Financing guarantee is an important means and key link to solve the financing difficulties of small- and medium-size enterprises. However, while financial guarantees alleviate the financing difficulties of SMEs, the complex guarantee relationships also constitute a new channel for credit risk contagion in the financial guarantee network. In this paper, we construct a model of credit risk contagion process of guarantee network based on SEIR and analyse the equilibrium point and stability of the model. Then, we (...) find the threshold value of risk contagion and further simulate the SEIR model dynamically to analyse the influence of each parameter of the model. The results show that the risk of the financing guarantee network begins to be widely contagious only when risk contagion threshold is greater than 1, and the conversion rate of exposed enterprises, removal rate of infected enterprises, nodal enterprises degree, and risk contagion rate have significant effects on the changes of individual density of susceptible, exposed, infected, and recovered enterprises. Combining the above findings, it is of great theoretical and practical significance to propose relevant countermeasures for credit risk control of financial guarantee network. (shrink)

There is no well-built theory on credit risk measurement and decision analysis for financial big data, and an effective and scientific evaluation system for them has not been formed. A review of them can contribute to grasping the abovementioned topics, understanding current issues, analyzing research problems, mastering research challenges, and predicting future research directions. Besides, this paper points out four research directions of credit risk measurement and decision analysis for financial big data. Moreover, this paper can (...) provide some guidance directions and insights for practitioners, researchers, financial institutions, and government departments who have an interest in complex decision-making in big data. (shrink)

In this note, we consider a Merton model for default risk, where the firm’s value is driven by a Brownian motion and a compound Poisson process.

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)

We investigate the association between a wide range of community-level environmental, social, and governance (ESG) outcomes and the credit risk of U.S. municipal finance fixed-income securities. We develop a novel dataset of multiple ESG outcomes for U.S. counties and connect it to a 2001-2020 panel of municipal bonds issued within those counties. Overall, we find supportive evidence that collective increases in community-level ESG factors (i.e., ESG outcomes) are associated with reductions in credit risk for U.S. municipal (...) finance instruments over time. We theorize that these associations arise from variations in investor perceptions and manifested changes in fiscal health over time as a function of changing ESG outcomes. Post hoc analyses leveraging quasi-exogenous shocks to uncertainty, as well as connecting ESG outcomes to various measures of fiscal health at the county-year level, and credit ratings at the bond-year level, help validate this theory. Our research suggests that even socially agnostic investors should investigate the environmental and social performance of a municipality as part of their credit due diligence. (shrink)

Based on a unique data set on US direct microloans, we study the funding determinants of interest-free peer-to-peer crowdlending aimed at borrowers in the US. By performing logistic regressions on funding success and Tobit regressions on the reversed funding time, the existence of a social underwriting by a third-party trustee and information in the description texts fostering the investors’ trust are shown to be the main predictors of successful funding. Regarding social impact, the possibility to empower women and groups of (...) borrowers appeals to the investors, whereas empowerment of the family or community beyond the borrowers themselves appears to remain unappreciated. When examining the vulnerability of the borrowers as a predictor, the results manifest differences amongst the attitudes of the investors towards social impact. In the subsample of non-endorsed loans, the investors appear to prefer to support borrowers with an immigration background. In contrast, this is not the case with endorsed loans. (shrink)

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)

There is no doubt that a negative perception of financial risk and a lack of credit risk management adversely impact business growth and business owners’ wellbeing. Past studies suggest that most Nigerian traders have poor risk perceptions and manage risk poorly. A business coaching program within rational-emotive behavior therapy framework was evaluated in order to determine its effects on financial risk perception and credit risk management among Nigerian traders. This study used an (...) open-label parallel randomized control design. This 8-weeks coaching program had 60 traders in the REBT-based business coaching group and 59 traders in the control group. The mixed-model repeated measures ANOVA was utilized for analysis of the study data. Results show that relative to a control group, traders’ financial risk perception [F = 637.29, p < 0.001, ω2 = 0.69] and credit risk management [F = 795.51, p < 0.001, ω2 = 0.80] significantly improved after participating in REBT-based business coaching program. This study shows that REBT-based business coaching program is integral to increasing financial risk perception and credit risk management among Nigerian traders. This study contributes to the advancement of business coaching program within the rational-emotive behavior therapy framework for market traders, and its application in similar situations. The study considered the benefits of business coaching program for market traders from a developing country, which is a rarely studied group. It is highly recommended that professionals study the relationship between REBT-based business coaching and economic decision-making within various organizational contexts. (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)

In recent years, supply chain finance is exploited to solve the financing difficulties of small- and medium-sized enterprises. SME credit risk assessment is a critical part in the SCF system. The diffusion of SME credit risk may cause serious consequences, leading the whole supply chain finance system unstable and insecure. Compared with traditional credit risk assessment models, the supply chain relationship, credit condition of SME, and core enterprises should all be considered to rate (...) SME credit risk in SCF. Traditional methods mix all indicators from different index systems. They cannot give a quantitative result on how these index systems work. Furthermore, traditional credit risk assessment models are heavily dependent on the number of annotated SME data. However, it is implausible to accumulate enough credit risky SMEs in advance. In this paper, we propose an adaptive heterogenous multiview graph learning method to tackle the small sample size problem for SMEs’ credit risk forecasting. Three graphs are constructed by using indicators from supply chain operation, SME financial indicator, and nonfinancial indicator individually. All the graphs are integrated in an adaptive manner, providing a quantitative explanation on how the three parts cooperate. The experimental analysis shows that the proposed method has good performance for determining whether SME is risky or nonrisky in SCF. From the perspective of SCF, SME financing ability is still the main factor to determine the credit risk of SME. (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)

