{"product_id":"socialbehavioral-modeling-for-complex-systems-9781119484967","title":"SocialBehavioral Modeling for Complex Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming. Recent scientific and technological advances have created exciting opportunities for such improvements. However, the book also identifies crucial scientific, ethical, and cultural challenges to be met if social-behavioral modeling is to achieve its potential. Doing so will require new methods, data sources, and technology. The volume discusses these, including those needed to achieve and maintain high standards of ethics and privacy. The result should be a new generation of modeling that willadvance science and, separately, aid decision-making on major social and security-related subjects despite the myriad uncertainties and complexities of social phenomena.\u003c\/p\u003e \u003cp\u003eIntended to be relatively comprehensivein scope, the volume balances theory-driven, data-driven, and hybrid approaches. The latter may be rapidly iterative, as when artificial-inte\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eForeword xxvii\u003c\/p\u003e \u003cp\u003eList of Contributors xxxi\u003c\/p\u003e \u003cp\u003eAbout the Editors xli\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xliii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Introduction and Agenda \u003c\/b\u003e1\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Understanding and Improving the Human Condition: A Vision of the Future for Social-Behavioral Modeling \u003c\/b\u003e\u003cb\u003e3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJonathan Pfautz, Paul K. Davis, and Angela O’Mahony\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChallenges 5\u003c\/p\u003e \u003cp\u003eAbout This Book 10\u003c\/p\u003e \u003cp\u003eReferences 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Improving Social-Behavioral Modeling \u003c\/b\u003e\u003cb\u003e15\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePaul K. Davis and Angela O’Mahony\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAspirations 15\u003c\/p\u003e \u003cp\u003eClasses of Challenge 17\u003c\/p\u003e \u003cp\u003eInherent Challenges 17\u003c\/p\u003e \u003cp\u003eSelected Specific Issues and the Need for Changed Practices 20\u003c\/p\u003e \u003cp\u003eStrategy for Moving Ahead 32\u003c\/p\u003e \u003cp\u003eSocial-Behavioral Laboratories 39\u003c\/p\u003e \u003cp\u003eConclusions 41\u003c\/p\u003e \u003cp\u003eAcknowledgments 42\u003c\/p\u003e \u003cp\u003eReferences 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Ethical and Privacy Issues in Social-Behavioral Research 49\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRebecca Balebako, Angela O’Mahony, Paul K. Davis, and Osonde Osoba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eImproved Notice and Choice 50\u003c\/p\u003e \u003cp\u003eUsable and Accurate Access Control 52\u003c\/p\u003e \u003cp\u003eAnonymization 53\u003c\/p\u003e \u003cp\u003eAvoiding Harms by Validating Algorithms and Auditing Use 55\u003c\/p\u003e \u003cp\u003eChallenge and Redress 56\u003c\/p\u003e \u003cp\u003eDeterrence of Abuse 57\u003c\/p\u003e \u003cp\u003eAnd Finally \u003ci\u003eThinking Bigger\u003c\/i\u003e About What Is Possible 58\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Foundations of Social-Behavioral Science 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Building on Social Science: Theoretic Foundations for Modelers 65\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenjamin Nyblade, Angela O’Mahony, and Katharine Sieck\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eBackground 65\u003c\/p\u003e \u003cp\u003eAtomistic Theories of Individual Behavior 66\u003c\/p\u003e \u003cp\u003eSocial Theories of Individual Behavior 75\u003c\/p\u003e \u003cp\u003eTheories of Interaction 80\u003c\/p\u003e \u003cp\u003eFrom Theory to Data and Data to Models 88\u003c\/p\u003e \u003cp\u003eBuilding Models Based on Social Scientific Theories 92\u003c\/p\u003e \u003cp\u003eAcknowledgments 