Mutually Assured Deregulation Dynamics

• Mutually Assured Deregulation is a strategic concept where actors, from regulators to corporations, deliberately weaken rules due to competitive and incentive pressures.
• It spans multiple domains—including regulatory capture, chaotic many-player dynamics, federal offsetting policies, financial risk measurement, and AI geopolitics—each with unique mechanisms.
• Empirical models and simulations highlight that altering key parameters like influence, cost structures, or risk metrics is essential for mitigating deregulation spirals.

Searching arXiv for the cited papers and closely related work on regulatory capture, many-player game dynamics, federal regulation, financial risk regulation, and AI governance. Mutually Assured Deregulation denotes a class of strategic dynamics in which multiple actors, facing competitive pressure or distorted incentives, converge on weakening or offsetting regulation because unilateral restraint is perceived as costly. Across the literature, the term is used in analytically distinct but structurally related ways: as a bilateral collusion problem between corporations and regulators in regulatory capture (Albino et al., 2013); as a strategic response to chaotic many-player technology environments in which stable regulatory equilibria may not exist (Kusnezov et al., 2017); as a hierarchical intergovernmental pattern in which subnational institutions counteract national regulation (Lucas et al., 2019); as a financial-regulatory equilibrium claim tied to the gaming of objective risk metrics (Maymin et al., 2010); and, in AI governance, as an international race dynamic in which states dismantle safety guardrails for fear of falling behind competitors (Abiri, 17 Aug 2025). A unifying feature is reciprocal incentive compatibility for deregulation or de facto deregulation: actors do not necessarily deny the presence of risk, but they behave as though relaxing constraints is privately rational under prevailing strategic conditions.

1. Conceptual definition and scope

In the regulatory-capture formulation, mutually assured deregulation arises when a corporation and a regulator can both obtain nonnegative expected payoffs from collusion. Using the notation $B$ for the corporate benefit from favorable regulation, $C$ for the regulator’s cost of favoring the corporation, $T$ for a transfer from corporation to regulator, and $\alpha \in [0,1]$ for influence, the expected utilities are given as

$$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$

Collusion is feasible when

$\alpha \cdot C \le T \le \alpha \cdot B,$

with a nonempty interval only if $B > C$, and equivalently, since $\alpha>0$, the critical threshold for capture is presented as $\alpha \cdot B > C$ (Albino et al., 2013). In this usage, “mutually assured deregulation” is a formalization of profitable collusion under mutual communication and influence.

In the many-player technology-governance formulation, the concept instead refers to reciprocal abandonment of onerous controls in environments where strategic interaction is highly dimensional, adaptive, and potentially chaotic. The extracted synthesis of the many-player learning model states that when $p$ and $C$0 are large and $C$1, the system lies in a chaotic regime without a stable Nash equilibrium to discover; under these conditions, actors rationally gravitate toward reciprocal deregulation because binding regulation lags behind or worsens intrinsic strategic instability (Kusnezov et al., 2017). Here the term does not denote direct collusion but a self-reinforcing equilibrium absence.

In federalism and institutional-economics work, the phrase is applied to hierarchical institutional interdependence: national regulation imposes burdens, while economically free states counteract those burdens through lower taxes, limited government spending, and flexible labor markets, thereby preserving market incentives and offsetting job destruction (Lucas et al., 2019). In this setting, “deregulation” is often not literal repeal of federal rules; it is an intergovernmental neutralization of their practical effects.

In financial regulation, the concept is tied to the claim that any objective risk-measurement rule invites systematic gaming, causing banks to concentrate on assets that appear safer because of sampling noise. The extracted exposition argues that, in a repeated-game sense, complete deregulation is the only subgame-perfect outcome because regulators cannot credibly improve outcomes with algorithmic rules, while banks under no regulation are disciplined by depositors (Maymin et al., 2010).

