Hill climbing, as articulated within the framework of adaptive systems by Jordan Hall, fundamentally represents a local optimization strategy where an agent incrementally moves towards higher value states in a given fitness landscape. By continually making local decisions based on immediate available information, this process aims to discover peaks of utility or performance without necessitating foresight into the global topography of possibilities. While pragmatic in swiftly finding local maxima, hill climbing is intrinsically limited by its myopia, potentially leading to suboptimal plateaus that hinder reaching the absolute optimum—akin to a traveler scaling the nearest visible summit, unaware of taller mountains beyond the fog. In complex, rapidly evolving environments, refining this approach with mechanisms for broader exploration becomes crucial to circumventing stagnation and achieving more holistic adaptation.

See also: adjacent possible, artificial intelligence, feedback loop, game theory, low-hanging fruit