Uncertainty: a taxonomy
Uncertainty: a taxonomy
Foreword
Preface
Acknowledgements
The evolution of models
The foundations of risk and uncertainty
Uncertainty: a taxonomy
Model risk and uncertainty: a survey of the institutional landscape
Model specification risk and uncertainty
Model operation risk and uncertainty
Data, models and their purpose
Artificial intelligence in finance: a synthesis of human and machine
A deeper dive into machine learning methods: their opportunities, limitations, risks and uncertainties
Measurement of risk and estimation of uncertainty in prediction models
Using models under risk and uncertainty
When models fail
Epilogue: models and the future
By “uncertain” knowledge I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty.… The sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention.… About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know!
J. M. Keynes (1937)
3.1 INTRODUCTION: ASPECTS OF UNCERTAINTY
The bestselling author and popular influencer in risk circles, Nassim Taleb, talking about economics and economists, provides the following anecdote (Taleb 2010):
A trader listened to the firm’s “chief” economist’s predictions about gold, then lost a bundle. The trader was asked to leave the firm. He then angrily asked the boss who was firing him, “Why do you fire me alone, not the economist? He too is responsible for the loss.” The boss: “You idiot, we are not firing you for losing money – we are firing you for listening to the economist.”
In Taleb’s world, economists are depicted in a less than flattering light for
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