Are We Intentionally Limiting Urban Planning and Intelligence? A Causal Evaluative Review and Methodical Redirection for Intelligence Systems

The chronic growth of networked complexities in today's world, now require highly efficient evolvable systems. However, diverse open issues and inabilities are facing urban planning practice and social sciences due to the limitations of artificial intelligence planning tools. These incapacities have relatively limited our ability to perceive and handle possible present and future temperamental situations in socio-physical contexts and in real- time modes. Here, we theoretically present two simple philosophical and systematic causal models to help software engineers to understand this philosophical and complexity dilemma from an urban planning perspective. The first model evaluates the reliance on perceptual and bounding trajectories. It discusses discrete and finite-expert systems that perceive specific parts of self-organization's complexities, while bounding limited facets only of general intelligence to address certain issues in urban planning and social contexts. This implies the second causal model that is based on aligning to urban self-organizational happenings, by putting philosophical foundations for a responsive artificial superintelligence (ASI). This proposed ASI is based on connecting between complex adaptive systems in our contexts by open-endedly hosting and operating in finite expert systems to refiect different fields and functions, toward asymptotic in finite intellectual capacity.