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)

Credit theorists claim to explain the incompatibility of luck and knowledge and also what makes knowledge valuable. If the theory works as well as they think, it accomplishes a lot. Unsurprisingly, however, some epistemologists remain unsure. Jennifer Lackey, for instance, proposes a dilemma that suggests credit theories are either too strong or too weak. Her criticism has been hard to overcome. This paper suggests a modified account of knowledge as credit for true belief that allows credit (...) theorists to better counter Lackey’s criticism. I call my version of credit theory Risk Sensitive Credit. Under my account, an agent deserves credit just in case she believes truly on account of her reasonably accurate epistemic risk assessment. This assessment need not include higher order beliefs or even enter into conscious thought. Recent work in cognitive science, for instance, suggests that our visual faculties, in the absence of our direct awareness, work in accordance with a risk sensitive framework. This research will be referenced to help explain the dynamics of barn facade Gettier cases. (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)

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)

Using the promulgation of Green Credit Guidelines in China as the research setting, this paper exploits a quasi-natural experiment to examine the impact of green credit policy on the stock price crash risk of heavy-polluting firms. The results show that green credit policy significantly increases the risk of stock price crash of heavy-polluting firms. Such impact is transmitted through increased financial constraints and reduced information transparency. In addition, we find that the impact of green (...) class='Hi'>credit policy on the stock price crash risk is more pronounced in firms with weak external governance and a small size. Our findings provide policy implications for mitigating corporate risks and promoting corporate sustainability. (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)

(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.

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

In recent years, scholars in the social sciences and humanities have turned their attention to how the rise of digital technologies is reshaping political life in contemporary society. Here, we analyze this issue by distinguishing between two classification technologies typical of pre-digital and digital eras that differently constitute the relationship between individuals and groups. In class-based systems, characteristic of the pre-digital era, one’s status as an individual is gained through membership in a group in which salient social identities are shared (...) in common with other group members. In attribute-based systems, characteristic of the digital era, one’s status as an individual is determined by virtue of possession of a set of attributes that need not be shared with others. We argue that differences between these two types of classification technologies have important implications for how persons attach (or fail to attach) to groups, and therefore what kinds of political mobilization are possible. We illustrate this argument by examining contention over the use of gender as a variable in the pricing of risk in insurance and credit – two markets in which individuals directly encounter class-based and attribute-based systems of classification, respectively. (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.

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)

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here (...) I argue that this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought. (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)

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)

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)

This article critically evaluates and contests the flagship benefit delivery system Universal Credit for lone mothers by focusing on some of the ethical challenges it poses, as well as some key implications it holds for social work with lone mothers and their children. Universal Credit was first introduced in the United Kingdom (UK) in 2008, and echoes conditionality-based welfare policies adopted by neoliberal governments internationally on the assumption that paid employment offers a route out of poverty for citizens. (...) However, research evidence suggests that the risks of conditionality polices for lone parents can often include increased poverty, a deterioration in mental health or even destitution posed by paternalistic sanctions or precarious low-paid employment, which can undermine parenting capacities and children’s well-being. The article also critically appraises and questions challenges posed by an increased reliance upon contractual ethics by governments, alongside the wider behaviour-modifying policies of the workfare-orientated state. This includes that working-class lone mothers can erroneously be stigmatised as representing a morally challenged dependent burden through activation policies and risk-averse social work practices. (shrink)

We take a fresh look at the concerns about credit card pricing and empirically investigate whether the Credit CARD Act of 2009 has been successful in addressing those concerns. The rational choice theory of credit card pricing, which posits that issuers use back-end fees to adjust the price of credit to reflect new information about borrowers’ credit risk, predicts that issuers will respond to the Act by using alternative ways to price risk. In (...) contrast, the behavioral economics theory, which posits that issuers use back-end fees because they are not salient to consumers, predicts that issuers will respond by increasing unregulated non-salient prices. If the market is competitive, we argue that the CARD Act should also result in increases in some salient, up-front prices. But we show that if issuers have market power, reductions in non-salient fees may not result in concomitant increases in salient charges. We test these predictions using two datasets on credit card contract terms before and after the CARD Act rules went into effect. We find that the rules have substantially reduced the back-end fees directly regulated by the Act, including late fees and over-the-limit fees. However, unregulated contract terms, such as annual fees and purchase interest rates, have changed little. Post-CARD Act, consumers continue to face high long-term prices and low short-term prices, and imperfectly rational consumers still find it difficult to understand the cost of credit card borrowing. We thus consider potential improvements to the regulatory framework. We argue that improved disclosures that present to consumers the aggregate cost of credit under the contract, based on information about the borrower’s likely use of credit, would improve consumer outcomes. Furthermore, we suggest that regulators, rather than focusing on prices that are “too high,” should consider limiting the ability of issuers to charge introductory teaser interest rates that are in a sense “too low. (shrink)

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

In this study, we accessed information about the university credits of all teachers born after 1971 in Sweden as a means of ascertaining the development of their subject knowledge. We examined the university credits they earned during pre-service and in-service training. Data comes from registers Gothenburg Educational Longitudinal Database and the teacher register. We linked GOLD to the teacher register in order to describe the knowledge development of teachers in compulsory school 1998–2014. Special focus was on Swedish language and mathematics. (...) Multiple regression and multilevel growth modeling were used as our main methods. Results show an increase in pre-service credits during the time period and more credits in Swedish language than in mathematics. To analyze teachers’ in-service training, we followed the development of their university credits over time. Teachers with higher prerequisites in terms of grade point average tended to gain more credits in-service. The study included discussions on ideas and the implications for future research. (shrink)