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Recent findings about the capabilities of smart animals such as
corvids or octopi and novel types of artificial intelligence (AI),
from social robots to cognitive assistants, are provoking the
demand for new answers for meaningful comparison with other kinds
of intelligence. This book fills this need by proposing a universal
theory of intelligence which is based on causal learning as the
central theme of intelligence. The goal is not just to describe,
but mainly to explain queries like why one kind of intelligence is
more intelligent than another, whatsoever the intelligence. Shiny
terms like "strong AI," "superintelligence," "singularity" or
"artificial general intelligence" that have been coined by a
Babylonian confusion of tongues are clarified on the way.
Christian Hugo Hoffmann undermines the citadel of risk assessment
and management, arguing that classical probability theory is not an
adequate foundation for modeling systemic and extreme risk in
complex financial systems. He proposes a new class of models which
focus on the knowledge dimension by precisely describing market
participants' own positions and their propensity to react to
outside changes. The author closes his thesis by a synthetical
reflection on methods and elaborates on the meaning of
decision-making competency in a risk management context in banking.
By choosing this poly-dimensional approach, the purpose of his work
is to explore shortcomings of risk management approaches of
financial institutions and to point out how they might be overcome.
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