Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas (Springer Proceedings in Complexity)

This book contains a selection of the latest research in the field of Computational Social Science (CSS) methods, uses, and results, as presented at the 2021 annual conference of the Computational Social Science Society of the Americas (CSSSA). Computational social science (CSS) is the science that investigates social and behavioral dynamics through social simulation, social network analysis, and social media analysis. The CSSSA is a professional society that aims to advance the field of computational social science in all areas, including basic and applied orientations, by holding conferences and workshops, promoting standards of scientific excellence in research and teaching, and publishing research findings and results. About the Book Contents About the Editors Effects of Assortativity on Consensus Formation with Heterogeneous Agents 1 Introduction 2 Assortativity in Reddit 3 Method 3.1 Information Diffusion Models 3.2 Simulation Procedure 4 Results 5 Conclusion References Quantifying Polish Anti-semitism in Twitter: A Robust Unsupervised Approach with Signal Processing 1 Background 2 Prior Literature 3 Unsupervised Summarization to Fulfill Heuer’s Vision 4 The ‘Polish Holocaust Research’ Dataset 4.1 Data Collection 4.2 Data Processing 5 Results 6 Conclusion References On Modeling Evolution in Continuous Spaces 1 Introduction 2 Conceptual Model of Continuous and Clustered Cultural Evolution 3 Background on Vowels 4 Model 1: Single Vowel, Continuously Varying 4.1 Under Drift Alone 4.2 Under Influencers 5 Model 2: Simple Selection 6 Model 3: Preference for Separation 7 Model 4: Avoidance and Merger 8 Model 5: Probe and Adjust 9 Discussion and Conclusion References Economic Sanctions and Consumer Behavior in Target States: An Agent-Based Model of Boycott Movements 1 Introduction 2 Economic Sanctions, Public Opinion, and Consumer Behavior in Target States 3 Model 4 Results 5 Discussion and Conclusion References Scheduler Dependencies in Agent-Based Models: A Case-Study Using a Contagion Model 1 Introduction 2 Fidelity to Social Processes and Relevant ABM Mechanisms 3 Case Study 3.1 Description of the Model 3.2 Experimental Design 3.3 Results: Social Mimicry Modulates Scheduler-Dependent Differences 4 Conclusion References Exploring the Impact of Social Network Density and Agent Openness on Societal Polarization 1 Introduction 2 Related Work 2.1 Opinion Dynamics 2.2 Polarization 3 Variables 3.1 Independent Variables 3.2 Dependent Variables 4 Model 4.1 Purpose 4.2 Entities, State Variables and Scales 4.3 Process Overview and Scheduling 4.4 Initialization 5 Hypothesis 6 Results 6.1 H1a and H1b 6.2 H2a and H2b 7 Discussion and Future Work References Learning Actor Preferences by Evolution 1 Introduction 2 Some Use Cases for Behavior Evolution 3 Introduction to SCAMP 4 Evolving Preferences 4.1 Use Case 4.2 Experimental Framework 4.3 Evolutionary Process 4.4 Analysis of Results 5 Discussion References Drivers and Predictors of COVID-19 Vaccine Hesitancy in Virginia 1 Introduction 2 Data 2.1 Vaccine Hesitancy 2.2 Vaccine Hesitancy Drivers 2.3 Demographics/Predictors 3 Method 3.1 Multilevel Analysis: Identifying Vaccine-Hesitant Zip Codes 3.2 Level-2 Predictor for Intercept-As-Output: Identifying the Predictors of the Vaccine Hesitancy 4 Results 4.1 Summary Statistics 4.2 Identifying Vaccine-Hesitant Zip Codes 4.3 Vaccine Hesitancy Drivers 5 Discussion and Conclusions References The Spreading-Activation Framework Does not Explain the Effects of Degree and Clustering on Spoken Word Recognition 1 Introduction 1.1 Cognitive Network Science & Phonological Neighborhood Networks 1.2 Structural Influences on Cognitive Processing 1.3 The Spreading-Activation Framework 2 Simulation 0 2.1 Purpose 2.2 Setup 2.3 Results 3 Simulation 1 3.1 Purpose 3.2 Setup 3.3 Results 3.4 Discussion 4 General Discussion and Conclusions 4.1 The Spreading-Activation Algorithm 4.2 Cognitive Network Science and Agent-Based Modeling 5 Additional References References Engineering Decentralized Enterprises: Emergent Mission Accomplishment Without Centralized Command and Control 1 Introduction 1.1 [Centralized] Command and Control 1.2 Decentralized Command and Control 1.3 Mission as an Emergent System Property 1.4 Distributed Situational Awareness 1.5 The Challenge Problem (Asteroid Impact) 2 Foundational Prior Work 2.1 Cooperative Distributed Problem Solving vs. C2 2.2 Previous Work on the Study of Complex Systems 3 Simulation Approach 3.1 Why Use an Agent-Based Model? 3.2 Four Pillars of Functional Emergence 3.3 Optimizing to a Scenario 4 Simulation Implementation 4.1 Experimental Simulation Parameters 4.2 Experiment Execution 5 Design of Experiments and Results 5.1 Parametric Changes 5.2 Experiment 1: Effects of Toggling DSA Parameters 5.3 Experiment 2: Exquisite Few vs. Crude Many 6 Discussion 6.1 Experiment 1 6.2 Experiment 2 7 Next Steps 7.1 Threat Modeling 7.2 Deeper Analysis A ODD+D A.1 Overview A.2 Design Concepts A.3 Details - Implementation B DOE Parameters C Experimental Methodology References An Agent-Based Approach to Classical Competitive Prices 1 Introduction 2 Classical-Marxian Competitive Prices 3 The Model 3.1 Agents 3.2 Exchange 3.3 Production 3.4 Capital Mobility 4 Simulations 4.1 Initialisation and Baseline Parameters 4.2 Results 5 Conclusions and Further Work References Machine Learning Reveals Adaptive COVID-19 Narratives in Online Anti-Vaccination Network 1 Introduction 2 Data and Machine Learning Methods 3 Results and Discussion 4 Limitations of the Study 5 Conclusion 6 Appendix References Agentization of Two Population-Driven Models of Mathematical Biology 1 Introduction 2 Discrete Logistic Equation with Time Delay 2.1 Relaxation of Specifications - Equilibrium 2.2 Relaxation of Specifications - Extinction 3 Models of the Stochastic Behavior of Genes in Finite Populations 3.1 Relaxation of Specification - Constant Population 3.2 Weak Selection 4 Conclusions References Correction to: Machine Learning Reveals Adaptive COVID-19 Narratives in Online Anti-Vaccination Network Correction to: Chapter “Machine Learning Reveals Adaptive COVID-19 Narratives in Online Anti-Vaccination Network” in: Z. Yang and E. von Briesen (Eds.): Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas, SPCOM, https://doi.org/10.1007/978-3-030-96188-6_12 Author Index