HELIOS -- Hybrid Evaluation of Lifecycle and Impact of Outstanding Science v-2.0

TL;DR

The paper enhances the HELIOS model by transforming it into a dynamic, predictive framework that uses non-linear functions, growth models, and uncertainty quantification to better assess and forecast technological impact and maturity.

Contribution

It introduces non-linear normalization, S-curve forecasting, dynamic weighting, and uncertainty quantification to improve HELIOS's predictive capabilities.

Findings

Enables probabilistic forecasts of technology impact.

Identifies critical inflection points in technology development.

Provides a nuanced understanding of technology trajectories.

Abstract

This paper presents a substantial enhancement of the HELIOS (Hybrid Evaluation of Lifecycle and Impact of Outstanding Science) model, transforming it from a static assessment tool into a dynamic and predictive framework for technological maturity. It addresses the limitations of the original model, which relied on linear normalization and fixed weights. Key modifications include the adoption of non-linear normalization functions (sigmoids), the integration of S-curve growth models for forecasting key indicators (Investment, Publications, Patents, Adoption, Regulation), the implementation of dynamic weighting schemes based on lifecycle phases, the application of non-linear aggregation functions to capture synergies and redundancies, and the incorporation of uncertainty quantification techniques such as Monte Carlo simulations. These advanced mathematical formulations enable HELIOS to provide probabilistic forecasts, identify critical inflection points, and offer a more nuanced understanding of a technology's trajectory. This makes it an invaluable tool for strategic planning, R&D investment evaluation, and policy-making in the domain of emerging technologies.