94\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 How Big and How Certain? A New Approach to Defining Levels of Analysis for Modeling Social Science Topics 101\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMatthew E. Brashears\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 101\u003c\/p\u003e \u003cp\u003eTraditional Conceptions of Levels of Analysis 102\u003c\/p\u003e \u003cp\u003eIncompleteness of Levels of Analysis 104\u003c\/p\u003e \u003cp\u003eConstancy as the Missing Piece 107\u003c\/p\u003e \u003cp\u003ePutting It Together 111\u003c\/p\u003e \u003cp\u003eImplications for Modeling 113\u003c\/p\u003e \u003cp\u003eConclusions 116\u003c\/p\u003e \u003cp\u003eAcknowledgments 116\u003c\/p\u003e \u003cp\u003eReferences 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Toward Generative Narrative Models of the Course and Resolution of Conflict 121\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSteven R. Corman, Scott W. Ruston, and Hanghang Tong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eLimitations of Current Conceptualizations of Narrative 122\u003c\/p\u003e \u003cp\u003eA Generative Modeling Framework 125\u003c\/p\u003e \u003cp\u003eApplication to a Simple Narrative 126\u003c\/p\u003e \u003cp\u003eReal-World Applications 130\u003c\/p\u003e \u003cp\u003eChallenges and Future Research 133\u003c\/p\u003e \u003cp\u003eConclusion 135\u003c\/p\u003e \u003cp\u003eAcknowledgment 137\u003c\/p\u003e \u003cp\u003eLocations, Events, Actions, Participants, and Things in the Three Little Pigs 137\u003c\/p\u003e \u003cp\u003eEdges in the Three Little Pigs Graph 139\u003c\/p\u003e \u003cp\u003eReferences 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 A Neural Network Model of Motivated Decision-Making in Everyday Social Behavior 145\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eStephen J. Read and Lynn C. Miller\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 145\u003c\/p\u003e \u003cp\u003eOverview 146\u003c\/p\u003e \u003cp\u003eTheoretical Background 147\u003c\/p\u003e \u003cp\u003eNeural Network Implementation 151\u003c\/p\u003e \u003cp\u003eConclusion 159\u003c\/p\u003e \u003cp\u003eReferences 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Dealing with Culture as Inherited Information 163\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLuke J. Matthews\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eGalton’s Problem as a Core Feature of Cultural Theory 163\u003c\/p\u003e \u003cp\u003eHow to Correct for Treelike Inheritance of Traits Across Groups 167\u003c\/p\u003e \u003cp\u003eDealing with Non independence in Less Treelike Network Structures 173\u003c\/p\u003e \u003cp\u003eFuture Directions for Formal Modeling of Culture 178\u003c\/p\u003e \u003cp\u003eAcknowledgments 181\u003c\/p\u003e \u003cp\u003eReferences 181\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Social Media, Global Connections, and Information Environments: Building Complex Understandings of Multi-Actor Interactions 187\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGene Cowherd and Daniel Lende\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA New Setting of Hyperconnectivity 187\u003c\/p\u003e \u003cp\u003eThe Information Environment 188\u003c\/p\u003e \u003cp\u003eSocial Media in the Information Environment 189\u003c\/p\u003e \u003cp\u003eIntegrative Approaches to Understanding Human Behavior 190\u003c\/p\u003e \u003cp\u003eThe Ethnographic Examples 192\u003c\/p\u003e \u003cp\u003eConclusion 202\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Using Neuroimaging to Predict Behavior: An Overview with a Focus on the Moderating Role of Sociocultural Context 205\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSteven H. Tompson, Emily B. Falk, Danielle S. Bassett, and Jean M. Vettel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 205\u003c\/p\u003e \u003cp\u003eThe Brain-as-Predictor Approach 206\u003c\/p\u003e \u003cp\u003ePredicting Individual Behaviors 208\u003c\/p\u003e \u003cp\u003eInterpreting Associations Between Brain Activation