Статті в журналах: "Decision under risk"

1

Mishra, Sandeep. "Decision-Making Under Risk." Personality and Social Psychology Review 18, no. 3 (April 25, 2014): 280–307. http://dx.doi.org/10.1177/1088868314530517.

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2

De Sousa, José, Anne-Célia Disdier, and Carl Gaigné. "Export decision under risk." European Economic Review 121 (January 2020): 103342. http://dx.doi.org/10.1016/j.euroecorev.2019.103342.

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3

Hewig, Johannes, Thomas Straube, Ralf H. Trippe, Nora Kretschmer, Holger Hecht, Michael G. H. Coles, and Wolfgang H. R. Miltner. "Decision-making under Risk: An fMRI Study." Journal of Cognitive Neuroscience 21, no. 8 (August 2009): 1642–52. http://dx.doi.org/10.1162/jocn.2009.21112.

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Recent research has focused on decision-making under risk and its neural bases. Two kinds of bad decisions under risk may be defined: too risky decisions and too cautious decisions. Here we show that suboptimal decisions of both kinds lead to increased activity in the anterior cingulate cortex in a Blackjack gambling task. Moreover, this increased activity is related to the avoidance of the negatively evaluated decision under risk. These findings complement other results suggesting an important role of the dorsal anterior cingulate cortex in reward-based decision-making and conflict resolution.

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4

Lee-Stronach, Chad. "Moral priorities under risk." Canadian Journal of Philosophy 48, no. 6 (December 2018): 793–811. http://dx.doi.org/10.1080/00455091.2017.1415104.

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AbstractMany moral theories are committed to the idea that some kinds of moral considerations should be respected, whatever the cost to ‘lesser’ types of considerations. A person's life, for instance, should not be sacrificed for the trivial pleasures of others, no matter how many would benefit. However, according to the decision-theoretic critique of lexical priority theories, accepting lexical priorities inevitably leads us to make unacceptable decisions in risky situations. It seems that to operate in a risky world, we must reject lexical priorities altogether. This paper argues that lexical priority theories can, in fact, offer satisfactory guidance in risky situations. It does so by equipping lexical priority theories with overlooked resources from decision theory.

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5

Venezian, Emilio, and Jeroen Suijs. "Cooperative Decision Making under Risk." Journal of Risk and Insurance 68, no. 3 (September 2001): 536. http://dx.doi.org/10.2307/2678127.

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6

Adam, Robert, Paul M. Bays, and Masud Husain. "Rapid decision-making under risk." Cognitive Neuroscience 3, no. 1 (October 12, 2011): 52–61. http://dx.doi.org/10.1080/17588928.2011.613988.

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7

He, Ying, and Rui-Hua Huang. "Risk attributes theory: Decision making under risk." European Journal of Operational Research 186, no. 1 (April 2008): 243–60. http://dx.doi.org/10.1016/j.ejor.2007.01.012.

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8

Grechuk, Bogdan, and Michael Zabarankin. "Risk averse decision making under catastrophic risk." European Journal of Operational Research 239, no. 1 (November 2014): 166–76. http://dx.doi.org/10.1016/j.ejor.2014.04.042.

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9

Lu, Jingyi, Xuesong Shang, and Bingjie Li. "Self–Other Differences in Decision-Making Under Risk." Experimental Psychology 65, no. 4 (July 2018): 226–35. http://dx.doi.org/10.1027/1618-3169/a000404.

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Abstract. Decisions made for others reflect not only decision-makers’ cognitive and emotional states but also decision-makers’ interpersonal concerns. People who make choices for others will potentially be blamed for unappealing outcomes by others. Therefore, we hypothesize that individuals will seek sure gains (which increase individuals’ responsibility for desirable outcomes) and avoid sure losses (which decrease individuals’ responsibility for undesirable outcomes) when making risky decisions for others more than when making such decisions for themselves. The results of two studies show that making decisions for others (vs. oneself) promotes risk-averse choices over gains. This effect may be driven by the perceived responsibility associated with different options. When both options exhibit variance in outcomes, such self–other difference disappears. However, no self–other difference over losses was observed. Taken together, our research highlights interpersonal concerns in making decisions for others, as well as the behavioral consequences of these concerns in decisions under risk.

