Agent-Based Modeling

2017 ◽  
pp. 36-51
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complexity Science ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Linear Behavior ◽  
Complex Adaptive ◽  
Economic Markets ◽  
The Common ◽  
Policy Problems

Agent based modeling is one of many tools, from the complexity sciences, available to investigate complex policy problems. Complexity science investigates the non-linear behavior of complex adaptive systems. Complex adaptive systems can be found across a broad spectrum of the natural and human created world. Examples of complex adaptive systems include various ecosystems, economic markets, immune response, and most importantly for this research, human social organization and competition / cooperation. The common thread among these types of systems is that they do not behave in a mechanistic way which has led to problems in utilizing traditional methods for studying them. Complex adaptive systems do not follow the Newtonian paradigm of systems that behave like a clock works whereby understanding the workings of each of the parts provides an understanding of the whole. By understanding the workings of the parts and a few external rules, predictions can be made about the behavior of the system as a whole under varying circumstances. Such systems are labeled deterministic (Zimmerman, Lindberg, & Plsek, 1998).

scholarly journals A Novel Formal Agent-Based Simulation Modeling Framework of an AIDS Complex Adaptive System

2013 ◽  
Vol 5 (3) ◽  
pp. 33-53 ◽  
Author(s):  
Amnah Siddiqa ◽  
Muaz Niazi
Keyword(s):  
Complex Adaptive Systems ◽  
Formal Specification ◽  
Simulation Modeling ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Modeling Framework ◽  
Agent Based ◽  
Complex Adaptive

HIV/AIDS spread depends upon complex patterns of interaction among various subsets emerging at population level. This added complexity makes it difficult to study and model AIDS and its dynamics. AIDS is therefore a natural candidate to be modeled using agent-based modeling, a paradigm well-known for modeling Complex Adaptive Systems (CAS). While agent-based models are well-known to effectively model CAS, often times models can tend to be ambiguous and using only using text-based specifications (such as ODD) making models difficult to be replicated. Previous work has shown how formal specification may be used in conjunction with agent-based modeling to develop models of various CAS. However, to the best of the authors’ knowledge, no such model has been developed in conjunction with AIDS. In this paper, we present a Formal Agent-Based Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS. FABS-AIDS employs the use of a formal specification model in conjunction with an agent-based model to reduce ambiguity as well as improve clarity in the model definition. The proposed model demonstrates the effectiveness of using formal specification in conjunction with agent-based simulation for developing models of CAS in general and, social network-based agent-based models, in particular.

Applying complex adaptive systems to agent-based models for social programme evaluation

2018 ◽  
pp. 312-326 ◽  
Author(s):  
Professor Michael E. Wolf-Branigin ◽  
Dr William G. Kennedy ◽  
Dr Emily S. Ihara ◽  
Dr Catherine J. Tompkins
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Programme Evaluation ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Adaptive ◽  
Social Programme

scholarly journals Patterns of Learning in Dynamic Technological System Lifecycles—What Automotive Managers Can Learn from the Aerospace Industry?

2018 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Daniel Guffarth ◽  
Mathias Knappe
Keyword(s):  
Organizational Learning ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Aerospace Industry ◽  
Patent Citation ◽  
Industry Life Cycle ◽  
Patent Citation Analysis ◽  
Product Technique ◽  
Complex Adaptive ◽  
The Common

Not only with respect to the common overlaps within the market of urban air mobility, but also in terms of their requirement profile with regard to the systemic core, all mobility industries are converging. This article focuses on the required patterns of learning in order to cope with these changes, and what automotive managers can learn from the aerospace industry in this context. As organizational learning is the central parameter of economic evolution, and technology develops over trajectory shifts, companies are, at the very least, cyclically forced to learn ambidextrously, or are squeezed out of the market. They have to act and react as complex adaptive systems in their changing environment. Especially in these dynamics, ambidextrous learning is identified to be a conditio sine qua non for organizational success. Especially the combination of efficiency-oriented internal exploitation with an explorative and external-oriented open innovation network turns out to be a superior strategy. By combining patent data, patent citation analysis and data on the European Framework Programs, we show that there are temporal differences, i.e., position of the product in the product, technique, technology, and industry life cycle. Furthermore, we draw a conclusion dependent on the systemic product character, which enforces different learning requirements concerning supply chain position and, as an overarching conclusion, we identify product structure to be decisive for how organizational learning should be styled.

In Silico Biology

2010 ◽  
pp. 55-74 ◽  
Author(s):  
Dimitri Perrin ◽  
Heather J. Ruskin ◽  
Martin Crane
Keyword(s):  
Immune Response ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Chain Structure ◽  
Communication Strategy ◽  
Agent Based ◽  
Large Numbers ◽  
Complex Adaptive ◽  
In Silico Biology ◽  
Over Time

Biological systems are typically complex and adaptive, involving large numbers of entities, or organisms, and many-layered interactions between these. System behaviour evolves over time, and typically benefits from previous experience by retaining memory of previous events. Given the dynamic nature of these phenomena, it is non-trivial to provide a comprehensive description of complex adaptive systems and, in particular, to define the importance and contribution of low-level unsupervised interactions to the overall evolution process. In this chapter, the authors focus on the application of the agent-based paradigm in the context of the immune response to HIV. Explicit implementation of lymph nodes and the associated lymph network, including lymphatic chain structure, is a key objective, and requires parallelisation of the model. Steps taken towards an optimal communication strategy are detailed.

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