Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and (...) precision all reaching 97.23%. The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

Cet essai a été présenté à l’atelier sur La démocratie de l’espace et l’espace de la démocratie, qui a eu lieu à Newcastle, en Angleterre, le 11 janvier 2008. Une version antérieure a été présentée à l’Université de Tokyo le 13 novembre 2007. Il sera publié en néerlandais, traduit par Freek Jansens, sous le titre “het plannen van ruimtes van (on)mogelijkheid” dans une collection éditée par Maarten Hajer et Jantine Grijzen sur les questions de politique contemporaine. Il a été traduit (...) de l’anglais par Martin Blanchard et révisé par Daniel Weinstock. (shrink)

Convergence research is driven by specific and compelling problems and requires deep integration across disciplines. The potential of convergence research is widely recognized, but questions remain about how to design, facilitate, and assess such research. Here we analyze a seven-year, twelve-million-dollar convergence project on sustainable climate risk management to answer two questions. First, what is the impact of a project-level emphasis on the values that motivate and tie convergence research to the compelling problems? Second, how does participation in convergence projects (...) shape the research of postdoctoral scholars who are still in the process of establishing themselves professionally? We use an interview-based approach to characterize what the project specifically enabled in each participant’s research. We find that (a) the project pushed participants’ research into better alignment with the motivating concept of convergence research and that this effect was stronger for postdoctoral scholars than for more senior faculty. (b) Postdocs’ self-assessed understanding of key project themes, however, appears unconnected to metrics of project participation, raising questions about training and integration. Regarding values, (c) the project enabled heightened attention to values in the research of a large minority of participants. (d) Participants strongly believe in the importance of explicitly reflecting on values that motivate and pervade scientific research, but they question their own understanding of how to put value-focused science into practice. This mismatch of perceived importance with poor understanding highlights an unmet need in the practice of convergence science. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. (...) The dataset was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...) framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Impostor syndrome or Impostor phenomenon is a belief that a person thinks their success is due to luck or external factors, not their abilities. This psychological trait is present in certain groups like women. In this paper, we propose a neutrosophic trait measure to represent the psychological concept of the trait-anti trait using refined neutrosophic sets. This study analysed a group of 200 undergraduate students for impostor syndrome, perfectionism, introversion and self-esteem: after the COVID pandemic break in 2021. Data labelling (...) was carried out using these neutrosophic trait measures. Machine learning models like Support Vector Machine(SVM), K-nearest neighbour (K-NN), and random forest were used to model the data; SVM provided the best accuracy of 92.15%. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over (...) a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

This study delves into the intricate relationship between an artist’s background (including nationality and gender) and the popularity of their artworks in the Museum of Modern Art (MoMA) in New York. Leveraging statistical methods, including Chi-squared tests and ANOVA, significant correlations between an artist’s nationality, gender, and the popularity of their artworks were identified. Time series analysis further underscored evolving trends in MoMA’s acquisition patterns over the years. The research also utilized a Random Forest classification model to predict (...) artwork popularity, achieving an accuracy rate of 0.75. The findings shed light on the dynamics of art acquisitions and offer implications for diversifying and enhancing museum collections in the future. (shrink)

In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent–child pairs of nodes as hidden variables for handling the partial occlusions. Moreover, the (...) visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts. (shrink)

In this study, we predict the different levels of performance in a Nintendo Entertainment System (NES) Tetris session based on the score and the number of matches played by the players. Using the first 45 seconds of gameplay, a Random Forest Classifier was trained on the five keys used in the game obtaining a ROC_AUC score of 0.80. Further analysis revealed that the number of down keys (forced drop) and the number of left keys (left translation) are the (...) most relevant keys in this task, showing that by merely including the data from these two keys our Random Forest Classifier reached a ROC_AUC score of 0.83.We conclude that the keylogger data during the early phases of a game session can be successfully used to predict performance in longer sessions of Tetris. (shrink)

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a (...) data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

