Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics

TL;DR

This paper demonstrates how Neural Ordinary Differential Equations can effectively model and predict poverty dynamics using socio-economic time-series data, aiding policymakers in decision-making.

Contribution

It applies Neural ODEs to socio-economic data, showing their effectiveness in capturing complex poverty dynamics and providing reliable projections.

Findings

Neural ODE model accurately reproduces observed poverty data.

The approach captures continuous socio-economic transitions effectively.

Neural ODEs can support policy decisions for poverty alleviation.

Abstract

Poverty is a complex dynamic challenge that cannot be adequately captured using predefined differential equations. Nowadays, artificial machine learning (ML) methods have demonstrated significant potential in modelling real-world dynamical systems. Among these, Neural Ordinary Differential Equations (Neural ODEs) have emerged as a powerful, data-driven approach for learning continuous-time dynamics directly from observations. This chapter applies the Neural ODE framework to analyze poverty dynamics in the Indian state of Odisha. Specifically, we utilize time-series data from 2007 to 2020 on the key indicators of economic development and poverty reduction. Within the Neural ODE architecture, the temporal gradient of the system is represented by a multi-layer perceptron (MLP). The obtained neural dynamical system is integrated using a numerical ODE solver to obtain the trajectory of over time. In backpropagation, the adjoint sensitivity method is utilized for gradient computation during training to facilitate effective backpropagation through the ODE solver. The trained Neural ODE model reproduces the observed data with high accuracy. This demonstrates the capability of Neural ODE to capture the dynamics of the poverty indicator of concrete-structured households. The obtained results show that ML methods, such as Neural ODEs, can serve as effective tools for modeling socioeconomic transitions. It can provide policymakers with reliable projections, supporting more informed and effective decision-making for poverty alleviation.