Political Science Observations

Mapping AI's Global Spread: Methodology Framework

A multi-dimensional approach to tracking artificial intelligence adoption and impact across Earth's surface

Data Collection

Multi-source intelligence gathering

Spatial Analysis

Geographic distribution mapping

Network Modeling

Diffusion pathway analysis

Impact Assessment

Socio-economic correlation

Spatial-Temporal Analysis

Using geographic information systems (GIS) to map AI adoption density across regions and track changes over time.

AI_Density(x,y,t) = Σ wᵢ · Iᵢ(x,y,t)

Key Metrics:

Spatial Autocorrelation (Moran's I): Measures clustering patterns

Hot Spot Analysis (Getis-Ord Gi*): Identifies significant clusters

Kernel Density Estimation: Smooths point data into continuous surfaces

Diffusion Modeling

Applying epidemiological and innovation diffusion models to predict AI adoption patterns.

dA/dt = β·A·(N-A) - γ·A + ε·∇²A

Bass Diffusion Model:

f(t) = [p + q·(A(t)/m)]·[m - A(t)]

Where p=innovation coefficient, q=imitation coefficient, m=market potential

Network Propagation

Modeling AI spread through economic, academic, and social networks using graph theory.

P(i→j) = 1 / (1 + exp(-Σ wₖ·xₖ))

Network Metrics:

Betweenness Centrality: Identifies key diffusion bridges

Eigenvector Centrality: Measures influence within network

Community Detection: Finds natural adoption clusters

Multi-Agent Simulation

Using agent-based modeling to simulate AI adoption decisions based on local interactions and global trends.

Uᵢ = β₁·Xᵢ + β₂·Σⱼ Aᵢⱼ·Yⱼ + εᵢ

Agent Decision Factors:

Economic utility, social influence, institutional pressure, technological readiness

Data Sources & Integration

Economic Indicators

AI investment patterns, startup density, patent filings, venture capital flow, R&D expenditure

Technical Infrastructure

Compute capacity, data center locations, broadband penetration, cloud service adoption

Human Capital

AI research publications, talent concentration, educational programs, skill surveys

Policy & Governance

AI regulations, national strategies, ethical guidelines, international cooperation

76%

Global AI Adoption Growth (2020-2024)

Countries with National AI Strategies

$320B

Global AI Investment (2024)

3.2M

AI Professionals Worldwide

Advanced Analytical Framework

Spectral Graph Wavelets

Using multi-scale analysis to detect AI adoption patterns at different geographic resolutions.

W_f(s,n) = Σ_{m=0}^{N-1} g(sλ_m) f̂(m) φ_m(n)

Hawkes Processes

Modeling self-exciting adoption patterns where early adoptions increase likelihood of nearby adoptions.

λ(t) = μ + Σ_{t_i < t} α·exp(-β(t-t_i))