Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are (...) two types of credit risk default models in this regard: structural and reduced-form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models. (shrink)

Economic models describe individuals in terms of underlying characteristics, such as taste for some good, sympathy level for another player, time discount rate, risk attitude, and so on. In real life, such characteristics change through experiences: taste for Mozart changes through listening to it, sympathy for another player through observing his moves, and so on. Models typically ignore change, not just for simplicity but also because it is unclear how to incorporate change. I introduce a general axiomatic framework for (...) defining, analysing and comparing rival models of change. I show that seemingly basic postulates on modelling change together have strong implications, like irrelevance of the order in which someone has his experiences and ‘linearity’ of change. This is a step towards placing the modelling of change on solid axiomatic grounds and enabling non-arbitrary incorporation of change into economic models. (shrink)

This article presents a comprehensive framework for valuing financial instruments subject to credit risk. In particular, we focus on the impact of default dependence on asset pricing, as correlated default risk is one of the most pervasive threats in financial markets. We analyze how swap rates are affected by bilateral counterparty credit risk, and how CDS spreads depend on the trilateral credit risk of the buyer, seller, and reference entity in a contract. Moreover, we study the (...) effect of collateralization on valuation, since the majority of OTC derivatives are collateralized. The model shows that a fully collateralized swap is risk-free, whereas a fully collateralized CDS is not equivalent to a risk-free one. (shrink)

This paper argues that the reduced-form jump diffusion model may not be appropriate for credit risk modeling. To correctly value hybrid defaultable financial instruments, e.g., convertible bonds, we present a new framework that relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage is (...) mainly due to convertible underpricing. Empirically, however, we do not find evidence supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price. (shrink)

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)

In this chapter, one considers finance at its very foundations, namely, at the place where assumptions are being made about the ways to measure the two key ingredients of finance: risk and return. It is well known that returns for a large class of assets display a number of stylized facts that cannot be squared with the traditional views of 1960s financial economics (normality and continuity assumptions, i.e. Brownian representation of market dynamics). Despite the empirical counterevidence, normality and continuity (...) assumptions were part and parcel of financial theory and practice, embedded in all financial practices and beliefs. Our aim is to build on this puzzle for extracting some clues revealing the use of one research strategy in academic community, model tinkering defined as a particular research habit. We choose to focus on one specific moment of the scientific controversies in academic finance: the ‘leptokurtic crisis’ opened by Mandelbrot in 1962. The profoundness of the crisis came from the angle of the Mandelbrot’s attack: not only he emphasized an empirical inadequacy of the Brownian representation, but also he argued for an inadequate grounding of this representation. We give some insights in this crisis and display the model tinkering strategies of the financial academic community in the 1970s and the 1980s. (shrink)

Risk propagation encompasses a plethora of techniques for analyzing how risk “spreads” in a given system. Albeit commonly used in technical literature, the very notion of risk propagation turns out to be a conceptually imprecise and overloaded one. This might also explain the multitude of modeling solutions that have been proposed in the lit- erature. Having a clear understanding of what exactly risk is, how it be quantified, and in what sense it can be propagated (...) is fundamental for devising high-quality risk assessment and decision-making solutions. In this paper, we exploit a previous well-established work about the nature of risk and related notions with the goal of providing a proper interpre- tation of the different notions of risk propagation, as well as revealing and harmonizing the alternative semantics for the links used in common risk propagation graphs. Finally, we discuss how these results can be leveraged in practice to model risk propagation scenarios. (shrink)

Enterprise Risk Management involves the process of identification, evaluation, treatment, and communication regarding risks throughout the enterprise. To support the tasks associated with this process, several frameworks and modeling languages have been proposed, such as the Risk and Security Overlay (RSO) of ArchiMate. An ontological investigation of this artifact would reveal its adequacy, capabilities, and limitations w.r.t. the domain of risk and security. Based on that, a language redesign can be proposed as a refinement. Such analysis (...) and redesign have been executed for the risk elements of the RSO grounded in the Common Ontology of Value and Risk. The next step along this line of research is to address the following research problems: What would be the outcome of an ontological analysis of security-related elements of the RSO? That is, can we identify other semantic deficiencies in the RSO through an ontological analysis? Once such an analysis is provided, can we redesign the security elements of the RSO accordingly, in order to produce an improved artifact? Here, with the aid of the Reference Ontology for Security Engineering (ROSE) and the ontological theory of prevention behind it, we address the remaining gap by proceeding with an ontological analysis of the security-related constructs of the RSO. The outcome of this assessment is an ontology-based redesign of the ArchiMate language regarding security modeling. In a nutshell, we report the following contributions: (1) an ontological analysis of the RSO that identifies six limitations concerning security modeling; (2) because of the key role of the notion of prevention in security modeling, the introduction of the ontological theory of prevention in ArchiMate; (3) a well-founded redesign of security elements of ArchiMate; and (4) ontology-based security modeling patterns that are logical consequences of our proposal of redesign due to its underlying ontology of security. As a form of evaluation, we show that our proposal can describe risk treatment options, according to ISO 31000. Finally, besides presenting multiple examples, we proceed with a real-world illustrative application taken from the cybersecurity domain. (shrink)