and Behavior 210\u003c\/p\u003e \u003cp\u003ePredicting Aggregate Out-of-Sample Group Outcomes 211\u003c\/p\u003e \u003cp\u003ePredicting Social Interactions and Peer Influence 214\u003c\/p\u003e \u003cp\u003eSociocultural Context 215\u003c\/p\u003e \u003cp\u003eFuture Directions 219\u003c\/p\u003e \u003cp\u003eConclusion 221\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Social Models from Non-Human Systems 231\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTheodore P. Pavlic\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eEmergent Patterns in Groups of Behaviorally Flexible Individuals 232\u003c\/p\u003e \u003cp\u003eModel Systems for Understanding Group Competition 239\u003c\/p\u003e \u003cp\u003eInformation Dynamics in Tightly Integrated Groups 246\u003c\/p\u003e \u003cp\u003eConclusions 254\u003c\/p\u003e \u003cp\u003eAcknowledgments 255\u003c\/p\u003e \u003cp\u003eReferences 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Moving Social-Behavioral Modeling Forward: Insights from Social Scientists 263\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMatthew Brashears, Melvin Konner, Christian Madsbjerg, Laura McNamara, and Katharine Sieck\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eWhy Do People Do What They Do? 264\u003c\/p\u003e \u003cp\u003eEverything Old Is New Again 264\u003c\/p\u003e \u003cp\u003eBehavior Is Social, Not Just Complex 267\u003c\/p\u003e \u003cp\u003eWhat is at Stake? 270\u003c\/p\u003e \u003cp\u003eSensemaking 272\u003c\/p\u003e \u003cp\u003eFinal Thoughts 275\u003c\/p\u003e \u003cp\u003eReferences 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Informing Models with Theory and Data 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Integrating Computational Modeling and Experiments: Toward a More Unified Theory of Social Influence 281\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMichael Gabbay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 281\u003c\/p\u003e \u003cp\u003eSocial Influence Research 283\u003c\/p\u003e \u003cp\u003eOpinion Network Modeling 284\u003c\/p\u003e \u003cp\u003eIntegrated Empirical and Computational Investigation of Group Polarization 286\u003c\/p\u003e \u003cp\u003eIntegrated Approach 299\u003c\/p\u003e \u003cp\u003eConclusion 305\u003c\/p\u003e \u003cp\u003eAcknowledgments 307\u003c\/p\u003e \u003cp\u003eReferences 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Combining Data-Driven and Theory-Driven Models for Causality Analysis in Sociocultural Systems 311\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAmy Sliva, Scott Neal Reilly, David Blumstein, and Glenn Pierce\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 311\u003c\/p\u003e \u003cp\u003eUnderstanding Causality 312\u003c\/p\u003e \u003cp\u003eEnsembles of Causal Models 317\u003c\/p\u003e \u003cp\u003eCase Studies: Integrating Data-Driven and Theory-Driven Ensembles 321\u003c\/p\u003e \u003cp\u003eConclusions 332\u003c\/p\u003e \u003cp\u003eReferences 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Theory-Interpretable, Data-Driven Agent-Based Modeling 337\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWilliam Rand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eThe Beauty and Challenge of Big Data 337\u003c\/p\u003e \u003cp\u003eA Proposed Unifying Principle for Big Data and Social Science 340\u003c\/p\u003e \u003cp\u003eData-Driven Agent-Based Modeling 342\u003c\/p\u003e \u003cp\u003eConclusion and the Vision 353\u003c\/p\u003e \u003cp\u003eAcknowledgments 354\u003c\/p\u003e \u003cp\u003eReferences 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Bringing the Real World into the Experimental Lab: Technology-Enabling Transformative Designs 359\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLynn C. Miller, Liyuan Wang, David C. Jeong, and Traci K. Gillig\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding, Predicting, and Changing Behavior 359\u003c\/p\u003e \u003cp\u003eSocial Domains of Interest 360\u003c\/p\u003e \u003cp\u003eThe SOLVE Approach 365\u003c\/p\u003e \u003cp\u003eExperimental Designs for Real-World Simulations 368\u003c\/p\u003e \u003cp\u003eCreating Representative Designs for Virtual Games 371\u003c\/p\u003e \u003cp\u003eApplications in Three Domains of Interest 375\u003c\/p\u003e \u003cp\u003eConclusions 376\u003c\/p\u003e \u003cp\u003eReferences 380\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Online Games for Studying Human Behavior 387\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKiran Lakkaraju, Laura Epifanovskaya, Mallory Stites, Josh Letchford, Jason Reinhardt, and Jon Whetzel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 387\u003c\/p\u003e \u003cp\u003eOnline Games and Massively Multiplayer Online Games for Research 388\u003c\/p\u003e \u003cp\u003eWar Games and Data Gathering for Nuclear Deterrence Policy 390\u003c\/p\u003e \u003cp\u003eMMOG Data to Test International Relations Theory 393\u003c\/p\u003e \u003cp\u003eAnalysis and Results 397\u003c\/p\u003e \u003cp\u003eGames as Experiments: The Future of Research 403\u003c\/p\u003e \u003cp\u003eFinal Discussion 405\u003c\/p\u003e \u003cp\u003eAcknowledgments 405\u003c\/p\u003e \u003cp\u003eReferences 405\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Using Sociocultural Data from Online Gaming and Game Communities 407\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSean Guarino, Leonard Eusebi, Bethany Bracken, and Michael Jenkins\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 407\u003c\/p\u003e \u003cp\u003eCharacterizing Social Behavior in Gaming 409\u003c\/p\u003e \u003cp\u003eGame-Based Data Sources 412\u003c\/p\u003e \u003cp\u003eCase Studies of SBE Research in Game Environments 422\u003c\/p\u003e \u003cp\u003eConclusions and Future Recommendations 437\u003c\/p\u003e \u003cp\u003eAcknowledgments 438\u003c\/p\u003e \u003cp\u003eReferences 438\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 An Artificial Intelligence\/Machine Learning Perspective on Social Simulation: New Data and New Challenges 443\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOsonde Osoba and Paul K. Davis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eObjectives and Background 443\u003c\/p\u003e \u003cp\u003eRelevant Advances 443\u003c\/p\u003e \u003cp\u003eData and Theory for Behavioral Modeling and Simulation 454\u003c\/p\u003e \u003cp\u003eConclusion and Highlights 470\u003c\/p\u003e \u003cp\u003eAcknowledgments 472\u003c\/p\u003e \u003cp\u003eReferences 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Social Media Signal Processing 477\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePrasanna Giridhar and Tarek Abdelzaher\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eSocial Media as a Signal Modality 477\u003c\/p\u003e \u003cp\u003eInterdisciplinary Foundations: Sensors, Information, and Optimal Estimation 479\u003c\/p\u003e \u003cp\u003eEvent Detection and Demultiplexing on the Social Channel 481\u003c\/p\u003e \u003cp\u003eConclusions 492\u003c\/p\u003e \u003cp\u003eAcknowledgment 492\u003c\/p\u003e \u003cp\u003eReferences 492\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Evaluation and Validation Approaches for Simulation of Social Behavior: Challenges and Opportunities 495\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEmily Saldanha, Leslie M. Blaha, Arun V. Sathanur, Nathan Hodas, Svitlana Volkova, and Mark Greaves\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eOverview 495\u003c\/p\u003e \u003cp\u003eSimulation Validation 498\u003c\/p\u003e \u003cp\u003eSimulation Evaluation: Current Practices 499\u003c\/p\u003e \u003cp\u003eMeasurements, Metrics, and Their Limitations 500\u003c\/p\u003e \u003cp\u003eProposed Evaluation Approach 507\u003c\/p\u003e \u003cp\u003eConclusions 515\u003c\/p\u003e \u003cp\u003eReferences 515\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Innovations in Modeling 521\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 The Agent-Based Model Canvas: A Modeling Lingua Franca for Computational Social Science 523\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIvan Garibay, Chathika