This article presents a structured and systematic approach for identifying and modelling compliance risks. The sophistication with which modern business is carried out and the unprecedented access to a global market means that businesses are exposed to increasing and diverse regulatory requirements in and across jurisdictions. Compliance with such requirements is practically challenging, partly due to the complexity of regulatory environments. One possibility in this regard is a risk-based approach to compliance, where resources are allocated to those compliance issues (...) that are most risky. Despite the need for risk-based compliance, few specific methods and techniques for identifying and modelling compliance risks have been developed. Due to the lack of methodological and tool support, compliance risk identification often involves unstructured brainstorming, with uncertain outcomes. The proposed approach consists of a five-step process for the structured identification and assessment of compliance risks. This process aims at facilitating the identification of compliance risks and their documentation in a consistent and reusable fashion. As part of the process, the article provides a systematic approach for a graphical modelling of compliance risks, which aims at facilitating communication among experts from different backgrounds. The creation of graphical models can be partly automated based on natural language patterns for regulatory requirements. Furthermore, the structuring of the compliance requirement in a template aims at simplifying the modelling of compliance risks and facilitating a potential future automated model. (shrink)

This article applies the concept of prudence to develop the characteristics of responsible risk-modeling practices in the insurance industry. A critical evaluation of the risk-modeling process suggests that ethical judgments are emergent rather than static, vague rather than clear, particular rather than universal, and still defensible according to the discipline’s established theory, which will support a range of judgments. Thus, positive moral guides for responsible behavior are of limited practical value. Instead, by being prudent, modelers can (...) improve their ability to deal with the ethical and technical complexity of the risk-modeling process. While the application of prudence to resolve ethical challenges in risk modeling, an issue of practical importance to managers, is a first in the literature, the practice of applying an ethical lens to issues of pragmatic importance for managers is well established in Maak and Pless (J Bus Ethics 66:99–115, 2006a ; Responsible leadership, 2006b ) among others. (shrink)

I examine the construction and evaluation of machine learning binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models (...) is underdetermined by the available data, and that this makes it necessary for ML modellers to make social value judgments in determining the error costs used in ML optimization. I thus suggest that the assessment of the inductive risk with respect to the social values of the intended users is an integral part of the construction and evaluation of ML classification models. I also discuss the implications of this conclusion for the philosophical debate concerning inductive risk. (shrink)

Predictive risk modelling is a computational method used to generate probabilities correlating events. The output of such systems is typically represented by a statistical score derived from various related and often arbitrary datasets. In many cases, the information generated by such systems is treated as a form of evidence to justify further action. This paper examines the nature of the information generated by such systems and compares it with more orthodox notions of evidence found in epistemology. The paper focuses (...) on a specific example to illustrate the issues: The New Zealand Government has proposed implementing a predictive risk modelling system which purportedly identifies children at risk of a maltreatment event before the age of five. Timothy Williamson’s conception of epistemology places a requirement on knowledge that it be explanatory. Furthermore, Williamson argues that knowledge is equivalent to evidence. This approach is compared to the claim that the output of such computational systems constitutes evidence. While there may be some utility in using predictive risk modelling systems, I argue, since an explanatory account of the output of such algorithms that meets Williamson’s requirements cannot be given, doubt is cast upon the resulting statistical scores as constituting evidence on generally accepted epistemic grounds. The algorithms employed in such systems are geared towards identifying patterns which turn out to be good correlations. However, rather than providing information about specific individuals and their exposure to risk, a more valid explanation of a high probability score is that the particular variables related to incidents of maltreatment are just higher amongst certain subgroups in a population than they are amongst others. The paper concludes that any justification of the information generated by such systems is generalised and pragmatic at best and the application of this information to individual cases raises various ethical issues. (shrink)

