CrisisDB Explorer
Exploring power transitions and elite dynamics using the Crisis Database
What is this?
This is an interactive exploration of the CrisisDB Power Transitions dataset, testing predictions from Peter Turchin's Structural Demographic Theory (SDT).
SDT predicts that institutional complexity leads to elite overproduction — more administrative levels mean more elite positions, more competition, and more intra-elite conflict during power transitions.
Data: 3,447 power transitions from 264 polities, merged with Seshat complexity metrics. This is a subset of the full CrisisDB.
Explore the Patterns
What do polities at different complexity levels typically experience? Configure parameters and explore historical patterns from 3,447 observed transitions.
Configure Polity Profile
Using observed Markov transition rates
Compare to Real Polity
Historical Patterns at Complexity 5
Based on 25 polities, 352 transitions
Simulation
Click "Simulate" to see one possible trajectory based on historical rates
Detailed Findings
1. Elite Overproduction
Does administrative complexity predict intra-elite conflict?
Elite Overproduction: Complexity → Conflict
Administrative levels vs intra-elite conflict rate
2. Violence Contagion
Does a violent transition increase the probability of subsequent violence?
Violence Contagion
Markov transition dynamics: violence is "sticky"
Transition Matrix
| → Peaceful | → Violent | |
|---|---|---|
| Peaceful | 78% | 22% |
| Violent | 40% | 60% |
Convergence to Equilibrium
The system spends ~36% of time in violent states at equilibrium.
3. Temporal Dynamics
If the system were stationary, we'd expect constant rates across centuries. It isn't.
4. Polity Trajectories
Aggregate statistics mask divergent paths. Some polities maintain stability for centuries; others spiral into violence.
5. Ruler Tenure
Do violent usurpers reign shorter?
Ruler Tenure: Survival by Accession Type
Does violent accession predict shorter reigns?
Which Violence Types Shorten Reigns Most?
Military revolts have the strongest effect: usurpers who seize power via coup reign 4 years shorter on average.
Violence Begets Violence
Rulers who seized power violently are 2.5x more likely to be removed violently. Chi-square p < 0.0001.
6. Transitions Over Time
When and where do power transitions cluster? Explore the temporal distribution.
Power Transitions Over Time
1,862 transitions from -500 to 1500
7. Notable Patterns
Outliers and trajectories that complicate the complexity-conflict relationship.
Complexity Without Conflict
Venice III (admin=5, conflict=5%, n=41), Egypt Classic Old Kingdom (admin=7, conflict=0%, n=20), and Northern Song (admin=7, conflict=11%, n=9) maintained complex bureaucracies with low intra-elite violence during transitions.
Succession institutions may buffer elite competition even at high administrative complexity.
Byzantine Degradation
Intra-elite conflict rates escalate across Byzantine phases: I (56%, n=25) → II (50%, n=22) → III (100%, n=10). Every power transition in the late Byzantine period involved intra-elite violence.
Declining territorial base with persistent elite expectations — consistent with SDT predictions.
Mamluk Escalation
Similar trajectory in Mamluk Egypt: I (68%, n=19) → II (73%, n=11) → III (80%, n=10).
Highest sustained conflict rates in the dataset. Military slave systems may carry structural instability in succession.
8. Toward Prediction: Violence as a Dynamical System
Political violence has structure. Can we move from description to prediction?
Equilibrium or Illusion?
The Markov chain converges to 36% violent at stationarity. But the temporal dynamics (Section 3) show the system is not stationary — rates shift across centuries. Are polities oscillating between basins of attraction (stable vs. crisis regimes), or is the drift driven by external factors?
Critical Transitions
Byzantine Empire: 56% → 50% → 100% violence. Mamluk: 68% → 73% → 80%. These trajectories resemble tipping points more than gradual drift. Complex systems theory predicts early warning signals before critical transitions (rising autocorrelation, critical slowing down). Can we detect them in the historical record?
Hidden States
A Hidden Markov Model could formalize this: a latent state (stable vs. crisis) generating observed transitions with different probabilities. The polity trajectories in Section 4 suggest at least two distinct regimes. What triggers the switch between them?
Open Questions
These require additional variables beyond power transitions:
- Economic stress as a trigger: Does fiscal crisis or resource scarcity predict the transition from stable to crisis regimes? (Requires CrisisDB economic variables)
- Population dynamics: Turchin's SDT predicts that population pressure drives elite overproduction. Can we measure this interaction directly? (Requires Seshat demographic data)
- Institutional resilience: Venice maintained stability for a millennium. What institutional features predict resistance to violence contagion? (Requires Seshat institutional complexity variables)
- Regional contagion: Does violence in one polity increase the probability of violence in neighboring polities? (Requires spatial analysis across the full CrisisDB)
9. Robustness Check
Do findings hold when excluding polities with fewer than 5 transitions?
All Polities
Excluding Sparse Polities (n < 5)
Methodology & Limitations
Data Sources
- CrisisDB: Power transitions with mechanism coding (P/IP/A/IA)
- Seshat Equinox 2022: Administrative levels and complexity metrics
- Merged on polity name (n = 87 with ≥5 transitions)
Limitations
- Partial CrisisDB subset (power_transitions.csv only)
- Correlation does not imply causation
- Selection bias toward well-documented polities
- Merging introduces data loss
Source Quality
Polities with fewer than 5 recorded transitions are flagged as sparse — too few data points for reliable violence rate estimates. Use the filter above to exclude these 39 polities and verify that findings hold.
Acknowledgments
This work builds on data and theory from Peter Turchin and the Complexity Science Hub Vienna. CrisisDB and Seshat are maintained by the Seshat: Global History Databank team.
Special thanks to Jenny Reddish, Jakob Zsambok, and Peter Turchin for feedback on source quality and methodology, and to Daniel Kondor for ongoing work on the power transitions data.