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s4h-evolution-fitness-landscape

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Maps the solution space as peaks and valleys to understand local optima, adaptive valleys, and path dependence. Triggers: 'are we stuck', 'local maximum', 'fitness landscape', 'why can't we get to the next level', 'path dependence', 'trapped by success', 'incremental improvement is not working', 'we need a discontinuous leap', 'adjacent possible'.

skills/s4h-evolution-fitness-landscape/SKILL.md

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# Evolution: Fitness Landscape

Sewall Wright introduced the fitness landscape metaphor in 1932: imagine every possible combination of traits mapped onto a surface, with elevation representing fitness. Populations move across this surface by natural selection — uphill, always, toward higher fitness. The metaphor contains a profound trap: gradient-following reliably finds local peaks but may never reach the global optimum if that optimum is separated from the current position by a valley — a region of lower fitness that must be crossed to reach higher ground.

Stuart Kauffman formalised the implications. In a rugged landscape (many local peaks of varying heights), adaptive evolution produces a rich variety of stable-but-suboptimal solutions. Path dependence is total: where you end up depends entirely on where you started, because the trajectory of selection is irreversible and local. In an ultra-smooth landscape (one peak), selection reliably finds the global optimum. In a chaotic landscape (fitness changes with every step), selection fails entirely — there is no stable higher ground to climb toward.

This tool maps the fitness landscape of a problem, strategy space, or technology domain. It identifies where the current entity sits, which kind of landscape this is, what the local peaks look like, what valleys must be crossed to reach higher ground, and whether valley-crossing is currently viable. The practical question is almost always: are we trapped on a local peak, and if so, what does it cost to get off it?

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## Your Process

**Step 1: Define the Landscape**
Specify the axes and the fitness measure. Every landscape has:
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