LEADER 03691nam 22006252 450 001 9910455333203321 005 20151005020622.0 010 $a1-107-12302-X 010 $a0-511-11944-5 010 $a0-511-32513-4 010 $a1-280-16224-4 010 $a0-521-80234-2 010 $a0-511-04781-9 010 $a0-511-15330-9 010 $a0-511-49353-3 035 $a(CKB)111056485620316 035 $a(EBL)201876 035 $a(OCoLC)559126611 035 $a(SSID)ssj0000258154 035 $a(PQKBManifestationID)11939489 035 $a(PQKBTitleCode)TC0000258154 035 $a(PQKBWorkID)10254530 035 $a(PQKB)10693436 035 $a(UkCbUP)CR9780511493539 035 $a(MiAaPQ)EBC201876 035 $a(Au-PeEL)EBL201876 035 $a(CaPaEBR)ebr5006345 035 $a(CaONFJC)MIL16224 035 $a(EXLCZ)99111056485620316 100 $a20090304d2001|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA theory of case-based decisions /$fItzhak Gilboa and David Schmeidler$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2001. 215 $a1 online resource (x, 199 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-00311-3 311 $a0-511-01295-0 320 $aIncludes bibliographical references and index. 327 $a1. Prologue. 1. The scope of this book. 2. Meta-theoretical vocabulary. 3. Meta-theoretical prejudices -- 2. Decision rules. 4. Elementary formula and interpretations. 5. Variations and generalizations. 6. CBDT as a behaviorist theory. 7. Case-based prediction -- 3. Axiomatic derivation. 8. Highlights. 9. Model and result. 10. Discussion of the axioms. 11. Proofs -- 4. Conceptual foundations. 12. CBDT and expected utility theory. 13. CBDT and rule-based systems -- 5. Planning. 14. Representation and evaluation of plans. 15. Axiomatic derivation -- 6. Repeated choice. 16. Cumulative utility maximization. 17. The potential -- 7. Learning and induction. 18. Learning to maximize expected payoff. 19. Learning the similarity function. 20. Two views of induction: CBDT and simplicism. 330 $aGilboa and Schmeidler provide a paradigm for modelling decision making under uncertainty. Unlike the classical theory of expected utility maximization, case-based decision theory does not assume that decision makers know the possible 'states of the world' or the outcomes, let alone the decision matrix attaching outcomes to act-state pairs. Case-based decision theory suggests that people make decisions by analogies to past cases: they tend to choose acts that performed well in the past in similar situations, and to avoid acts that performed poorly. It is an alternative to expected utility theory when both states of the world and probabilities are neither given in the problem nor can be easily constructed. The authors describe the general theory and its relationship to planning, repeated choice problems, inductive inference, and learning; they highlight its mathematical and philosophical foundations and compare it with expected utility theory as well as with rule-based systems. 606 $aDecision making$xMathematical models 615 0$aDecision making$xMathematical models. 676 $a658.4/033 700 $aGilboa$b Itzhak$0478597 702 $aSchmeidler$b David$f1939- 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910455333203321 996 $aA theory of case-based decisions$92449380 997 $aUNINA