LEADER 04194nam 22007572 450 001 9910807679103321 005 20151005020622.0 010 $a1-107-23957-5 010 $a1-107-22899-9 010 $a1-283-52200-4 010 $a9786613834454 010 $a1-139-52713-4 010 $a1-139-52832-7 010 $a1-139-52593-X 010 $a1-139-53179-4 010 $a1-139-53060-7 010 $a0-511-99771-X 035 $a(CKB)2560000000090097 035 $a(EBL)977169 035 $a(OCoLC)804845628 035 $a(SSID)ssj0000738960 035 $a(PQKBManifestationID)11458052 035 $a(PQKBTitleCode)TC0000738960 035 $a(PQKBWorkID)10671130 035 $a(PQKB)10745162 035 $a(UkCbUP)CR9780511997716 035 $a(Au-PeEL)EBL977169 035 $a(CaPaEBR)ebr10583243 035 $a(CaONFJC)MIL383445 035 $a(MiAaPQ)EBC977169 035 $a(PPN)261317040 035 $a(EXLCZ)992560000000090097 100 $a20110111d2012|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aQuantum models of cognition and decision /$fJerome R. Busemeyer, Peter D. Bruza$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2012. 215 $a1 online resource (xiv, 407 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a1-107-41988-3 311 $a1-107-01199-X 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: 1. Why use quantum theory for cognition and decision? Some compelling reasons; 2. What is quantum theory? An elementary introduction; 3. What can quantum theory predict? Predicting question order effects on attitudes; 4. How to apply quantum theory? Accounting for human probability judgment errors; 5. Quantum inspired models of concept combination; 6. An application of quantum theory to conjoint memory recognition; 7. Quantum-like models of human semantic space; 8. What about quantum dynamics? More advanced principles; 9. What is the quantum advantage? Applications to decision making; 10. How to model human information processing using quantum information theory; 11. Can quantum systems learn? Quantum updating; 12. What are the future prospects for quantum cognition and decision?. 330 $aMuch of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modeling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision. 517 3 $aQuantum Models of Cognition & Decision 606 $aDecision making$xMathematical models 606 $aStatistical decision 606 $aCognition$xMathematical models 606 $aQuantum theory 615 0$aDecision making$xMathematical models. 615 0$aStatistical decision. 615 0$aCognition$xMathematical models. 615 0$aQuantum theory. 676 $a530.12 686 $aPSY008000$2bisacsh 700 $aBusemeyer$b Jerome R.$01690603 702 $aBruza$b Peter David$f1962- 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910807679103321 996 $aQuantum models of cognition and decision$94066385 997 $aUNINA