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Articles de revues sur le sujet « Uncertainty risk »

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Auteur : Grafiati
Publié le 11 mars 2023

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1

Bélyácz, Iván, et Katalin Daubner. « Uncertainity of risk and increasing risk of uncertainty in business decisions ». Economy & ; finance 8, no 3 (2021) : 264–312. http://dx.doi.org/10.33908/ef.2021.3.2.

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Our paper follows the development of theory regarding the position of risk and uncertainty in economics from the publication of works by Knight (1921) and Keynes (1921) until the recent past. The starting point is presented by the relevant remarks of the thinkers of classical economics. Next, we describe the turning point related to Knight and Keynes and reveal the theoretical roots of risk taking. In the core chapter of the paper the authors make an attempt to re-interpret “animal spirits” as the intention for risk taking. A separate chapter is devoted to the relationship of rational choice and risk, and another one about the canonisation of risk in economics. In further parts of the paper, we examine the intentions to relativize the difference between risk and uncertainty, the negligence of uncertainty in the neo-classical system, the attempts to merge risk and uncertainty and the disruption of the unity of risk taking and risk bearing. Finally, the authors come to the conclusion that Knight’s and Keynes’ doctrines of risk and uncertainty have stood the test of time.
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Griffin, R. L. « Uncertain about uncertainty in pest risk analysis ». Acta Horticulturae, no 1105 (décembre 2015) : 315–20. http://dx.doi.org/10.17660/actahortic.2015.1105.45.

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A. Kuzmin, Evgeny. « Risk and uncertainty in concept of corporate lifecycle ». Problems and Perspectives in Management 15, no 1 (29 mars 2017) : 107–14. http://dx.doi.org/10.21511/ppm.15(1).2017.11.

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Regularly changed destructive periods in organizational development mean that the lifecycle exists. A nature of its formation hides a number of important conceptual regularities. One aspect of these trends is relationship between distribution of uncertainty and risks in lifecycle models, underlying motives of their formation and determining participation in development of organizational immunity. A closer definition of these issues is an objective of this research. The paper reviews the history of the lifecycle concept, gives its analysis and possible applications in management studies. In the analytical review of literature, there is an attempt of theoretical systematization for some provisions from the concept on consistency and continuity of stages turnover, on conditions of their identification and a nonlinear path. For discussions of the scientific community, the author presents hypotheses of the available effect of compression (density) in development stages, as well as heterogenic risk concentration. There is an assumption that economic systems have different orders for both the general and short lifecycles. Based on generalized theoretical and methodological provisions of stages in the lifecycle phases, the author attempts to combine functional and evolutionary models. The author also details distinctive features in the process of control over uncertainty and risks in the sequence of development stages.
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Leicester, R. H. « Uncertainty and risk ». Australian Journal of Structural Engineering 18, no 1 (2 janvier 2017) : 1–2. http://dx.doi.org/10.1080/13287982.2017.1335963.

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May, Robert. « Risk and uncertainty ». Nature 411, no 6840 (juin 2001) : 891. http://dx.doi.org/10.1038/35082158.

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Carter, Tony. « Risk and Uncertainty ». Journal of Hospital Marketing & ; Public Relations 20, no 1 (12 janvier 2010) : 66–86. http://dx.doi.org/10.1080/15390940903451014.

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Toma, Simona-Valeria, Mioara Chiriţă et Daniela Şarpe. « Risk and Uncertainty ». Procedia Economics and Finance 3 (2012) : 975–80. http://dx.doi.org/10.1016/s2212-5671(12)00260-2.

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Schendel, Dan. « Risk and uncertainty ». Strategic Entrepreneurship Journal 1, no 1-2 (2007) : 53–55. http://dx.doi.org/10.1002/sej.17.

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Brzezicka, Justyna, et Radoslaw Wisniewski. « Identifying Selected Behavioral Determinants of Risk and Uncertainty on the Real Estate Market ». Real Estate Management and Valuation 22, no 2 (8 juillet 2014) : 30–40. http://dx.doi.org/10.2478/remav-2014-0015.