Most forms of human papillomavirus can create alterations on a woman's cervix that can lead to cervical cancer in the long run, while others can produce genital or epidermal tumors. Cervical cancer is a leading cause of morbidity and mortality among women in low- and middle-income countries. The prediction of cervical cancer still remains an open challenge as there are several risk factors affecting the cervix of the women. By considering the above, the cervical cancer risk factor dataset from KAGGLE (...) data warehouse is executed for predicting the cervical cancer risk classes. The cervical cancer data set is normalised with incomplete data and Pattern Calibration. Secondly, the interpretive data analysis is carried out, and the target feature's dispersion of the cervical cancer risk is visualised. Thirdly, several classifiers are fitted to the unprocessed data set, and the performance is measured with pre and post feature scaling. Fourth, oversampling methodologies are applied to the pre - processed data set. Fifth, the oversampled dataset by differment methods are applied to all the classifiers and the performance is compared with pre and post feature scaling. Sixth, Precision, recall, Fscore, accuracy, and running time are some of the metrics used in performance analysis. The code is written in Python and executed with Anaconda Navigator on the Spyder framework. The findings of the experiments reveal that the Random forest classifier tends to sustain 96% accuracy pre and post scaling for unporocessed dataset. Similarly the same classifier tends to sustain 98% accuracy for all the oversampling techniques. (shrink)

In this study, we extracted facial action units (AUs) data during a Hearthstone tournament to investigate behavioural differences between expert, intermediate, and novice players. Our aim was to obtain insights into the nature of expertise and how it may be tracked using non-invasive methods such as AUs. These insights may shed light on the endogenous responses in the player and at the same time may provide information to the opponents during a competition. Our results show that player expertise may be (...) characterised by specific patterns in facial expressions. More specifically, AU17 (chin raiser), AU25 (lips apart), and AU26 (jaws drop) intensity responses during gameplay may vary according to players' expertise. Such results were obtained by training a random forest classifier to test whether we can use these three AUs alone to accurately detect players' expertise. The classifier reached 0.75 accuracy on 5-fold cross-validation, after balancing the class weights, and 0.85 after having applied the Synthetic Minority Over-sampling Technique (SMOTE) function. These results suggest that AUs can be effectively used to discriminate different levels of expertise in competitive video game players. (shrink)

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and (...) compared with others. (shrink)

Concurring to information from the Centresfor Illness Control and Anticipation, instruction and religion are both capable indicators of lasting or dissolving unions. The chance of a marriage finishing in separate was lower for individuals with more knowledge, with over half of relational unions of those who did not complete high school having finished in separate compared with roughly 30 percent of relational unions of college graduates. With this overview, the divorce rate dataset from UCI dataset repository is used for predicting (...) the divorce class target with the following contributions. Firstly, the Divorce rate dataset is subjected with the data cleaning and exploratory data analysis. Secondly, the data set is settled with different classifiers to look at the classification before and after feature scaling. Thirdly, the dataset is processed with various cross validation of training and testing dataset i.e 80:20, 30:70, 40:60, 50:50 to improve the accuracy of all the classifiers. Fourth, the dataset is processed with 15, 20 and 30 components of principal component analysis and then applied with all classifier algorithm to analyze the accuracy of divorce rate prediction. Fifth, the performance analysis is done with precision, recall, accuracy, fscore and running time to infer the classification before and after feature scaling. Experimental results show that the Random Forest classifier is found to have the accuracy of 98% for all PCA reduced dataset with 15, 20 and 30 components. The result showsthat Random Forest classifier is found to have the accuracy of 98% for 40:60, 50:50 of training and testing dataset. (shrink)

In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes are (...) fed into Classification methods of Machine Learning are been used to predict risk of heart disease. (shrink)

Vietnam’s forests are at risk - carbon offset schemes could be the best chance of saving them, say Dr. Quan-Hoang Vuong and Minh-Hoang Nguyen. The value of forests is deeply ingrained in Vietnamese culture. Rừng vàng, biển bạc” [“forests of gold and seas of silver”] is both a metaphor for Vietnam, and a description of its natural wealth. The phrase is everywhere, from political speeches to daily conversation, as is Nhất phá sơn lâm, nhì đâm hà bá [“the worst crime (...) is to destroy the mountains and forests, the second worst is to kill the river”]. Deforestation is a Vietnamese taboo. (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the (...) Global Forest Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

