An optimization principle for deriving nonequilibrium statistical models of Hamiltonian dynamics

A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. As in standard projection operator methods, a set of resolved variables is selected to capture the slow, macroscopic behavior of the system, and the family of quasi-equilibrium probability densities on phase space corresponding to these resolved variables is employed as a statistical model. The macroscopic dynamics of the mean resolved variables is determined by optimizing over paths of these probability densities. Specifically, a cost function is introduced that quantifies the lack-of-fit of such paths to the underlying microscopic dynamics; it is an ensemble-averaged, squared-norm of the residual that results from submitting a path of trial densities to the Liouville equation. The evolution of the macrostate is estimated by minimizing the time integral of the cost function. The value function for this optimization satisfies the associated Hamilton-Jacobi equation, and it determines the optimal relation between the statistical parameters and the irreversible fluxes of the resolved variables, thereby closing the reduced dynamics. The resulting equations for the macroscopic variables have the generic form of governing equations for nonequilibrium thermodynamics, and they furnish a rational extension of the classical equations of linear irreversible thermodynamics beyond the near-equilibrium regime. In particular, the value function is a thermodynamic potential that extends the classical dissipation function and supplies the nonlinear relation between thermodynamics forces and fluxes

Using Coloured Cognitive Mapping (CCM) for Design Science Research

Design Science Research (DSR) is a research paradigm for research that undertakes to solve general problems through the invention and evaluation of new or improved technologies. Once DSR is completed, practitioners may make use of the new technology to solve particular instances of the generalised problem (and thereby make improvements in a problematic situation). In order to effectively solve a generalised problem, it is important (among other things) for DSR researchers to (1) understand the problem, its causes, and the conditions that allow a problem to continue or hinder its solution, (2) develop a shared problem understanding among collaborating DSR researchers, (3) creatively think of alternative potential avenues and means to solve (or reduce or alleviate) the problem, and (4) develop and convey design theories about the utility of a developed design artefact to solve a problem. This paper describes how Coloured Cognitive Mapping (CCM) can be used for these purposes in the context of DSR and provides evidence of its utility for those purposes through description of an application of CCM to DSR and more formal evaluation through teaching CCM to DSR researchers and surveying them for their opinions about its utility and features