In frontier AI governance, the term refers to a geopolitical race dynamic driven by what Gilad Abiri calls the “Regulation Sacrifice”: the systematic abandonment of safety oversight justified by competitive imperatives. Each state’s attempt to accelerate by removing licensing, red-team evaluations, or disclosure mandates pressures rivals to do the same, producing shared insecurity rather than durable advantage (Abiri, 17 Aug 2025).

2. Bilateral collusion, influence, and regulatory capture

Albino, Hu, and Bar-Yam model the interaction between corporations and regulators as a game with mutual influence, explicitly addressing how communication enables collusion and how profits can be split (Albino et al., 2013). In the extracted step-by-step exposition, the corporation chooses whether to offer a transfer $C$2, and the regulator chooses a favorable ($C$3) or unfavorable ($C$4) regulatory outcome. The influence parameter is defined as

$C$5

measuring how much the offer increases the probability of favorable regulation.

Under the simplifying assumptions $C$6 and $C$7, expected utilities become

$C$8

The feasibility band

$C$9

captures the set of transfers that leave both parties weakly better off. The extracted analysis then identifies several benchmark transfers. Under a minimal-payment strategy, $T$0, so the regulator is approximately indifferent and the corporation retains surplus $T$1. Under equal-split bargaining,

$T$2

and each party receives $T$3. A weighted split can be written as

$T$4

The numerical illustration uses $T$5 and $T$6. For $T$7, the feasible interval is $T$8, with total surplus $T$9; for $\alpha \in [0,1]$0, the interval is $\alpha \in [0,1]$1, with surplus $\alpha \in [0,1]$2; for $\alpha \in [0,1]$3, the interval is $\alpha \in [0,1]$4, with surplus $\alpha \in [0,1]$5 (Albino et al., 2013). As $\alpha \in [0,1]$6 or $\alpha \in [0,1]$7 grows, the collusive surplus $\alpha \in [0,1]$8 grows.

This formulation presents mutually assured deregulation as a precise incentive condition rather than a metaphor. The relevant claim is not merely that capture can occur, but that it is jointly rational whenever expected corporate benefits can both cover regulator costs and leave surplus to divide. The paper’s abstract further emphasizes that capture is likely in the real world because benefits often far outweigh costs, and it identifies countermeasures: strict separation, independent market knowledge and research by regulators, regulatory and market transparency, regulatory accountability for market failures, widely distributed regulatory control, and anti-corruption enforcement (Albino et al., 2013). The extracted exposition reframes these as two control levers: reduce $\alpha \in [0,1]$9 or increase $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$0.

3. Many-player instability and equilibrium-free deregulation

The paper on whether some technologies are beyond regulatory regimes develops a many-player game-theoretic learning model for domains such as cyber, where the number of actors and the number of strategic options are both large (Kusnezov et al., 2017). In the extracted formal setup, there are $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$1 players, each with $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$2 pure strategies and mixed strategy vector $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$3. Payoffs $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$4 are modeled using a maximum-entropy random-matrix ansatz with correlation parameter $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$5, where $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$6 corresponds to zero-sum coupling, $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$7 to uncorrelated payoffs, and $$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$8 to positively correlated payoffs.

Learning proceeds through Experience-Weighted Attraction dynamics. The strategy-choice rule is

$$U_C = \alpha \cdot B - T,\qquad U_R = T - \alpha \cdot C.$$9

where $\alpha \cdot C \le T \le \alpha \cdot B,$0 is the intensity of choice. Attractions update as

$\alpha \cdot C \le T \le \alpha \cdot B,$1

with $\alpha \cdot C \le T \le \alpha \cdot B,$2 as a memory-decay or forgetting rate (Kusnezov et al., 2017).

