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This paper uses decision-theoretic principles to obtain new insights into theassessment and updating of probabilities. First, a new foundation ofBayesianism is given. It does not require infinite atomless uncertainties asdid Savage s classical result, AND can therefore be applied TO ANY finiteBayesian network.It neither requires linear utility AS did de Finetti sclassical result, AND r ntherefore allows FOR the empirically AND normativelydesirable risk r naversion.Finally, BY identifying AND fixing utility IN anelementary r nmanner, our result can readily be applied TO identify methods OFr nprobability updating.Thus, a decision - theoretic foundation IS given r ntothe computationally efficient method OF inductive reasoning r ndeveloped BYRudolf Carnap.Finally, recent empirical findings ON r nprobability assessmentsare discussed.It leads TO suggestions FOR r ncorrecting biases IN probabilityassessments, AND FOR an alternative r nto the Dempster - Shafer belieffunctions that avoids the reduction TO r ndegeneracy after multiple updatings.rn

Decision Principles to justify Carnap's Updating Method and to Suggest Corrections of Probability Judgments (Invited Talks)