scholarly journals Advancing child maltreatment prevention through the application of complex adaptive systems: development of an agent-based model

2010 ◽  
Vol 16 (Supplement 1) ◽  
pp. A185-A185 ◽  
Author(s):  
R. W. Puddy
Keyword(s):  
Child Maltreatment ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Systems Development ◽  
Agent Based Model ◽  
Agent Based ◽  
Child Maltreatment Prevention ◽  
Complex Adaptive

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

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).

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.

Sign in / Sign up

Export Citation Format

Share Document