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Abstract Various market behaviors can be characterized as risky or uncertain, thus their observation is important to the real estate market system. The extensive use of behavioral factors facilitates their implementation and studies in relation to the real estate market system. The behavioral approach has established its own instrumentation which enables elements of risk and uncertainty to be quantified. The belief that behavioral determinants connected with risk and uncertainty occur in the real estate market system (REMS) is the research hypothesis of the present paper which aims to: 1) analyze risk and uncertainty in the REM system using the behavioral approach, 2) identify and systematize behavioral determinants of risk and uncertainly shaping the REM, 3) develop a methodology for measuring behavioral determinants of risk and uncertainty in the REM, 4) present the significance of behavioral determinants of risk and uncertainty on the example of the Olsztyn REM. The aims were realized by means of a laboratory experiment conducted on a representative sample, as well as through the statistical verification of the obtained results. The article builds on current knowledge regarding the mechanisms of how the REM operates by introducing new issues connected with the behavioral approach to the topic of risk and uncertainty.
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Zhai, Jia, Haitao Zheng, Manying Bai et Yunyun Jiang. « An Uncertain Alternating Renewal Insurance Risk Model ». Mathematical Problems in Engineering 2020 (6 juillet 2020) : 1–13. http://dx.doi.org/10.1155/2020/3856323.

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The claim process in an insurance risk model with uncertainty is traditionally described by an uncertain renewal reward process. However, the claim process actually includes two processes, which are called the report process and the payment process, respectively. An alternative way is to describe the claim process by an uncertain alternating renewal reward process. Therefore, this paper proposes an insurance risk model under uncertain measure in which the claim process is supposed to be an alternating renewal reward process and the premium process is regarded as a renewal reward process. Then, the paper also gives the inverse uncertainty distribution of the insurance risk process. The expression of ruin index and the uncertainty distribution of the ruin time are derived which both have explicit expressions based on given uncertainty distributions. Finally, several examples are provided to illustrate the modeling ideas.
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11

Krebs, John R. « Risk, uncertainty and regulation ». Philosophical Transactions of the Royal Society A : Mathematical, Physical and Engineering Sciences 369, no 1956 (13 décembre 2011) : 4842–52. http://dx.doi.org/10.1098/rsta.2011.0174.

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This paper reviews the relationship between scientific evidence, uncertainty, risk and regulation. Risk has many different meanings. Furthermore, if risk is defined as the likelihood of an event happening multiplied by its impact, subjective perceptions of risk often diverge from the objective assessment. Scientific evidence may be ambiguous. Scientific experts are called upon to assess risks, but there is often uncertainty in their assessment, or disagreement about the magnitude of the risk. The translation of risk assessments into policy is a political judgement that includes consideration of the acceptability of the risk and the costs and benefits of legislation to reduce the risk. These general points are illustrated with reference to three examples: regulation of risk from pesticides, control of bovine tuberculosis and pricing of alcohol as a means to discourage excessive drinking.
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12

Liu, Yuhan, et Dan A. Ralescu. « Risk Index in Uncertain Random Risk Analysis ». International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no 04 (août 2014) : 491–504. http://dx.doi.org/10.1142/s021848851450024x.

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In many systems, randomness and uncertainty are present simultaneously. Uncertain random variables provide a tool to deal with these inexact phenomena. This paper proposes a concept of risk index to quantify the risk of an uncertain random system. In addition, a risk index theorem is proved in order to calculate the risk index, and is applied to series systems, parallel systems and standby systems. Finally, a concept of expected loss is suggested for an uncertain random system.
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Sturloni, Giancarlo. « Handling uncertainty ». Journal of Science Communication 03, no 04 (9 décembre 2005) : R01. http://dx.doi.org/10.22323/2.03040701.

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The management of health risks related to scientific and technological innovations has been the focus of a heated debate for a few years now. In some cases, like the campaigns against the use of GMOs in agriculture, this debate has degenerated into a political and social dispute. Even risk analysis studies, which appeared in the 1970s in the fields of nuclear physics and engineering and were later developed by social sciences as well, have given completely different, and at times contradictory, interpretations that, in turn, have given rise to bitter controversies.
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Almeida, Dora, Andreia Dionísio, Isabel Vieira et Paulo Ferreira. « Uncertainty and Risk in the Cryptocurrency Market ». Journal of Risk and Financial Management 15, no 11 (14 novembre 2022) : 532. http://dx.doi.org/10.3390/jrfm15110532.

