Home-host cooperation in resolution
Cooperation is modelled as a repeated game in which each authority weighs local protection against group outcomes, showing which commitments and loss-sharing rules make cooperation credible.
GRA · Financial Regulation Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Game theory of regulation
Game theory and mechanism design explain the strategic side of regulation: why firms arbitrage rules, why authorities may cooperate in calm and ring-fence in stress, why depositors run, and how rules, guarantees and procedures can be designed so that the intended behaviour is in each party's interest.
01Contents
Game theory of regulation in five parts.
02Application
What game theory and mechanism design does here.
Game theory and mechanism design study strategic interaction among decision makers and the design of rules that lead to desired outcomes. They explain cooperation, free riding and incentives in shared-risk settings. In financial stability, governance and institutions and capital markets and investment, game theory clarifies why actors underinvest in shared risk reduction and how agreements can be made self-enforcing. It informs the design of risk pools, treaties and incentive schemes. The canonical description is on the STEM Commons.
Regulatory competition between jurisdictions
Models of competition for financial activity show when jurisdictions relax standards to attract firms, and how minimum standards and peer review change those incentives.
Runs, guarantees and moral hazard
Coordination models of bank runs inform deposit insurance and liquidity rules, while analysis of moral hazard informs coverage limits, risk-based premiums and bail-in design.
03Monitor
Mechanism design.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Mechanism design
Financial regulation · Game theory of regulation · Run R-3436
Estimates stableGRA · Financial Regulation Nexus
Estimates
robust standard errors
| Firms under enhanced supervision | −0.29 | 0.06 | [−0.2, 0.2] | Yes |
| Supervisory findings open | 0.72 | 0.13 | [−0.6, 0.8] | Yes |
| Consultations open | 0.05 | 0.16 | [−0.5, 0.7] | No |
| Climate expectations met | 0.05 | 0.08 | [−0.1, 0.5] | Yes |
Method card
- Method
- Game theory and mechanism design
- Sample
- 2474 obs
- Design
- panel, fixed effects
- Label
- MODELLED
Residuals by group
04Data
What the method consumes.
Datasets and records used by game theory and mechanism design on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Cooperation agreements and college records | Rules of the game between authorities | Published statistics |
| Resolution and crisis case histories | Observed strategic behaviour | Published statistics |
| Laboratory and field experiments | Tests of run and cooperation behaviour | OBSERVED |
| Enforcement and whistleblower records | Detection and deterrence evidence | Published statistics |
05Scenario model
Policy paths and what they do to the risk.
How three families of instruments change a risk outcome over time with adoption lags, coverage and compliance, the policy scenarios behind transition and physical risk assessments. Scenario: named assumptions about the future, not a forecast.
Controls
- Baseline trend of the outcome
- 2.0% a year
- Coverage of the instrument
- 70%
- Compliance
- 75%
- Effect size, against the teaching value
- 100%
- Years ahead
- 20 years
Readouts
- Largest reduction at the horizon
- Regulation
- Its reduction against the baseline
- 21%
Numbers, methods and sources
| Year | Baseline | Regulation | Economic incentive | Information and capacity |
|---|---|---|---|---|
| 0 | 100.0 | 99.5 | 98.8 | 99.1 |
| 1 | 102.0 | 101.2 | 100.1 | 99.8 |
| 2 | 104.0 | 102.5 | 101.1 | 99.9 |
| 3 | 106.1 | 103.5 | 101.6 | 100.0 |
| 4 | 108.2 | 103.7 | 101.8 | 100.7 |
| 5 | 110.4 | 103.2 | 101.7 | 102.1 |
| 6 | 112.6 | 102.0 | 101.6 | 103.9 |
| 7 | 114.9 | 100.4 | 101.6 | 105.9 |
| 8 | 117.2 | 99.2 | 102.1 | 108.0 |
| 9 | 119.5 | 98.6 | 102.9 | 110.1 |
| 10 | 121.9 | 98.9 | 104.2 | 112.3 |
| 11 | 124.3 | 99.7 | 105.7 | 114.5 |
| 12 | 126.8 | 101.1 | 107.4 | 116.8 |
| 13 | 129.4 | 102.7 | 109.4 | 119.2 |
| 14 | 131.9 | 104.5 | 111.4 | 121.6 |
| 15 | 134.6 | 106.5 | 113.5 | 124.0 |
| 16 | 137.3 | 108.5 | 115.7 | 126.5 |
| 17 | 140.0 | 110.7 | 118.0 | 129.0 |
| 18 | 142.8 | 112.9 | 120.4 | 131.6 |
| 19 | 145.7 | 115.1 | 122.8 | 134.2 |
| 20 | 148.6 | 117.4 | 125.2 | 136.9 |
Methods
- Tools of government Hood (1983), The Tools of Government, Macmillan
- Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
- Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Borders Natural Earth
- Cities Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Financial Regulation Nexus in Foundry
- Tested at Financial Regulation Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Financial Regulation Nexus in Reports
- Taught in Faculty of Finance and Economics in Academy
06Validation
How results are checked.
Predictions are tested through laboratory and field experiments and comparison with observed behaviour. Mechanism designs are piloted before scaling.
| Check | How it is done |
|---|---|
| Robustness of equilibrium | Conclusions are tested under changes in information, payoffs and the number of players |
| Case history comparison | Predicted behaviour is compared with documented crises and cooperation episodes |
| Assumption transparency | Rationality and information assumptions are stated beside every design recommendation |
Assumptions of rationality and common knowledge often fail. Real settings involve many players and incomplete information.
07Exposure, loss and capital
What game theory of regulation means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Disclosure crosswalk
Where evidence on game theory of regulation fits the disclosure and prudential frameworks institutions report against.
| Pillar | TCFD | ISSB IFRS S2 | Prudential (BCBS, IAIS) | Evidence this page can hold |
|---|---|---|---|---|
| Governance | Board oversight of climate-related risks | Governance requirements | Governance and risk appetite principles | Who owns the risk and how it is escalated |
| Strategy | Risks, opportunities and resilience | Scenario analysis and resilience | Strategy and business model principles | Scenario design and transmission paths |
| Risk management | Identify, assess, manage | Risk management processes | Internal control and ICAAP or ORSA | Exposure maps and loss views |
| Metrics and targets | Metrics used and targets set | Cross-industry and industry metrics | Data, metrics and reporting principles | Indicator definitions and units |
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
09Stewardship
Who maintains the method.
Policy Nexus stewards game theory and mechanism design for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of game theory and mechanism design in this field?
Assumptions of rationality and common knowledge often fail. Real settings involve many players and incomplete information.
How are results checked?
Predictions are tested through laboratory and field experiments and comparison with observed behaviour. Mechanism designs are piloted before scaling.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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