The concept of randomness has been unjustly neglected in recent philosophical literature, and when philosophers have thought about it, they have usually acquiesced in views about the concept that are fundamentally flawed. After indicating the ways in which these accounts are flawed, I propose that randomness is to be understood as a special case of the epistemic concept of the unpredictability of a process. This proposal arguably captures the intuitive desiderata for the concept of randomness; at least it should suggest (...) that the commonly accepted accounts cannot be the whole story and more philosophical attention needs to be paid. 1. Randomness in science1.1Random systems1.2Random behaviour1.3Random sampling1.4Caprice, arbitrariness and noise2. Concepts of randomness2.1Von Mises/church/martin-löf randomness2.2KCS-randomness3. Randomness is unpredictability: preliminaries3.1Process and product randomness3.2Randomness is indeterminism?4. Predictability4.1Epistemic constraints on prediction4.2Computational constraints on prediction4.3Pragmatic constraints on prediction4.4Prediction defined5. Unpredictability6. Randomness is unpredictability6.1Clarification of the definition of randomness6.2Randomness and probability6.3Subjectivity and context sensitivity of randomness7. Evaluating the analysis[R]andomness … is going to be a concept which is relative to our body of knowledge, which will somehow reflect what we know and what we don't know. Henry E. Kyburg, Jr ([1974], p. 217)Phenomena that we cannot predict must be judged random. Patrick Suppes ([1984], p. 32). (shrink)

We consider two ways one might use algorithmic randomness to characterize a probabilistic law. The first is a generative chance* law. Such laws involve a nonstandard notion of chance. The second is a probabilistic* constraining law. Such laws impose relative frequency and randomness constraints that every physically possible world must satisfy. While each notion has virtues, we argue that the latter has advantages over the former. It supports a unified governing account of non-Humean laws and provides independently motivated solutions to (...) issues in the Humean best-system account. On both notions, we have a much tighter connection between probabilistic laws and their corresponding sets of possible worlds. Certain histories permitted by traditional probabilistic laws are ruled out as physically impossible. As a result, such laws avoid one variety of empirical underdetermination, but the approach reveals other varieties of underdetermination that are typically overlooked. (shrink)

Forests play fundamental roles in the Earth’s ecosystems. With the great capability of carbon sequestration, tropical forests are expected to contribute substantially to reducing the CO2 in Earth’s atmosphere. However, global tropical forest areas have declined drastically over the last few decades due to pressures from socio-economic development pursuit. The current essay aims to demonstrate the ongoing global deforestation crisis and its underlying drivers and discuss the vital roles of tropical forests in the socio-economic development in the face of (...) climate change when developed countries start to tighten the carbon emission control regulations (e.g., European Union’s Carbon Border Adjustment Mechanism). It suggests why developing countries in tropical regions, particularly Vietnam, should not sacrifice forests for short- and medium-term socio-economic goals but capitalize on them to leverage long-term development. Moreover, evidence of Vietnamese political leaders’ commitment to forest protection and sustainable development is also provided. (shrink)

In 1947 Donald Cary Williams claimed in The Ground of Induction to have solved the Humean problem of induction, by means of an adaptation of reasoning first advanced by Bernoulli in 1713. Later on David Stove defended and improved upon Williams’ argument in The Rational- ity of Induction (1986). We call this proposed solution of induction the ‘Williams-Stove sampling thesis’. There has been no lack of objections raised to the sampling thesis, and it has not been widely accepted. In our (...) opinion, though, none of these objections has the slightest force, and, moreover, the sampling thesis is undoubtedly true. What we will argue in this paper is that one particular objection that has been raised on numerous occasions is misguided. This concerns the randomness of the sample on which the inductive extrapolation is based. (shrink)

The practice of trading forest carbon credits has gained significant attention as a strategy to combat climate change. By allowing companies to buy and sell credits representing forest carbon sequestration, it aims to create financial incentives for forest preservation. However, a recent report highlights the dangers of commodifying forest carbon if such financial mechanisms overshadow other vital environmental and social values. To understand the complexities of this issue, we can turn to the mindsponge theory (MT), particularly (...) its information-value nexus hypothesis, for valuable insights. (shrink)

Are there any truly ontologically random events? This paper argues that randomness is an unavoidably epistemic concept and therefore ascription of ontological randomness to any particular event or series of events can never be justified.

