The HELIOS Model (Hybrid Evaluation of Lifecycle and Impact of Outstanding Science) is a comprehensive framework designed to evaluate the maturity of emerging technologies by integrating multiple key indicators. The model combines data from R&D investment, scientific publications, patents, adoption levels, and regulatory frameworks to position each technology within its lifecycle phase.
HELIOS [1] provides a composite index that simultaneously reflects both the state of scientific development (research impact) and technological advancement (diffusion and investment) of a technology. This hybrid approach draws inspiration from established frameworks such as NASA's Technology readiness level (TRL) and Rogers' adoption categories, which link technological evolution with user acceptance. [2]
In the HELIOS framework, each variable indicates a complementary aspect of maturity: sustained growth in investment and publications typically precedes phases of technological expansion, while an elaborate regulatory environment points to a more consolidated technology.
The HELIOS index is calculated as a weighted average of five normalized variables (I, P, Pt, A, R) representing investment, publications, patents, adoption, and regulation, each scaled to the range [0,1]:
Where the weights sum to 1. A typical weight distribution might be:
The resulting HELIOS value ranges from 0 (very early-stage technology) to 1 (high maturity).
Each key variable is measured using standardized criteria and scales:
Annual investment amount (public + private) in USD, normalized by dividing by the highest recorded level or sectoral target. Typical scoring ranges:
Number of academic articles in the related discipline per year, normalized against historical maximum. Example ranges:
Number of patent families published annually in the field. Similar normalization to publications: [3]
Degree of technology implementation or usage, estimated as market penetration following the diffusion of innovations model:
Maturity level of legal frameworks and standards, qualitatively assessed:
The current state of the five variables can be represented graphically using a radar chart, where each dimension (investment, publications, patents, adoption, regulation) is measured from 0 to 1. The resulting surface reflects the technology maturity profile. Additionally, the typical Sigmoid function (S-Curve) illustrates the overall maturity trajectory: its maximum slope indicates the inflection point (rapid growth phase) and the final saturation level marks complete maturity.
| Variable | Metric | Normalization | Typical Weight |
|---|---|---|---|
| Investment (I) | Annual R&D spend (USD) | I/Imax | 0.25 |
| Publications (P) | Articles per year | P/Pmax | 0.25 |
| Patents (Pt) | Patent families per year | Pt/Ptmax | 0.20 |
| Adoption (A) | % market penetration | tiered scale | 0.25 |
| Regulation (R) | Quality of legal standards | tiered scale | 0.05 |
To illustrate HELIOS, consider quantum computing with recent data:
Using suggested weights:
A value of ~0.65 indicates an early growth stage, consistent with rapid expansion in patents and investment but limited adoption. This suggests quantum computing is still far from saturation, with the S-curve's increasing slope indicating that the mass adoption tipping point may be approaching.
HELIOS values near 0.5–0.7 correspond to technologies in the development/early adoption phase, while indices close to 1 indicate maturity or stagnation. The model enables:
The original HELIOS model was a linear evaluation tool, based on normalization and fixed weights, to assess the lifecycle and impact of scientific and technological developments.
The enhanced version extends its scope with non-linear normalization (e.g., sigmoid functions), S-curve growth models for forecasting (investment, publications, patents, adoption, regulation), and dynamic weights across lifecycle phases. It also applies non-linear aggregation (e.g., Choquet integral, OWA operator) to capture synergies and redundancies, and integrates Uncertainty quantification methods such as Monte Carlo simulations.
These upgrades turn HELIOS into a probabilistic forecasting framework, able to detect inflection points and support strategic planning, R&D investment, and policy-making in emerging technologies. [7]