Modelling Excessive Internet Use:s Revision of R. Davis's Cognitive-Behavioural Model of Pathological Internet Use This article proposes a new model of excessive Internet use. The point of departure for the present study was the Cognitive-Behavioural Model of Pathological Internet Use developed by R. Davis. The original model was modified so as to improve its explanatory power. Data were collected from 405 participants aged from 18 to 55 in various Polish towns and cities. The following instruments were administered to the participants: (...) The Temperament and Character Inventory, J. Kuhl's Action Control Scale, The Berlin Social Support Scale, The Coping Inventory for Stressful Situations, The Excessive Internet Use Risk Scale, The Reasons for Internet Use and the Personal Data Questionnaire. The new model of excessive Internet use was empirically tested and proved to be satisfactory. (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 poster presents results from applying agent-based modeling to an exploration of risk attitudes and rational decision making in the context of group interaction. We are also interested in the place of agent-based modeling and computational philosophy within the computational humanities. Computational philosophy has not typically been included in Digital Humanities; computational work has been done using philosophy texts as a source for analysis (Kinney 2022; Malaterre et al. 2021; Fletcher et al. 2021; Zahorec et al. 2022), (...) but there are few examples of philosophical arguments being made based on computation (Zahorec et al. 2022). Modeling is largely accepted as a computational humanist method (McCarty 2004; So 2022), but typically models are based on data rather than designed simulations as a method analogous to a thought experiment (Mayo-Wilson / Zollman 2021). Agent-based modeling is not as common in computational humanities, but we are interested in its potential; for instance, see work modeling correspondence networks to better understand lost information and biases in correspondence data sets (Buarque / Vogl 2023), or work simulating past human societies (Romanowska et al. 2021). Our project explores Lara Buchak’s theory of risk-weighted rational decision making by extending it to the context of group populations and interactions. Buchak has proposed risk-weighted expected utility maximization, which incorporates individual risk attitudes into the standard expected utility (EU) calculation based on utility and probability of outcomes (Buchak 2017). This theory provides an explanation for differences of behavior seen in human populations, while still allowing those different choices to be rational. Buchak’s theory has previously only been applied to individual decision making; our work uses agent-based modeling to develop and analyze simulations which incorporate a variety of risk attitudes (risk avoidance and risk seeking) into game theoretic interactions. This project is a collaboration between a philosopher and a research software engineer; we have worked together to develop simulations that implement agents with risk attitudes making risky choices. This presentation focuses on results from an implementation of a hawk/dove game with multiple risk attitudes and risk attitude adjustment. In the hawk/dove game cooperation, or playing dove, is better for the population, but acting aggressively, or playing hawk, is better for an individual if they are in a neighborhood of doves. To set up the game, we place agents on a grid and have them play against each of their neighbors, and accumulate payoffs based on the success of their plays. We define risk attitudes 0 through 9, where that number corresponds to the minimum number of neighbors playing dove for an agent to play hawk. An agent with risk attitude 0 is most risk seeking and always plays hawk; an agent with risk attitude 1 will play hawk if at least one neighbor plays dove; an agent with risk attitude 9 always takes the safe choice and plays dove; an agent with risk attitude 4 or 5 is risk neutral (breaking ties in different ways), which corresponds to expected utility. In this simulation we randomly set agent initial risk attitudes, and then every ten rounds agents compare their payoffs with their neighbors and adopt the most successful risk attitude in their neighborhood. The simulation is coded to stop once risk attitude adjustments stabilize. We define 13 different population states based on primary and second majority of risk averse, risk inclined, or risk moderate, and analysis of over 1 million runs indicate that while it’s more likely to stabilize with the population having become majority risk inclined, that is not the only outcome; we also see cases where the population stabilizes as risk neutral, risk avoidant, and many cases with no clear majority. This diversity of stable populations provides support for the argument that no one risk attitude is uniquely rational, but rather that risk attitudes are conventional. This matches what we observe in the real world: that different populations have different risk attitudes. Our simulations include numerous parameters to vary grid size; adjustment frequency, strategy, and wealth comparison; neighborhood sizes for observing previous choice, playing against, and comparing payoffs for adjusting risk attitudes; and different distributions for initializing risk attitudes. Analysis of parameters and adjusted risk attitudes shows that the strongest correlations are the initial distribution of risk attitudes, which tend to be preserved. * * * This poster was accepted and presented at the DH2024 conference in Washington, D.C. (shrink)

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behaviour change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behaviour change scale. Structural equation modelling examined three progressive (...) models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behaviour change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behaviour change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behaviour change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behaviour change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behaviour change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behaviour change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models. (shrink)

Most research on risk aversion in behavioral science with human subjects has focused on a component of risk aversion that does not adapt itself to context. More recently, studies have explored risk aversion adaptation to changing circumstances in sequential decision-making tasks. It is an open question whether one can identify evidence, at the single subject level, for such risk aversion adaptation. We conducted a behavioral experiment on human subjects, using a sequential decision making task. We developed (...) a model-based approach for estimating the adaptation of risk-taking behavior with single-trial resolution by modelling a subject’s goals and internal representation of task contingencies. Using this model-based approach, we estimated the subject-specific adaptation of risk aversion depending on the current task context. We found striking inter-subject variations in the adaptation of risk-taking behavior. We show that these differences can be explained by differences in subjects’ internal representations of task contingencies and goals. We discuss that the proposed approach can be adapted to a wide range of experimental paradigms and be used to analyze behavioral measures other than risk aversion. (shrink)

Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our (...) study undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research. (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)

The paper re-expresses arguments against the normative validity of expected utility theory in Robin Pope (1983, 1991a, 1991b, 1985, 1995, 2000, 2001, 2005, 2006, 2007). These concern the neglect of the evolving stages of knowledge ahead (stages of what the future will bring). Such evolution is fundamental to an experience of risk, yet not consistently incorporated even in axiomatised temporal versions of expected utility. Its neglect entails a disregard of emotional and financial effects on well-being before a particular (...) class='Hi'>risk is resolved. These arguments are complemented with an analysis of the essential uniqueness property in the context of temporal and atemporal expected utility theory and a proof of the absence of a limit property natural in an axiomatised approach to temporal expected utility theory. Problems of the time structure of risk are investigated in a simple temporal framework restricted to a subclass of temporal lotteries in the sense of David Kreps and Evan Porteus (1978). This subclass is narrow but wide enough to discuss basic issues. It will be shown that there are serious objections against the modification of expected utility theory axiomatised by Kreps and Porteus (1978, 1979). By contrast the umbrella theory proffered by Pope that she has now termed SKAT, the Stages of Knowledge Ahead Theory, offers an epistemically consistent framework within which to construct particular models to deal with particular decision situations. A model by Caplin and Leahy (2001) will also be discussed and contrasted with the modelling within SKAT (Pope, Leopold and Leitner 2007). (shrink)

This article examines the epistemology of risk assessment in the context of financial modelling for the purposes of making loan underwriting decisions. A financing request for a company in the paper and pulp industry is considered in some detail. The paper and pulp industry was chosen because it is subject to some specific risks that have been identified and studied by bankers, investors and managers of paper and pulp companies and certain features of the industry enable analysts to quantify (...) the impact of specific risk events of a given dimension on a company's future financial performance. While companies in other industries may be subject to similar risk factors, the impact of risk events may be more difficult to gauge in those industries. The ability of financial analysts to model the impact of a risk event, and hence quantify a credit risk, increases the predictive accuracy of the model. I argue that bankers and regulators should recognise the uncertainty associated with unquantifiable credit risk in financial models, and they should view this uncertainty as a credit risk factor in and of itself. Evaluating the relative degree to which credit risk is quantifiable in financial models is a potentially significant yet largely unrecognised tool for credit risk management. I consider some possible applications of this assessment tool for managing risk within the banking industry. (shrink)

Ainslie does not formally incorporate risk and uncertainty in his framework for modelling impulses and willpower. To provide a complete account of the motivational bases of choice behaviour, Ainslie should extend his framework to incorporate risk attitudes and subjective beliefs.

The main purpose of the paper is to develop a concept of _model uncertainty_ as opposed to the existing and well-established concept of model risk. Up to date the broad literature on probability not only developed complete probability systems, but also correctly noticed limitations of probability calculus. Despite the acknowledgement of such probability restrictions, drawbacks of modeling are often related to model risk. We present an argument here to distinguish a feature limiting models even further: model uncertainty. (...) The tenets of it already exist in the literature on probability, but were not properly emphasized while the idea of model risk was developed. Our plan it to start with a broad overview of the existing knowledge about probability in order to start with fundamental principles. From them we are deriving a new concept of model uncertainty. (shrink)

(2013). Modeling herding behavior and its risks. Journal of Economic Methodology: Vol. 20, Methodology, Systemic Risk, and the Economics Profession, pp. 6-18. doi: 10.1080/1350178X.2013.774843.

