Computing the uncertainty associated with the control of ecological and biological systems

Abstract

Recently, I showed that ecological and biological networks can be controlled by coupling their dynamics to evolutionary modelling. This provides numerous solutions to the goal of guiding a system's behaviour towards the desired result. In this paper, I face another important question: how reliable is the achieved solution? In other words, which is the degree of uncertainty about getting the desired result if values of edges and nodes were a bit different from optimized ones? This is a pivotal question, because it's not assured that while managing a certain system we are able to impose to nodes and edges exactly the optimized values we would need in order to achieve the desired results. In order to face this topic, I have formulated here a 3-parts framework (network dynamics - genetic optimization - stochastic simulations) and, using an illustrative example, I have been able to detect the most reliable solution to the goal of network control. The proposed framework could be used to: a) counteract damages to ecological and biological networks, b) safeguard rare and endangered species, c) manage systems at the least possible cost, and d) plan optimized bio-manipulations.

Keywords

• genetic algorithms
• network control
• stochastic simulations
• uncertainty