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10

Backus, G. B. C., V. R. Eidman, and A. A. Dijkhuizen. "Farm decision making under risk and uncertainty." Netherlands Journal of Agricultural Science 45, no. 2 (July 1, 1997): 307–28. http://dx.doi.org/10.18174/njas.v45i2.520.

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Relevant portions of the risk literature are reviewed, relating them to observed behaviour in farm decision-making. Relevant topics for applied agricultural risk research are proposed. The concept of decision making under risk and uncertainty is discussed by reviewing the theory of Subjective Expected Utility and its limitations. Subjective Expected Utility theory is the major framework for thinking systematically through complex issues of decision. Limitations of Subjective Expected Utility theory were that its application to unique decisions is doubtful, that it does not contribute to difficulties in determining the available decision alternatives, and that it is cast in a timeless setting, making the theoretic framework to a very limited extent helpful to solve real world decision problems. Most empirical studies indicate that farmers are risk neutral to slightly risk averse. It is doubtful whether decision makers could be classified according to their risk preferences. A presented overview of applied risk responses reveals much attention for diversification of the enterprise and of production practices, maintaining reserves, and farm expansion. Research reports on observed problems in farm decision making behaviour are lacking. Proposed topics for agricultural risk research include the assessment of the need for a strategic change, the creation of databases to determine both the (co)variances of input and output prices, the effectiveness of various kinds of decision support for different decision problems, and methods for applied scenario analysis to deal with long-run risk.

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11

Zapien, Nicolle Marie. "Decision Science, Risk Perception, and Infidelity." SAGE Open 7, no. 1 (January 2017): 215824401668681. http://dx.doi.org/10.1177/2158244016686810.

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Decision scientists have revealed and described, through empirical study, the ways in which we tend to make decisions, and in particular, the ways we make decisions under conditions of uncertainty and that involve risk. The findings from these studies demonstrate the heuristics we use in making judgments and ways in which we process information compared with subject matter experts and logical and statistical principles. These findings have been applied to public policy issues and environmental safety, among other areas of social and political import, but have largely not been applied to our understanding of important personal decisions that are also made under uncertainty and risk and which have important personal and sometimes social consequences. This article aims to consider extra-marital affairs as an example of a personal decision that is made under uncertainty and with risk, and to apply decision science models to the decision-making that occurs in these cases. The hope is that decision scientists, psychologists, and clinicians who treat instances of infidelity, as well as the public, can benefit from what is known about how these decisions are made, which so often lead to regret. Theoretical implications for other personal decisions made under uncertainty with risk will be discussed.

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12

MANCINI, FRANCESCO. "RESPONSIBILITY, GUILT, AND DECISION UNDER RISK." Psychological Reports 93, no. 7 (2003): 1077. http://dx.doi.org/10.2466/pr0.93.7.1077-1079.

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13

MANCINI, FRANCESCO. "RESPONSIBILITY, GUILT, AND DECISION UNDER RISK." Psychological Reports 93, no. 8 (2003): 1077. http://dx.doi.org/10.2466/pr0.93.8.1077-1079.

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14

Gatev, Geo I. "Decision-making under risk and uncertainty." International Journal of Nuclear Governance, Economy and Ecology 1, no. 1 (2006): 82. http://dx.doi.org/10.1504/ijngee.2006.008705.

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15

Mancini, Francesco, and Amelia Gangemi. "Responsibility, Guilt, and Decision under Risk." Psychological Reports 93, no. 3_suppl (December 2003): 1077–79. http://dx.doi.org/10.2466/pr0.2003.93.3f.1077.

