Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves

Hands-On Museum Exhibit

Agent-Based Modeling has been known as an alternative scientific method to explain complex behaviors. This Interactive Qualifying Project investigated this theme to build a learning-by-doing model that can provide an understanding and a simulation of a phenomenon, how partygoers form smaller groups in a party. Provided a current gradual loss of interest among patrons, through the accomplishments of this project, the project introduces to museums another possible solution for their current problem, an interactive exhibit that uses Agent-Based Modeling

Data-driven Travel Demand Modelling and Agent-based Traffic Simulation in Amsterdam Urban Area

AbstractThe goal of this project is the development of a large-scale agent-based traffic simulation system for Amsterdam urban area, validated on sensor data and adjusted for decision support in critical situations and for policy making in sustainable city development, emission control and electric car research. In this paper we briefly describe the agent-based simulation workflow and give the details of our data- driven approach for (1) modeling the road network of Amsterdam metropolitan area extended by major national roads, (2) recreating the car owners population distribution from municipality demographic data, (3) modeling the agent activity based on travel survey, and (4) modeling the inflow and outflow boundary conditions based on the traffic sensor data. The models are implemented in scientific Python and MATSim agent-based freeware. Simulation results of 46.5 thousand agents -with travel plans sampled from the model distributions- show that travel demand model is consistent, but should be improved to correspond with sensor data. The next steps in our project are: extensive validation, calibration and testing of large-scale scenarios, including critical events like the major power outage in the Netherlands (doi:10.1016/j.procs.2015.11.039), and modelling emissions and heat islands caused by traffic jams

A Comparative Review on Computational Modeling Paradigms. A Study on Case-Based Modeling and Political Terrorism

We review the advances in Case-Based Computational Modeling on Political Analysis issues. Starting in early „70s, the research on political terrorism has been challenged by the latest advances of terrorism computational modeling research. Nowadays Political Analysis community has a wider perspective over the terrorism research aims, methodology and instruments. Widening up this perspective is not a matter of political analysis and research only, it is as well a long-term effect of an interdisciplinary style which has been adopted within the area by acknowledging the scientific advances and support of the Computational Modeling and Simulation as a specific scientific research method. Computational Modeling includes several research frameworks. The Case-Based Modeling is analysed and evaluated on a comparative basis with Agent-Based Modeling in a study on political terrorism phenomena

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Emergence and Artificial Life

This paper focuses on emergent phenomena and the utilization of computer simulations, basically agent-based modeling to understand emergent phenomena. Agent-based simulation models have a promising future in the social sciences, from management to economies, political science, sociology and anthropology. This paper attempts to realize their full scientific potential by reviewing recent applications in engineering management and addresses the set of challenges confronted by this method. Common methodology for constructing an agent-based model is also discussed with the aim of highlighting how artificial life and management can be brought together to develop decision making aid tools

Science as a Social System and Virtual Research Environment

The accumulation of gradual changes in scientific landscape and research practice due to the Internet has the potential to enhance the quality of both cognitive and social aspects of science and scientists. New types of research outputs, modes of scientific communication and new circulation mechanisms, as well as enhanced opportunities for scientific re-use and measuring research impact, in combination with new approaches to research assessment and evaluation are all having profound effects on the social system of science. To be sure that these innovations will not break the social sustainability of the science community, it will be valuable to develop a model of science as a tool for computer simulation of social consequences from possible innovations within virtual research environment. Focusing on possible social problems related to these new virtual research environments this short paper provides a brief analysis of the current situation in science (challenges, problems, main actors), general views on model of science (landscape, main agents, important properties, etc.) and on areas where simulation can contribute to better understanding of possible futures for the scientific community.Virtual Research Environment, Science System Social Sustainability, Agent Based Modeling

Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation

Subject of the present study is the agent-based computer simulation of Agent Island. Agent Island is a macroeconomic model, which belongs to the field of monetary theory. Agent-based modeling is an innovative tool that made much progress in other scientific fields like medicine or logistics. In economics this tool is quite new, and in monetary theory to this date virtual no agent-based simulation model has been developed. It is therefore the topic of this study to close this gap to some extend. Hence, the model integrates in a straightforward way next to the common private sectors (i.e. households, consumer goods firms and capital goods firms)and as an innovation a banking system, a central bank and a monetary circuit. Thereby, the central bank controls the business cycle via an interest rate policy; the according mechanism builds on the seminal idea of Knut Wicksell (natural rate of interest vs. money rate of interest). In addition, the model contains also many Keynesian features and a flow-of-funds accounting system in the tradition of Wolfgang Stützel. Importantly, one objective of the study is the validation of Agent Island, which means that the individual agents (i.e. their rules, variables and parameters) are adjusted in such a way that on the aggregate level certain phenomena emerge. The crucial aspect of the modeling and the validation is therefore the relation between the micro and macro level: Every phenomenon on the aggregate level (e.g. some stylized facts of the business cycle, the monetary transmission mechanism, the Phillips curve relationship, the Keynesian paradox of thrift or the course of the business cycle) emerges out of individual actions and interactions of the many thousand agents on Agent Island. In contrast to models comprising a representative agent, we do not apply a modeling on the aggregate level; and in contrast to orthodox GE models, true interaction between heterogeneous agents takes place (e.g. by face-to-face-trading).Multi-agent system , agent-based macroeconomic computer simulation , stock-flow consistent, monetary theory , Keynesian model, Wicksellian model, monetary policy

The Multiscale Systems Immunology project: software for cell-based immunological simulation

<p>Abstract</p> <p>Background</p> <p>Computer simulations are of increasing importance in modeling biological phenomena. Their purpose is to predict behavior and guide future experiments. The aim of this project is to model the early immune response to vaccination by an agent based immune response simulation that incorporates realistic biophysics and intracellular dynamics, and which is sufficiently flexible to accurately model the multi-scale nature and complexity of the immune system, while maintaining the high performance critical to scientific computing.</p> <p>Results</p> <p>The Multiscale Systems Immunology (MSI) simulation framework is an object-oriented, modular simulation framework written in C++ and Python. The software implements a modular design that allows for flexible configuration of components and initialization of parameters, thus allowing simulations to be run that model processes occurring over different temporal and spatial scales.</p> <p>Conclusion</p> <p>MSI addresses the need for a flexible and high-performing agent based model of the immune system.</p