Emergent phenomena appear when interacting components give rise to novel
behaviors not seen at the scale of their individual parts, resulting in new behaviors and complex systems that are very difficult to predict and understand through analysis of their individual components alone. More specifically, a population of microsystems unpredictably interacts to produce a new macrosystem. However, the unpredictable element of emergent systems can be removed by changing the observer perspective in a manner that adds information. This can be performed in space, as with an expansion of the observer’s 3D frame of reference as if ‘zooming out’, in time, such as through increasing the observation period to capture the emergence of repetitive system behaviors, and in a ‘five-dimensional’ region of
space, time and probability, through assigning observation probability ranges to the observer.
Therefore, through the use of simplified mathematical models, the unpredictability of
emergent phenomena is shown to be relative and based on observer perspective in coordinate space for both classical and chaotic systems. This emphasizes the importance of studying emergent phenomena, because with the addition of information, higher dimensions of perspective, and computational power, something unpredictable at one point in history may become predictable at a later time in history.
*A note on generative AI: ChatGPT/OpenAI and Grok/XAI were used to verify calculations and expand some equation sets into time or probability coordinates, but all writing and ideas presented are original theoretical pieces.”