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We hypothesize that individuals' choices (risk-seeking/risk-aversion) depend on moral values and, in particular, on how subjects evaluate themselves as guilty or as victims of a wrong rather than on the descriptions of the outcomes as given in the options and evaluated accordingly as gains or losses (framing effect). People who evaluate themselves as victims are expected to show a risk-seeking preference (context of innocence). People who evaluate themselves as guilty are expected to show a risk-averse preference (context of guilt). Responses of 232 participants to a decision problem were compared in four different conditions involving two-story formats (innocence/guilt) and two-question-options formats (gain/loss). Regardless of the format of the question options, the story format appears to be an important determinant of individuals' preferences.

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16

Johnson, Joseph G., and Jerome R. Busemeyer. "Decision making under risk and uncertainty." Wiley Interdisciplinary Reviews: Cognitive Science 1, no. 5 (August 2, 2010): 736–49. http://dx.doi.org/10.1002/wcs.76.

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17

Olson, Lars J. "Multivariate decision-making under risk aversion." Journal of Economic Theory 50, no. 1 (February 1990): 193–203. http://dx.doi.org/10.1016/0022-0531(90)90091-w.

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18

Uzhga-Rebrov, Oleg, and Galina Kuleshova. "Probability Weighting in Decision-Making Tasks under Risk." Information Technology and Management Science 25 (December 9, 2022): 49–54. http://dx.doi.org/10.7250/itms-2022-0006.

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The analysis of alternative decisions and the choice of the optimal – in a given sense – decision is an integral part of people’s purposeful activity in all areas of their social life. Many formal approaches have been proposed to solve these problems. One such approach is expected utility theory, which correctly models individuals’ subjective preferences and attitudes to risk. For a very long time this theory was the leading approach for decision making under conditions of risk. However, numerous practical studies have shown its weakness: the theory did not explicitly use subjective perceptions of decision outcome probabilities in optimal decision-making processes. This research has led to the creation and development of approaches to explicitly consider the probabilities of outcomes in decision making. This paper provides a critical analysis of the descriptive properties of expected utility theory and presents various forms of probability weighting functions.

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19

Van Bossuyt, Douglas, Chris Hoyle, Irem Y. Tumer, and Andy Dong. "Risk attitudes in risk-based design: Considering risk attitude using utility theory in risk-based design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26, no. 4 (November 2012): 393–406. http://dx.doi.org/10.1017/s0890060412000261.

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AbstractEngineering risk methods and tools account for and make decisions about risk using an expected-value approach. Psychological research has shown that stakeholders and decision makers hold domain-specific risk attitudes that often vary between individuals and between enterprises. Moreover, certain companies and industries (e.g., the nuclear power industry and aerospace corporations) are very risk-averse whereas other organizations and industrial sectors (e.g., IDEO, located in the innovation and design sector) are risk tolerant and actually thrive by making risky decisions. Engineering risk methods such as failure modes and effects analysis, fault tree analysis, and others are not equipped to help stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions. This article presents a novel method for translating engineering risk data from the expected-value domain into a risk appetite corrected domain using utility functions derived from the psychometric Engineering Domain-Specific Risk-Taking test results under a single-criterion decision-based design approach. The method is aspirational rather than predictive in nature through the use of a psychometric test rather than lottery methods to generate utility functions. Using this method, decisions can be made based upon risk appetite corrected risk data. We discuss development and application of the method based upon a simplified space mission design in a collaborative design-center environment. The method is shown to change risk-based decisions in certain situations where a risk-averse or risk-tolerant decision maker would likely choose differently than the expected-value approach dictates.

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20

Li, Hong, and Zhong Yao. "Construction Project Risk Decision-Making Based on Grey Multi-Objective Decision-Making." Applied Mechanics and Materials 235 (November 2012): 323–28. http://dx.doi.org/10.4028/www.scientific.net/amm.235.323.