Currently, under the conditions of permanent financial risks that hamper the sustainable economic growth in the financial sector, the development of evaluation and risk management methods both regulated by Basel II and III and others seem to be of special importance. The reputation risk is one of significant risks affecting reliability and credibility of commercial banks. The importance of reputation risk management and the quality of their assessment remain relevant as the probability of decrease in or loss (...) of business reputation influences the financial results and the degree of customers’, partners’ and stakeholders’ confidence. By means of imitating modeling based on Bayesian Networks and the fuzzy data analysis, the article characterizes the mechanism of reputation risk assessment and possible losses evaluation in banks by plotting normal and lognormal distribution functions. Monte-Carlo simulation is used to calculate the probability of losses caused by reputation risks. The degree of standardized histogram similarity is determined on the basis of the fuzzy data analysis applying Hamming distance method. The tree-like hierarchy based on the OWA-operator is used to aggregate the data with Fishburne's coefficients as the convolution scales. The mechanism takes into account the impact of criteria, such as return on equity, goodwill value, the risk assets ratio, the share of the productive assets in net assets, the efficiency ratio of interest bearing liabilities, the risk ratio of credit operations, the funding ratio and reliability index on the business reputation of the bank. The suggested methods and recommendations might be applied to develop the decision-making mechanism targeted at the implementation of reputation risk management system in commercial banks as well as to optimize risk management technologies. (shrink)

Enterprise Risk Management and security have become a fundamental part of Enterprise Architecture, so several frameworks and modeling languages have been designed to support the activities associated with these areas. Archi- Mate’s Risk and Security Overlay is one of such proposals, endorsed by The Open Group. We investigate the capabilities of the proposed security-related con- structs in ArchiMate with regard to the necessities of enterprise security modeling. Our analysis relies on a well-founded reference ontology of security (...) to uncover ambiguity, missing modeling elements, and other deficiencies of the security mod- eling capabilities in ArchiMate. Based on this ontologically-founded analysis, we propose a redesign of security aspects of ArchiMate to overcome its original limitations. (shrink)

The spatial-ecological modeling is relating sustainable dispersions with social development. Sustainability with spatial-ecological model gives attention to urban environments in the design review management to comply with Earth’s system. Naturally exchanged patterns of ecosystems have consistent and periodic cycles to preserve energy flows and materials in Earth’s system. The Probabilistic Risk Assessment (PRA) technique is utilized to assess the safety of an industrial complex. The other analytical approach is the Failure-Safe Mode and Effect Analysis (FMEA) for critical components. (...) The plant safety parameters are identified for engineering topology as employed in assessment safety of industrial ecology. In particular, the most severe accidental release of hazardous gaseous is postulated, analyzed and assessment in industrial region. The IAEA-safety assessment procedure is used to account the duration and rate of discharge of liquid chlorine. The ecological model of plume dispersion width and concentration of chlorine gas in the downwind direction is determined using Gaussian Plume Model in urban and rural areas and presented with SURFER®. The prediction of accident consequences is traced in risk contour concentration lines. The local greenhouse effect is predicted with relevant conclusions. The spatial-ecological model is predicted for multiple factors distribution schemes of multi-criteria analysis. The input–output analysis is explored from the spillover effect, and we conducted Monte Carlo simulations for sensitivity analysis. Their unique structure is balanced within “equilibrium patterns”, such as the composite index for biosphere with collective structure of many distributed feedback flows. These dynamic structures are related to have their physical and chemical properties and enable a gradual and prolonged incremental pattern. While this spatial model structure argues from ecology, resource savings, static load design, financial and other pragmatic reasons, the outcomes are not decisive in an artistic/architectural perspective. The hypothesis is deployed to unify analytic and analogical spatial structure in development urban environments using optimization loads as an example of integrated industrial structure where the process is based on engineering topology of systems ecology. (shrink)

