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This Festschrift celebrates Teddy Seidenfeld and his seminal
contributions to philosophy, statistics, probability, game theory
and related areas. The 13 contributions in this volume, written by
leading researchers in these fields, are supplemented by an
interview with Teddy Seidenfeld that offers an abbreviated
intellectual autobiography, touching on topics of timeless interest
concerning truth and uncertainty. Indeed, as the eminent
philosopher Isaac Levi writes in this volume: "In a world dominated
by Alternative Facts and Fake News, it is hard to believe that many
of us have spent our life's work, as has Teddy Seidenfeld, in
discussing truth and uncertainty." The reader is invited to share
this celebration of Teddy Seidenfeld's work uncovering truths about
uncertainty and the penetrating insights they offer to our common
pursuit of truth in the face of uncertainty.
While probabilistic logics in principle might be applied to solve a
range of problems, in practice they are rarely applied - perhaps
because they seem disparate, complicated, and computationally
intractable. This programmatic book argues that several approaches
to probabilistic logic fit into a simple unifying framework in
which logically complex evidence is used to associate probability
intervals or probabilities with sentences. Specifically, Part I
shows that there is a natural way to present a question posed in
probabilistic logic, and that various inferential procedures
provide semantics for that question, while Part II shows that there
is the potential to develop computationally feasible methods to
mesh with this framework. The book is intended for researchers in
philosophy, logic, computer science and statistics. A familiarity
with mathematical concepts and notation is presumed, but no
advanced knowledge of logic or probability theory is required.
While probabilistic logics in principle might be applied to solve a
range of problems, in practice they are rarely applied - perhaps
because they seem disparate, complicated, and computationally
intractable. This programmatic book argues that several approaches
to probabilistic logic fit into a simple unifying framework in
which logically complex evidence is used to associate probability
intervals or probabilities with sentences. Specifically, Part I
shows that there is a natural way to present a question posed in
probabilistic logic, and that various inferential procedures
provide semantics for that question, while Part II shows that there
is the potential to develop computationally feasible methods to
mesh with this framework. The book is intended for researchers in
philosophy, logic, computer science and statistics. A familiarity
with mathematical concepts and notation is presumed, but no
advanced knowledge of logic or probability theory is required.
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