Decision Principles to justify Carnap's Updating Method and to Suggest
  Corrections of Probability Judgments (Invited Talks)
  This paper uses decision-theoretic principles to obtain new insights into the
assessment and updating of probabilities. First, a new foundation of
Bayesianism is given. It does not require infinite atomless uncertainties as
did Savage s classical result, AND can therefore be applied TO ANY finite
Bayesian network.It neither requires linear utility AS did de Finetti s
classical result, AND r ntherefore allows FOR the empirically AND normatively
desirable risk r naversion.Finally, BY identifying AND fixing utility IN an
elementary r nmanner, our result can readily be applied TO identify methods OF
r nprobability updating.Thus, a decision - theoretic foundation IS given r nto
the computationally efficient method OF inductive reasoning r ndeveloped BY
Rudolf Carnap.Finally, recent empirical findings ON r nprobability assessments
are discussed.It leads TO suggestions FOR r ncorrecting biases IN probability
assessments, AND FOR an alternative r nto the Dempster - Shafer belief
functions that avoids the reduction TO r ndegeneracy after multiple updatings.r
n