This article describes the peculiarities of the formation and modeling of the enterprise risk management system in modern conditions. The main purpose of the research is further development of theoretical and methodical principles and development of practical recommendations aimed at improvement of management of business risks of wholesale enterprises. The critical analysis of literature sources and approaches to solving problems of enterprise risk management testifies to the lack of attention of scientists of the present-day to the investigated (...) problem in the sphere of activity of enterprises of wholesale trade. The urgency of this problem is caused by the existing losses of wholesale companies due to imperfect management as a result of the influence of various business risks. The methodological basis of the research is the system of methods used to obtain the final results: theoretical generalization, scientific abstraction and comparison – to deepen the essence of the concept of "business risk"; analysis and synthesis, grouping – for classification of business risks in the sphere of wholesale trade; methods of analysis, synthesis and logical generalization – to obtain conclusions during determination of the state and problems of realization of business activity in the sphere of wholesale trade of enterprises of Ukraine; systematic approach – for improvement of system of management of business risks of enterprises of wholesale trade on the basis of commercial values. The object of the research is the business risk in the wholesale trade environment. In the article the further development of the risk-based apparatus has been made, the negative consequences of the impact of business risks on the activities of the wholesale trade enterprises have been clarified, the enterprise risk management system of the enterprises has been built, which will reflect an integrated approach to its realization, practical recommendations on the formation of a model of management of business risks of the wholesale trade enterprises on the basis of commercial values. The results of the survey can be useful for business structures, which are focused on improving the management of business risks. (shrink)

This paper provides a method for characterizing space events using the framework of conceptual spaces. We focus specifically on estimating and ranking the likelihood of collisions between space objects. The objective is to design an approach for anticipatory decision support for space operators who can take preventive actions on the basis of assessments of relative risk. To make this possible our approach draws on the fusion of both hard and soft data within a single decision support framework. Contextual data (...) is also taken into account, for example data about space weather effects, by drawing on the Space Domain Ontologies, a large system of ontologies designed to support all aspects of space situational awareness. The framework is coupled with a mathematical programming scheme that frames a mathematically optimal approach for decision support, providing a quantitative basis for ranking potential for collision across multiple satellite pairs. The goal is to provide the broadest possible information foundation for critical assessments of collision likelihood. (shrink)

In Risk Management, security issues arise from complex relations among objects and agents, their capabilities and vulnerabilities, the events they are involved in, and the value and risk they ensue to the stakeholders at hand. Further, there are patterns involving these relations that crosscut many domains, ranging from information security to public safety. Understanding and forming a shared conceptualization and vocabulary about these notions and their relations is fundamental for modeling the corresponding scenarios, so that proper security (...) countermeasures can be devised. Ontologies are instruments developed to address these conceptual clarification and terminological systematization issues. Over the years, several ontologies have been proposed in Risk Management and Security Engineering. However, as shown in recent literature, they fall short in many respects, including generality and expressivity - the latter impacting on their interoperability with related models. We propose a Reference Ontology for Security Engineering (ROSE) from a Risk Treatment perspective. Our proposal leverages on two existing Reference Ontologies: the Common Ontology of Value and Risk and a Reference Ontology of Prevention, both of which are grounded on the Unified Foundational Ontology (UFO). ROSE is employed for modeling and analysing some cases, in particular providing clarification to the semantically overloaded notion of Security Mechanism. (shrink)

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be (...) positive or negative. Moreover, the model can naturally capture wrong or right way risk. (shrink)

The paper explores the influence of greenwash on green trust and discusses the mediation roles of green consumer confusion and green perceived risk. The research object of this study focuses on Taiwanese consumers who have the purchase experience of information and electronics products in Taiwan. This research employs an empirical study by means of the structural equation modeling. The results show that greenwash is negatively related to green trust. Therefore, this study suggests that companies must reduce their greenwash (...) behaviors to enhance their consumers’ green trust. In addition, this study finds out that green consumer confusion and green perceived risk mediate the negative relationship between greenwash and green trust. The results also demonstrate that greenwash is positively associated with green consumer confusion and green perceived risk which would negatively affect green trust. It means that greenwash does not only negatively affect green trust directly but also negatively influence it via green consumer confusion and green perceived risk indirectly. Hence, if companies would like to reduce the negative relationship between greenwash and green trust, they need to decrease their consumers’ green consumer confusion and green perceived risk. (shrink)

This article presents a new model for valuing a credit default swap (CDS) contract that is affected by multiple credit risks of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty (...) class='Hi'>risk completely in the CDS market. (shrink)

The mental risk poses a high threat to the individuals, especially overseas demographic, including expatriates in comparison to the general Arab demographic. Since Arab countries are renowned for their multicultural environment with half of the population of students and faculties being international, this paper focuses on a comprehensive analysis of mental health problems such as depression, stress, anxiety, isolation, and other unfortunate conditions. The dataset is developed from a web-based survey. The detailed exploratory data analysis is conducted on the (...) dataset collected from Arab countries to study an individual’s mental health and indicative help-seeking pointers based on their responses to specific pre-defined questions in a multicultural society. The proposed model validates the claims mathematically and uses different machine learning classifiers to identify individuals who are either currently or previously diagnosed with depression or demonstrate unintentional “save our souls” (SOS) behaviors for an early prediction to prevent risks of danger in life going forward. The accuracy is measured by comparing with the classifiers using several visualization tools. This analysis provides the claims and authentic sources for further research in the multicultural public medical sector and decision-making rules by the government. (shrink)