The central extracted result is an informal threshold for chaos. Defining $\alpha \cdot C \le T \le \alpha \cdot B,$3, when $\alpha \cdot C \le T \le \alpha \cdot B,$4,

$\alpha \cdot C \le T \le \alpha \cdot B,$5

If $\alpha \cdot C \le T \le \alpha \cdot B,$6, the dynamics are stable and converge to a fixed point coinciding with a Nash equilibrium; if $\alpha \cdot C \le T \le \alpha \cdot B,$7, no attracting fixed point exists and trajectories are generically chaotic (Kusnezov et al., 2017). The proof sketch invokes the spectrum of a large random Jacobian and the condition $\alpha \cdot C \le T \le \alpha \cdot B,$8 for stability.

The extracted synthesis connects this directly to mutually assured deregulation. In democratized-technology domains, $\alpha \cdot C \le T \le \alpha \cdot B,$9 and $B > C$0 are very large, $B > C$1 is small, and $B > C$2 is large, so $B > C$3; regulation conceived as equilibrium selection is therefore misaligned with the strategic structure of the domain. Attempts to impose treaties or export controls can drive the system deeper into chaos, local or regional rules are ineffective when actors move to weakly regulated jurisdictions, and norms-based appeals are characterized as equivalent to forcing $B > C$4, which the extraction describes as tantamount to heavy coercion and unlikely to be sustainable or robust (Kusnezov et al., 2017).

This suggests a distinct meaning of mutually assured deregulation: not collusive bargain, but a reciprocal strategic stand-off in which no actor dares impose or retain strict controls unilaterally because the environment does not support stable compliance equilibria. The same extraction points toward a possible alternative paradigm, “control of chaos,” involving minimal, well-targeted feedback interventions such as real-time threat-information sharing platforms, micro-sanction regimes, and preparedness architectures that adapt continuously rather than relying on static rulebooks (Kusnezov et al., 2017).

4. Hierarchical institutions and subnational counter-regulation

The analysis of federal regulation, job creation, and state economic freedom introduces the concept of hierarchical institutional interdependence: the net effect of a federal rule depends on the state-level institutional environment in which firms operate (Lucas et al., 2019). Drawing on market-preserving federalism, the extracted framework specifies three requirements: regional governments as principal economic policymakers, unrestricted interregional trade, and hard budget constraints on state governments. Within this structure, state economic freedom operates as a countervailing mechanism.

The empirical model is a three-way fixed-effects regression: $B > C$5 where $B > C$6 is the percentage change or log-change in industry-level federal restrictions, $B > C$7 is the lagged state economic freedom index, and controls include median income, unemployment rate, population, poverty rate, population density, and number of firms (Lucas et al., 2019).

The data cover approximately 2,698 U.S. counties, 20 major industries, and 2003–2015, yielding about 463,000 observations after matching. Federal regulation is measured using RegData; state economic freedom comes from the Frasier Institute’s Economic Freedom of North America index; and net job creation comes from the Census Bureau’s Quarterly Workforce Indicators (Lucas et al., 2019).

The extracted key estimates from Model 3 are $B > C$8 and $B > C$9, both with $\alpha>0$0. In a state with average economic freedom $\alpha>0$1, a $\alpha>0$2 increase in industry-level federal restrictions implies approximately $\alpha>0$3 net jobs. A one-standard-deviation increase in economic freedom ($\alpha>0$4) offsets about $\alpha>0$5 jobs, described as roughly four fewer jobs destroyed. At $\alpha>0$6, the effect is approximately $\alpha>0$7 jobs; at $\alpha>0$8, approximately $\alpha>0$9 jobs, which the extraction states is not statistically different from zero (Lucas et al., 2019).

The heterogeneity results indicate that this moderation accrues strictly to older firms. For young firms aged $\alpha \cdot B > C$0–$\alpha \cdot B > C$1 year, $\alpha \cdot B > C$2 and $\alpha \cdot B > C$3, both not significant. For mature firms aged $\alpha \cdot B > C$4 years, $\alpha \cdot B > C$5 and $\alpha \cdot B > C$6, both significant at $\alpha \cdot B > C$7; at average $\alpha \cdot B > C$8, a $\alpha \cdot B > C$9 increase in regulation implies approximately $p$0 jobs, and a one-standard-deviation increase in $p$1 offsets about $p$2 jobs. The moderation is driven by the tax freedom and labor market freedom components, not the government-spending freedom sub-index (Lucas et al., 2019).

