionary biology. (shrink)

This article adopts a minimal definition of biological usage to demonstrate that the debate over biological function encompasses two distinct dimensions: descriptive and prescriptive. In the descriptive dimension, biological usage serves as the final arbiter for evaluating different accounts of biological function. Conversely, in the prescriptive dimension, accounts are formulated despite biological usage. The main thesis of this article is that the descriptive/prescriptive distinction helps make better sense of the biological function debate from a novel perspective. This is elucidated by (...) meticulously analyzing the two dimensions and subsequently providing a global overview of the debate. (shrink)