Gunaratne, Niloofar Yousefi, and Steve Scheinert\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 523\u003c\/p\u003e \u003cp\u003eThe Language Gap 527\u003c\/p\u003e \u003cp\u003eThe Agent-Based Model Canvas 530\u003c\/p\u003e \u003cp\u003eConclusion 540\u003c\/p\u003e \u003cp\u003eReferences 541\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Representing Socio-Behavioral Understanding with Models 545\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAndreas Tolk and Christopher G. Glazner\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 545\u003c\/p\u003e \u003cp\u003ePhilosophical Foundations 546\u003c\/p\u003e \u003cp\u003eThe Way Forward 562\u003c\/p\u003e \u003cp\u003eAcknowledgment 563\u003c\/p\u003e \u003cp\u003eDisclaimer 563\u003c\/p\u003e \u003cp\u003eReferences 564\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Toward Self-Aware Models as Cognitive Adaptive Instruments for Social and Behavioral Modeling 569\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLevent Yilmaz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 569\u003c\/p\u003e \u003cp\u003ePerspective and Challenges 571\u003c\/p\u003e \u003cp\u003eA Generic Architecture for Models as Cognitive Autonomous Agents 575\u003c\/p\u003e \u003cp\u003eThe Mediation Process 578\u003c\/p\u003e \u003cp\u003eCoherence-Driven Cognitive Model of Mediation 581\u003c\/p\u003e \u003cp\u003eConclusions 584\u003c\/p\u003e \u003cp\u003eReferences 585\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Causal Modeling with Feedback Fuzzy Cognitive Maps 587\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOsonde Osoba and Bart Kosko\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 587\u003c\/p\u003e \u003cp\u003eOverview of Fuzzy Cognitive Maps for Causal Modeling 588\u003c\/p\u003e \u003cp\u003eCombining Causal Knowledge: Averaging Edge Matrices 592\u003c\/p\u003e \u003cp\u003eLearning FCM Causal Edges 594\u003c\/p\u003e \u003cp\u003eFCM Example: Public Support for Insurgency and Terrorism 597\u003c\/p\u003e \u003cp\u003eUS–China Relations: An FCM of Allison’s Thucydides Trap 603\u003c\/p\u003e \u003cp\u003eConclusion 611\u003c\/p\u003e \u003cp\u003eReferences 612\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Simulation Analytics for Social and Behavioral Modeling 617\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSamarth Swarup, Achla Marathe, Madhav V. Marathe, and Christopher L. Barrett\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 617\u003c\/p\u003e \u003cp\u003eWhat Are Behaviors? 619\u003c\/p\u003e \u003cp\u003eSimulation Analytics for Social and Behavioral Modeling 624\u003c\/p\u003e \u003cp\u003eConclusion 628\u003c\/p\u003e \u003cp\u003eAcknowledgments 630\u003c\/p\u003e \u003cp\u003eReferences 630\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Using Agent-Based Models to Understand Health-Related Social Norms 633\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGita Sukthankar and Rahmatollah Beheshti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 633\u003c\/p\u003e \u003cp\u003eRelated Work 634\u003c\/p\u003e \u003cp\u003eLightweight Normative Architecture (LNA) 634\u003c\/p\u003e \u003cp\u003eCognitive Social Learners (CSL) Architecture 635\u003c\/p\u003e \u003cp\u003eSmoking Model 639\u003c\/p\u003e \u003cp\u003eAgent-Based Model 641\u003c\/p\u003e \u003cp\u003eData 645\u003c\/p\u003e \u003cp\u003eExperiments 646\u003c\/p\u003e \u003cp\u003eConclusion 652\u003c\/p\u003e \u003cp\u003eAcknowledgments 652\u003c\/p\u003e \u003cp\u003eReferences 652\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Lessons from a Project on Agent-Based Modeling 655\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMirsad Hadzikadic and Joseph Whitmeyer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 655\u003c\/p\u003e \u003cp\u003eACSES 656\u003c\/p\u003e \u003cp\u003eVerification and Validation 661\u003c\/p\u003e \u003cp\u003eSelf-Organization and Emergence 665\u003c\/p\u003e \u003cp\u003eTrust 668\u003c\/p\u003e \u003cp\u003eSummary 669\u003c\/p\u003e \u003cp\u003eReferences 670\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Modeling Social and Spatial Behavior in Built Environments: Current Methods and Future Directions 673\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDavide Schaumann and Mubbasir Kapadia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 673\u003c\/p\u003e \u003cp\u003eSimulating Human Behavior – A Review 675\u003c\/p\u003e \u003cp\u003eModeling Social and Spatial Behavior with MAS 678\u003c\/p\u003e \u003cp\u003eDiscussion and Future Directions 685\u003c\/p\u003e \u003cp\u003eAcknowledgments 687\u003c\/p\u003e \u003cp\u003eReferences 687\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Multi-Scale Resolution of Human Social Systems: A Synergistic Paradigm for Simulating Minds and Society 697\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMark G. Orr\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 697\u003c\/p\u003e \u003cp\u003eThe Reciprocal Constraints Paradigm 699\u003c\/p\u003e \u003cp\u003eDiscussion 706\u003c\/p\u003e \u003cp\u003eAcknowledgments 708\u003c\/p\u003e \u003cp\u003eReferences 708\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Multi-formalism Modeling of Complex Social-Behavioral Systems 711\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMarco Gribaudo, Mauro Iacono, and Alexander H. Levis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003ePrologue 711\u003c\/p\u003e \u003cp\u003eIntroduction 713\u003c\/p\u003e \u003cp\u003eOn Multi-formalism 718\u003c\/p\u003e \u003cp\u003eIssues in Multi-formalism Modeling and Use 719\u003c\/p\u003e \u003cp\u003eIssues in Multi-formalism Modeling and Simulation 734\u003c\/p\u003e \u003cp\u003eConclusions 736\u003c\/p\u003e \u003cp\u003eEpilogue 736\u003c\/p\u003e \u003cp\u003eReferences 737\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 Social-Behavioral Simulation: Key Challenges 741\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKathleen M. Carley\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 741\u003c\/p\u003e \u003cp\u003eKey Communication Challenges 742\u003c\/p\u003e \u003cp\u003eKey Scientific Challenges 743\u003c\/p\u003e \u003cp\u003eToward a New Science of Validation 748\u003c\/p\u003e \u003cp\u003eConclusion 749\u003c\/p\u003e \u003cp\u003eReferences 750\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 Panel Discussion:Moving Social-Behavioral Modeling Forward 753\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAngela O’Mahony, Paul K. Davis, Scott Appling, Matthew E. Brashears, Erica Briscoe, Kathleen M. Carley, Joshua M. Epstein, Luke J. Matthews, Theodore P. Pavlic, William Rand, Scott Neal Reilly, William B. Rouse, Samarth Swarup, Andreas Tolk, Raffaele Vardavas, and Levent Yilmaz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eSimulation and Emergence 754\u003c\/p\u003e \u003cp\u003eRelating Models Across Levels 765\u003c\/p\u003e \u003cp\u003eGoing Beyond Rational Actors 776\u003c\/p\u003e \u003cp\u003eReferences 784\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Models for Decision-Makers 789\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34 Human-Centered Design of Model-Based Decision Support for Policy and Investment Decisions 791\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWilliam B. Rouse\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 791\u003c\/p\u003e \u003cp\u003eModeler as User 792\u003c\/p\u003e \u003cp\u003eModeler as Advisor 792\u003c\/p\u003e \u003cp\u003eModeler as Facilitator 793\u003c\/p\u003e \u003cp\u003eModeler as Integrator 797\u003c\/p\u003e \u003cp\u003eModeler as Explorer 799\u003c\/p\u003e \u003cp\u003eValidating Models 800\u003c\/p\u003e \u003cp\u003eModeling Lessons Learned 801\u003c\/p\u003e \u003cp\u003eObservations on Problem-Solving 804\u003c\/p\u003e \u003cp\u003eConclusions 806\u003c\/p\u003e \u003cp\u003eReferences 807\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35 A Complex Systems Approach for Understanding the Effect of Policy and Management Interventions on Health System Performance 