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Cryptocurrency investments are often perceived as uncertain and risky. In this study, we assessed if this is indeed the case, using a sample of seven cryptocurrencies and considered a period that encompassed the first real global shock in the life of these relatively new financial assets, the COVID-19 pandemic. Uncertainty was evaluated using Shannon’s symbolic entropy. To measure risk, we use value-at-risk and conditional value-at-risk. The results indicate that, except for Tether, the analyzed cryptocurrencies’ returns exhibited similar patterns of uncertainty and risk. Levels of uncertainty were close to the maximum values, but high uncertainty is not always associated with high risk. During the pandemic crisis, uncertainty increased while risk decreased, suggesting that the considered assets may have safe haven properties.
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15

Spiegelhalter, David. « Risk and Uncertainty Communication ». Annual Review of Statistics and Its Application 4, no 1 (7 mars 2017) : 31–60. http://dx.doi.org/10.1146/annurev-statistics-010814-020148.

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16

Ribbens, Eric. « The Risk of Uncertainty ». Ecology 78, no 7 (octobre 1997) : 2272–73. http://dx.doi.org/10.1890/0012-9658(1997)078[2272:trou]2.0.co;2.

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Carrington, Clark D., et P. Michael Bolger. « Uncertainty and Risk Assessment ». Human and Ecological Risk Assessment : An International Journal 4, no 2 (avril 1998) : 253–57. http://dx.doi.org/10.1080/10807039891284325.

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18

BROECKE, MARCEL. « Uncertainty in Risk Assessment ». Annals of the New York Academy of Sciences 895, no 1 UNCERTAINTY I (décembre 1999) : xii. http://dx.doi.org/10.1111/j.1749-6632.1999.tb08072.x.

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Kitchenham, B., et S. Linkman. « Estimates, uncertainty, and risk ». IEEE Software 14, no 3 (1997) : 69–74. http://dx.doi.org/10.1109/52.589239.

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20

Tversky, Amos, et Craig R. Fox. « Weighing risk and uncertainty. » Psychological Review 102, no 2 (1995) : 269–83. http://dx.doi.org/10.1037/0033-295x.102.2.269.

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Madansky, Albert. « Probability, Uncertainty and Risk ». International Journal of Evidence & ; Proof 1, special (décembre 1997) : 326–30. http://dx.doi.org/10.1177/1365712797001special11.

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22

Williams, Allan M., et Vladimír Baláž. « Tourism Risk and Uncertainty ». Journal of Travel Research 54, no 3 (24 février 2014) : 271–87. http://dx.doi.org/10.1177/0047287514523334.

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Goucher, Wendy. « The risk of uncertainty ». Computer Fraud & ; Security 2011, no 2 (février 2011) : 11–14. http://dx.doi.org/10.1016/s1361-3723(11)70017-3.

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Morgan, M. Granger, Max Henrion, Samuel C. Morris et Deborah A. L. Amaral. « Uncertainty in risk assessment ». Environmental Science & ; Technology 19, no 8 (août 1985) : 662–67. http://dx.doi.org/10.1021/es00138a002.

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Shrader-Frechette, Kristin. « Risk Assessment and Uncertainty ». PSA : Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988, no 2 (janvier 1988) : 504–17. http://dx.doi.org/10.1086/psaprocbienmeetp.1988.2.192908.

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Dangol, Ramesh, et Anthony Kos. « Knightian uncertainty and risk ». Journal of Strategy and Management 7, no 4 (11 novembre 2014) : 337–53. http://dx.doi.org/10.1108/jsma-02-2014-0010.

