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988			_aSpringer_Engineering_2019
999			_c114395 _d114395 _x1
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020			_a9783030203092
024	7		_a10.1007/978-3-030-20309-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.76 .I58 _b2019 EB
245	0	0	_aComplex adaptive systems : _bviews from the physical, natural, and social sciences _cedited by Ted Carmichael, Andrew J. Collins, Mirsad Hadžikadić
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (VIII, 250 páginas) _b76 ilustraciones, 55 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aUnderstanding Complex Systems _x1860-0832
490	0		_aEngineering (Springer-11647)
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	3		_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		7	_2embne _aAgentes inteligentes (Programas de ordenador) _9160930
650		7	_2embne _aComplejidad computacional _9165250
700	1		_aCarmichael, Ted _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aCollins, Andrew J _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHadžikadić, Mirsad _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030203078
776	0	8	_iPrinted edition: _z9783030203085
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-20309-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI