Human Reasoning Research Articles

Fuzzy Analytical Hierarchy Process for Assessing Damage in Old Masonry Buildings: A Case Study

ABSTRACT This study presents an approach using a fuzzy analytical hierarchy process to assist experts during inspection campaigns of old masonry buildings. The approach is developed through a case study on the assessment of damages to old masonry buildings. Fuzzy systems are preferred due to their ability to handle uncertainties by simulating human reasoning during decision-making. The problem is broken down into a hierarchy of 37 criteria and sub-criteria to enable informed decisions. The weights of these criteria are calculated using the FAHP approach, which is commonly used in multi-criteria decision-making. Experts then assign scores to estimate the global score, reflecting the overall damage to the historical structure. This overall level of damage is associated with a colour, indicating the degree of construction degradation. The methodology is illustrated in the assessment of the health condition of the historic monument of Emir Abdelkader’s staff, with results found to be in agreement with those obtained from previous expertise on the historical building. The study concludes that the method adopted is an effective scientific tool that can be of great support to experts in their task of assessing the damage caused to old Masonry buildings.

Artificial Intelligence Security through Input Data Quality Steps

In times of digital transformation, data generation constitutes a new type of asset, as it requires new forms of organizational learning, giving rise to new business models. The focus of all this transformation is Artificial Intelligence (AI), a subfield of Computer Science responsible for creating computational resources with capabilities similar to human reasoning for automated problem-solving. Elements of mathematics and engineering are used to reproduce aspects of human intelligence, as well as insights from other areas such as philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory, cybernetics, and linguistics. Hence the name AI. Machines learn to speak, write, interpret data, and solve problems through AI, a tool that is now essential for Industry 4.0, as a transition from industrial society to the knowledge and digital economy. In turn, Generative AI uses multimodal tools to work with elements such as spoken language, images, sounds, body movements, etc. All this technology requires innovations in companies through the adoption of frameworks that enable the selection and storage of qualified data. This original article was developed based on extensive bibliographic research and scientific materials. Its objective is to demonstrate the relevance of data analysis as a way to apply compliance practices to AI. As a result of this research, there was a recognized need for greater attention to input data quality processes for generative AI training.

Relational reasoning in wild bumblebees revisited: the role of distance

In reasoning tasks, non-human animals attend more to relational than to object similarity. It is precisely this focus on relational similarity that has been argued to explain the reasoning gap between humans and other animals. Work with humans has revealed that objects placed near each other are represented to be more similar than objects placed farther apart. Will distance between objects also affect non-human animals’ abilities to represent and reason about objects? To test this, wild bumblebees were presented with a spatial reasoning task (with competing object matches) in which the objects or features alone (colour, shape) were placed close together or far apart. Bumblebees spontaneously attended to objects over relations, but only when the objects were far apart. Features alone were not strong enough to drive object matching—suggesting that bumblebees bound colour and shape into their object representations. These findings question whether the ability to focus on and compare objects is what makes human abstract reasoning unique.

Human Conditional Reasoning in Answer Set Programming

AbstractGiven a conditional sentence “ ${\varphi}\Rightarrow \psi$ " (if ${\varphi}$ then $\psi$ ) and respective facts, four different types of inferences are observed in human reasoning: Affirming the antecedent (AA) (or modus ponens) reasons $\psi$ from ${\varphi}$ ; affirming the consequent (AC) reasons ${\varphi}$ from $\psi$ ; denying the antecedent (DA) reasons $\neg\psi$ from $\neg{\varphi}$ ; and denying the consequent (DC) (or modus tollens) reasons $\neg{\varphi}$ from $\neg\psi$ . Among them, AA and DC are logically valid, while AC and DA are logically invalid and often called logical fallacies. Nevertheless, humans often perform AC or DA as pragmatic inference in daily life. In this paper, we realize AC, DA and DC inferences in answer set programming. Eight different types of completion are introduced, and their semantics are given by answer sets. We investigate formal properties and characterize human reasoning tasks in cognitive psychology. Those completions are also applied to commonsense reasoning in AI.

Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.

