This book is a practical and theoretical guide that demonstrates how to leverage investment data in numerical models despite uncertainty and ambiguity. The author presents innovative methods that incorporate fuzzy set theory to overcome the imprecision of expert opinions and appraisals. Through real
Fuzzy Investment Decision Making with Examples
β Scribed by Cengiz Kahraman, Elif HaktanΔ±r
- Publisher
- Springer
- Year
- 2024
- Tongue
- English
- Leaves
- 269
- Edition
- 2024
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This book is a practical and theoretical guide that demonstrates how to leverage investment data in numerical models despite uncertainty and ambiguity. The author presents innovative methods that incorporate fuzzy set theory to overcome the imprecision of expert opinions and appraisals. Through real industry case studies and comparative analyses, the book provides a comprehensive understanding of how these novel approaches can be implemented to measure robustness.
This book is a must-read for managers involved in investment decision making, for economists, lecturers, as well as M.Sc. and Ph.D. students studying investment decision-making.
π SIMILAR VOLUMES
<p><br>This book describes five qualitative investment decision-making methods based on the hesitant fuzzy information. They are: (1) the investment decision-making method based on the asymmetric hesitant fuzzy sigmoid preference relations, (2) the investment decision-making method based on the hesi
<p><p>Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prev
<p><p>Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prev
<p>In the literature of decision analysis it is traditional to rely on the tools provided by probability theory to deal with problems in which uncertainty plays a substantive role. In recent years, however, it has become increasingly clear that uncertainty is a mulΒ tifaceted concept in which some o