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In order to make Construction project risk decision under multiple objectives and high uncertainty, this paper adopts grey multi-objective decision-making method to a highway construction project in Huanghua city to make a choice from the feasible alternatives. It proves that grey multi-objective decision-making result is a relatively optimal solution, which offers method for enterprises to make decisions practicably, accessibly and effectively under risks.

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21

Smith, K., and P. A. Hancock. "Managing Risk under Time Stress." Proceedings of the Human Factors Society Annual Meeting 36, no. 13 (October 1992): 1019–23. http://dx.doi.org/10.1177/154193129203601322.

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The evolution of automated and semi-automated systems is rendering continuous regulation relatively obsolete, leaving periodic “management” interventions as the main way in which operators exercise control. Consequently, the human is now more frequently required to respond in uncertain, unusual, or “emergency” conditions. Such circumstances connote high stress environments. Consequently, the research reported here investigates expertise at decision making under stress. The source of stress is ubiquitous in occurrence, namely time pressure. We present a process model that explains and predicts the decision behavior of skilled operators as they manage risk under time stress. The model identifies three components of decision making, (1) attention, (2) assessment, and (3) intervention. Attention (1) scans widely among information displays and focuses action narrowly upon one of three procedures for (2) assessing the attended information. Separate procedures assess (α) the risks posed by the environment, (β) risks generated by interacting with the environment, and (α) uncertainty about those risks. The uniquely appropriate intervention (3) is selected by a small set of rules that match heuristically the assessments of risk and uncertainty to a short list of alternative actions. The model is validated with respect to the operation of skilled operators in the domain of currency exchange. In comparing performance versus simulation data, the model identifies the one procedure that resists automation - the assessment of risks posed by the environment. This assessment involves causal arguments that often rely upon extensive domain knowledge. In contrast, attention to displays, heuristic matching, and the procedures for assessing uncertainty and the risk of interaction can be delegated to an automated decision support system. This result has clear implications for the the design of systems to support skilled decision making under emergency conditions: decision support systems for dynamic environments like currency trading must notify the operator of the occurrence of system parameters that require assessments of environmental risk and incorporate these assessments into automated procedures that recommend appropriate interventions.

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22

Mueller, Silke M., Johannes Schiebener, Margarete Delazer, and Matthias Brand. "Risk approximation in decision making: approximative numeric abilities predict advantageous decisions under objective risk." Cognitive Processing 19, no. 3 (January 22, 2018): 297–315. http://dx.doi.org/10.1007/s10339-018-0854-9.

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23

Russell, Roberta S., and Marilyn Smith. "Reeling In Outsourcing: Evaluating Supply Chain Risk And Reward Under Economic Uncertainty." Journal of Service Science (JSS) 2, no. 1 (June 1, 2009): 57–64. http://dx.doi.org/10.19030/jss.v2i1.4290.

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In todays climate of economic uncertainty, companies that have relied on outsourcing in the past to curtail costs are increasingly reeling in their outsourcing decision to more appropriately balance supply chain risk and reward. This paper provides a review of the literature on reversing supply chain outsourcing and frames the sourcing decision in terms of multiple options, including multi- sourcing, near sourcing, and in-sourcing. A decision tree model is presented to aid the decision maker in evaluating the expected value of various sourcing decisions when risks and returns are explicitly considered. Trends and conditions that influence the outsourcing decision are also discussed.

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24

Tsubaki, Hiroe, and Houju Hori Jr. "Risk-based Social Decision under Different Stakeholders." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 43, no. 1 (2016): 13–20. http://dx.doi.org/10.2333/jbhmk.43.13.

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25

Navas, Juan F., Raquel Vilar-López, José C. Perales, Trevor Steward, Fernando Fernández-Aranda, and Antonio Verdejo-García. "Altered Decision-Making under Risk in Obesity." PLOS ONE 11, no. 6 (June 3, 2016): e0155600. http://dx.doi.org/10.1371/journal.pone.0155600.

