Agent Based Computational Model Aided Approach to Improvise the Inequality-Adjusted Human Development Index (IHDI) for Greater Parity in Real Scenario Assessments

TL;DR

This paper proposes an agent-based computational model to improve the accuracy of the Inequality-adjusted Human Development Index (IHDI) for better societal assessment, addressing limitations of traditional indices in dynamic social systems.

Contribution

It introduces a novel agent-based modeling approach to refine IHDI, capturing non-linear social dynamics often missed by conventional indices.

Findings

Identifies shortcomings of current IHDI in dynamic social contexts

Proposes an agent-based model to enhance IHDI assessment

Suggests improved policy evaluation through refined index

Abstract

To design, evaluate and tune policies for all-inclusive human development, the primary requisite is to assess the true state of affairs of the society. Statistical indices like GDP, Gini Coefficients have been developed to accomplish the evaluation of the socio-economic systems. They have remained prevalent in the conventional economic theories but little do they have in the offing regarding true well-being and development of humans. Human Development Index (HDI) and thereafter Inequality-adjusted Human Development Index (IHDI) has been the path changing composite-index having the focus on human development. However, even though its fundamental philosophy has an all-inclusive human development focus, the composite-indices appear to be unable to grasp the actual assessment in several scenarios. This happens due to the dynamic non-linearity of social-systems where superposition principle cannot be applied between all of its inputs and outputs of the system as the system's own attributes get altered upon each input. We would discuss the apparent shortcomings and probable refinement of the existing index using an agent based computational system model approach.