A major objective of this monograph is to present an agent-based simulation of artificial populations. The focus is on possible unexpected or catastrophic events that may spontaneously appear in simulations. A short recall of the tenets of the theory of catastrophes is given. Several examples of artificial society simulations are provided as the main topic of the book. With agent-based modeling, possible catastrophes and unexpected events in artificial populations are simulated. The book presents a new modeling and simulation tool, applied to social system simulation. The models are coded in the object- and agent-oriented language Bluesss (Blues Simulation System), related to the C++ language. The program code consists of a series of generic declarations of processes. Each of them includes a number of events that are coded in C++. At the runtime, a population of objects is generated. All the objects (agents) start to execute their own events, and interact with one another. During the simulations it is possible to observe the macro-behavior of the population, where some unexpected or "catastrophic" events occur. The examples include a stock market crash, catastrophes in extended prey–predator systems, growing organisms and cancer, epidemics, social inequality and economic decay, mass-service systems, and more. Remarks on possible simultaneous events are also included.
Author(s): Stanisław Raczyński
Series: Evolutionary Economics and Social Complexity Science, 27
Publisher: Springer
Year: 2021
Language: English
Pages: 199
City: Cham
Preface
Acknowledgements
Contents
About the Author
Chapter 1: Catastrophes and Agent-Based Models
1.1 Catastrophes
1.2 Discrete Events and Agent-Based Models
1.2.1 Discrete Events and DEVS
1.2.2 Some Software Tools
1.2.3 The Event Queue
1.2.4 Agent-Based Models
References
Chapter 2: BLUESSS Simulation Package
2.1 BLUESSS Concepts
2.2 BLUESSS Code, Program Structure
2.3 Inheritance
2.4 BLUESSS Code Generators, Continuous and Discrete Simulation Support
2.5 Variance Analysis
References
Chapter 3: Behavior Patterns of an Artificial Society
3.1 Introduction
3.2 Agent-Based Models
3.3 Simulation Tool
3.4 The Model
3.4.1 Process food
3.4.2 Process agent
3.4.2.1 Event moves
3.4.2.2 Event rwalk
3.4.2.3 Event foodseek
3.4.2.4 Event gregarious
3.4.2.5 Event attract
3.4.2.6 Event repro
3.4.2.7 Event dies
3.4.2.8 Event agres (aggression)
3.4.2.9 Event works
3.5 Simulations
3.5.1 Experiment 1
3.5.2 Experiment 2 (unexpected behavior)
3.5.3 Experiment 3-aggression
3.5.4 Experiment 4-Work
3.6 Conclusions
References
Chapter 4: Extended Prey-Predator Model
4.1 Introduction
4.2 Continuous Model
4.2.1 Simple Simulation
4.2.2 Uncertainty and Differential Inclusions
4.3 Agent-Based Simulation
4.3.1 General Remarks
4.3.2 Simulation Tool
4.4 The Model
4.4.1 Resources and Agent Types
4.4.2 Implementation, Processes, and Events
4.4.3 The Food Resource
4.4.4 Agents
4.4.5 Process Static
4.4.6 Process Control
4.5 Simulations
4.5.1 Basic Simulation Mode
4.5.2 Incoming Agents, Low System Isolation
4.5.3 Gregarious Effect
4.5.4 Group Forming-Simulation Mode 2
4.5.5 A Slow Catastrophe; Static Agents
Appendix-Model Data Specification
The Food Resource
Agents
Static Agents (SA) Mode
References
Chapter 5: Stock Market: Uncertainty and Catastrophes
5.1 Introduction
5.2 Continuous Model
5.2.1 Model Equations
5.2.2 Differential Inclusion Solver
5.2.3 The Reachable Set Example
5.3 Agent-Based Model
5.3.1 Processes and Events
5.3.1.1 Process Company
5.3.1.2 Process Agent
5.3.1.3 Event Buy
5.3.1.4 Process Brokers
5.3.1.5 Process monit
5.3.2 Other Elements
5.4 Simulation Tool: BLUESSS Implementation
5.5 The Simulations
5.5.1 Experiment 1
5.5.2 Experiment 2
5.5.3 Experiment 3
5.6 Final Remarks
References
Chapter 6: Epidemics
6.1 Introduction
6.2 Continuous Models
6.2.1 Susceptible-Infectious-Removed Models
6.2.2 Differential Inclusions and Uncertainty
6.2.3 Examples of Reachable Sets
6.3 Agent-Based Model
6.3.1 General Concepts
6.3.2 The Model
6.3.2.1 Agent Events
6.4 Simulations
6.4.1 Fast Propagation
6.4.2 Lower Trip Frequency
6.4.3 Near Cities
6.4.4 Long Epidemics-Adverse Conditions
6.5 Conclusion
References
Chapter 7: Growing Organism and Cancer
7.1 Introduction
7.2 Agent-Based Modeling
7.2.1 How It Works
7.2.2 Simulation Tool
7.3 The Model
7.3.1 Overview
7.3.2 Implementation
7.3.2.1 Process Cell
7.4 Simulations
7.4.1 The Growth of the Organism and Aging
7.4.2 Infection and Defense
7.4.3 The Cancer
7.5 Conclusion
References
Chapter 8: Work, Salary, and Gini
8.1 Introduction
8.2 Inequality and the Gini Coefficient
8.3 Agent-Based Modeling
8.4 Simulation Tool
8.5 The Model
8.5.1 Work, Income, and Object Function
8.5.2 Implementation
8.5.3 Simulations
References
Chapter 9: Waiting Lines
9.1 Introduction
9.2 Queuing Model Generator (QMG)
9.2.1 Overview
9.2.2 QMG Blocks
9.2.3 Additional Entity Actions: The SVOP Function
9.3 Simulations
9.3.1 Simple Model, Useless Statistics
9.3.2 The Bus Stop Paradox
9.3.3 Queue and Server Chain
9.3.4 Conveyors with Feedback
9.4 Conclusion
References
Chapter 10: Simultaneous Events, Semi-Discrete Events, and Chicken
10.1 Introduction
10.2 The Chicken Game
10.3 Simulation and Model Convergence
10.4 Three Body Collision
10.4.1 Compliance Collision
10.4.2 Elastic Collision: Compliance Zero
10.5 Conclusion
References
Index