Do forest owners’ levels of education or value profiles explain their responses to climate change? The cultural cognition thesis has cast serious doubt on the familiar and often criticized "knowledge deficit" model, which says that laypeople are less concerned about climate change because they lack scientific knowledge. Advocates of CCT maintain that citizens with the highest degrees of scientific literacy and numeracy are not the most concerned about climate change. Rather, this is the group in which cultural polarization is (...) greatest, and thus individuals with more limited scientific literacy and numeracy are more concerned about climate change under certain circumstances than those with higher scientific literacy and numeracy. The CCT predicts that cultural and other values will trump the positive effects of education on some forest owners' attitudes to climate change. Here, using survey data collected in 2010 from 766 private forest owners in Sweden and Germany, we provide the first evidence that perceptions of climate change risk are uncorrelated with, or sometimes positively correlated with, education level and can be explained without reference to cultural or other values. We conclude that the recent claim that advanced scientific literacy and numeracy polarizes perceptions of climate change risk is unsupported by the forest owner data. In neither of the two countries was university education found to reduce the perception of risk from climate change. Indeed in most cases university education increased the perception of risk. Even more importantly, the effect of university education was not dependent on the individuals' value profile. (shrink)

In this paper we consider conditional random quantities (c.r.q.’s) in the setting of coherence. Based on betting scheme, a c.r.q. X|H is not looked at as a restriction but, in a more extended way, as \({XH + \mathbb{P}(X|H)H^c}\) ; in particular (the indicator of) a conditional event E|H is looked at as EH + P(E|H)H c . This extended notion of c.r.q. allows algebraic developments among c.r.q.’s even if the conditioning events are different; then, for instance, we can give (...) a meaning to the sum X|H + Y|K and we can define the iterated c.r.q. (X|H)|K. We analyze the conjunction of two conditional events, introduced by the authors in a recent work, in the setting of coherence. We show that the conjoined conditional is a conditional random quantity, which may be a conditional event when there are logical dependencies. Moreover, we introduce the negation of the conjunction and by applying De Morgan’s Law we obtain the disjoined conditional. Finally, we give the lower and upper bounds for the conjunction and disjunction of two conditional events, by showing that the usual probabilistic properties continue to hold. (shrink)

Alexander Rosenberg (1994) claims that the omniscient viewpoint of the evolutionary process would have no need for the concept of random drift. However, his argument fails to take into account all of the processes which are considered to be instances of random drift. A consideration of these processes shows that random drift is not eliminable even given a position of omniscience. Furthermore, Rosenberg must take these processes into account in order to support his claims that evolution is (...) deterministic and that evolutionary biology is an instrumental science. (shrink)

This research note challenges the idea that Random Somatic Mutations are entirely random, highlighting their non-equiprobable nature and their influence on evolution, involution, or indeterminacy. It recalls the Neutrosophic Theory of Evolution, extending Darwin’s theory, and emphasizes the importance of distinguishing between different senses of ‘random mutation’ in evolutionary theory.

n this text, we revisit part of the analysis of anti-entropy in Bailly and Longo (2009} and develop further theoretical reflections. In particular, we analyze how randomness, an essential component of biological variability, is associated to the growth of biological organization, both in ontogenesis and in evolution. This approach, in particular, focuses on the role of global entropy production and provides a tool for a mathematical understanding of some fundamental observations by Gould on the increasing phenotypic complexity along evolution. Lastly, (...) we analyze the situation in terms of theoretical symmetries, in order to further specify the biological meaning of anti-entropy as well as its strong link with randomness. (shrink)

Early work on the frequency theory of probability made extensive use of the notion of randomness, conceived of as a property possessed by disorderly collections of outcomes. Growing out of this work, a rich mathematical literature on algorithmic randomness and Kolmogorov complexity developed through the twentieth century, but largely lost contact with the philosophical literature on physical probability. The present chapter begins with a clarification of the notions of randomness and probability, conceiving of the former as a property of a (...) sequence of outcomes, and the latter as a property of the process generating those outcomes. A discussion follows of the nature and limits of the relationship between the two notions, with largely negative verdicts on the prospects for any reduction of one to the other, although the existence of an apparently random sequence of outcomes is good evidence for the involvement of a genuinely chancy process. (shrink)

On absolute random and restricted random .