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26

Pablo, Amy L. "Reconciling predictions of decision making under risk." Journal of Managerial Psychology 12, no. 1 (February 1997): 4–20. http://dx.doi.org/10.1108/02683949710164217.

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27

Daradkeh, Mohammad. "Information visualisation for decision support under risk." International Journal of Information and Decision Sciences 9, no. 3 (2017): 276. http://dx.doi.org/10.1504/ijids.2017.086786.

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28

Daradkeh, Mohammad. "Information visualisation for decision support under risk." International Journal of Information and Decision Sciences 9, no. 3 (2017): 276. http://dx.doi.org/10.1504/ijids.2017.10007807.

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29

Buckley, James J. "Entropy Principles in Decision Making Under Risk." Risk Analysis 5, no. 4 (December 1985): 303–13. http://dx.doi.org/10.1111/j.1539-6924.1985.tb00186.x.

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30

Yarkoni, T., and T. S. Braver. "Strategic modulation of decision-making under risk." NeuroImage 47 (July 2009): S118. http://dx.doi.org/10.1016/s1053-8119(09)71095-x.

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31

Hollard, Guillaume, Hela Maafi, and Jean-Christophe Vergnaud. "Consistent inconsistencies? Evidence from decision under risk." Theory and Decision 80, no. 4 (October 30, 2015): 623–48. http://dx.doi.org/10.1007/s11238-015-9518-8.

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32

Matos, Manuel A. "Decision under risk as a multicriteria problem." European Journal of Operational Research 181, no. 3 (September 2007): 1516–29. http://dx.doi.org/10.1016/j.ejor.2005.11.057.

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33

Pahlke, Julius, Sebastian Strasser, and Ferdinand M. Vieider. "Responsibility effects in decision making under risk." Journal of Risk and Uncertainty 51, no. 2 (October 2015): 125–46. http://dx.doi.org/10.1007/s11166-015-9223-6.

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34

Philippe, Fabrice. "Tools for decision making under imprecise risk." Statistical Papers 43, no. 1 (January 2002): 95–110. http://dx.doi.org/10.1007/s00362-001-0088-1.

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35

Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. "Salience Theory of Choice Under Risk." Quarterly Journal of Economics 127, no. 3 (June 20, 2012): 1243–85. http://dx.doi.org/10.1093/qje/qjs018.

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Abstract We present a theory of choice among lotteries in which the decision maker's attention is drawn to (precisely defined) salient payoffs. This leads the decision maker to a context-dependent representation of lotteries in which true probabilities are replaced by decision weights distorted in favor of salient payoffs. By specifying decision weights as a function of payoffs, our model provides a novel and unified account of many empirical phenomena, including frequent risk-seeking behavior, invariance failures such as the Allais paradox, and preference reversals. It also yields new predictions, including some that distinguish it from prospect theory, which we test.

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36

Bruce, A. C., and J. E. V. Johnson. "Decision-Making under Risk: Effect of Complexity on Performance." Psychological Reports 79, no. 1 (August 1996): 67–76. http://dx.doi.org/10.2466/pr0.1996.79.1.67.

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This paper examined the effect of complexity on performance in decision-making under risk employing a database of 1161 randomly sampled betting decisions made during 1987 in offcourse horserace betting offices situated throughout the UK. The setting for the study is characterised by important ecological advantages over laboratory or alternative naturalistic settings. Complexity is defined in terms of both the number of alternatives in the decision-maker's choice set (number of horses in a race) and the complexity of the set of attributes for each horserace (handicap vs nonhandicap races). Performance was not adversely affected by an increase in the number of alternatives and only to a limited extent by an increase in attribute-based complexity. The robustness of the results is reinforced by adjustments which acknowledge and control for the differential random chance of correct decision-making across groups of events with differing complexity.