The COVID-19 pandemic had a global impact and created an unprecedented emergency in healthcare and other related frontline sectors. Various Artificial-Intelligence-based models were developed to effectively manage medical resources and identify patients at high risk. However, many of these AI models were limited in their practical high-risk applicability due to their "black-box" nature, i.e., lack of interpretability of the model. To tackle this problem, Explainable Artificial Intelligence (XAI) was introduced, aiming to explore the "black box" behavior of machine learning models and offer definitive and interpretable evidence. XAI provides interpretable analysis in a human-compliant way, thus boosting our confidence in the successful implementation of AI systems in the wild. In this regard, this study explores the use of model-agnostic XAI models, such as SHapley Additive exPlanations values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), for COVID-19 symptom analysis in Indian patients toward a COVID severity prediction task. Various machine learning models such as Decision Tree Classifier, XGBoost Classifier, and Neural Network Classifier are leveraged to develop Machine Learning models. The proposed XAI tools are found to augment the high performance of AI systems with human interpretable evidence and reasoning, as shown through the interpretation of various explainability plots. Our comparative analysis illustrates the significance of XAI tools and their impact within a healthcare context. The study suggests that SHAP and LIME analysis are promising methods for incorporating explainability in model development and can lead to better and more trustworthy ML models in the future.

Harmonizing Artificial Intelligence with Islamic Values - A Thoughtful Analysis of Religious, Social, and Economic Impacts of Technological Advancements

The research explored the integration of AI technologies with Islamic principles and their implications for religious, social, and economic structures in Muslim-majority countries. It used qualitative design, semi-structured interviews, and case studies to analyze digitization of the Holy Qur’an and Hadith, and AI in banking compliance with Shariah laws, focusing on lexical-semantic, syntactical, grammatical, and pragmatic issues. A few of the such AI-based Qur’an tools include KSU Qur’an (مشروع المصحف الإلكتروني بجامعة الملك سعود), Islam web, Qur’anic Arabic Corpus, Almonagib alQur’any (المنقب القرآني), Tanzil (Tanzil Documents), The Qur’an (Al-Qur’an (القرآن) - Online Qur’an Project - Translation and Tafsir) and the Noble Qur’an (The Noble Qur’an – القرآن الكريم). Islamic Fintech, which adheres to Shariah principles, is embracing innovation to streamline transactions and comply with the Holy Qur’an. AI virtual assistants like ‘Aisyah’ are being adopted by Islamic banks to streamline transactions and eliminate risks like leverage and uneven maturity. The regulatory management of Islamic FinTech includes issues like Islamic cryptocurrency rules, money creation, and eliminating ‘riba’. In Malaysia and Indonesia, digital zakat distribution and classification systems are being developed using AI to accurately classify those in need and compute financial percentages for each group. Thematic analysis revealed that human-inspired, analytical, and humanized AI has led to its significant impact on various aspects of life, society, and employment. Nonetheless, AI cannot substitute for the profound comprehension and knowledge of Islamic jurists, alongside human reasoning and intellectual competence. Job displacement and ethical concerns of accuracy, transparency, fairness, and accountability remain significant challenges for society.

Towards human-like perception: Learning structural causal model in heterogeneous graph

Heterogeneous graph neural networks have become popular in various domains. However, their generalizability and interpretability are limited due to the discrepancy between their inherent inference flows and human reasoning logic or underlying causal relationships for the learning problem. This study introduces a novel solution, HG-SCM (Heterogeneous Graph as Structural Causal Model). It can mimic the human perception and decision process through two key steps: constructing intelligible variables based on semantics derived from the graph schema and automatically learning task-level causal relationships among these variables by incorporating advanced causal discovery techniques. We compared HG-SCM to seven state-of-the-art baseline models on three real-world datasets, under three distinct and ubiquitous out-of-distribution settings. HG-SCM achieved the highest average performance rank with minimal standard deviation, substantiating its effectiveness and superiority in terms of both predictive power and generalizability. Additionally, the visualization and analysis of the auto-learned causal diagrams for the three tasks aligned well with domain knowledge and human cognition, demonstrating prominent interpretability. HG-SCM’s human-like nature and its enhanced generalizability and interpretability make it a promising solution for special scenarios where transparency and trustworthiness are paramount.

