General Problem-Solving Ability in Natural Systems as a Model for Computation

Abstract

Natural systems have demonstrated the ability to solve a wide range of adaptive problems as well as the ability to self-assemble in a self-sustaining way that enables them to exponentially increase impact on outcomes related to those problems. In the case of photosynthesis nature solved the problem of harnessing the energy in sunlight and then leveraged self-assembling and self-sustaining processes so that exponentially increasing impact on that problem is reliably achievable. Rather than having to budget a given amount of resources to create a mature tree, where those resources might not be reliably available, tree seedlings self-assemble in a self-sustaining way from very few resources to grow from having the capability of photosynthesis accompanying a single leaf, to the capability of photosynthesis accompanying what might be millions of leaves. If the patterns underlying this adaptive problem-solving could be abstracted so that they are generally applicable, they might be applied to social and other problems occurring at scales that currently are not reliably solvable. One is the Sustainable Development Goals (SDGs) funding gap. The funding believed to be required to address the SDGs is difficult to estimate, and may be anywhere between $2 trillion and $6 trillion USD per year. However, bridging the gap between the funding required to meet these goals and the funding available to do so is universally acknowledged to be a difficult and unsolved problem. This paper explores how abstracting the pattern for general problem-solving ability that nature has used to solve the problem of exponentially increasing impact on collective problems, and that nature has proven to be effective for billions of years, might be reused to solve “wicked problems” from implementing an Artificial General Intelligence (AGI) to funding sustainable development at the scale required to transform Africa and the world.