Can a theory of complex time explain aging across physical and biological systems?
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Time in complex systems operates concurrently at different scales, runs at multiple rates, and integrates the function of numerous connected systems. We describe these processes as “complex time” as opposed to the simple, regular clock time of physical phenomena. In complex time age includes explicitly the coupling between information gain and information loss.
Within living systems, larger animals tend to live longer. Indeed, average life span can be predicted to scale as the 1/4 power of body mass. This implies that all aspects of aging (information loss), to include genetic, cellular, and cognitive, are slowed down in larger animals. And these aging processes can be modified through mechanisms of adaptation (information gain).
A theory of complex time, or adaptation and aging in complex systems, should include the many scales and connections among dynamical processes, and recognize that the effective age of an individual is dependent on the age of their environment, technology, and culture.
Twentieth-century humans have access to antibiotics, anesthetics, and laser surgery; biologically identical medieval humans used urine as an antiseptic and trepanning to treat migraines. Age is not only biological it is environmental, technological and cultural.
Even companies age. This is measured in terms of the statistical risk of “death:” a function of the number of years that they have been in existence. For a large group of publicly traded companies, we observe a half-life of survival of around 7-10 years, with the longest-lived companies in the tail of an exponential distribution.
This research theme aims to transform our understanding, treatment and control of complex time encompassing natural biological and disease phenomena, social systems, and technology by bridging deep theoretical ideas with areas of both empirical and practical depth.
The benefits of such an approach include conceiving of bold new experiments, sharing data and insights across fields, making powerful methods of analysis and tools of complexity science more broadly known and applied.
This five-year research theme is sponsored by the James S. McDonnell Foundation.