Debiasing thinking among non-WEIRD reasoners

Human reasoning has been shown to be biased in a variety of situations. While most studies have focused on samples of WEIRD participants (from Western Educated Industrialized Rich and Democratic societies), the sparse non-WEIRD data on the topic suggest an even stronger propensity for biased reasoning. This could be explained by a competence issue (people lack the ability to integrate logical knowledge into their reasoning) or a performance issue (people possess the logical knowledge but do not know it is relevant). We addressed this question using a debiasing paradigm with the base-rate task on a sample of non-industrialized people, the Himba of Namibia. After a short training, most participants were debiased, lending credence to the performance account. Debiasing was however to some extent boosted by schooling and living environment suggesting that competence also plays a role (in that more acquired knowledge allows for a higher training benefit). Results imply that debias interventions can be successfully employed to boost sound reasoning around the world.

Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining.

Fuzzy utility (FU) pattern mining with an advantage in human reasoning has become one of the interesting topics in studies of knowledge discovery. The discovered information in FU pattern mining from real-life quantitative databases with item profits is suitable for interpreting data from a human perspective because it is not expressed using numerical values but linguistic terms which consist of natural languages. State-of-the-art approaches in this literature provide extended results by considering temporal factors, such as seasons, which can be influential in real-life situations. However, they still suffer from scalability issues because they are based on level-wise approaches which generate a number of candidates. In this article, we propose a scalable and efficient approach with a novel data structure for mining high temporal FU patterns without generating candidates. Efficient pruning techniques and algorithms are presented to improve the performance of the proposed approach. Performance experiments on both real and synthetic datasets show that the suggested algorithm has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability.

Reasoning Through Narrative

AbstractA peculiar feature of our species is that we settle what to believe, value, and do by reasoning through narratives. A narrative is adiachronic, information-rich story that contains persons, objects, and at least one event. When we reason through narrative, we usenarrative to settle what to do, to make predictions, to guide normative expectations, and to ground which reactive attitudes we think areappropriate in a situation. Narratives explain, justify, and provide understanding. Narratives play a ubiquitous role in human reasoning. Andyet, narratives do not seem up to the task. Narratives are often unmoored representations (either because they are do not purport to referto the actual world, or because they are grossly oversimplified, or because are known to be literally false). Against this, I argue thatnarratives guide our reasoning by shaping our grasp of modal structure: what is possible, probable, plausible, permissible, required,relevant, desirable and good. Narratives are good guides to reasoning when they guide us to accurate judgments about modal space. Icall this the modal model of narrative. In this paper, I develop an account of how narratives function in reasoning, as well as an account ofwhen reasoning through narrative counts as good reasoning.

Mathematics Educators Perception On Ways Of Resolving Human Development Issues In Nigeria Through Mathematics

This study investigated ways of resolving human development issues in Nigeria through Mathematics. The study adopted a descriptive survey research design. The population of the study consisted of 724 Mathematics educators from the Education Zones in Cross River State. A sample of 258 respondents was drawn using a convenient non-probability sampling technique by applying Yamane’s formula. Three research questions were raised to guide the study. A structured questionnaire of 15 items was used to collect data on ways of resolving human development issues in Nigeria through Mathematics. The instrument was validated by three experts; two of them were Mathematics experts while the other was from Tests and Measurement unit of the department of Educational Foundations, all in University of Calabar. The reliability of the instrument was calculated using Cronbach Alpha. The questionnaire had a reliability coefficient 77 data collected were analyzed using descriptive statistics of mean and standard deviation. Among the findings were that Mathematics can be applied in resolving human development issues in Nigeria through helping an individual to forecast for the future, carry out research on resolving human development issues and to take decision. It was recommended among others that: there should be effective and functional mathematics education at all levels in order to transform skills realized for mathematics to solve the nation’s security challenges; Since mathematics enhances the level of human reasoning and further develop the mind, technical and generic courses undertaken by staff in any industry should continuously incorporate some level of mathematics in their training curricula. This will further develop the minds of our present and future leaders for better decision making and be more scientific in general management and public administration; Mathematics educators should be involved in all government programmes for resolving human development issues.
 

