Multi-Agent-Based Simulation XIX
19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers
Published on 19. June 2019
IX, 153 pages
978-3-030-22270-3 (ISBN)
Description
This book constitutes the thoroughly refereed post-conference proceedings of the 19th International Workshop on Multi-Agent-Based Simulation, MABS 2019, held in Stockholm Sweden, in July 2018 as part of the Federated AI Meeting, FAIM 2018.
The 10 revised full papers included in this volume were carefully selected from 15 submissions. They focus on finding efficient solutions to model complex social systems in such areas as economics, management, and organisational and social sciences. In all these areas, agent theories, metaphors, models, analysis, experimental designs, empirical studies, and methodological principles, converge into simulation as a way of achieving explanations and predictions, exploration and testing of hypotheses, better designs and systems.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Product notice
Reflowable
Illustrations
IX, 153 p. 104 illus., 85 illus. in color.
File size
16,78 MB
ISBN-13
978-3-030-22270-3 (9783030222703)
DOI
10.1007/978-3-030-22270-3
Schweitzer Classification
Other editions
Additional editions
Paul Davidsson | Harko Verhagen
Multi-Agent-Based Simulation XIX
19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers
Book
06/2019
Springer
€53.49
Shipment within 7-9 days
Content
- Intro
- Preface
- Organization
- Contents
- Using Agent-Based Modelling to Inform Policy - What Could Possibly Go Wrong?
- Abstract
- 1 Introduction - The Cautionary Example of the North Atlantic Cod Fisheries Collapse
- 2 Some Pitfalls in Using Agent-Based Modelling for Policy
- 3 The Engineering/Predictive Paradigm for Policy Making
- 4 Using Agent-Based Modelling for Uncertainty Analysis
- 5 Concluding Discussion
- Acknowledgements
- References
- A Comparison of Two Historical Trader Societies - An Agent-Based Simulation Study of English East India Company and New-Julfa
- 1 Introduction
- 2 Review of Systems
- 3 Simulation Model of Two Systems
- 4 Simulation Results
- 5 Discussion
- References
- Indirect Influence Manipulation with Partial Observability
- 1 Introduction
- 2 Related Work
- 3 Concept Interaction
- 4 Target Degree
- 5 Experimental Approach
- 6 Results
- 7 Conclusions and Future Work
- References
- Impact of Trust on Agent-Based Simulation for Supply Chains
- 1 Introduction
- 2 Beer Game
- 3 State of the Art
- 4 Agent-Based SCM Simulation Model
- 4.1 Supply Chain Model
- 4.2 Agent Model
- 4.3 Ordering by Anchoring and Adjustment Strategy
- 4.4 Working Capital Model
- 4.5 Supplier Trust Model
- 4.6 Supplier Recommendation Model
- 4.7 Agent Profiles
- 4.8 Unreliable Peers
- 4.9 Liars
- 5 Simulation and Results
- 5.1 Simulation Cycle
- 5.2 Implementation
- 5.3 Fixed Input Parameters
- 5.4 Experiment 1: Impact of Peer Recommendations
- 5.5 Experiment 2: Impact of Lies
- 6 Conclusions
- References
- PASHAMAMA: An Agricultural Process-Driven Agent-Based Model of the Ecuadorian Amazon
- 1 Introduction
- 2 Context
- 2.1 Historical Context
- 2.2 Related Works
- 3 Overview
- 3.1 Purpose
- 3.2 Entities, State Variables, and Scales
- 3.3 Process Overview and Scheduling
- 4 Design Concepts
- 5 Details
- 5.1 Initialization
- 5.2 Input Data
- 5.3 Submodels
- 6 First Results
- 7 Conclusion and Perspectives
- References
- The MASON Simulation Toolkit: Past, Present, and Future
- 1 Introduction
- 2 Development History
- 3 MASON's Design
- 4 Where Is MASON Going?
- 4.1 Making MASON More Robust
- 4.2 Making MASON Distributed
- 4.3 Making MASON More Coder-Friendly
- 4.4 Making MASON More Community-Friendly
- 4.5 Development Plan
- 5 Conclusion
- References
- American vs German Teams: The Case of Human Resources Consumption
- Abstract
- 1 Introduction
- 2 Team Assembly Overview
- 3 The Team Building Model
- 3.1 TBM Model Parameters
- 3.2 Explaining TBM Model
- 4 Simulation Results and Analysis
- 4.1 American Teams
- 4.2 German Teams
- 4.3 Hybrid Teams
- 5 Conclusion and Future Work
- References
- Simulation of Bank Transaction Data
- 1 Introduction
- 2 Data Generation by Simulation
- 3 Financial Data Simulation Framework
- 4 Money-Laundering Scenarios Simulation
- 4.1 Scenario 1: Transactions Just Under the Reporting Limit
- 4.2 Scenario 2: Larger Amounts of Cash Deposits
- 4.3 Scenario 3: Simulation of Structuring Behavior
- 5 Evaluation
- 5.1 Evaluation on Fraudulent Account Detection
- 5.2 Comparison of Simulated Data to Real Dataset
- 6 Conclusions
- References
- The Temporality Model Time Scheduling Approach: A Practical Application
- 1 Introduction
- 2 Related Work
- 2.1 The Time-Stepped Approach
- 2.2 The Event-Driven Approach
- 2.3 The Mixed Approach
- 2.4 The Temporality Model Approach
- 3 Case Study
- 3.1 The Agent Model
- 3.2 The Environment Modeling Elements
- 4 The SKUADCityModel
- 4.1 SKUAD
- 4.2 The SimSKUAD Simulation Platform
- 4.3 The SKUADCityModel
- 5 Confrontation of the Different Approaches
- 6 Conclusion and Further Work
- References
- Multi-agent Systems Society for Power and Energy Systems Simulation
- Abstract
- 1 Introduction
- 2 Multi-agent Systems Society
- 3 Ontologies for MAS Interoperability
- 3.1 EMO Specification
- 3.2 Additional Modules
- 3.3 Complementary Ontologies
- 4 Case Study
- 5 Conclusions
- References
- Modeling Herd Behavior Caused by Evacuation Decision Making Using Response Threshold
- 1 Introduction
- 2 The Response Threshold Model
- 3 The Evacuation Decision Model
- 4 The Evacuation Simulation
- 4.1 Setup
- 4.2 Results and Analysis
- 4.3 Impact of Uneven Spatial Distributions
- 5 Cognitive Aggregation and Physical Factors
- 6 Population and Physical Factors
- 7 Discussion
- 8 Conclusion
- References
- Author Index
