A system refers to “a regularly interacting or interdependent group of items forming a unified whole” (Merriam-Webster, n.d.). A complex system refers to a system in which the behavior of the system as a whole is unknown/unpredictable from the individual parts or components of the system.
For example, electrical appliances, such as cameras and computers, can be disassembled into parts, then reassembled back together and function again as machines. Composed of numerous parts and difficult to build, these appliances may be considered complicated, but are not complex systems. On the other hand, you could hypothetically take a human being apart into pieces, but assembling the pieces back together would not give you a human being. This is because the relationship or the interaction between the parts of a human being plays a critical role; the understandings of the individual parts do not add up to an understanding of the human being as a whole. Similarly, a flock of birds as a system displays unique movement patterns that cannot be predicted from the individual movements of each bird.
From car traffic to a line of ants to brains to cities, complex systems are found everywhere. Complex Systems Science is an interdisciplinary field of study that explores how emergent properties form within various sorts of complex systems, why synchronized phenomena occur, and many other questions.
In Kito Lab, we work primarily with complex systems that “create value” in the economy or the society. In particular, we often focus on the network created by the relationships between the components of a system.
The relationships between multiple components, i.e. a network, is studied in a field called “network science.” A brain, for example, can be considered a network of neurons strangled in a complex manner. Many other complex systems, such as interpersonal relationships, food chains, and the internet, are complex networks.
Many complex networks “create value” in the economy or the society - like business relationships and supply chains. People working in a team to achieve results more efficiently or to inspire new ideas can be a subject for study in network science. Corporate strategies may not seem like a network at first glance, but very well be considered one because they cannot be evaluated individually but only in relation to other existing strategies with which they inevitably interact.
Data analysis, modeling, simulation, and theory construction are all valued approaches in our lab. In addition to utilizing state-of-the-art methods and theories in network science, we encourage cooperation with experts in related fields including but not limited to economics, management, sociology, geology, and cognitive physiology.
Corporate strategy (diversification strategy, competitive strategy, cooperative strategy, “coopetitive” strategy, mergers and acquisition strategy), supply chain, regional cluster, organizational structure, teamwork, leadership, collaboration, innovation, bounded rationality, resilience, robustness, efficiency, adaptability, prediction, etc.
Network science (complex network, social network), multi-agent simulation, system theory, emergence theory, complex system, data analytics (big data, small data, rare data), machine learning, text analytics, etc.