In the field of physics, equations that govern physical systems often assume that variables like temperature and chemical potential can be measured with precise accuracy. However, this assumption fails to acknowledge the inherent uncertainties that exist in real-world scenarios. Factors such as natural fluctuations, instrument errors, environmental interactions, and the dynamic nature of systems over time can introduce uncertainties that impact the accuracy of these models and the subsequent predictions about the behavior of physical systems.
Uncertainty: A Crucial Element in Physics
Physicists argue that the uncertainty inherent in thermodynamic parameters themselves, which is incorporated into equations governing the system’s energetic behavior, can have a significant influence on experimental outcomes. They are now considering ways to modify the equations of stochastic thermodynamics to incorporate this type of uncertainty. Uncertainty in parameters like temperature, pressure, or volume not only affects the information about the system’s initial state but also its evolution over time.
This uncertainty has implications for a variety of natural and engineered systems, impacting phenomena such as cell reactions and the optimal configuration of optical tweezers. The precision of the measured components that are incorporated into the equations directly affects the accuracy of physical models. Scientists Jan Korbel and David Wolpert have identified an overlooked inaccuracy that has wide-ranging implications, spanning from cell biology to artificial intelligence.
Impact of Uncertainty on Energy and Environmental Systems
The article also explores the influence of extreme random events on the inversion analysis of energy and environmental systems and the unreliability of planning schemes. It proposes a Copula-based interval cost–benefit stochastic programming (CICS) approach to address extreme random uncertainties. The study, using Yulin city as an example, designs nine constraint-violation scenarios and six coal-reduction scenarios. The findings reveal that as the industrial energy supply constraint-violation level increases, the system cost and pollutant emissions decrease.
Addressing Uncertainty: A Multidisciplinary Approach
In addition, the Copula-based stochastic programming method captures more uncertain information and achieves a higher marginal net present value rate compared to traditional chance-constrained programming. This paper provides a comprehensive review of the transfer and quantification of the concept of entropy in multidisciplinary fields, with a focus on its future applications and research directions in organizational management psychology. It examines the conceptual evolution of entropy in disciplines such as physics, information theory, and psychology, and scrutinizes the quantification methods of entropy in a multidisciplinary context.
The research also reviews how individuals cope with uncertainty in entropy increase. The paper redefines psychological entropy from the perspective of organizational management psychology and proposes an “entropy-based proactive control model” at the individual level, encompassing dimensions such as learning orientation, goal orientation, change orientation, and risk-taking. Recognizing and accounting for these uncertainties is crucial for scientists and engineers when applying theoretical models to practical situations. This enables them to develop more robust predictions and understand the limitations of their models.