Lockdowns, or modern quarantines, involve the use of novel restrictive non-pharmaceutical interventions (NPIs) to suppress the transmission of COVID-19. In this paper, I aim to critically analyze the emerging history and philosophy of lockdowns, with an emphasis on the communication of health evidence and risk for informing policy decisions. I draw a distinction between evidence-based and modeling-based decision-making. I argue that using the normative framework of evidence-based medicine would have recommended against the use of lockdowns. I first review (...) the World Health Organization’s evidence-based pandemic preparedness plans for respiratory viruses. I then provide a very brief history of COVID-19 modeling, which was cited as justification for the use of lockdowns in the U.K., the U.S., and much of the world. I focus on the so-called Imperial College model designed by Neil Ferguson et al. as well as the so-called Oxford model designed by José Lourenço et al. I analyze the evidence-based pandemic response known as ‘mitigation’, and I compare it with Ferguson et al.’s experimental strategy known as ‘suppression’. I summarize the strengths and weaknesses of these strategies based on their diametric aims and each model’s parametric assumptions. Based on my critical analysis of the suppression strategy, I attempt to expose what has been called the ‘logic of lockdowns’, which Sunetra Gupta of the Oxford model group has suggested is flawed. Finally, I consider Trisha Greenhalgh’s objection to evidence-based policy based on the precautionary principle, and I attempt to offer a response. I conclude with a brief narrative review of the emerging randomized evidence on restrictive NPIs, which seems to support my claim that mitigation was the strategy that would have been recommended by evidence-based medicine. If this is true, then COVID-19 modeling may serve as an important reminder of the enduring lesson of evidence-based medicine: that one should always ‘Trust the Evidence!’ for better health policy. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously (...) considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

Climate change mitigation has become a paradigm case both for externalities in general and for the game-theoretic model of the Tragedy of the Commons (ToC) in particular. This situation is worrying, as we have reasons to suspect that some models in the social sciences are apt to be performative to the extent that they can become self-fulfilling prophecies. Framing climate change mitigation as a hardly solvable coordination problem may force us into a worse situation, by changing real-world behaviour to fit (...) our model, rather than the other way around. But while this problem of the performativity of the ToC has been noted in a recent paper in this journal by Matthew Kopec, his proposed strategies for dealing with their self-fulfilling nature fall short of providing an adequate solution. Instead of relying on the idea that modelling assumptions are always strictly speaking false, this paper shows that the problem may be better framed as a problem of underdetermination between competing explanations. Our goal here is to provide a framework for choosing between this set of competing models that allows us to avoid a ‘Russian Roulette’-like situation in which we gamble with existential risk. (shrink)

Bartha and DesRoches (2021) and Steel and Bartha (2023) argue that we should understand the precautionary principle as the injunction to maximise lexical utilities. They show that the lexical utility model has important pragmatic advantages. Moreover, the model has the theoretical advantage of satisfying all axioms of expected utility theory except continuity. In this paper I raise a trilemma for any attempt at modelling the precautionary principle with lexical utilities: it permits choice cycles or leads to paralysis or implies that (...) the smallest value difference that is possible in a context has extreme axiological implications. (shrink)

Climate engineering with stratospheric sulfate aerosol injections (SSAI) has the potential to reduce risks of injustice related to anthropogenic emissions of greenhouse gases. Relying on evidence from modeling studies, this paper makes the case that SSAI could have the potential to reduce many of the key physical risks of climate change identified by the Intergovernmental Panel on Climate Change. Such risks carry potential injustice because they are often imposed on low-emitters who do not benefit from climate change. Because SSAI (...) has the potential to reduce those risks, it thereby has the potential to reduce the injustice associated with anthropogenic emissions. While acknowledging important caveats, including uncertainty in modeling studies and the potential for SSAI to carry its own risks of injustice, the paper argues that there is a strong case for continued research into SSAI, especially if attention is paid to how it might be used to reduce emissions-driven injustice. (shrink)

To find the neural substrates of consciousness, researchers compare subjects’ neural activity when they are aware of stimuli against neural activity when they are not aware. Ideally, to guarantee that the neural substrates of consciousness—and nothing but the neural substrates of consciousness—are isolated, the only difference between these two contrast conditions should be conscious awareness. Nevertheless, in practice, it is quite challenging to eliminate confounds and irrelevant differences between conscious and unconscious conditions. In particular, there is an often-neglected confound that (...) is crucial to eliminate from neuroimaging studies: task performance. Unless subjects’ task performance is matched (and hence perceptual signal processing is matched), researchers risk finding the neural correlates of perception, rather than conscious perception. Here, we discuss the theoretical motivations for the performance matching framework and review empirical demonstrations of, and theoretical inferences derived from, obtaining differences in consciousness while controlling for task performance. We summarize signal detection theoretic modeling frameworks that explain how it is that we can derive performance-matched differences in consciousness without the effect being trivially driven by differences in criterion setting, and also provide principles for designing experimental paradigms that yield performance-matched differences in awareness. Finally, we address potential technical and theoretical issues that stem from matching performance across conditions of awareness, and we introduce the notion of “triangulation” for designing comprehensive experimental sets that can better reveal the neural substrates of consciousness. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research (...) institutions, and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

