00853nam0 2200265 450 00002361020090226130933.0012576830320090226d1993----km-y0itay50------baengUSa-------001yyStatistical methods in econometricsRamu RamanathanSan DiegoAcademic Pressc1993x, 405 p.ill.23 cmStatistical methods in econometrics44584EconomiaMetodi statistici330.01519520Economia. Matematica statisticaRamanathan,Ramu251855ITUNIPARTHENOPE20090226RICAUNIMARC000023610211/23157/L/VNAVA22009211/257623NAVA22009Statistical methods in econometrics44584UNIPARTHENOPE01229nam 2200325 450 991013781360332120221118224547.0(CKB)3240000000064426(NjHacI)993240000000064426(EXLCZ)99324000000006442620221112d1858 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierOld New Yorkor, Reminiscences of the past sixty yearsBeing an enlarged and revised edition of the anniversary discourse delivered before the New York historical society, (November 17, 1857,) /John W. FrancisNew York :C. Roe,1858.1 online resource (554 pages)Old New Yorkor, Reminiscences of the past sixty years. Being an enlarged and revised edition of the anniversary discourse delivered before the New York historical society,ReminiscingReminiscing.646.504Francis John W.298052NjHacINjHaclBOOK9910137813603321Old New Yorkor, Reminiscences of the past sixty years2962113UNINA05521nam 2200661 a 450 991043782310332120200520144314.01-283-74213-694-007-3852-810.1007/978-94-007-3852-2(CKB)2670000000280522(EBL)971837(OCoLC)817917018(SSID)ssj0000790471(PQKBManifestationID)11428975(PQKBTitleCode)TC0000790471(PQKBWorkID)10745752(PQKB)10798384(DE-He213)978-94-007-3852-2(MiAaPQ)EBC971837(PPN)258864923(PPN)168336421(EXLCZ)99267000000028052220120831d2013 uy 0engur|n|---|||||txtccrCognitive agent-based computing-I a unified framework for modeling complex adaptive systems using agent-based & complex network-based methods /Muaz A. Niazi, Amir Hussain1st ed. 2013.New York Springer20131 online resource (65 p.)SpringerBriefs in cognitive computation,2212-6023Description based upon print version of record.94-007-3851-X Includes bibliographical references and index.Cognitive Agent-basedComputing-I; Acknowledgments; Contents; Acronyms; Abstract; 1 Introduction; 1.1...About the AgentAgent Concept; 1.2...A Framework for Complex Adaptive Systems; 1.3...Modeling CASCAS; 1.4...Motivation; 1.5...Aims and Objectives; 1.6...Overview of the Briefs; References; 2 A Unified Framework; 2.1...Overview of the Proposed Framework; 2.2...Proposed Framework Levels Formulated in Terms of CASCAS Study Objectives; 2.3...Proposed Framework Levels Formulated in Relation to Available Data Types; 2.4...Overview of the Rest of the Parts; 2.4.1 Overview of Case Studies; 2.4.2 Outline of the BriefsReferences3 Complex Adaptive Systems; 3.1...Overview; 3.2...Complex Adaptive Systems (CASCAS); 3.2.1 The Seven Basics of CASCAS; 3.2.2 Emergence; 3.3...Examples of CASCAS; 3.3.1 Natural CASCAS Example 1: CAS in Plants; 3.3.2 Natural CASCAS Example 2: CAS in Social Systems; 3.3.3 Artificial CASCAS Example 1: Complex Adaptive Communication Networks; 3.3.4 Artificial CASCAS Example 2: Simulation of Flocking Boids; References; 4 Modeling CASCAS; 4.1...AgentAgent-based Modeling and Agent-based Computing; 4.1.1 AgentAgent-oriented ProgrammingAgentAgent-Oriented Programming4.1.2 Multi-agentagent Oriented Programming4.1.3 AgentAgent-based or Massively Multiagent Modeling; 4.1.4 Benefits of AgentAgent-based Thinking; 4.2...A Review of an AgentAgent-based Tool; 4.2.1 NetLogo Simulation: An Overview; 4.2.1.1 Overview of NetLogo for Modeling Complex Interaction ProtocolsOverview of NetLogo for Modeling Complex Interaction Protocols; 4.2.1.2 Capabilities in Handling a Range of Input Values; 4.2.1.3 Range of Statistics and Complex Metrics; 4.3...Verification and Validation of SimulationSimulation Models; 4.3.1 Overview; 4.3.2 Verification and Validation of ABMs4.3.3 Related Work on V&V of ABMABM4.4...Overview of Communication Network Simulators; 4.4.1 Simulation of WSNs; 4.4.2 Simulation of P2P Networks; 4.4.3 Simulation of Robotic Swarms; 4.4.4 ABMABM for Complex Communication Networks SimulationSimulation; 4.5...Complex Network Modeling; 4.5.1 Complex Network Methods; 4.5.2 Theoretical Basis; 4.5.3 Centralities and Other Quantitative Measures; 4.5.3.1 Clustering Coefficient; 4.5.3.2 Matching Index; 4.5.4 Centrality Measures; 4.5.4.1 Degree Centrality; 4.5.4.2 Eccentricity Centrality; 4.5.4.3 Closeness Centrality4.5.4.4 Shortest Path Betweenness Centrality4.5.5 Software Tools for Complex NetworksComplex Networks; 4.6...Conclusions; References; IndexComplex Systems are made up of numerous interacting sub-components. Non-linear interactions of these components or agents give rise to emergent behavior observable at the global scale. Agent-based modeling and simulation is a proven paradigm which has previously been used for effective computational modeling of complex systems in various domains. Because of its popular use across different scientific domains, research in agent-based modeling has primarily been vertical in nature. The goal of this book is to provide a single hands-on guide to developing cognitive agent-based models for the exploration of emergence across various types of complex systems. We present practical ideas and examples for researchers and practitioners for the building of agent-based models using a horizontal approach - applications are demonstrated in a number of exciting domains as diverse as wireless sensors networks, peer-to-peer networks, complex social systems, research networks and epidemiological HIV.SpringerBriefs in Cognitive Computation,2212-6023Social systemsMathematical modelsSocial sciencesMathematical modelsSocial systemsMathematical models.Social sciencesMathematical models.006.3Niazi Muaz A1058427Hussain A(Amir)1750483MiAaPQMiAaPQMiAaPQBOOK9910437823103321Cognitive agent-based computing-I4185128UNINA