Factory floors throughout the global economy are rapidly transforming themselves into potentially fertile laboratories for research in the cognitive sciences. The information revolution has challenged our understanding of perception and cognition. Innovations in information technologies have also provided us with new methods and environments for the study of cognition. On the business and economic front, information technology is supporting the development of new corporate information systems-Enterprise Systems-that will revolutionize the decision-making, reporting and reward environments in corporations. These systems are pervasive (...) and transforming. At all levels, employees will be presented with potentially unlimited amounts of accurate, real-time information and will be expected to use that information effectively. The possibilities for research, especially for modeling the learning and decision-making processes, are unparalleled. Research conducted in these environments offers extraordinary opportunities to address research questions formerly requiring limited synthetic environmental situations. Data collected in firms as they deploy enterprise information systems can be more extensive and accurate and more easily quantified than data from traditional synthetic experiments. Research programs in these settings can also address more complex problems of human interaction and collaborativity. This article describes the aspects of enterprise systems that will be of greates interest to cognitive scientists. It also outlines the nature of potential collaboration between economists and cognitive scientists and offers a list of outstanding questions for systems design and development. (shrink)

There continues to be significant confusion about the goals, scope, and nature of modelling practice in neuroeconomics. This article aims to dispel some such confusion by using one of the most recent critiques of neuroeconomic modelling as a foil. The article argues for two claims. First, currently, for at least some economic model of choice behaviour, the benefits derivable from neurally informing an economic model do not involve special tractability costs. Second, modelling in neuroeconomics is best understood within Marr’s three-level (...) of analysis framework and in light of a co-evolutionary research ideology. The first claim is established by elucidating the relationship between the tractability of a model, its descriptive accuracy, and its number of variables. The second claim relies on an explanation of what it can take to neurally inform an economic model of choice behaviour. 1 Introduction2 Neurally Informed Models of Choice: A Case Study2.1 A case study on risk-sensitive choice2.1.1 Target and modelling framework2.1.2 Research question and hypotheses2.1.3 Competitive models of risk-sensitive behaviour2.1.4 Model-based fMRI: from economics to brains and back2.2 Neurally informed modelling3 Tractability: When Does Size Matter?4 Neural Integration and the Co-evolutionary Research Ideology5 Conclusion. (shrink)

Utility discounting in intertemporal economic modelling has been viewed as problematic, both for descriptive and normative reasons. However, positive utility discount rates can be defended normatively; in particular, it is rational for future utility to be discounted to take into account model-independent outcomes when decision-making under risk. The resultant values will tend to be smaller than descriptive rates under most probability assignments. This also allows us to address some objections that intertemporal considerations will be overdemanding. A principle for utility (...) discount rates is suggested which is rooted in probability discounting. Utility discounting is defended against objections from Parfit (1984) and Broome (2005); Broome (2012). A sample utility discount rate is estimated. [Open access]. (shrink)

This paper presents a new decision theory for modelling choice under risk. The new theory is a two-parameter generalization of expected utility theory. The proposed theory assumes that a decision maker: behaves as if maximizing expected utility; but may experience disappointment when the utility of a lottery’s outcome falls short of the expected utility of the lottery; and may have a preference for gambling. The proposed theory can rationalize the fourfold pattern of risk attitudes; the common ratio effect (...) and the reverse thereof ; the Allais paradox in classical common consequence problems and the reverse Allais paradox—in common consequence problems with an even split of a probability mass; violations of the betweenness axiom; switching behavior in the Samuelson’s example; violations of ordinal, upper and lower cumulative independence ; and preference reversals between valuations and choice. In application to insurance, the theory can rationalize full insurance with an actuarially unfair premium and aversion to probabilistic insurance. In application to optimal portfolio investment, the theory can rationalize the equity premium puzzle. (shrink)

While genomic and proteomic information describe the overall cellular machinery available to an organism, the metabolic profile of an individual at a given time provides a canvas as to the current physiological state. Concentration levels of relevant metabolites vary under different conditions, in particular, in the presence or absence of different disorders. Metabolite concentrations thus mediate an important link between chemistry and biology, contributing to a systems-wide understanding of biological processes and pathways. However, there are a number of challenges in (...) the ontological representation of such information. Firstly, concentration information is numeric and ranges over continuous values, while ontologies consist of discrete classes. Secondly, ontologies usually model only what is certain, and their logical formalisms are adapted to reasoning from certain axioms to logical deductions, however, the link between chemicals and diseases via concentration levels, like many threshold phenomena, is both uncertain and vague. In this paper we evaluate the representation of this knowledge using a combination of concrete domains and probabilistic reasoning. We parse concentration values from HMDB and create an ontology able to distinguish normal from abnormal concentrations and able to evaluate a probabilistic risk category for the presence of an associated disorder. (shrink)

A de minimis risk is defined as a risk that is so small that it may be legitimately ignored when making a decision. While ignoring small risks is common in our day-to-day decision making, attempts to introduce the notion of a de minimis risk into the framework of decision theory have run up against a series of well-known difficulties. In this paper, I will develop an enriched decision theoretic framework that is capable of overcoming two major obstacles (...) to the modelling of de minimis risk. The key move is to introduce, into decision theory, a non-probabilistic conception of risk known as normic risk. (shrink)

We agree with McCullough, Coverdale and Chervenak1 that ‘medical educators and academic leaders are in a pivotal and powerful position to role model’ to counter ‘incivility’ in medicine, which can include ‘dismissing’ or ‘demeaning others’. They note that ‘women may be at greater risk for experiencing incivility compared with men’, as may other individuals who experience ‘patterns of disrespect based on minority status’. The authors promote ‘professionalism’ and ‘etiquette’ to foster civility within medicine. Yet theory and experience suggest that (...) medical educators and academic leaders are also in powerful positions to mobilise these concepts to perpetuate exclusions from, and biases within, medicine, upholding racist, sexist and ableist norms. Feminist methodology reveals how ethical theorising historically excluded views and experiences of marginalised individuals and groups and provides frameworks for interrogating supposedly universal norms. Presuming dominant values and experiences reflect universal ones can further marginalise already oppressed groups and individuals. A feminist analysis reveals how ‘professionalism’ or ‘etiquette’ are historically shaped by white or male identity and experience, including norms of attire, hair, accessories, speech or behaviour. Part of the hidden curriculum of medicine becomes intuiting how leaders implicitly or explicitly define ‘professionalism’. McCullough et al define ‘professionalism’ in medicine as ‘sustained commitments to scientific and clinical excellence… making the protection and promotion of health-related interest the physician’s primary concern …. (shrink)

The argument from inductive risk has become widely accepted as good reason to reject the value-free ideal. The literature that follows is then focused on where inductive risk judgements are required and whose values ought to determine them. The purpose of this paper is twofold. Firstly, to offer motivation for aiming at the value-free ideal, and therefore avoiding inductive risk. To do so I show that there is a tension between principles in science ethics and value encroachment (...) because of inductive risk. Secondly, I offer a renewed defence of Jeffrey’s response to the argument from inductive risk. By appealing to theories in epistemology about rational belief modelling, I argue that the Bayesian belief model offers a suitable alternative to current belief modelling in science, despite criticism that is either explicit in the literature or fairly expected to arise. (shrink)

Nietzsche offers us an account of how different drives interact with one another; it is rich but also appears to risk the homunculus fallacy. Competing attempts to deflect this charge on his behalf share an implicit consensus about the ‘epistemic ends’ of the account: they assume Nietzsche is trying to provide true explanations of psychological phenomena. I argue against this consensus. I claim that Nietzsche's characterisations of drive interaction are to be taken as fictive and are not intended to (...) have explanatory value. They nevertheless facilitate genuine epistemic achievement. Drawing on Catherine Elgin's account of the epistemic role of idealised models in science, I argue that Nietzsche's account of drive interaction is a ‘model of the mind’ that, despite relying on falsehoods, can exemplify features of our psychology that aid us in making novel predictions. We then see that Nietzsche neatly sidesteps the homunculus fallacy; we can further understand more fully what Nietzsche hopes his drive psychology will teach us. We can now resolve, for example, outstanding interpretative puzzles about the relationship between psychic integration and Nietzsche’s distinctive notion of spiritual health. (shrink)