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37

Pedersen, Anya, Robert Göder, Samuel Tomczyk, and Patricia Ohrmann. "Risky decision-making under risk in schizophrenia: A deliberate choice?" Journal of Behavior Therapy and Experimental Psychiatry 56 (September 2017): 57–64. http://dx.doi.org/10.1016/j.jbtep.2016.08.004.

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38

Benke, Theresa, Josef Marksteiner, Beatrix Ruepp, Elisabeth M. Weiss, and Laura Zamarian. "Decision Making under Risk in Patients Suffering from Schizophrenia or Depression." Brain Sciences 11, no. 9 (September 7, 2021): 1178. http://dx.doi.org/10.3390/brainsci11091178.

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Studies have reported difficulties in decision making for patients with schizophrenia or depression. Here, we investigated whether there are differences between schizophrenia patients, depressed patients, and healthy individuals (HC) when decisions are to be made under risk and cognitive flexibility is required. We were also interested in the relationships between decision making, cognitive functioning, and disease severity. Thirty HC, 28 schizophrenia patients, and 28 depressed patients underwent structured clinical assessments and were assessed by the Positive and Negative Syndrome Scale or Hamilton Rating Scale. They performed the Probability-Associated Gambling (PAG) Task and a neuropsychological test battery. Both patient groups obtained lower scores than HC in memory and executive function measures. In the PAG task, relative to HC, depressed patients made slower decisions but showed a comparable number of advantageous decisions or strategy flexibility. Schizophrenia patients were slower, riskier, and less flexible compared to HC. For them, the decision making behavior correlated with the symptom severity. In both groups, decision making scores correlated with memory and executive function scores. Patients with schizophrenia or depression may have difficulties under risk when quick and flexible decisions are required. These difficulties may be more pronounced in patients who have marked cognitive deficits or severe clinical symptoms.

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39

Janová, J. "Crop plan optimization under risk on a farm level in the Czech Republic." Agricultural Economics (Zemědělská ekonomika) 60, No. 3 (March 27, 2014): 123–32. http://dx.doi.org/10.17221/77/2013-agricecon.

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The precise agricultural operations research models claiming complex solutions and specialized software tools may appear too complicated to truly support the decision making process on the farm level. The specific balance between the simplifications made and the correspondence to the real problem must be found when building decision support for a local farmer. In this paper, a decision support model for agricultural production planning in the Czech Republic is developed covering both the randomness of parameters entering the problem and the complex crop succession requirements. Crucially, the model can be approached using software tools commonly available to farmers. The structure of the simplified model is described in detail, its validity proved by the recently suggested simulation procedures and the general applicability of the model structuring for a local agriculture decision support is discussed.  

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40

Trifunovic, Dejan. "Investment choice under uncertainty: A review essay." Ekonomski anali 50, no. 167 (2005): 141–70. http://dx.doi.org/10.2298/eka0567141t.

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An investment opportunity whose return is perfectly predictable, hardly exists at all. Instead, investor makes his decisions under conditions of uncertainty. Theory of expected utility is the main analytical tool for description of choice under uncertainty. Critics of the theory contend that individuals have bounded rationality and that the theory of expected utility is not correct. When agents are faced with risky decisions they behave differently, conditional on their attitude towards risk. They can be risk loving, risk averse or risk neutral. In order to make an investment decision it is necessary to compare probability distribution functions of returns. Investment decision making is much simpler if one uses expected values and variances instead of probability distribution functions.

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41

Delazer, M., H. Sinz, L. Zamarian, H. Stockner, K. Seppi, G. K. Wenning, T. Benke, and W. Poewe. "Decision making under risk and under ambiguity in Parkinson's disease." Neuropsychologia 47, no. 8-9 (July 2009): 1901–8. http://dx.doi.org/10.1016/j.neuropsychologia.2009.02.034.

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42

Wang, Zeng Zhong, and Yu Xin Zhang. "Risk Based Decision Making for Bridge under Multi Hazard." Applied Mechanics and Materials 105-107 (September 2011): 1215–19. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.1215.