Many epidemics consist in individuals spreading infection to others. From the population perspective, they also have population characteristics important in modeling, explaining, and intervening in epidemics. I analyze epidemiology’s contemporary population perspective through the example of epidemics by examining two central principles attributed to Geoffrey Rose: a distinction between the causes of cases and the causes of incidence, and between “high-risk” and “population” strategies of prevention. Both principles require revision or clarification to capture the sense in which they (...) describe distinct perspectives on the same phenomenon, each perspective capturing a different level of contrastive analysis. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly evolved, offering transformative capabilities for business decision-making. This paper explores how AI can be leveraged to enhance strategic decision-making in business contexts. It examines the integration of AI-driven analytics, predictive modeling, and automation to improve decision accuracy and operational efficiency. By analyzing current applications and case studies, the paper highlights the opportunities AI presents, including enhanced data insights, risk management, and personalized customer experiences. Additionally, it addresses the challenges businesses face in adopting (...) AI, such as data privacy concerns, integration issues, and the need for skilled personnel. The paper concludes with recommendations for effectively implementing AI solutions to maximize their benefits while navigating potential pitfalls. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the (...) challenges businesses face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (shrink)

The use of evolutionary game theory to explain the evolution of human norms and the behavior of humans who act according to those norms is widespread. Both the aims and motivation for its use are clearly articulated by Harms and Skyrms (2008) in the following passage: "A good theory of evolution of norms might start by explaining the evolution of altruism in Prisoner’s Dilemma, of Stag Hunting, and of the equal split in the symmetric bargaining game. These are not well-explained (...) by classical game theory based on rational choice. From a technical point of view, they present different theoretical challenges. In the bargaining game, there are an infinite number of equilibria with no principled (rational choice) way to select the cooperative one. In Stag Hunt there are only two, but the non-cooperative one is selected by risk-dominance. In Prisoner’s Dilemma the state of mutual cooperation is not a Nash equilibrium at all, and cooperation flies in the face of the rational-choice principle that one does not choose less rather than more. In contrast to rational choice theory, the most common tool of evolutionary game theory is the replicator dynamics, in which the propagation rate of each strategy is determined by its current payoffs. These dynamics have a rationale in both biological and cultural evolutionary modeling, and sometimes tell us things that rational choice theory does not." We agree with the first sentence in this quotation: a good theory about the behavior under norms ought to explain altruism in the Prisoner’s Dilemma (PD), playing Stag in Stag Hunt (SH), and offering equal splits in the symmetric Nash bargaining game (NB). We also agree with Harms and Skyrms about the difference in technical challenges each of these games poses. Finding a single mechanism, even one as broadly understood as evolution, that could solve these challenges en masse is no doubt a tall order. Nonetheless, in this paper, we present a single, simple, modification to SH, NB, and a general n-player PD that does just that: we introduce deontological autonomy into the models. (shrink)

As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. At its core, (...) the framework emphasizes the importance of harnessing vast datasets related to energy production, consumption, environmental impact, and financial performance. By leveraging machine learning algorithms and predictive modeling techniques, financial stakeholders can gain deeper insights into the risks and opportunities associated with green energy investments. Key components of the framework include data collection and aggregation, risk assessment, impact measurement, and investment optimization. Data sources range from traditional financial indicators to environmental metrics, social impact assessments, and geopolitical factors. Through comprehensive data analysis, financial institutions can assess the long-term viability and sustainability of green energy projects, while also evaluating potential social and environmental impacts. Risk assessment methodologies within the framework consider both financial risks, such as market volatility and regulatory uncertainty, and non-financial risks, such as climate change impacts and community resilience. By integrating these factors into risk models, investors can make more informed decisions that mitigate potential losses and maximize returns. Furthermore, impact measurement tools enable stakeholders to quantify the environmental and social benefits of green energy investments. By tracking metrics such as carbon emissions reduction, energy efficiency improvements, and job creation, investors can assess the contribution of their portfolios towards broader sustainability objectives. Finally, the framework incorporates investment optimization strategies that align financial goals with environmental objectives. Through portfolio diversification, asset allocation, and innovative financial instruments such as green bonds and impact investing funds, financial institutions can allocate capital more efficiently towards green energy projects. The conceptual framework presented herein offers a systematic approach to integrating data-driven methodologies into sustainable finance practices. By leveraging advanced analytics and comprehensive datasets, financial stakeholders can drive the transition towards green energy while simultaneously achieving financial returns and positive environmental outcomes. (shrink)

