Theories of Decision-Making Under Risk
Robert Rieg,
Ute Vanini and
Werner Gleißner
Additional contact information
Robert Rieg: Aalen University
Ute Vanini: Kiel University of Applied Science
Werner Gleißner: Future Value Group AG
Chapter Chapter 2 in Enterprise Risk Management, 2025, pp 19-49 from Springer
Abstract:
Abstract Enterprise risk management (ERM) should support managerial decision-making to increase firm performance and firm value. Although decisions are made every day, few people realize that decisions are fundamentally structured in terms of cost and benefits and risks and opportunities. These factors must be analyzed to understand how decisions are made and how they can be improved. The underlying structure is not always visible in risk decisions and risk models in practice, so it is important to be aware of it when individuals in organizations build risk models and make decisions under risk. Such decisions should consider expected values, risk attitudes, and risk diversification which are explained in this chapter.
Keywords: Beta factor; Capital Asset Pricing Model (CAPM); Capital market line; Certainty equivalent; Decision-making; Diversifying risks; Enterprise risk management (ERM); Expected utility; Principal-agent theory; Portfolio theory; Risk attitude; Risk premium (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-031-86425-4_2
Ordering information: This item can be ordered from
http://www.springer.com/9783031864254
DOI: 10.1007/978-3-031-86425-4_2
Access Statistics for this chapter
More chapters in Springer Texts in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().