The scales of a system’s behavior determine what it can do. By comparing these scales to the tasks for which the system is designed, we can see whether or not it can achieve its goals and why. Multiscale Analysis proves useful in the study of large organizations, such as healthcare, the military, and corporations.
Complex Systems Modeling
NECSI uses conceptual and mathematical models to characterize the multiscale complexity of mathematical, physical and social systems, and solve a mystery of complex systems: strong emergence.
Exercising in the space of possibilities, Y. Bar-Yam (August 23, 2018).
Logic and generalization, Y. Bar-Yam (July 9, 2018).
Fixing science using a new science of science, Y. Bar-Yam (March 28, 2018).
Risk and opportunity in the space of possibilities, Y. Bar-Yam (March 28, 2018).
Multiscale information theory and the marginal utility of information, B. Allen, B.C. Stacey, Y. Bar-Yam (June 13, 2017).
From big data to important information, Y. Bar-Yam (April 25, 2016).
An information-theoretic formalism for multiscale structure in complex systems, B. Allen, B.C. Stacey, Y. Bar-Yam (September 16, 2014).
The limits of phenomenology: From behaviorism to drug testing and engineering. Y. Bar-Yam (2013).
Computationally tractable pairwise complexity profile, Y. Bar-Yam, D. Harmon, Y. Bar-Yam (May, 2013).
Information flow through a chaotic channel: prediction and postdiction at finite resolution, R. Metzler, Y. Bar-Yam, M. Kardar (2004).
A Mathematical Theory of Strong Emergence using Multiscale Variety, Y. Bar-Yam (2004).
Multiscale Variety in Complex Systems,Y. Bar-Yam (2004).
A self-stabilizing, robust region finder applied to color and optical flow pictures, M. Ben-Ezra, M. Werman, Y. Bar-Yam (May, 2001).
Sensitivity of ballistic deposition to pseudorandom number generators, R.M. D'Souza, Y. Bar-Yam, M. Kardar (1998).
NECSI identifies the inherent limitations of traditional engineering for highly complex challenges, and shows the potential of an evolutionary approach.
About Engineering Complex Systems: Multiscale Analysis and Evolutionary Engineering, Y. Bar-Yam, in Engineering Self Organising Systems: Methodologies and Applications, S. Brueckner, G. Di Marzo Serugendo, A. Karageorgos, R. Nagpal Eds. (2005).
Generalizing our understanding of information to include scale provides an important tool for characterizing any system.
Comparison of the roughness scaling of the surface topography of Earth and Venus, G. E. Crooks, Y. Bar-Yam, S. V. Buldyrev, H. E. Stanley (September 7, 2018).
Renormalization of sparse disorder in the Ising model, Y. Bar-Yam, S.P. Patil (May 31, 2018).
Multiscale complexity of correlated Gaussians, R. Metzler, Y. Bar-Yam (2005).
Multiscale complexity/entropy, Y. Bar-Yam (2004).
Multiscale analysis of information correlations in an infinite-range, ferromagnetic Ising system, S. Gheorghiu-Svirschevski, Y. Bar-Yam, Phys Rev E 70, 066115 (2004).
Sum rule for multiscale representations of Kinematic systems,Y. Bar-Yam (2002).
Models of human social interaction developed by NECSI provide insight on the future of human civilization as well as achieving individual well-being.
A mathematical theory of interpersonal interactions and group behavior, Y. Bar-Yam, D. Kantor (December 30, 2018).
The inherent instability of disordered systems, T. Bar-Yam, O. Lynch, Y. Bar-Yam (December 2, 2018).
Environmental Complexity: Information for human-environment well-being. Y. Bar-Yam, A. Davidson (2006).
Complexity Rising: From human beings to human civilization, a complexity profile, Y. Bar-Yam (2002).
A. Davidson, M.H. Teicher, Y. Bar-Yam, The role of environmental complexity in the well being of the elderly, Complexity Chaos Nursing 3, 5 (1997).