When developing computational models to analyze the tradeoffs between climate risk management strategies (i.e., mitigation, adaptation, or geoengineering), scientists make explicit and implicit decisions that are influenced by their beliefs, values and preferences. Model descriptions typically include only the explicit decisions and are silent on value judgments that may explain these decisions. Eliciting scientists’ mental models, a systematic approach to determining how they think about climate risk management, can help to gain a clearer understanding of their modeling (...) decisions. In order to identify and represent the role of values, beliefs and preferences on decisions, we used an augmented mental models research approach, namely values-informed mental models (ViMM). We conducted and qualitatively analyzed interviews with eleven climate risk management scientists. Our results suggest that these scientists use a similar decision framework to each other to think about modeling climate risk management tradeoffs, including eight specific decisions ranging from defining the model objectives to evaluating the model’s results. The influence of values on these decisions varied between our scientists and between the specific decisions. For instance, scientists invoked ethical values (e.g., concerns about human welfare) when defining objectives, but epistemic values (e.g., concerns about model consistency) were more influential when evaluating model results. ViMM can (i) enable insights that can inform the design of new computational models and (ii) make value judgments explicit and more inclusive of relevant values. This transparency can help model users to better discern the relevance of model results to their own decision framing and concerns. (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)

The aim of this study is to examine the factors influencing ethical business decision-making on environmental issues, among employees of SMEs. To do so, a survey study was performed with 394 top managers of SMEs in the UAE using a questionnaire, and the data was statistically evaluated using SmartPLS 3.0. The results suggest that prior technology use has significant positive relationships with ethical decision-making and the level of risk acceptance. Furthermore, perceived competitive pressure has significant positive relationships with ethical (...) decision-making and confidence level. In comparison, the level of risk acceptance and confidence level have significant positive relationships with ethical decision-making. Results also demonstrate that the level of risk acceptance mediates the relationship between prior technology use and ethical decision-making. There is also a significant and positive association between perceived competitive pressure and ethical decision-making with a mediating impact of the confidence level. (shrink)

Quantitative assessments of when infectious disease outbreaks end are crucial, as resources targeted towards outbreak responses typically remain in place until outbreaks are declared over. Recent improvements and innovations in mathematical approaches for determining when outbreaks end provide public health authorities with more confidence when making end-of-outbreak declarations. Although quantitative analyses of outbreaks have a long history, more complex mathematical and statistical methodologies for analysing outbreak data were developed early in the 20th century and continue to be refined. Historically, such (...) methodologies focused primarily on factors affecting the early and middle phases of an outbreak, with less attention given to determining how and when outbreaks end. This review discusses mathematical modelling methods from the last 20 years that have been developed for determining the ends of infectious disease outbreaks, and considers factors that affect the accuracy of such determinations. When disease surveillance systems provide timely and representative data to inform models, the timings of end-of-outbreak declarations can be fine-tuned to allow outbreaks to be declared over quickly and with a low risk of being incorrect. Premature declarations that outbreaks are over can undermine earlier achievements in disease control and may result in a resurgence of cases, but unnecessary delays in declaring outbreaks over can cause significant economic and social harm. Appropriate declarations that balance the benefits of relaxing control measures against the risk of a surge in cases allow public health resources to be conserved (and economic and social pressures to be reduced) while limiting the potential for additional transmission. (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)

Engineers must deal with risks and uncertainties as a part of their professional work and, in particular, uncertainties are inherent to engineering models. Models play a central role in engineering. Models often represent an abstract and idealized version of the mathematical properties of a target. Using models, engineers can investigate and acquire understanding of how an object or phenomenon will perform under specified conditions. This paper defines the different stages of the modeling process in engineering, classifies the various sources (...) of uncertainty that arise in each stage, and discusses the categories into which these uncertainties fall. The paper then considers the way uncertainty and modeling are approached in science and the criteria for evaluating scientific hypotheses, in order to highlight the very different criteria appropriate for the development of models and the treatment of the inherent uncertainties in engineering. Finally, the paper puts forward nine guidelines for the treatment of uncertainty in engineering modeling. (shrink)

Findings about the desire for health-risk information are heterogeneous and sometimes contradictory. In particular, they seem to be at variance with established psychological theories of information-seeking behavior.The present paper posits the decision about treating illness with medicine as the causal determinant for the expected net value of information, and attempts to explain idiosyncrasies in information-seeking behavior by using the notion of decision sensitivity to incoming information.Furthermore, active information avoidance is explained by modeling the expected emotional distress potentially brought (...) about by “bad news” as a disutility factor in pay-off maximization.In this context two notions of uncertainty are distinguished: an epistemic uncertainty related to the prognostic probability assigned to the risk, and an emotional uncertainty related to the expected damage. Health-risk information can both reduce epistemic and increase emotional uncertainty, giving rise to idiosyncratic processing strategies. (shrink)