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Purpose – The purpose of this paper is to propose a new way to distinguish a firm’s dynamic capabilities from operational capabilities. Design/methodology/approach – Conceptual paper/literature review. Findings – Current literature on dynamic capabilities posits that dynamic capabilities are those firm capabilities that can induce change in other capabilities, while operational capabilities are static. Distinguishing between these capabilities in this manner is not helpful because changes occur continuously in all capabilities to at least some extent. In addition, empirical studies show that even task-level operational capabilities can change on their own and can induce change in other capabilities. In contrast to focussing on the presence or lack of change to determine if a capability is dynamic or operational, this paper distinguishes between them by determining a priori the expected nature of the outcome. By focussing on the outcomes of change rather than the changes themselves, this paper proposes that capabilities should be considered operational if they produce outcomes that can be predicted using probability distribution while those capabilities that produce outcomes that cannot be predicted using probability distribution should be considered dynamic. Research limitations/implications – Future research on dynamic capabilities should not only investigate whether or not change is occurring, but the outcome of change to understand whether a change is precipitated by dynamic or operational capabilities. Originality/value – Existing dynamic capabilities literature is unclear about how to distinguish between dynamic capabilities from operational capabilities. Previous research attempts to distinguish these capabilities by arguing dynamic capabilities are those firm capabilities that can induce change in other capabilities, while operational capabilities are static and do not induce change. This is not particularly helpful. A clear distinction between dynamic and operational capabilities could facilitate further advancement of the dynamic capability literature; this study makes a rudimentary effort to distinguish between them.
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Dixon, Mandy, et Femi Oyebode. « Uncertainty and risk assessment ». Advances in Psychiatric Treatment 13, no 1 (janvier 2007) : 70–78. http://dx.doi.org/10.1192/apt.bp.105.002022.

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Risk assessment forms an integral part of clinical practice. Traditionally, risk has been portrayed as a binary concept, and its assessment regarded as a test that can be correctly or incorrectly classified. However, this article discusses how risk assessments are less straightforward than is commonly perceived and are often complicated by multiple forms of uncertainty. These uncertainties arise where psychiatrists are unsure about their interpretation of information, where information is missing, or where interpretation of the risk situation is open to challenge. They centre on doubts about the accuracy and the defensibility of assessment of patients' risk status and the need for risk containment. Like other professionals, psychiatrists adopt a range of strategies to resolve uncertainties. These strategies, which often involve some ‘risk-taking’, enable the practising clinician to make a more confident decision. There is an argument for including ‘certainty’ as a theoretical feature of risk assessment in psychiatry and for recognising it as a multifaceted phenomenon. There is also an argument for considering with greater precision the manner in which uncertainty is managed within psychiatric risk assessments.
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Buzen, J. P. « Computation, Uncertainty and Risk ». Computer Journal 55, no 7 (29 juin 2012) : 838–47. http://dx.doi.org/10.1093/comjnl/bxs076.

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Alaszewski, Andy, et Patrick Brown. « Risk, uncertainty and knowledge ». Health, Risk & ; Society 9, no 1 (mars 2007) : 1–10. http://dx.doi.org/10.1080/13698570601183033.

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Leggio, Karyl B. « Managing uncertainty and risk ». Quarterly Review of Economics and Finance 44, no 5 (décembre 2004) : 633–35. http://dx.doi.org/10.1016/j.qref.2004.07.001.

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Mousavi, Shabnam, et Gerd Gigerenzer. « Risk, uncertainty, and heuristics ». Journal of Business Research 67, no 8 (août 2014) : 1671–78. http://dx.doi.org/10.1016/j.jbusres.2014.02.013.

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Norman, Alfred L., et David W. Shimer. « Risk, uncertainty, and complexity ». Journal of Economic Dynamics and Control 18, no 1 (janvier 1994) : 231–49. http://dx.doi.org/10.1016/0165-1889(94)90077-9.

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Istiak, Khandokar, et Apostolos Serletis. « Risk, uncertainty, and leverage ». Economic Modelling 91 (septembre 2020) : 257–73. http://dx.doi.org/10.1016/j.econmod.2020.06.010.

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Hong, Zhen, C. K. M. Lee et Linda Zhang. « Procurement risk management under uncertainty : a review ». Industrial Management & ; Data Systems 118, no 7 (13 août 2018) : 1547–74. http://dx.doi.org/10.1108/imds-10-2017-0469.

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Purpose The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps. Design/methodology/approach Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed. Findings Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper. Research limitations/implications Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty. Originality/value The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.
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Abeysinghe, Sudeepa. « An Uncertain Risk : The World Health Organization's Account of H1N1 ». Science in Context 27, no 3 (28 juillet 2014) : 511–29. http://dx.doi.org/10.1017/s0269889714000167.