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The bridges are of much importance to the transportation network system, as they connect key highways at crucial nodes. This paper reviews and discussed risk-based methodologies to assess the risk acceptability and cost-effectiveness of protective measures for bridge under multiple manmade or natural hazards. Three risk acceptance criteria based on fatality risks, failure probabilities and net benefit assessment were described and discussed. The decision support framework accompanying these risk acceptance criteria considers hazard probabilities, value of human life, physical and indirect damages, risk reduction and protective measure costs were studied and an illustrative example was presented.

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43

Ricci, Paolo F., Louis A. Cox, and Thomas R. MacDonald. "Precautionary principles: a jurisdiction-free framework for decision-making under risk." Human & Experimental Toxicology 23, no. 12 (December 2004): 579–600. http://dx.doi.org/10.1191/0960327104ht482oa.

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Fundamental principles of precaution are legal maxims that ask for preventive actions, perhaps as contingent interim measures while relevant information about causality and harm remains unavailable, to minimize the societal impact of potentially severe or irreversible outcomes. Such principles do not explain how to make choices or how to identify what is protective when incomplete and inconsistent scientific evidence of causation characterizes the potential hazards. Rather, they entrust lower jurisdictions, such as agencies or authorities, to make current decisions while recognizing that future information can contradict the scientific basis that supported the initial decision. After reviewing and synthesizing national and international legal aspects of precautionary principles, this paper addresses the key question: How can society manage potentially severe, irreversible or serious environmental outcomes when variability, uncertainty, and limited causal knowledge characterize their decisionmaking? A decision Rational choice of an action from among various alternatives-requires accounting for costs, benefits and the change in risks associated with each candidate action. Decisions under any form of the precautionary principle reviewed must account for the contingent nature of scientific information, creating a link to the decision/response models to the current set of regulatory defaults such as the linear, non-threshold models. This increase in the number of defaults is an important improvement because most of the variants of the precautionary principle require cost-defined as a choice that makes preferred consequences more likely-analytic principle of expected value of information (VOI), to show the relevance of new information, relative to the initial (and smaller) set of data on which the decision was based. We exemplify this seemingly simple situation using risk management of BSE. As an integral aspect of causal analysis under risk, the methods developed in this paper permit the addition of non-linear, hormetic dose-analytic solution is outlined that focuses on risky decisions and accounts for prior states of information and scientific beliefs that can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision-making under risk, inherent to precautionary decision-making, these (Bayesian) methods help decision-makers and stakeholders because they formally account for probabilistic outcomes, new information, and are consistent and replicable. benefit balancing. Specifically, increasing the set of causal defaults accounts for beneficial effects at very low doses. We also show and conclude that quantitative risk assessment dominates qualitative risk assessment, supporting the extension of the set of default causal models.

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Hagen, Ole. "Decisions under risk: A descriptive model and a technique for decision making." European Journal of Political Economy 7, no. 3 (October 1991): 381–405. http://dx.doi.org/10.1016/0176-2680(91)90020-4.

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Lambregts, Timo R., Paul van Bruggen, and Han Bleichrodt. "Insurance decisions under nonperformance risk and ambiguity." Journal of Risk and Uncertainty 63, no. 3 (November 26, 2021): 229–53. http://dx.doi.org/10.1007/s11166-021-09364-7.

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AbstractAn important societal problem is that people underinsure against risks that are unlikely or occur in the far future, such as natural disasters and long-term care needs. One explanation is that uncertainty about the risk of non-reimbursement induces ambiguity averse and risk prudent decision makers to take out less insurance. We set up an insurance experiment to test this explanation. Consistent with the theoretical predictions, we find that the demand for insurance is lower when the nonperformance risk is ambiguous than when it is known and when decision makers are risk prudent. We cannot attribute the lower take-up of insurance to our measure of ambiguity aversion, probably because ambiguity attitudes are richer than aversion alone.