Sortition, i.e. random appointment for public duty, has been employed by societies throughout the years as a firewall designated to prevent illegitimate interference between parties in a legal case and agents of the legal system. In judicial systems of modern western countries, random procedures are mainly employed to select the jury, the court and/or the judge in charge of judging a legal case. Therefore, these random procedures play an important role in the course of a case, and (...) should comply with some principles, such as transparency and complete auditability. Nevertheless, these principles are neglected by random procedures in some judicial systems, which are performed in secrecy and are not auditable by the involved parties. The assignment of cases in the Brazilian Supreme Court is an example of such a procedure, for it is performed using procedures unknown to the parties involved in the judicial cases. This article presents a review of how sortition has been historically employed by societies and discusses how Mathematical Statistics may be applied to random procedures of the judicial system, as it has been applied for almost a century on clinical trials, for example. A statistical model for assessing randomness in case assignment is proposed and applied to the Brazilian Supreme Court. As final remarks, guidelines for the development of good randomization procedures are outlined. (shrink)

Data from the International Space Station reveals that, in some tropical forests, a small percentage of forest leaves are exposed to extreme temperatures. Scientists are concerned and warn that this trend may continue to escalate. This is a consequence of the Earth’s warming process, causing tropical forests to become excessively hot, potentially surpassing the maximum temperature threshold for leaves to photosynthesize. The ultimate result could be the collapse of the flora system.

A puzzle of an unmarked clock, used by Timothy Williamson to question the KK principle, was separately adapted by David Christensen and Adam Elga to critique a principle of Rational Reflection. Both authors, we argue, flout the received relationship between ideal agency and the classical distinction between systematic and random error, namely that ideal agents are subject only to the latter. As a result, these criticisms miss their mark.

Our latest contribution to the world’s battle against climate and biodiversity crises was an expert commentary on Vietnamese forests and the prospect of carbon credits trade. The article appeared in the Land and Climate Review on Feb. 2, 2024.

Review of "Exploring Randomness" (200) and "The Unknowable" (1999) by Gregory Chaitin.

In this study, we consider that enabling young people to experience direct contact with nearby natural environments can positively influence their knowledge and feelings about the biodiversity that occurs there, contributing to its protection and conservation for current and future generations. In this study, we explore how teenagers (n = 17) aged between 13 and 17 years old describe and perceive the nearby natural environment before and after an interpretive trail in Una, Bahia, Brazil. Participants were asked to draw the (...) Atlantic Forest with colored pencils on white paper and, based on the drawing, they answered the following questions: “What is in your drawing? and “What is happening in your drawing?,” in addition to other information such as the title of the drawing, difficulty of the activity, and sociodemographic aspects. Content analysis was used to analyze the information collected. From the drawings and responses of the participants, categories related to knowledge, experiences, and types of relationships with the visited place emerged. (shrink)

Explaining the behaviour of ecosystems is one of the key challenges for the biological sciences. Since 2000, new-mechanicism has been the main model to account for the nature of scientific explanation in biology. The universality of the new-mechanist view in biology has been however put into question due to the existence of explanations that account for some biological phenomena in terms of their mathematical properties (mathematical explanations). Supporters of mathematical explanation have argued that the explanation of the behaviour of ecosystems (...) is usually provided in terms of their mathematical properties, and not in mechanistic terms. They have intensively studied the explanation of the properties of ecosystems that behave following the rules of a non-random network. However, no attention has been devoted to the study of the nature of the explanation in those that form a random network. In this paper, we cover that gap by analysing the explanation of the stability behaviour of the microbiome recently elaborated by Coyte and colleagues, to determine whether it fits with the model of explanation suggested by the new-mechanist or by the defenders of mathematical explanation. Our analysis of this case study supports three theses: (1) that the explanation is not given solely in terms of mechanisms, as the new-mechanists understand the concept; (2) that the mathematical properties that describe the system play an essential explanatory role, but they do not exhaust the explanation; (3) that a non-previously identified appeal to the type of interactions that the entities in the network can exhibit, as well as their abundance, is also necessary for Coyte and colleagues’ account to be fully explanatory. From the combination of these three theses we argue for the necessity of an integrative pluralist view of the nature of behaviour explanation when this is given by appealing to the existence of a random network. (shrink)

