Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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Preferences, risk, and environmental policy Location: Lab 2 Session Chair: Ibrahim Tahri, International Institute for Applied System Analysis | |
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Alternative Ways of Information Processing as a Source of Sustainable and Rational Peer Disagreement 1: School of Economics and ReSEES Research Laboratory, Athens University of Economics and Business, Athens, Greece; Department of Technology, Management and Economics, Technical University of Denmark, Kongens Lyngby, Denmark; SDSN, Global Climate Hub, Athens, Greece; Sustainable Development Unit, ATHENA Information Technologies Research Center, Athens, Greece;; 2: Department of Banking and Financial Management, University of Piraeus, Greece;; 3: Department of Economics, University of Macedonia Consider an event of interest B and another event A which is viewed as information for B. When a decision maker (DM) evaluates the effect of A on B, she evaluates the degree to which she asserts the indicative conditional "if A then B", written as A —→ B. In the context of the standard Bayesian confirmation theory, the degree of assertability, As(A —→ B) is given by DM's subjective conditional probability P (B | A). However, the Bayesian interpretation is not the only rational interpretation of As(A —→ B). An alternative interpretation is that As(A —→ B) goes by the probability that the proposition A —→ B is true, that is by DM's unconditional probability P (A —→ B). It is now widely accepted that there is no interpretation of "—→" that ensures the genral validity of P (A —→ B) = P (B | A). Hence, there are multiple truth-conditional interpretations of "—→" each corresponding to a distinct way of information processing. One of these interpretations, namely the material implication of the Propositional Logic, competes favorably with the Bayesian interpretation on normative grounds. As a result, two decision makers can disagree about their posterior probabilities of B even if they share the same information A and have identical prior probability functions. | |