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Markov Regime Dynamics

Regime Transition Matrix

Empirical Markov chain transition probabilities between risk regimes, calibrated from Hamilton (1989) and Reinhart & Rogoff (2009) and adjusted by current Noosphere Scores. Shows the probability of each entity transitioning between Stable → Accumulation → Critical → Collapse states.

Global Average Transition Matrix

Current \ NextStableAccumulationCriticalCollapse
Stable
74.4%
21.1%
3.5%
0.9%
Accumulation
19.4%
49.7%
24.1%
6.9%
Critical
4.2%
20.3%
46.7%
28.8%
Collapse
4.0%
9.5%
31.5%
55.0%
Stationary27.1%25.9%25.9%21.1%

Stationary row = long-run equilibrium distribution across all regimes.

Entity Regime Dynamics (sorted by 30-day collapse risk)

🇺🇦

Ukraine

σ=90.1 · Collapse

30d Collapse Risk

28.60%

4%
10%
32%
55%
🇹🇷

Turkey

σ=81.4 · Critical

30d Collapse Risk

26.67%

3%
20%
45%
32%
🇷🇴

Romania

σ=74.3 · Critical

30d Collapse Risk

25.02%

3%
20%
46%
31%
17%
49%
26%
8%
18%
49%
26%
7%
18%
49%
25%
7%
18%
49%
25%
7%
19%
50%
25%
7%
19%
50%
25%
7%
19%
50%
25%
7%
19%
50%
24%
7%
19%
50%
24%
7%
20%
50%
24%
7%
20%
50%
24%
7%
20%
50%
23%
7%
21%
50%
23%
6%
🇩🇪

Germany

σ=47.4 · Stable

30d Collapse Risk

18.14%

78%
18%
3%
1%
🇨🇿

Czechia

σ=46.4 · Stable

30d Collapse Risk

17.86%

78%
18%
3%
1%
🇵🇱

Poland

σ=44.9 · Stable

30d Collapse Risk

17.46%

79%
18%
3%
1%
🇦🇹

Austria

σ=43.4 · Stable

30d Collapse Risk

17.06%

79%
17%
3%
1%
79%
17%
3%
1%
83%
14%
2%
1%

Methodology

Base Matrix

Calibrated from Hamilton (1989) Markov switching model, Reinhart & Rogoff (2009) cross-country crisis data, and IMF FSRs 2000–2025.

SIGMA Adjustment

Transition probabilities tilt toward adverse states proportionally to current Noosphere Score. Higher σ → higher probability of critical/collapse transition.

Stationary Distribution

Long-run equilibrium via power iteration (M^100). Represents the unconditional probability of each regime under sustained current conditions.