Liquidity risk arises from the inability to unwind or hedge trading positions at the prevailing market prices. The risk of liquidity is a wide and complex topic as it depends on several factors and causes. While much has been written on the subject, there exists no clear-cut mathematical description of the phenomena and typical market risk modeling methods fail to identify the effect of illiquidity risk. In this paper, we do not propose a definitive one (...) either, but we attempt to derive novel mathematical algorithms for the dynamic modeling of trading volumes during the closeout period from the perspective of multiple-asset portfolio, as well as for financial entities with different subsidiary firms and multiple agents. The robust modeling techniques are based on the application of initial-value-problem differential equations technique for portfolio selection and risk management purposes. This paper provides some crucial parameters for the assessment of the trading volumes of multiple-asset portfolio during the closeout period, where the mathematical proofs for each theorem and corollary are provided. Based on the new developed econophysics theory, this paper presents for the first time a closed-form solution for key parameters for the estimation of trading volumes and liquidity risk, such as the unwinding constant, half-life, and mean lifetime and discusses how these novel parameters can be estimated and incorporated into the proposed techniques. The developed modeling algorithms are appealing in terms of theory and are promising for practical econophysics applications, particularly in developing dynamic and robust portfolio management algorithms in light of the 2007–2009 global financial crunch. In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things data analytics, expert systems in finance, FinTech, and within big data ecosystems. (shrink)

Individuals make decisions under uncertainty every day. Decisions are based on in- complete information concerning the potential outcome or the predicted likelihood with which events occur. In addition, individuals’ choices often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision making are difficult to capture using conventional, linear mathematical models. Here, we present data from a 2-choice task with variable risk between sure loss and risky loss to illustrate how a simple nonlinear dynamical system (...) can be employed to capture the dynamics of human decision making under uncertainty (i.e., multistability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlin- ear complexities of human decision making are discussed as well as the degree to which the theory of nonlinear dynamical systems might offer an alternative framework for understanding human decision making processes. (shrink)

Prevention is a pervasive phenomenon. It is about blocking an effect before it happens or stopping it as it unfolds: vaccines prevent (the unfolding of) diseases; seat belts prevent events causing serious injuries; circuit breaks prevent the manifestation of overcurrents. Many disciplines in the information sciences deal with modeling and reasoning about prevention. Examples include risk and security management as well as medical and legal informatics. Having a proper conceptualization of this phenomenon is crucial for devising proper (...) class='Hi'>modeling mechanisms and tools to support these disciplines. Forming such a conceptualization is a matter of Formal Ontology. In fact, prevention and related notions have become a topic of interest in this area. In this paper, with the support of Unified Foundational Ontology (UFO), we conduct an ontological analysis of this and other related notions, namely, the notions of countermeasures and countermeasure mechanisms, including the notion of antidotes. As a result of this conceptual clarification process, we propose an ontology-based reusable module extending UFO and capturing the relations between these elements. Finally, we employ this module to address a few cases in risk management. (shrink)

In order to be capable of exhibiting a wide range of cooperative behavior, a computer-based dialog system must have available assumptions about the current user's goals, plans, background knowledge and (false) beliefs, i.e., maintain a so-called “user model”. Apart from cooperativity aspects, such a model is also necessary for intelligent coherent dialog behavior in general. This article surveys recent research on the problem of how such a model can be constructed, represented and used by a system during its interaction with (...) the user. Possible applications, as well as potential problems concerning the advisability of application, are then discussed. Finally, a number of guidelines are presented which should be observed in future research to reduce the risk of a potential misuse of user modeling technology. (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)

