Multi-Agent-Based Simulation XIX

Name: Multi-Agent-Based Simulation XIX | 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers
Brand: Springer
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19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers

Paul Davidsson Harko Verhagen(Herausgeber*in)

Springer (Verlag)

Erschienen am 19. Juni 2019

IX, 153 Seiten

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978-3-030-22270-3 (ISBN)

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Beschreibung

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Inhalt

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

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