Innovameter: Agent-based modeling of innovation determinants in American and European countries

Area of Science:

• Economics
• Computational Social Science
• Innovation Studies

Background:

• Innovation dynamics are influenced by factors like technology access, education, and life expectancy.
• Existing models may not fully capture the complex interplay of these variables on national innovation performance.
• The Global Innovation Index (GII) serves as a key metric for assessing national innovation capacity.

Purpose of the Study:

• To develop and validate an agent-based model, the Innovameter, for simulating innovation dynamics.
• To identify and quantify the contribution of key socio-economic and technological variables to the Global Innovation Index.
• To provide data-driven insights for policy recommendations aimed at enhancing national innovation performance.

Main Methods:

• Agent-based modeling (Innovameter) with two abstraction levels: country (global) and individual (local).
• Data analysis using correlation analysis and multiple hierarchical regressions.
• Experimental simulations using data from 30 countries, followed by nonparametric correlation analysis.

Main Results:

• The Innovameter model successfully parameterized key variables influencing the GII, including birth rates, life expectancy, ICT use, R&D, age, schooling, and income.
• Correlation indices confirmed relationships between model-predicted outcomes and actual GII scores.
• The study identified specific variables with significant contributions to innovation performance.

Conclusions:

• The Innovameter provides a robust framework for understanding and simulating national innovation dynamics.
• Policy interventions focusing on identified key variables can potentially improve a country's Global Innovation Index.
• The model offers a valuable tool for comparative analysis and strategic planning in innovation policy.