Summary: Complex sociotechnical systems (CSS) are on the rise. These are systems comprising of technology subsystems that are central to their performance and having societal, political and economic relevance and impact. Their complexity arises from structural, behavioral, evaluative and nested dimensions. CSS have emerged as part of a solution towards addressing critical contemporary issues such as national security, climate change, productivity and global economy.
System dynamics offers an approach of creating data-based dynamic models that policy and decision makers can authoritatively and reliably use in this ‘Big Data’ era.
Critical stages in the dynamic modeling of CSS are highlighted and emphasized in this paper. In brief terms, the advent of Big Data has made formal statistical parameter estimate feasible and robust. This has enabled use of models to arrive at the appropriate hypothesis. Policy-oriented dynamic modeling considers long-term development trajectories of key variables a major concern. Hence output evaluations are based on pattern characteristics of the model behavior. Use of pattern-recognition algorithms simplifies such exercises. Use of pattern-recognition algorithms enables automation of similarity evaluation and classification activities. Other key benefits of pattern-recognition under discussion in this paper include enabling determination of the parameter range within which the model behavior is robust and acceptable; extensive and efficient exploration of behavior-space of models; and support of identification of robust policies that lead to desired behavior modes.
Author(s): Wafula Muliaro, Jomo Kenyatta University of Agriculture and Technology, Kenya
Prof Joseph Muliaro Wafula is the Chair of the technical advisory board of the Africa Open Science Platform (AOSP), Chair of the Innovative Open Data and Visualization (iODaV) Subtaskforce of the AFRICA-ai-JAPAN Project. And chair of the Kenya National Industrial Training Authority-Transport Storage and Communication sector. He is a member of the editorial board of the Data Science Journal. Prof Muliaro is also a member of the committee of the United Nations SDGs on Agriculture and Climate Change Pillars of Kenya.
He holds a BSc. Science (Hons) (Kenyatta University), MSc. Physics (University of Nairobi), M.Phil. Microelectronic Engineering and Semiconductor Physics (University of Cambridge –UK), Summer Doctoral Program (Berkman Centre for Internet & Society/Oxford Internet Institute’s -Harvard University Law School), and PhD Information Technology (JKUAT).
He is a recipient of two IBM awards namely: the 2016 IBM Shared University Research Award on Open Data Cloud Project for JKUAT for building an open data platform for researchers in Africa, and the 2014 IBM MEA Award, for capacity building in Mobile Application development. He won the Kenya Open Data Champion Award of 2016.
He is a fellow of the Computer Society of Kenya and the Cambridge Commonwealth Society and has published book chapters, a book and research papers in peer reviewed international journals. He has a university teaching experience of 24 years. He is currently an Associate Professor in the Department of Computing at JKUAT and the founder Director of the ICT Centre of Excellence and Open Data (iCEOD).
Wafula Muliaro, Jomo Kenyatta University of Agriculture and Technology, Kenya