Over the past two decades, gamblers have begun taking mathematics into account more seriously than ever before. While probability theory is the only rigorous theory modeling the uncertainty, even though in idealized conditions, numerical probabilities are viewed not only as mere mathematical information, but also as a decision-making criterion, especially in gambling. This book presents the mathematics underlying the major games of chance and provides a precise account of the odds associated with all gaming events. It begins by explaining (...) in simple terms the meaning of the concept of probability for the layman and goes on to become an enlightening journey through the mathematics of chance, randomness and risk. It then continues with the basics of discrete probability, combinatorics and counting arguments for those interested in the supporting mathematics. These mathematic sections may be skipped by readers who do not have a minimal background in mathematics; these readers can skip directly to the Guide to Numerical Results to pick the odds and recommendations they need for the desired gaming situation. Doing so is possible due to the organization of that chapter, in which the results are listed at the end of each section, mostly in the form of tables. The chapter titled The Mathematics of Games of Chance presents these games not only as a good application field for probability theory, but also in terms of human actions where probability-based strategies can be tried to achieve favorable results. Through suggestive examples, the reader can see what are the experiments, events and probability fields in games of chance and how probability calculus works there. The main portion of this work is a collection of probability results for each type of game. Each game s section is packed with formulas and tables. Each section also contains a description of the game, a classification of the gaming events and the applicable probability calculations. The primary goal of this work is to allow the reader to quickly find the odds for a specific gaming situation, in order to improve his or her betting/gaming decisions. Every type of gaming event is tabulated in a logical, consistent and comprehensive manner. The complete methodology and complete or partial calculations are shown to teach players how to calculate probability for any situation, for every stage of the game for any game. Here, readers can find the real odds, returned by precise mathematical formulas and not by partial simulations that most software uses. Collections of odds are presented, as well as strategic recommendations based on those odds, where necessary, for each type of gaming situation. The book contains much new and original material that has not been published previously and provides great coverage of probabilities for the following games of chance: Dice, Slots, Roulette, Baccarat, Blackjack, Texas Hold em Poker, Lottery and Sport Bets. Most of games of chance are predisposed to probability-based decisions. This is why the approach is not an exclusively statistical one, but analytical: every gaming event is taken as an individual applied probability problem to solve. A special chapter defines the probability-based strategy and mathematically shows why such strategy is theoretically optimal.". (shrink)

In this article, we describe a project in which philosophy, in combination with methods drawn from mental modeling, was used to structure dialogue among stakeholders in a region-scale climate adaptation process. The case study we discuss synthesizes the Toolbox dialogue method, a philosophically grounded approach to enhancing communication and collaboration in complex research and practice, with a mental modeling approach rooted in risk analysis, assessment, and communication to structure conversations among non-academic stakeholders who have a common interest (...) in planning for a sustainable future. We begin by describing the background of this project, including details about climate resiliency efforts in West Michigan and the Toolbox dialogue method, which was extended in this project from academic research into community organization involving the West Michigan Climate Resiliency Framework Initiative. This extension involved application of several methods, which are the focus of the Methods section. We then present and discuss preliminary results that suggest the potential for philosophical dialogue to enhance mutual understanding in complex community initiatives that focus on sustainable responses to climate change. Overall, the article supplies a detailed, instructive example of how philosophy can support policy-relevant decision-making processes at the community level. (shrink)

Can we build machines with which we can have interesting conversations? Observing the new optimism of AI regarding deep learning and new language models, we set ourselves an ambitious goal: We want to find out how far we can get in creating a digital replica of a philosopher. This project has two aims; one more technical, investigating of how the best model can be built. The other one, more philosophical, explores the limits and risks which are accompanied by the creation (...) of digital replicas. In cooperation with Daniel Dennett, we took his complete works to fine-tune a GPT-3. In this paper, we share our first results with some piloting models and discuss legal and ethical questions about creating and interacting with digital replicas. (shrink)

This article is devoted to the topical aspects of the transformation of society, science, and man in the context of E. László’s work «Macroshift». The author offers his own attempt to consider the attributes of macroshift and then use these attributes to operationalize further analysis, highlighting three essential elements: the world has come to a situation of technological indistinguishability between the natural and the artificial, to machines that know everything about humans. Antiquity aspired to beauty and saw beauty in realistic (...) art, but technology made indistinguishability possible, bringing the risks of deepfakes and post-truth. Following J. Bernal’s logic, the author distinguishes between different types of the future, comparing them with the Kantian triad of ethical questions: (1) What can we know? (2) What should we do? (3) What can I hope for? Under these conditions, the fact that technology has become a real tool of geopolitics is no longer just a matter for discussion, but a subject of deep philosophical consideration. AI is becoming not only an opportunity, but also a threat, on a scale that has never been seen before in our civilizational history. Therefore, the former, linear development of science gives way to nonlinearity. Society in the macroshift will be radically transformed, and the author identifies five main transformations of the current macroshift. The post-nonclassical approach in the analysis of scenarios for modeling the future, both accidentally possible and preferred, is becoming the most important tool of futurology and prognostics. The author describes the future around five social trends that will determine the economy and society of the coming decades. In addition, opportunities for the future development of science are proposed, subject to significant penetration of AI technologies. These technologies will have a fundamental impact on all aspects of our society, economy, and human existence. (shrink)

