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020 _a9783030203092
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024 7 _a10.1007/978-3-030-20309-2
_2doi
050 4 _aTA352-356
050 4 _aQC20.7.N6
072 7 _aTBJ
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072 7 _aTBJ
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072 7 _aGPFC
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082 0 4 _a515.39
_223
245 1 0 _aComplex Adaptive Systems
_h[electronic resource] :
_bViews from the Physical, Natural, and Social Sciences /
_cedited by Ted Carmichael, Andrew J. Collins, Mirsad Hadžikadić.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aVIII, 250 p. 76 illus., 55 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnderstanding Complex Systems,
_x1860-0840
505 0 _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.
520 _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.
650 0 _aDynamics.
_942537
650 0 _aNonlinear theories.
_93339
650 0 _aNonlinear Optics.
_911414
650 0 _aGraph theory.
_93662
650 1 4 _aApplied Dynamical Systems.
_932005
650 2 4 _aNonlinear Optics.
_911414
650 2 4 _aGraph Theory.
_93662
700 1 _aCarmichael, Ted.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_942538
700 1 _aCollins, Andrew J.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_942539
700 1 _aHadžikadić, Mirsad.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_942540
710 2 _aSpringerLink (Online service)
_942541
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030203078
776 0 8 _iPrinted edition:
_z9783030203085
830 0 _aUnderstanding Complex Systems,
_x1860-0840
_942542
856 4 0 _uhttps://doi.org/10.1007/978-3-030-20309-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77146
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