809\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJason Thompson, Rod McClure, and Andrea de Silva\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 809\u003c\/p\u003e \u003cp\u003eUnderstanding Health System Performance 811\u003c\/p\u003e \u003cp\u003eMethod 813\u003c\/p\u003e \u003cp\u003eModel Narrative 815\u003c\/p\u003e \u003cp\u003ePolicy Scenario Simulation 817\u003c\/p\u003e \u003cp\u003eResults 817\u003c\/p\u003e \u003cp\u003eDiscussion 824\u003c\/p\u003e \u003cp\u003eConclusions 826\u003c\/p\u003e \u003cp\u003eReferences 827\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36 Modeling Information and Gray Zone Operations 833\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCorey Lofdahl\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 833\u003c\/p\u003e \u003cp\u003eThe Technological Transformation of War: Counterintuitive Consequences 835\u003c\/p\u003e \u003cp\u003eModeling Information Operations: Representing Complexity 838\u003c\/p\u003e \u003cp\u003eModeling Gray Zone Operations: Extending Analytic Capability 842\u003c\/p\u003e \u003cp\u003eConclusion 845\u003c\/p\u003e \u003cp\u003eReferences 847\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37 Homo Narratus (The Storytelling Species): The Challenge (and Importance) of Modeling Narrative in Human Understanding 849\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChristopher Paul\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eThe Challenge 849\u003c\/p\u003e \u003cp\u003eWhat Are Narratives? 850\u003c\/p\u003e \u003cp\u003eWhat Is Important About Narratives? 851\u003c\/p\u003e \u003cp\u003eWhat Can Commands Try to Accomplish with Narratives in Support of Operations? 856\u003c\/p\u003e \u003cp\u003eMoving Forward in Fighting Against, with, and Through Narrative in Support of Operations 857\u003c\/p\u003e \u003cp\u003eConclusion: Seek Modeling and Simulation Improvements That Will Enable Training and Experience with Narrative 861\u003c\/p\u003e \u003cp\u003eReferences 862\u003c\/p\u003e \u003cp\u003e\u003cb\u003e38 Aligning Behavior with Desired Outcomes: Lessons for Government Policy from the Marketing World 865\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKatharine Sieck\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTechnique 1: Identify the Human Problem 867\u003c\/p\u003e \u003cp\u003eTechnique 2: Rethinking Quantitative Data 869\u003c\/p\u003e \u003cp\u003eTechnique 3: Rethinking Qualitative Research 876\u003c\/p\u003e \u003cp\u003eSummary 882\u003c\/p\u003e \u003cp\u003eReferences 882\u003c\/p\u003e \u003cp\u003e\u003cb\u003e39 Future Social Science That Matters for Statecraft 885\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKent C. Myers\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003ePerspective 885\u003c\/p\u003e \u003cp\u003eRecent Observations 885\u003c\/p\u003e \u003cp\u003eInteractions with the Intelligence Community 887\u003c\/p\u003e \u003cp\u003ePhronetic Social Science 888\u003c\/p\u003e \u003cp\u003eCognitive Domain 891\u003c\/p\u003e \u003cp\u003eReflexive Processes 893\u003c\/p\u003e \u003cp\u003eConclusion 895\u003c\/p\u003e \u003cp\u003eReferences 896\u003c\/p\u003e \u003cp\u003e\u003cb\u003e40 Lessons on Decision Aiding for Social-Behavioral Modeling 899\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePaul K. Davis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eStrategic Planning Is Not About Simply Predicting and Acting 899\u003c\/p\u003e \u003cp\u003eCharacteristics Needed for Good Decision Aiding 901\u003c\/p\u003e \u003cp\u003eImplications for Social-Behavioral Modeling 918\u003c\/p\u003e \u003cp\u003eAcknowledgments 921\u003c\/p\u003e \u003cp\u003eReferences 923\u003c\/p\u003e \u003cp\u003eIndex 927\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407064277335,"sku":"9781119484967","price":131.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119484967.jpg?v=1730498050","url":"https:\/\/bookcurl.com\/products\/socialbehavioral-modeling-for-complex-systems-9781119484967","provider":"Book Curl","version":"1.0","type":"link"}