Natural and Artificial Systems: Compare, Model or Engineer?

Abstract

Some areas of biological research use artificial means to explore the natural world. But how the natural and artificial are related across wideranging research areas is not always clear. Relations differ further for bioengineering fields. We propose a taxonomy which would serve to elucidate distinct relations; there are three ways in which the natural is linked to the artificial, corresponding with distinct methods of investigation: i) a comparative approach (natural vs artificial) in which artificial systems are treated in the same way as natural systems, ii) a modeling approach (natural via artificial) in which we use artificial systems to learn about features of natural ones, and iii) an engineering approach (natural pro artificial) in which natural systems are used to draw inspiration for artefacts. Ambiguities about and between these approaches limit the development of fields and impact negatively on interdisciplinary communication.

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

“…This would require a comprehension, at least in principle, of differences in numerical and orthodontic formalisms within the inscrutable hidden “black box” of algorithms [ 56 ]. Although the nature of ML optimization is purely mathematical, craniofacial feature optimization during growth is, above all, a matter of adaptation [ 57 , 58 , 59 ]. The best possible clinical-digital model may include neither the past nor the present, but only a situation calculated at every moment.…”

“…The best possible clinical-digital model may include neither the past nor the present, but only a situation calculated at every moment. When prognostic processes are conducted across both technological and morphological boundaries, new orthodontic theories could be derived through the pure power of technology [ 58 , 59 , 60 , 61 ]. Machines must be understandable and acceptable, even though the understandability of the algorithmic answers is often inversely proportional to the transparency and the complexity of the predictive models [ 46 ] ( Figure 5 ).…”

On the evolution of self-organised role-allocation and role-switching behaviour in swarm robotics: a case study

“…On the other hand, self-organisation models do not consider the evolutionary trajectories that may lead to task-allocation. Our long-term aim is to create models of the evolution of selforganised role-allocation and role-switching behaviour that can be used as "intuition-pump" for indicating potential evolutionary drivers and emergent behavioural rules capable of accounting for the collective behaviour of natural swarms (see Vassie and Morlino, 2012, for an epistemological account of robotic models). For example, biological evidence shows that behavioural specialisation in various insect societies evolve to minimise the costs of taskswitching.…”

Learning to Manipulate and Categorize in Human and Artificial Agents

“…The study was conducted by carrying out parallel experiments on human and artificial agents (Vassie & Morlino, ). This direct comparison was made to verify the extent to which observed data can be accounted for by a simple model implemented in experiments with artificial agents and to clear up the role played by humans' previous knowledge.…”