The article examines the process of formation inventory of the enterprise and determines the optimal volume of commodity resources for sale. A generalization of author’s approaches to the formation and evaluation of inventories of the enterprise is carried out. The marketing-logistic approach was applied for the purpose of distribution groups of commodity resources due to the risk of non-fulfillment the order for the supply of goods of the enterprise. In order to ensure an effective process of commodity provision of (...) the enterprises, the costs associated with the formation of inventories are determined. The formalized scheme of the formation commodity provision and the process of optimization inventory of the enterprise is offered. The analysis the structure of the company’s inventory is carried out, the volume of goods turnover is defined, the stocks are grouped for various clustering characteristics. To conduct the study, statistical information was used on commodity resources of the enterprises, statistical methods (grouping, structure analysis, estimation of dynamic series), tools for assessing the efficiency of inventory use, HML-FMR clustering were used. The necessity of using XYZ and ABC analysis is indicated in order to obtain more reliable results and forecast values of the product support of the enterprise. Economic-mathematical modeling is applied and graphically shown the difference in the formation of commodity resources by various features of HML-FMR clustering. The calculations allow the enterprise to determine the optimal amount of commodity resources in accordance with the needs of consumers and their solvent demand, to plan financial resources for the formation of inventories, to develop assortment policy in accordance with demand for products and their implementation. The results of calculations the volume of merchandising of the enterprise, taking into account the HML-FMR clustering affect the formation of final financial performance of the enterprise — income and profits. (shrink)

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)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise algorithmic (...) computation and to offer foundations for scientific computing. -/- The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. -/- In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. -/- In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

This is a paper about the methodology of normative ethics. I claim that much work in normative ethics can be interpreted as modelling, the form of inquiry familiar from science, involving idealised representations. I begin with the anti-theory debate in ethics, and note that the debate utilises the vocabulary of scientific theories without recognising the role models play in science. I characterise modelling, and show that work with these characteristics is common in ethics. This establishes the plausibility of my interpretation. (...) Taking methodological inspiration from modelling in science gives us new tools for managing idealisations, and a new perspective on pluralism. I demonstrate why this interpretation is a fruitful way of interpreting ethics, by looking at three case studies. First, I return to the anti-theory debate and argue that modelling opens up a new middle ground. Second, I argue that a modelling lens offers a new way of understanding impossibility theorems in population ethics, and their bearing on ethics as a whole. Finally, I show how viewing our work as modelling can be deployed in debates within ethics, using the debate over prioritarianism as an example. I close with further methodological suggestions for those who choose to see themselves as modellers. I discuss the role of counterexamples, our responses to moral disagreement, and the training of new ethicists. (shrink)

ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due to two (...) other features of contemporary model construction processes that Weisberg does not explicitly consider: the methods-drivenness and outcome-orientedness of modelling. 1Introduction 2Modelling as Indirect Representation 3The Design of the Lotka–Volterra Model by Volterra 3.1Volterra’s method of hypothesis 3.2The construction of the Lotka–Volterra model by Volterra 4The Design of the Lotka–Volterra Model by Lotka 4.1Physical biology according to Lotka 4.2Lotka’s systems approach and the Lotka–Volterra model 5Philosophical Discussion: Strategies and Tools of Modelling 5.1Volterra’s path from the method of isolation to the method of hypothesis 5.2The template-based approach of Lotka 5.3Modelling: methods-driven and outcome-oriented 6Conclusion. (shrink)

We introduce a number of logics to reason about collective propositional attitudes that are defined by means of the majority rule. It is well known that majoritarian aggregation is subject to irrationality, as the results in social choice theory and judgment aggregation show. The proposed logics for modelling collective attitudes are based on a substructural propositional logic that allows for circumventing inconsistent outcomes. Individual and collective propositional attitudes, such as beliefs, desires, obligations, are then modelled by means of minimal modalities (...) to ensure a number of basic principles. In this way, a viable consistent modelling of collective attitudes is obtained. (shrink)

This paper constructs a model of metaphysical indeterminacy that can accommodate a kind of ‘deep’ worldly indeterminacy that arguably arises in quantum mechanics via the Kochen-Specker theorem, and that is incompatible with prominent theories of metaphysical indeterminacy such as that in Barnes and Williams (2011). We construct a variant of Barnes and Williams's theory that avoids this problem. Our version builds on situation semantics and uses incomplete, local situations rather than possible worlds to build a model. We evaluate the resulting (...) theory and contrast it with similar alternatives, concluding that our model successfully captures deep indeterminacy. (shrink)

Global directional eliminativists deny that there is any global direction to time. This paper provides a way to understand everyday temporal assertions—assertions made outside the physics or metaphysics rooms, the truth of which appears to require that time has a global direction—on the assumption that global directional eliminativism is true.