When does it make sense to act randomly? A persuasive argument from Bayesian decision theory legitimizes randomization essentially only in tie-breaking situations. Rational behaviour in humans, non-human animals, and artificial agents, however, often seems indeterminate, even random. Moreover, rationales for randomized acts have been offered in a number of disciplines, including game theory, experimental design, and machine learning. A common way of accommodating some of these observations is by appeal to a decision-maker’s bounded computational resources. Making this suggestion both (...) precise and compelling is surprisingly difficult. Toward this end, I propose two fundamental rationales for randomization, drawing upon diverse ideas and results from the wider theory of computation. The first unifies common intuitions in favour of randomization from the aforementioned disciplines. The second introduces a deep connection between randomization and memory: access to a randomizing device is provably helpful for an agent burdened with a finite memory. Aside from fit with ordinary intuitions about rational action, the two rationales also make sense of empirical observations in the biological world. Indeed, random behaviour emerges more or less where it should, according to the proposal. (shrink)

This paper reviews current initiatives to establish carbon offset forests in suburban and peri-urban environments. While moments of density occur within urban territories the general spatial condition is one of fragmented and patchy networks made up of a heterogeneous mix of residential enclaves, industrial parks, waste sites, infrastructure easements interspersed with forests, agriculture, leftover voids and overlooked open space. These overlooked open spaces have the potential to form a new green urban structure of carbon offset forests as cities respond to (...) the carbon economy. Furthermore such urban spaces are not likely to diminish over time as cities are not only growing larger but are becoming more dispersed according to recent World Bank reports. This strong trend is not limited to developed or developing cities but is a worldwide phenomenon. In the future, policy incentives for carbon sequestration will drive land use change in rural and urban areas alike. The paper documents projects in Blacktown City Council in Western Sydney. (shrink)

Uncertainty, insufficient information or information of poor quality, limited cognitive capacity and time, along with value conflicts and ethical considerations, are all aspects thatmake risk managementand riskcommunication difficult. This paper provides a review of different risk concepts and describes how these influence risk management, communication and planning in relation to forest ecosystem services. Based on the review and results of empirical studies, we suggest that personal assessment of risk is decisive in the management of forest ecosystem services. The (...) results are used together with a reviewof different principles of the distribution of risk to propose an approach to risk communication that is effective aswell as ethically sound. Knowledge of heuristics and mutual information on both beliefs and desires are important in the proposed risk communication approach. Such knowledge provides an opportunity for relevant information exchange, so that gaps in personal knowledge maps can be filled in and effective risk communication can be promoted. (shrink)

Traditionally Ψ is used to stand in for both the mathematical wavefunction (the representation) and the quantum state (the thing in the world). This elision has been elevated to a metaphysical thesis by advocates of the view known as wavefunction realism. My aim in this paper is to challenge the hegemony of the wavefunction by calling attention to a little-known formulation of quantum theory that does not make use of the wavefunction in representing the quantum state. This approach, called Lagrangian (...) quantum hydrodynamics (LQH), is not an approximation scheme, but rather a full alternative formulation of quantum theory. I argue that a careful consideration of alternative formalisms is an essential part of any realist project that attempts to read the ontology of a theory off of the mathematical formalism. In particular, I show that LQH undercuts the central presumption of wavefunction realism and falsifies the claim that one must represent the many-body quantum state as living in a 3n-dimensional configuration space. I conclude by briefly sketching three different realist approaches one could take toward LQH, and argue that both models of the quantum state should be admitted. When exploring quantum realism, regaining sight of the proverbial forest of quantum representations beyond the Ψ is just the first step. (shrink)

One of the most influential and controversial schools of 20th century Thomism—especially in North America—is the “River Forest School” or “River Forest Thomism”. And one of the most influential and controversial theses associated with that school is the thesis that metaphysics cannot be established as a distinct and autonomous science unless one has already proven the existence of a positively immaterial being. The purpose of this paper is to show that River Forest Thomism cannot and should not (...) be reduced to that controversial thesis. As such, rejection of the thesis cannot and should not constitute a rejection of the school. Indeed, as soon as we understand what River Forest Thomism was really about, it will become clear that all Thomists should be River Forest Thomists. (shrink)

In this article, we provide three generators of propositional formulae for arbitrary languages, which uniformly sample three different formulae spaces. They take the same three parameters as input, namely, a desired depth, a set of atomics and a set of logical constants (with specified arities). The first generator returns formulae of exactly the given depth, using all or some of the propositional letters. The second does the same but samples up-to the given depth. The third generator outputs formulae with exactly (...) the desired depth and all the atomics in the set. To make the generators uniform (i.e. to make them return every formula in their space with the same probability), we will prove various cardinality results about those spaces. (shrink)