Inverse modelling

Analogies between organisational routines and genes, firms and phenotypes, economic activities and biological populations, and between the processes acting upon these entities, have been used to advance our qualitative understanding of mechanisms driving economic change. Yet, it remained unclear whether biological concepts can be used to quantitatively describe long-term economic change. Here, we use an inverse modelling framework together with economic time-series data to test whether eco-evolutionary processes can explain the collective dynamics of national economic activities. Comparing the support of different biologically inspired dynamic community models against a null model, we find evidence for positive interactions between economic activities and their spatial dispersal.

Ecological and economic systems are complex adaptive systems, composed of multiple entities which interact and undergo evolutionary processes. The complexity of these systems present a crucial challenge for underpinning their general organizational principles. This thesis develops innovative modelling techniques and advances our comprehension of the eco-evolutionary processes and feedbacks that shape these systems.

Ecosystems are involved in global biogeochemical cycles that regulate climate and provide essential services to human societies. Mechanistic models are required to describe ecosystem dynamics and anticipate their response to anthropogenic pressure, but their adoption has been limited in practice because of issues with parameter identification and because of model inaccuracies. We propose a machine learning (ML) framework relying on a mini-batch method combined with automatic differentiation and state-of-the-art optimizers. The proposed ML framework can efficiently learn from data and elucidate mechanistic pathways to improve our understanding and predictions of ecosystem dynamics.