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Beranová, M., and D. Martinovičová. "Application of the theory of decision making under risk and uncertainty at modelling of costs." Agricultural Economics (Zemědělská ekonomika) 56, No. 5 (June 1, 2010): 201–8. http://dx.doi.org/10.17221/88/2009-agricecon.

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The costs functions are mentioned mostly in the relation to the Break-even Analysis where they are presented in the linear form. But there exist several different types and forms of cost functions. Fist of all, it is necessary to distinguish between the short-run and long-run cost function that are both very important tools of the managerial decision making even if each one is used on a different level of management. Also several methods of estimation of the cost function's parameters are elaborated in the literature. But all these methods are based on the past data taken from the financial accounting while the financial accounting is not able to separate the fixed and variable costs and it is also strongly adjusted to taxation in the many companies. As a tool of the managerial decision making support, the cost functions should provide a vision to the future where many factors of risk and uncertainty influence economic results. Consequently, these random factors should be considered in the construction of cost functions, especially in the long-run. In order to quantify the influences of these risks and uncertainties, the authors submit the application of the Bayesian Theorem.

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Saisubramanian, Sandhya. "Adaptive Modeling for Risk-Aware Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9896–97. http://dx.doi.org/10.1609/aaai.v33i01.33019896.

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This thesis aims to provide a foundation for risk-aware decision making. Decision making under uncertainty is a core capability of an autonomous agent. A cornerstone for with long-term autonomy and safety is risk-aware decision making. A risk-aware model fully accounts for a known set of risks in the environment, with respect to the problem under consideration, and the process of decision making using such a model is risk-aware decision making. Formulating risk-aware models is critical for robust reasoning under uncertainty, since the impact of using less accurate models may be catastrophic in extreme cases due to overly optimistic view of problems. I propose adaptive modeling, a framework that helps balance the trade-off between model simplicity and risk awareness, for different notions of risks, while remaining computationally tractable.

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Szkudlarek, Piotr. "Regulatory decisions in the context of risk and uncertainty." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu 64, no. 10 (2020): 107–17. http://dx.doi.org/10.15611/pn.2020.10.08.

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The purpose of the article is to identify the propensity of decision-makers of the regulatory authority on the telecommunications market in Poland (Office of Electronic Communications, OEC) in making decisions under risk and uncertainty conditions and comparing them with a group of non-experts. The research outcomes reveal that decision-makers of the regulatory authority make in most cases decisions that are consistent with the prospect theory. In addition, OEC decision-makers in circumstances of uncertainty, retain the status quo and maintain a lower risk tendency in a decision-making situation with the same expected value but different variance. It was pointed out, at the same time, that their decisions did not differ from those taken by the group of non-experts. The findings presented in the article contribute to the discussion on the propensity of public decision-makers to make decisions under conditions of risk and uncertainty

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Zhang, Long, Kai Wang, Chunyan Zhu, Fengqiong Yu, and Xingui Chen. "Trait Anxiety Has Effect on Decision Making under Ambiguity but Not Decision Making under Risk." PLOS ONE 10, no. 5 (May 22, 2015): e0127189. http://dx.doi.org/10.1371/journal.pone.0127189.

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Martignon, Laura, and Kathryn Laskey. "Statistical literacy for classification under risk: an educational perspective." AStA Wirtschafts- und Sozialstatistisches Archiv 13, no. 3-4 (November 2019): 269–78. http://dx.doi.org/10.1007/s11943-019-00259-3.

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AbstractAfter a brief description of the four components of risk literacy and the tools for analyzing risky situations, decision strategies are introduced, These rules, which satisfy tenets of Bounded Rationality, are called fast and frugal trees. Fast and frugal trees serve as efficient heuristics for decision under risk. We describe the construction of fast and frugal trees and compare their robustness for prediction under risk with that of Bayesian networks. In particular, we analyze situations of risky decisions in the medical domain. We show that the performance of fast and frugal trees does not fall too far behind that of the more complex Bayesian networks.

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