Keiichiro Tokita

There are many things in this world that can be described by a simple equation, but when the number increases, the whole thing behaves in a very complex way. P. W. Anderson, one of the founders of complex systems science and a Nobel laureate in physics, left behind a famous phrase that beautifully expresses the essence of complex systems: “More is different”. Examples of the complex systems include ecosystems, metabolic systems in cells, immune systems, biological macromolecules, neural networks and deep learning, combinatorial optimization problems, information systems, economic systems, and social systems. We study how to understand and control those complex systems mathematically using methods such as nonlinear dynamics, statistical physics, and computational physics. At first glance, these problems may seem to be in completely different research fields, but they are mathematically closely related in that they are macroscopic phenomena that emerge when a large number of relatively simple things come together and influence each other through complex interactions. By looking at these phenomena from a unified perspective, it is not uncommon to gain a deeper understanding of each individual phenomenon. The evolutionary biologist E. O. Wilson, famous for his work on ants and as the founder of sociobiology, calls the integration of knowledge from various fields through complex systems science “consilience. We are trying to open up new research areas using a variety of methods, from analytical approaches in mathematics and physics to large-scale simulations using supercomputers and GPUs.