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ArgumentScientific uncertainty is fundamental to the management of contemporary global risks. In 2009, the World Health Organization (WHO) declared the start of the H1N1 Influenza Pandemic. This declaration signified the risk posed by the spread of the H1N1 virus, and in turn precipitated a range of actions by global public health actors. This article analyzes the WHO's public representation of risk and examines the centrality of scientific uncertainty in the case of H1N1. It argues that the WHO's risk narrative reflected the context of scientific uncertainty in which it was working. The WHO argued that it was attempting to remain faithful to the scientific evidence, and the uncertain nature of the threat. However, as a result, the WHO's public risk narrative was neither consistent nor socially robust, leading to the eventual contestation of the WHO's position by other global public health actors, most notably the Council of Europe. This illustrates both the significance of scientific uncertainty in the investigation of risk, and the difficulty for risk managing institutions in effectively acting in the face of this uncertainty.
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Shakshuki, Elhadi, Kumaraswamy Ponnambalam et Tassew Wodaj. « Risk analysis in environmental systems ». Canadian Journal of Civil Engineering 29, no 1 (1 février 2002) : 1–7. http://dx.doi.org/10.1139/l01-072.

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Uncertainty is an inherent feature of environmental systems, which makes probabilistic models important. Environmental risk assessment is an important but time consuming task. For large-scale systems, use of linear systems with uncertainty information on parameters and inputs is one of the few possible methods to assess risk. To estimate risk, it is necessary to have at least the first two moments of output variables. This paper describes an efficient method developed for second-moment analysis of linear systems with uncertain coefficients. The main objective is to provide the means and the variances of the output and to provide efficient formulation and automation of the moment equations. This method is demonstrated in two real-world applications of environmental modeling.Key words: uncertainty, second-moment methods, risk analysis, reliability, linear systems.
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Gilkeson, James H., et Stuart E. Michelson. « Risk Budgeting, Parameter Uncertainty,and Risk Realizations ». Journal of Investing 17, no 1 (29 février 2008) : 47–54. http://dx.doi.org/10.3905/joi.2008.701956.

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Ellis, M. E. « Risk Budgeting, Parameter Uncertainty, and Risk Realizations ». CFA Digest 38, no 3 (août 2008) : 63–65. http://dx.doi.org/10.2469/dig.v38.n3.28.

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Moura, Stephanie Tonn Goulart, Christian Daniel Falaster, Christine Elena Bianchi, Érica de Souza Mazato et Laura Taysa Espig. « How Institutions Shape Uncertainty and Risk ». Internext 16, no 3 (1 septembre 2021) : 238–51. http://dx.doi.org/10.18568/internext.v16i3.604.

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Purpose: The study proposes a conceptual framework on how institutions influence risk and uncertainty. Beyond the nuances in defining the concepts in the existing literature, the role of institutions in shaping risks and uncertainties remains understudied. This paper adopts the new institutional economics (NIE) perspective to revisit the concepts of risk and uncertainty and provide a deeper reflection about its interactions with formal and informal institutions. Method: Our conceptual model is based on four propositions that support a theoretical explanation about the relationships between institutions and uncertainties, institutions and risks, and uncertainties and risks. Findings: While formal institutions have a primary role in reducing uncertainties, informal institutions can be seen as a source of risk. These findings imply firms’ strategic decisions. In this regard, we also provide a research agenda for future empirical studies in the area. Originality/value: The study highlights the importance of institutions for companies to deal with risk and uncertainties. The institutions have a primary role in defining the “known part” of the uncertainty, allowing the companies to evaluate the different scenarios for decision-making. Theoretical/Methodological Contributions: This study differentiates risk and uncertainty interaction according to institutional theory. Additionally, we offer an academic discussion of how formal and informal institutions can shape risks and uncertainties.
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Phillips, Séamus. « Risky Business - The management of Risk and Uncertainty ». BMJ 319, Suppl S5 (1 novembre 1999) : 9911432a. http://dx.doi.org/10.1136/sbmj.9911432a.