Epistemological role of human reasoning in data-informed decision-making

Epistemological role of human reasoning in data-informed decision-making

Case Report: What—or who—killed Frank Ramsey? Some reflections on cause of death and the nature of medical reasoning

Philosopher Frank Ramsey died in 1930 aged only 26. There has been much speculation about the nature of his final illness and the sequence of events which led to his death. To prepare this case report, we traced Ramsey’s medical records and combined them with an extensive and unique dataset of contemporaneous sources. We use these to evaluate three possible explanations for Ramsey’s illness and its unexpectedly fatal trajectory—infectious (Weil’s disease), autoimmune (primary sclerosing cholangitis) and obstructive (gallstones). We explore how uncertainty surrounding each of these possibilities might have influenced Ramsey’s doctors’ thoughts and actions, including their ill-fated decision to perform the emergency operation that appears to have precipitated his final decline. We then reflect on the unfinished opus on which Ramsey was working when he died—on the nature of truth and how humans reason under conditions of uncertainty. We end with some thoughts linking Ramsey’s death to his philosophy.

Machine-guided discovery of a real-world rogue wave model.

Big data and large-scale machine learning have had a profound impact on science and engineering, particularly in fields focused on forecasting and prediction. Yet, it is still not clear how we can use the superior pattern-matching abilities of machine learning models for scientific discovery. This is because the goals of machine learning and science are generally not aligned. In addition to being accurate, scientific theories must also be causally consistent with the underlying physical process and allow for human analysis, reasoning, and manipulation to advance the field. In this paper, we present a case study on discovering a symbolic model for oceanic rogue waves from data using causal analysis, deep learning, parsimony-guided model selection, and symbolic regression. We train an artificial neural network on causal features from an extensive dataset of observations from wave buoys, while selecting for predictive performance and causal invariance. We apply symbolic regression to distill this black-box model into a mathematical equation that retains the neural network's predictive capabilities, while allowing for interpretation in the context of existing wave theory. The resulting model reproduces known behavior, generates well-calibrated probabilities, and achieves better predictive scores on unseen data than current theory. This showcases how machine learning can facilitate inductive scientific discovery and paves the way for more accurate rogue wave forecasting.

Can we change how people reason? Effects of instructions to reason differently and reasoning strategy.

Mental model (Johnson-Laird, 2001) and probabilistic theories (Oaksford & Chater, 2009) claim to provide distinct explanations of human reasoning. However, the dual strategy model of reasoning suggests that this distinction corresponds to different reasoning strategies, termed counterexample and statistical, respectively. There is clear evidence that most people have a preference for a given strategy, and that this predicts performance on a variety of forms of reasoning and judgment (Thompson & Markovits, 2021). To date, however, the evidence for this conclusion has been correlational in nature; in the current studies, we manipulated strategy use. To this end, we gave people (N = 885) explicit instructions to reason either using a counterexample strategy or a probabilistic strategy. In two studies, we observed that the ability to follow these instructions was constrained by people's spontaneous strategy use, and that the effect of instructions carried over to two subsequent forms of reasoning (a) belief-biased inferences and (b) base-rate judgments. Finally, the ability to follow instructions was correlated with reasoning accuracy on both tasks. These results provide strong evidence for the underlying reality of the dual strategy model and show that explicit instructions to reason differently can modify performance on different forms of reasoning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Why Dunhuang Survived Instead of Loulan -- An Analysis of the Revolution of the Silk Road

In the northwest of China, there is the Taklamakan Desert, one of the largest deserts in the world, and under the dust, there is a prosperous city hidden. At the same time, in the depths of the desert, a ruined ancient city was sleeping. The two cities we are going to introduce today - Dunhuang and Loulan, are two very similar but completely different cities. The ruins of one of the cities have been buried by wind and sand, but the other city is still full of vitality until today. Loulans disillusionment is not just a natural factor, but the result of many different choices. We will analyze the endings of these two cities from three aspects (nature, human reasons, and religious reasons), and I also hope that you can gain some thoughts about choice while reading.

