An Information-Theoretic Analysis of Flexible Efficient Cognition for Persistent Sustainable Production

Abstract

Amidst certainty, efficiency can improve sustainability by reducing resource consumption. However, flexibility is needed to be able to survive when uncertainty increases. Apropos, sustainable production cannot persist in the long-term without having both flexibility and efficiency. Referring to cognitive science to inform the development of production systems is well established. However, recent research in cognitive science encompassing flexibility and efficiency in brain functioning have not been considered previously. In particular, research by others that encompasses information (I), information entropy (H), relative entropy (D), transfer entropy (TE), and brain entropy. By contrast, in this paper, flexibility and efficiency for persistent sustainable production is analyzed in relation to these information theory applications in cognitive science and is quantified in terms of information. Thus, this paper is consistent with the established practice of referring to cognitive science to inform the development of production systems. However, it is novel in addressing the need to combine flexibility and efficiency for persistent sustainability in terms of cognitive functioning as modelled with information theory.

Design of Fuzzy Aided Cognition System for International Economic and Trade Information under Supply Chain Management

“…Only relevant feature information, such as distance, velocity, acceleration, target movement direction, and radial velocity, is considered for the purpose of simplicity. The introduction of radial velocity data has been thoroughly examined [ 21 – 23 ]. But the extraction of radial velocity requires a nonlinear motion model and nonlinear filtering method, which increases the complexity of the system.…”

Bio-Inspired Intelligent Systems: Negotiations between Minimum Manifest Task Entropy and Maximum Latent System Entropy in Changing Environments

“…As a consequence, ETO production is characterized by high latent entropy arising from being open to whatever customers in the business environment may have in mind, and the by risk of high manifest task entropy due to the uncertainty that can arise from being open to whatever customers may have in mind. Accordingly, intelligent ETO production and intelligent MTO production needs to be able to adapt to whatever new customer orders may arise from the business environment, but do so while minimizing manifest task entropy [55,56].…”

Synchronous Generative Development amidst Situated Entropy

“…Thus, a positive balance of situated entropy between people and environment is a balance that reduces physical disorder and information uncertainty but does not focus on entirely eliminating either. In other words, there is a balance between entropy pessimism [ 26 ] and entropy optimism [ 99 ], within which reducing entropy can increase efficiency but not to such an extent that there is no flexibility to develop amidst disturbances [ 7 , 8 ].…”

“…However, such imbalance between very low situated entropy experienced by people and very high situated entropy experienced by environments is also not positive. This is because systems characterized by very low entropy can be vulnerable to disturbances [ 7 , 8 ], and disturbances are inevitable [ 9 , 10 ], including natural disturbances that can destroy well-constructed infrastructure in a few hours [ 11 ].…”