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WONG., KAM L. « Uncertainty ! Uncertainty ! » Quality and Reliability Engineering International 3, no 1 (janvier 1987) : 1–2. http://dx.doi.org/10.1002/qre.4680030102.

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Liu, Ying, Liuying Ma, Luyang Sun, Xiao Zhang, Yunyun Yang, Qing Zhao et Zhigang Qu. « Risk-Based Maintenance Optimization for a Subsea Production System with Epistemic Uncertainty ». Symmetry 14, no 8 (11 août 2022) : 1672. http://dx.doi.org/10.3390/sym14081672.

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The lack of operation and maintenance data brings difficulties to traditional risk assessment based on probability methods. Therefore, experts are invited to evaluate the key performance indicators related to system risk. These evaluation results are usually described by ambiguous language, so they have epistemic uncertainty. Uncertainty theory is a branch of mathematics used to model experts’ degrees of belief. The uncertain measure has duality, that is, some symmetry, which means that the sum of the uncertain measure of an event and the uncertain measure of its complementary set is equal to 1. Therefore, the risk occurrence time of each basic event evaluated by experts is modeled by the uncertain variable in this article. Then, the risk assessment method of systems with epistemic uncertainty is proposed based on an uncertain fault tree analysis. Furthermore, two risk-based maintenance optimization models for systems with epistemic uncertainty are established. In particular, the leakage risk assessment method and the two risk-based maintenance optimization models for a subsea production system are considered, and the optimization results are given. The optimization results can help practitioners to warn of the leakage risk and make scientific maintenance decisions based on expert knowledge, so as to extend the service life of subsea production systems.
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Ma, Zhen. « Uncertainty, Risk, and Merit-Making ». Social Analysis 65, no 3 (1 septembre 2021) : 88–109. http://dx.doi.org/10.3167/sa.2021.650305.

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The rising importance of the tea business among the Bulang people of Yunnan province, Southwest China, is intimately linked to Theravada Buddhist ideologies and practices. Non-reciprocal merit-making provides a sense of control, and this is particularly important in an increasingly uncertain economic environment. More and more people were ready to engage in high-risk trading, and new rituals emerged precisely at a time when profit margins increased rapidly. The reinvention of local rituals helped people to control risk-taking and to morally legitimize ambiguous market behavior. The result is strong synergies between the ways uncertainty and risk are being addressed in the tea economy and in local religious practice: economic processes are changing religious practices just as much as religious practices are making a difference in economic behavior.
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Bekaert, Geert, Eric Engstrom et Yuhang Xing. « Risk, Uncertainty, and Asset Prices ». Finance and Economics Discussion Series 2005, no 40 (2005) : 1–53. http://dx.doi.org/10.17016/feds.2005.40.

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Bekaert, Geert, Eric Engstrom et Yuhang Xing. « Risk, Uncertainty, and Asset Prices ». Finance and Economics Discussion Series 2008, no 40r (2008) : 1–57. http://dx.doi.org/10.17016/feds.2005.40r.

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Bali, Turan G., et Hao Zhou. « Risk, Uncertainty, and Expected Returns ». Finance and Economics Discussion Series 2011, no 45 (septembre 2011) : 1–57. http://dx.doi.org/10.17016/feds.2011.45.

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Roeser, Sabine. « Uncertainty, risk and moral emotions ». Philosophers' Magazine, no 66 (2014) : 106–11. http://dx.doi.org/10.5840/tpm20146693.

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Pohorecki, Roman, et Gary E. Hill. « Risk and Uncertainty in Anesthesia ». Open Journal of Anesthesiology 03, no 07 (2013) : 338–44. http://dx.doi.org/10.4236/ojanes.2013.37072.

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Rayner, Steve, Mary Douglas, Joseph G. Morone, Edward J. Woodhouse, Arthur L. Stinchcombe et Carol A. Heimer. « Risk, Uncertainty, and Social Organization ». Contemporary Sociology 18, no 1 (janvier 1989) : 6. http://dx.doi.org/10.2307/2071900.

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Sbughea, Corina, et Anamaria Aldea. « Decisions under Risk and Uncertainty ». Risk in Contemporary Economy 1, no 1 (31 juillet 2021) : 443–49. http://dx.doi.org/10.35219/rce20670532138.

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