Prevention is a pervasive phenomenon. It is about blocking an effect before it happens or stopping it as it unfolds: vaccines prevent (the unfolding of) diseases; seat belts prevent events causing serious injuries; circuit breaks prevent the manifestation of overcurrents. Many disciplines in the information sciences deal with modeling and reasoning about prevention. Examples include risk and security management as well as medical and legal informatics. Having a proper conceptualization of this phenomenon is crucial for devising proper (...) class='Hi'>modeling mechanisms and tools to support these disciplines. Forming such a conceptualization is a matter of Formal Ontology. In fact, prevention and related notions have become a topic of interest in this area. In this paper, with the support of Unified Foundational Ontology (UFO), we conduct an ontological analysis of this and other related notions, namely, the notions of countermeasures and countermeasure mechanisms, including the notion of antidotes. As a result of this conceptual clarification process, we propose an ontology-based reusable module extending UFO and capturing the relations between these elements. Finally, we employ this module to address a few cases in risk management. (shrink)

A review is given on statistical and modelling issues in ecotoxicology. The issues discussed are:1. How to estimate an (almost) no effect concentration chemicals in the laboratory.

The essays on obesity in this issue frequently refer to the recent American Medical Association (AMA) declaration of obesity as a disease. In response to these essays, I describe and explore the significance of ‘risk–factor medicalization’ and how negative unintended consequences with this approach to disease modeling are exemplified in many of the essays. I also relate the essays’ content to the issue of physician hubris in the face of their own helplessness in aiding the obese patient.

Translation abstractSummary: Risks in the Making: The Mediating Role of Models in Water Management and Civil Engineering in the Netherlands. Reliance on models can make technological cultures susceptible to risks through the assumptions, uncertainties, and blind spots that may accompany modeling practices. Historian of science Peter Galison has described computer modeling practices as “trading zones”, conceptual spaces in which a shared language is hammered out in an attempt to facilitate collaboration between different social groups, such as engineers and (...) policymakers. Although such a shared language may enable collaboration between diverse groups, it may also make the relation between modeling practices and knowledge of risks less visible, since the shared language does not necessarily acknowledge how models produce knowledge about their world. In that respect, models have a ‘mediating’ role, since they are not straightforward representations of the world, but involve a process of translating phenomena into formalized representations that enable experimentation. Drawing on insights from Science and Technology Studies (STS) and empirical work in the domains of hydrology, hydrodynamics, geotechnical engineering, and ecology, this paper emphasizes the importance of understanding the mediating role of models in shaping the understanding that various actors hold with regard to water‐related risks. Failing to appreciate the mediating role of models hampers the ability of actors to understand the assumptions, uncertainties, and blind spots that accompany simulation practices, and may put technological cultures at risk. (shrink)

This survey was conducted in 2017 to investigate factors influencing social risk perception of biotechnologists and plant breeders in training toward GM food based on a conceptual model. A random sample of 210 biotechnologists and plant breeders in training was studied. Confirmatory factor analysis and the reliability tests have been used to verify the uni-dimensionality of the measurement scale, SEM also was carried out to determine the most parsimonious models with the best fit for social risk perception of (...) GM foods and path analysis was conducted to understand the exogenous variables introduced in the research model. The findings revealed that the engineers in training had moderate social risk perception regarding GM foods. Moreover, the results of structural equation modeling showed the capability of the model in predicting the social risk perceptions of engineers in training. The psychological attributes of risks, social benefit perception, attitude toward using technology, level of religiosity, and moral and ethical beliefs emerged as the most powerful predictors of the social risk perception. The social benefit perception and attitude toward using technology also mediated the effects of psychological attributes of risks, level of religiosity, and moral and ethical beliefs. The social benefit perception also had an indirect influence on the engineers in training’s social risk perception of GM foods. Finally, we recommend the application of the model developed by this study for better understanding of social risk perception of stakeholders to have a more informed view of the development and promotion of GM foods. (shrink)

This article has explored the impact of coronavirus disease 2019 -induced decline in consumer durables and mobility on nitrogen dioxide emission in Europe by providing empirical and graphical justifications based on consumer price index and gross domestic product deflator indexes. The empirical estimations show that carbon dioxide and NOx emission along with other greenhouse gases drastically decreased in the wake of COVID-19-induced lockdowns and decrease in the demand of consumer goods in Europe. This means that COVID-19 improved environment in the (...) European region. However, high cost makes COVID-19 an unstable solution to environmental woes where positive impact of COVID-19 on environment achieved in short run cannot be guaranteed in the long run. Besides environment, COVID-19 drastically curtailed economic activities and exposed them to the risk of economic crisis particularly in case of Europe. (shrink)