Examining the network structure among moral functioning components with network analysis

I explored the association between components constituting the basis for moral and optimal human functioning, i.e., moral reasoning, moral identity, empathy, and purpose, via network analysis. I employed factor scores instead of composite scores that most previous studies used for better accuracy in score estimation in this study. Then, I estimated the network structure among collected variables and centrality indicators. For additional information, the structure and indicators were compared between two groups, participants who engaged in civic activities highly versus lowly. The results demonstrated significant intra- and inter-scale associations in the network. The network structure was invariant across the two groups. Despite the network invariance, I found that the global connectivity between and centrality indicators of examined factors were higher among the high civic engagement group in general. I discussed the implications of the findings in research on moral functioning based on moral psychology and virtue ethics.

Business Sector Financial Performance Evaluation Using Automated Approach in Assisting Investors’ Decision-Making

Investors must carefully study and analyze a company's financial performance in order to avoid the risk of financial losses, which takes a lot of time due to the numerous criteria or financial statistics that must be taken into account. The investor's choice of the best company to invest in is further complicated by the limitations of human reasoning when faced with a large selection of choices. This study has proposed an automated system to help investors who plan to make investments by offering features to assess the financial performance of particular businesses within particular industries. The authors have suggested two strategies: TOPSIS and Fuzzy Logic approaches. Successful automated system development has been accomplished using these approaches. The approach allows investors the freedom to select from a range of performance evaluation criteria to establish the rankings. Investors can view the companies' rank in terms of their financial performance. Investors may also avoid getting overwhelmed with complicated, perhaps conflicting web information by employing the existing systems. It is observed that the proposed system is very user-friendly and managed to assist the investors’ decision-making in making investments. It should be noted that the developed systems can be useful not only to investors but also to students taking business courses. From our analysis, there is no just one right technique to analyze a company and assess its current standings. Therefore, in the future, we recommend introducing an aggregate method to business appraisal in order to further give potential investors a better depiction of the businesses.

A Joke Off-hand: Who Says Jordanians Keep a Straight Face?

Based on a sample of 75 Jokes extracted from YouTube clips which belong to a comic program called ‘A Joke Off-hand’, different Jordanian male age-groups are shown to highly welcome, appreciate, and interact with joke telling in public. The topics of jokes are varied, mainly involving hash-addict (26.66%), marriage (16%), body defects (6.66%), and school (6.66%) jokes. The total absence of political jokes and the very few sexual and religious jokes (two instances each) may be ascribed to Jordanians’ relatively conservative attitude towards exposing such sensitive themes publically in addition to being aware of censorship as reported by the program’s presenter in a TV interview. Jordanian jokes are shown to be influenced by the jokester’s age, which is clearly reflected in the structure and sophistication of the joke. In terms of linguistic resources, the bulk of the jokes (85.33%) follows human logical reasoning based on conversational implicature, which can readily travel into English through translation. The remainder of the sample consist of linguistic jokes which defy translation and must be largely annotated if they were to make sense. Word ambiguity and onomastics seem to be a prevalent feature of Jordanian linguistic jokes.

Does God Think the Same Way We Do? On the Logical Apophatism of Michał Heller

Apophatic theology is an approach in theology that emphasizes the limitation of human language and concepts in describing the nature of the Divine. Rooted in ancient religious traditions, apophatic theology has gained attention in contemporary discourse for its potential convergence with the scientific method. This paper expands on a novel application of this approach in which the formal methods of science such as logic and mathematics are engaged to inquire into how God thinks and to what degree the modes of human reasoning can be projected on the nature of the Divine mind. This application has been proposed by Michał Heller and is referred to as the logical apophatism. In the course of the analysis carried out in this paper more in-depth understanding of the logical apophatism has been obtained by contrasting it with classical approaches to negative theology such as the Triplex Via and supplementing with recent advances within the cognitive sciences. It is concluded that Heller’s use of the apophatic approach is manifestly non-standard and its novelty consists in the shift of emphasis from the negative character of the language of theology to the logic of the Divine mind and the logic that underpins the workings of the Universe.