Physical Information among Statistically Independent Processes in Coevolutionary Systems

There is a common misconcepton that statistically independent processes cannot share any information. In reality, the absence of statistical codependence does not preclude the existence of physical interactions. In order to bring out and characterize that hidden information in terms of microphysical interaction indicators, Rui A. P. Perdigão has formulated a new set of informaton measures and introduced them in [1]. The findings stress the relevance of taking nonlinear microphysical coevolution into account when formulating information measures, especially when a system is undergoing mixing among subsystems such as in thermodynamic coevolutionary settings.

[1] Perdigão, Rui A.P. (2018): Polyadic Entropy, Synergy and Redundancy among Statistically Independent Processes in Nonlinear Statistical Physics with Microphysical Codependence. Entropy 2018, 20(1), 26; doi:10.3390/e20010026

 

Beyond Coevolutionary Optimality in Non-Ergodic Statistical Physics: Unveiling The 5th Principle

Meteoceanics chairman Rui Perdigão has launched an international project on Theoretical Thermodynamics and Statistical Physics. The main goal entails the development of the new general principle governing the breach of coevolutionary optimality and unlocking the predictability of post-critical emergence in non-ergodic statistical physics, recently introduced by Rui Perdigão.

The project emerges from his general program on the Mathematical Physics and Predictability of Complex Coevolutionary Systems.

For a start, the record is made straight on coevolution. The current status of the art is dominated by dynamical system formulations that essentially revolve on kinematic geometry – where physics are notoriously absent. However, unlike popular belief, coevolution is not about a history of interaction between variables over time, i.e. it is not about kinematic geometry. Dynamic coupling, scale interactions and feedbacks are important in a dynamical system, but none of them entail coevolution.

Framing coevolution in the light of theoretical physics enables us to elicit not only its governing principles but also the associated thermodynamic optimality. This in turn is fundamental to lift the theory from an optimality-bound fate to a more realistic setting in which optimality itself is challenged. By eliciting underlying mechanisms governing such challenges, we unveil the fundamental laws governing the breach of optimality principles, generalising thermodynamic theory with a new overarching principle.

The links to the classical theories will be done by showing that all four principles of thermodynamics are in essence particular cases of our general principle.

As happens with all our purely scientific projects, this initiative is independent from any commercial, political or academic constraints. Our ultimate quest is the fundamental quest for knowledge.

Even so, the project provides a significant edge to design novel energy solutions, which we develop for trade through Meteoceanics Energy.

 

Mathematical Physics and Predictability of Complex Coevolutionary Systems

Meteoceanics chair Prof. Dr. Rui Perdigão has launched an international program on the mathematical physics of complexity. The program entails the theoretical establishment of the fundamental principles and general governing laws for complex systems, including the far from equilibrium coevolutionary dynamics where the ergodic assumptions of classical dynamical system theories do not work. The theory provides fundamental physical understanding and dynamic predictability in non-standard systems where there are no established invariants of motion, no attractors, no definite phase space, and no scale separability (i.e. where the classical scale interaction paradigms are not possible, and the predictability of both deterministic and stochastic formulations collapse).

A series of projects are set within this program, from fundamental mathematical physics to applications including complex system modelling, big data analytics, robust assessment of environmental conditions in fast-changing theatres of operation, dynamic risk assessment and decision support under trans-critical climate change.