LEADER 00730nam0-22002651i-450- 001 990002840440403321 035 $a000284044 035 $aFED01000284044 035 $a(Aleph)000284044FED01 035 $a000284044 100 $a20000920d1991----km-y0itay50------ba 101 1$aENG 200 1 $aCompetition or Credit Controls?$fDavid T. Llewellyn, Mark Holmes. 210 $aLondon$cIEA$d1991 215 $a103 p. 700 1$aLlewellyn,$bDavid T. L.$f<1943- >$0252235 702 1$aHolmes,$bMark 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002840440403321 952 $aU-2-TB$b3552 DEA$fECA 959 $aECA 996 $aCompetition or Credit Controls$9417477 997 $aUNINA DB $aING01 LEADER 00788cam2 22002771i 450 001 990005247700403321 005 20180525112213.0 035 $a000524770 100 $a19990530d1874----km-y0itay50------ba 101 0 $afre 105 $ay-------001yy 200 1 $a<>coscience$a<>Orestie$a<> tour Saint-Jacques 205 $aNouvelle ed. 210 $aParis$cLévy$d1874 215 $a334 p.$d18 cm 461 $1001000510259$12001$aThéatre complet 700 1$aDumas,$bAlexandre$f<1802-1870>$0176848 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005247700403321 952 $aP.3 A1(7;20)$bBibl.6606$fFLFBC 959 $aFLFBC 996 $aTour Saint-Jacques$9539143 996 $aCoscience$9539141 996 $aOrestie$9539142 997 $aUNINA LEADER 04857nam 22006135 450 001 9910337606403321 005 20251116212617.0 010 $a3-030-20309-3 024 7 $a10.1007/978-3-030-20309-2 035 $a(CKB)4100000008424379 035 $a(DE-He213)978-3-030-20309-2 035 $a(MiAaPQ)EBC5926578 035 $a(PPN)258059923 035 $a(EXLCZ)994100000008424379 100 $a20190614d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComplex Adaptive Systems $eViews from the Physical, Natural, and Social Sciences /$fedited by Ted Carmichael, Andrew J. Collins, Mirsad Had?ikadi? 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (VIII, 250 p. 76 illus., 55 illus. in color.) 225 1 $aUnderstanding Complex Systems,$x1860-0832 300 $aIncludes index. 311 08$a3-030-20307-7 327 $aThe Fundamentals of Complex Adaptive Systems -- A Cognitive-Consistency Based Model of Population Wide Attitude Change -- An Application of Agent Based Social Modeling in the DoD -- Agent Based Behavior Precursor Model of Insider IT Sabotage -- Formal Measures of Dynamical Properties: Tipping Points, Robustness, and Sustainability -- Identifying Unexpected Behaviors of Agent-based Models through Spatial Plots and Heat Maps -- Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) -- Stigmergy for Biological Spatial Modeling -- Strategic group formation in the El Farol bar problem -- SwarmFSTaxis: Borrowing a Swarm Communication Mechanism from Fireflies and Slime Mold -- Teaching Complexity as Transdisciplinarity. 330 $aThis book emerged out of international conferences organized as part of the AAAI Fall Symposia series, and the Swarmfest 2017 conference. It brings together researchers from diverse fields studying these complex systems using CAS and agent-based modeling tools and techniques. In the past, the knowledge gained in each domain has largely remained exclusive to that domain. By bringing together scholars who study these phenomena, the book takes knowledge from one domain to provide insight into others. Most interesting phenomena in natural and social systems include constant transitions and oscillations among their various phases ? wars, companies, societies, markets, and humans rarely stay in a stable, predictable state for long. Randomness, power laws, and human behavior ensure that the future is both unknown and challenging. How do events unfold? When do they take hold? Why do some initial events cause an avalanche while others do not? What characterizes these events? What are the thresholds that differentiate a sea change from a non-event? Complex adaptive systems (CAS) have proven to be a powerful tool for exploring these and other related phenomena. The authors characterize a general CAS model as having a large number of self-similar agents that: 1) utilize one or more levels of feedback; 2) exhibit emergent properties and self-organization; and 3) produce non-linear dynamic behavior. Advances in modeling and computing technology have led not only to a deeper understanding of complex systems in many areas, but they have also raised the possibility that similar fundamental principles may be at work across these systems, even though the underlying principles may manifest themselves differently. 410 0$aUnderstanding Complex Systems,$x1860-0832 606 $aComputational complexity 606 $aStatistical physics 606 $aPhysics 606 $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022 606 $aApplications of Nonlinear Dynamics and Chaos Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/P33020 606 $aApplications of Graph Theory and Complex Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/P33010 615 0$aComputational complexity. 615 0$aStatistical physics. 615 0$aPhysics. 615 14$aComplexity. 615 24$aApplications of Nonlinear Dynamics and Chaos Theory. 615 24$aApplications of Graph Theory and Complex Networks. 676 $a006.30285436 676 $a620 702 $aCarmichael$b Ted$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCollins$b Ace$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHadz?ikadic?$b Mirsad$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337606403321 996 $aComplex adaptive systems$91774983 997 $aUNINA