Hill Climbing and the Problem of Local Optima
In computer science, the Hill Climbing algorithm is an iterative technique for solving problems from a random starting point. Imagine an explorer being helicoptered into a hilly landscape and told to climb to the top of the nearest hill. They might not find the highest peak in the range, but they’ll find the highest point around where they started. This is known as the problem of ‘local optima’. You get around by running the process over and over again from a lot of different random starting points.
Moving away from mathematical search algorithms and into the realms of real world creative problem solving, we might find ourselves looking towards Edward de Bono’s lateral thinking. The basic principle is that our brains are designed to create patterns, and are very good at creating logical paths to ‘connect the dots’ when solving problems. The problem with this is that logical thinking is predictable, and is not capable of finding creative solutions. To do that, we must disrupt our disrupt our normal mode of thinking with some lateral movement and find new starting points. Enter lateral thinking.
The search for Blue Ocean Strategy
Kim and Mauborgne coined the term blue ocean strategy to define new market spaces in which nobody is yet competing. Using value innovation, the approach seeks to define new value propositions that are radically differentiated from existing market offerings. But how exactly do you find these new offerings?
If you start from your existing business model, and stick to the familiar territory of critical thinking, then it is unlikely that you will escape your own ‘local proxima’ problem. You will be stuck trying to find radical ideas from a known starting point, following known paths. You will stay very much in the red ocean.
To move beyond the confines of your existing business model, and those of your nearest competitors, you will need to think laterally.