In the last decade, the UK Government has attempted to implement improved processes and procedures in modelling and analysis in response to the Laidlaw report of 2012 and the Macpherson review of 2013. The Laidlaw report was commissioned after failings during the Intercity West Coast Rail (ICWC) Franchise procurement exercise by the Department for Transport (DfT) that led to a legal challenge of the analytical models used within the exercise. The Macpherson review looked into the quality assurance of Government analytical (...) models in the context of the experience with the Intercity West Coast franchise competition. This paper examines what progress has been made in the 8 years since the Laidlaw report in model building and best practise in government and proposes several recommendations for ways forward. This paper also discusses the Lords Science and Technology Committees of June 2020 that analysed the failings in the modelling of COVID. Despite going on to influence policy, many of the same issues raised within the Laidlaw and Macpherson Reports were also present in the Lords Science and Technology Committee enquiry. We examine the technical and organisational challenges to progress in this area and make recommendations for a way forward. (shrink)

What is the connection between modelling thought and modelling the brain? In a model (as understood here), we strip away from the modelled system some non-essential features and retain some essential ones. What are the essential features of thought that are to be re- tained in the model, and conversely, what are its inessential features, that may be stripped away in the model? According to a prevalent view in contemporary science and philoso- phy, thought is a computation, and therefore its (...) essential features are its computational features. A necessary part of the computational view of thought is the idea that the same computation can be realised by, or implemented in, physically heterogeneous systems, an idea known as “Multiple Realizability” of the computational features or properties by the physical ones. I will describe why the very idea of Multiple Realizability, especially in the case of mental computation, entails mind-body dualism, and explore some implications of this conclusion concerning the question of which are the essential features of thought to be retained in modeling it. (shrink)

The descriptions and theoretical laws scientists write down when they model a system are often false of any real system. And yet we commonly talk as if there were objects that satisfy the scientists’ assumptions and as if we may learn about their properties. Many attempt to make sense of this by taking the scientists’ descriptions and theoretical laws to define abstract or fictional entities. In this paper, I propose an alternative account of theoretical modelling that draws upon Kendall Walton’s (...) ‘make-believe’ theory of representation in art. I argue that this account allows us to understand theoretical modelling without positing any object of which scientists’ modelling assumptions are true. (shrink)

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent (...) in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational. (shrink)

The following considerations concern modelling Belief Dynamics (BD) not just in the sense of a formalization, but rather in the sense of building a computational model and implementing the corresponding data structures and algorithms of recomputing beliefs. The purpose of such a project is to illustrate some ideas about belief changes in a Web of Beliefs (WoB) to explore and deepen one's understanding of belief changes by trying to implement or improve corresponding algorithms.

I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised (...) as modelling idealisations motivated normatively. I then survey the landscape of methodological options: what might formal epistemologists be up to? I argue the choice is essentially binary: modelling or theorising. If NFE is theorising it is doing very poorly: generating false claims with no clear methodology for separating out what is to be taken seriously. Modelling, by contrast, is a successful methodology precisely suited to the management of useful falsehoods. Regarding NFE as modelling is not costless, however. First, our normative inferences are less direct and are muddied by the presence of descriptive idealisations. Second, our models are purpose-specific and limited in their scope. I close with suggestions for how to adapt our practice. (shrink)

(1) An introduction to the principles of conceptual modelling, combinatorial heuristics and epistemological history; (2) the examination of a number of perennial epistemological-methodological schemata: conceptual spaces and blending theory; ars inveniendi and ars demonstrandi; the two modes of analysis and synthesis and their relationship to ars inveniendi; taxonomies and typologies as two fundamental epistemic structures; extended cognition, cognitio symbolica and model-based reasoning; (3) Plato’s notions of conceptual spaces, conceptual blending and hypothetical-analogical models (paradeigmata); (4) Ramon Llull’s concept analysis and combinatoric (...) spaces; (5) Gottfried Leibniz’s development of compositional analysis and synthesis as a general modelling method and a paradigm for ars inveniendi; (6) Fritz Zwicky’s revival of the morphological method of analysis and construction, and its subsequent computerised applications. (shrink)

In this paper, I examine Reinforcement Learning modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang’s account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.