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Books on the topic 'Risk representation'

Author: Grafiati

Published: 9 November 2024

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1

Dilla, William N. Information representation, scaling, and experience in inherent risk judgments. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1991.

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2

Wyn, Grant. Corporatism in Britain: Effective representation or democracy at risk?. [s.l.]: Social Studies Review, 1986.

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3

Stone, Walter J. Republic at risk: Self-interest in American politics. Pacific Grove, Calif: Brooks/Cole Pub. Co., 1990.

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4

J, Quinn D., and Great Britain. Health and Safety Executive., eds. Development of an intermediate societal risk methodology: An investigation of FN curve representation. Norwich: HSE Books, 2003.

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5

Aven, Terje. Uncertainty in risk assessment: The representation and treatment of uncertainties by probabilistic and non-probabilistic methods. Chichester, West Sussex, United Kingdom: Wiley, 2014.

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6

Richard, Zielinski, and Massachusetts Continuing Legal Education, Inc. (1982- ), eds. Avoiding malpractice claims for family lawyers: Managing professional risk while providing high-quality representation. [Boston, Mass.]: MCLE, 2006.

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7

Zhao, Yongmao. She hui dai yi de jue qi: Taiwan zheng zhi yu she hui de ping xing fa zhan = The rise of social representation : the parallel development of politics and society in Taiwan. Taibei Shi: Han Lu tu shu chu ban you xian gong si, 2018.

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8

Lynnette, Fallon, and Massachusetts Continuing Legal Education, Inc. (1982- ), eds. Representations and warranties: Allocating the risk in acquisition agreements. Boston, MA: MCLE, 1993.

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9

E, Klechefski George, Hoel Michael K, National Media Laboratory, and Library of Congress. Preservation Directorate., eds. Risk analysis study for a representative magnetic tape collection. Washington, DC: Library of Congress, Preservation Directorate, 1998.

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10

Thomas, Dohmen, ed. Individual risk attitudes: New evidence from a large, representative, experimentally-validated survey. Bonn, Germany: IZA, 2005.

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11

Morgan, Edmund Sears. Inventing the people: The rise of popular sovereignty in England and America. New York: Norton, 1988.

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12

Office, General Accounting. High-risk program: Information on selected high-risk areas : information for House Majority Leader Richard Armey and Representative Pete Sessions. Washington, D.C: The Office, 1997.

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13

English, Kirstie Ken, and Lexi Webster. An overview of the risks and benefits of data representation for LGBTI+ people. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2024. http://dx.doi.org/10.4135/9781529690767.

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14

Clark, Nigel. Refiguring Nature: Ecology, Risk and Representation. Sage Publications, 2003.

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15

Refiguring Nature: Ecology, Risk and Representation. Sage Publications, 2003.

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16

Gil, Isabel Capeloa. Hazardous Future: Disaster, Representation and the Assessment. De Gruyter, 2015.

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17

Wulf, Christoph, and Isabel Capeloa Gil. Hazardous Future: Disaster, Representation and the Assessment of Risk. de Gruyter GmbH, Walter, 2015.

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18

Hazardous Future: Disaster, Representation and the Assessment of Risk. De Gruyter, Inc., 2015.

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19

Hazardous Future: Disaster, Representation and the Assessment of Risk. De Gruyter, Inc., 2015.

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20

Stone, Walter J., and James A. McCann. Republic at Risk: An Introduction to American Politics. University of Cambridge ESOL Examinations, 2021.

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21

Stone, Walter J. Republic at Risk: Self Interest in American Politics. Wadsworth Pub Co, 1989.

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22

Stone, Walter J., and James A. McCann. Republic at Risk: An Introduction to American Politics. Cambridge University Press, 2021.

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23

Stone, Walter J., and James A. McCann. Republic at Risk: An Introduction to American Politics. University of Cambridge ESOL Examinations, 2021.

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24

Aven, Terje, Enrico Zio, Piero Baraldi, and Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.

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25

Aven, Terje, Enrico Zio, Piero Baraldi, and Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.

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26

Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.

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27

Aven, Terje, Enrico Zio, Piero Baraldi, and Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Limited, John, 2014.

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28

Avoiding malpractice traps for employment lawyers: Managing professional risk while providing quality representation to your clients. [Boston, Mass.]: MCLE, 2005.

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29

Mongin, Philippe, and Marcus Pivato. Social Evaluation under Risk and Uncertainty. Edited by Matthew D. Adler and Marc Fleurbaey. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199325818.013.23.

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This chapter examines the problem of evaluating policies with either risky or uncertain consequences. Under risk, the probabilities are known and agreed, whereas under uncertainty, probabilities are subjective and subject to disagreement. In the case of risk, Harsanyi’s Social Aggregation Theorem derives a representation of social utility as a weighted sum of individual utilities. But when there is uncertainty, two different and conflicting evaluation criteria, that is, ex ante and ex post, become available. The chapter explores this problem and sketches solutions. Similarly, when equality becomes the guiding question, there is a tension between ex ante and ex post evaluations, each leading to a conceptual loss, and the chapter explores solutions given to this further problem. It also covers Harsanyi’s Impartial Observer Theorem and its recent developments, and discusses the question raised by Sen of whether the weighted sum of individual utilities obtained by Harsanyi makes genuine utilitarian sense.

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30

Soramäki, Kimmo. Computational Models of Financial Networks, Risk, and Regulatory Policies. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.16.

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This chapter introduces the concept of financial networks and reviews research in three of the most active research areas of financial systems: interbank payment networks, interbank exposure networks, and asset correlation networks. The financial crisis of 2007-2008 revealed the intertwined nature of modern financial systems. A promising methodology for capturing and modeling connections in the financial system is provided by network theory. The intricate structure of linkages between financial institutions, among sectors of the economy, and across financial systems can conveniently be captured by using a network representation. Empirical research on describing existing networks is presented, as well as new modeling and simulation approaches for financial risk that take into account the complex structure of financial markets and infrastructures.

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31

Thanatourism and Cinematic Representations of Risk. 2016.

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32

G, Johnson David. Representations of uncertainty in risk analysis. 1998.

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33

Keech, William R. Economic Politics in the United States: The Costs and Risks of Democracy. Cambridge University Press, 2013.

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34

Munier, Bertrand, Mark J. Machina, Mohammed Abdellaoui, and R. Duncan Luce. Uncertainty and Risk: Mental, Formal, Experimental Representations. Springer, 2010.

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35

Back, Kerry E. Representative Investors. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0007.

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There is a representative investor at any Pareto optimal competitive equilibrium. If investors have linear risk tolerance with the same cautiousness parameter, then there is a representative investor with the same utility function. When there is a representative investor, there is a factor model with the representative investor’s marginal utility of consumption as the factor. If the representative investor has constant relative risk aversion, then the risk‐free return and log equity premium can be calculated in terms of moments of aggregate consumption. The equity premium and risk‐free rate puzzles are explained. The coskewness‐cokurtosis pricing model and the Rubinstein option pricing model are derived.

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36

Baer, Marc. Rise and Fall of Radical Westminster, 1780-1890. Palgrave Macmillan, 2012.

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37

Representations and warranties: Allocating the risk in acquisitions agreements. Boston, MA: MCLE, 1995.

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38

Dadge, David. Rise of Crossborder and Representative Banking. Center for International Legal Studies, 1998.

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39

Hawkes, D. Faust Myth: Religion and the Rise of Representation. Palgrave Macmillan Limited, 2007.

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40

Hawkes, David. Faust Myth: Religion and the Rise of Representation. Palgrave Macmillan, 2008.

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41

Political culture in nineteenth-century Peru: The rise of the Partido Civil. Pittsburgh, Pa: University of Pittsburgh Press, 2004.

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42

Shea, Nicholas. Representation in Cognitive Science. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198812883.001.0001.

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The representational theory of mind (RTM) has given us the powerful insight that thinking consists of the processing of mental representations. Behaviour is the result of these cognitive processes and makes sense in the light of their contents. There is no widely accepted account of how representations get their content – of the metaphysics of representational content. That question, usually asked about representations at the personal level like beliefs and conscious states, is equally pressing for the subpersonal representations that pervade our best explanatory theories in cognitive science. This book argues that well-understood naturalistic resources can be combined to provide an account of subpersonal representational content. It shows how contents arise in a series of detailed case studies in cognitive science. The account is pluralistic, allowing that content is constituted differently in different cases. Building on insights from previous theories, especially teleosemantics, the accounts combine an appeal to correlational information and structural correspondence with an expanded notion of etiological function, which captures the kinds of stabilizing processes that give rise to content. The accounts support a distinction between descriptive and directive content. They also allow us to see how representational explanation gets its distinctive explanatory purchase.

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43

Back, Kerry E. Explaining Puzzles. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0011.

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Various models proposed to explain the equity premium or risk‐free rate puzzle are explained: external habits (Abel’s “catching up with the Joneses” model and the Campbell‐Cochrane model), rare disasters, Epstein‐Zin‐Weil utility, long run risks, and idiosyncratic uninsurable labor income risk. External habits allow the SDF to be variable without requiring high variability of consumption. The SDF for a representative investor with Epstein‐Zin‐Weil utility depends on consumption and the market return. It is most useful when the world is not IID, as in the long‐run risks model. With uninsurable labor income risk, there is no representative investor even if investors all have the same CRRA utility, and there is additional exibility to explain asset returns.

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44

The rise of the Norwegian Parliament. London: F. Cass, 2003.

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45

Hawkes, David. The Faust Myth: Religion and the Rise of Representation. Palgrave Macmillan, 2007.

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46

Moffitt, Benjamin. Global Rise of Populism: Performance, Political Style, and Representation. Stanford University Press, 2017.

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47

The Faust Myth: Religion and the Rise of Representation. Palgrave Macmillan, 2007.

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48

Weirich, Paul. Rational Responses to Risks. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190089412.001.0001.

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A philosophical account of risk, such as this book provides, states what risk is, which attitudes to it are rational, and which acts affecting risks are rational. Attention to the nature of risk reveals two types of risk, first, a chance of a bad event, and, second, an act’s risk in the sense of the volatility of its possible outcomes. The distinction is normatively significant because different general principles of rationality govern attitudes to these two types of risk. Rationality strictly regulates attitudes to the chance of a bad event and is more permissive about attitudes to an act’s risk. Principles of rationality governing attitudes to risk also justify evaluating an act according to its expected utility given that the act’s risk, if any, belongs to every possible outcome of the act. For a rational ideal agent, the expected utilities of the acts available in a decision problem explain the agent’s preferences among the acts. Maximizing expected utility is just following preferences among the acts. This view takes an act’s expected utility, not just as a feature of a representation of preferences among acts, but also as a factor in the explanation of preferences among acts. It takes account of an agent’s attitudes to an act’s risk without weakening the standard of expected-utility maximization. The view extends to evaluations of combination of acts, either simultaneous or in a sequence. Applications cover hedging, return-risk evaluation, professional advice, and government regulation.

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49

Thanatourism and Cinematic Representations of Risk: Screening the End of Tourism. Taylor & Francis Group, 2016.

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50

Tzanelli, Rodanthi. Thanatourism and Cinematic Representations of Risk: Screening the End of Tourism. Taylor & Francis Group, 2016.

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