In this literature, mutually assured deregulation is not a claim that all regulation disappears. Rather, the extracted synthesis states that as federal regulation expands and compresses net job creation, economically free states enact deregulatory or pro-market policies to neutralize federal burdens. This state-level counterbalance preserves regional employment and entrepreneurship, particularly among established firms, and in highly economically free states federal regulatory expansions have near-zero net effect on job creation (Lucas et al., 2019). A plausible implication is that “deregulation” here functions as institutional offsetting within a multilevel governance stack.

5. Financial regulation, objective risk metrics, and the case for complete deregulation

Maymin and Maymin argue that any objective risk measurement algorithm mandated by central banks induces more risk-taking and more concentrated systemic risk than would otherwise occur (Maymin et al., 2010). In the extracted reconstruction, there are $p$3 banks and $p$4 securities. Each security $p$5 has true but unobserved standard deviation $p$6 and mean $p$7, while banks observe sample standard deviations $p$8 computed from $p$9 past returns.

Under regulation, each bank chooses a portfolio $C$00 to maximize

$C$01

subject to the risk-capital constraint

$C$02

where $C$03 and $C$04 is the regulatory multiple. The constrained optimum satisfies

$C$05

scaled so that $C$06 (Maymin et al., 2010). Banks therefore invest more in assets with lower sample volatilities.

The extracted argument depends on several assumptions: risk neutrality plus limited liability under regulation, unbiased but noisy historical estimators, frictionless capital and trading, and symmetric information on $C$07 and $C$08. Because $C$09 is noisy, some assets will by chance appear unusually safe. Since all banks observe the same history and solve the same optimization problem, they overweight the same assets, creating systemic concentration (Maymin et al., 2010).

The theorem sketch centers on the sampling distribution of $C$10. If returns are Gaussian with true $C$11, then $C$12. The extracted Theorem 2.1 states that for any $C$13, the expected value of the lowest-$C$14 sample standard deviations satisfies

$C$15

where $C$16 is the $C$17-th quantile of $C$18 and $C$19 quickly as $C$20 grows (Maymin et al., 2010).

The numerical illustration uses $C$21 securities, each with true $C$22, and $C$23 monthly observations. For the lowest $C$24 tail, the extraction reports

$C$25

Thus about $C$26 assets out of $C$27 appear $C$28 “too safe.” With $C$29 and $C$30, intended leverage is $C$31, while true leverage is $C$32, described as a $C$33 increase in risk beyond the regulator’s targets (Maymin et al., 2010).

The extracted exposition compares three regimes: continued algorithmic regulation, full nationalization, and full deregulation. It concludes that in a repeated-game sense, the only subgame-perfect outcome is complete deregulation, defined by abolishing deposit insurance and exogenous risk-capital rules so that banks internalize true risk and depositors impose market discipline (Maymin et al., 2010). This is a maximalist usage of mutually assured deregulation, extending beyond offsetting or selective relaxation toward elimination of the regulatory apparatus that generates the exploitable algorithmic game.

6. International AI competition and the “Regulation Sacrifice”

Gilad Abiri’s essay explicitly names mutually assured deregulation as a geopolitical AI-governance dynamic (Abiri, 17 Aug 2025). The core construct is the “Regulation Sacrifice,” defined in the extracted text as “the systematic abandonment of safety oversight justified by competitive imperatives.” States assume that each month of additional development speed, unconstrained by licensing, red-team evaluations, or disclosure mandates, is more valuable for national security than the risk reduction achieved through oversight. Because AI capabilities diffuse rapidly, however, temporary leads vanish while deregulation-induced vulnerabilities persist (Abiri, 17 Aug 2025).

The extracted quantitative evidence uses Stanford’s 2025 AI Index. Between January 2024 and February 2025, performance gaps between the best U.S. and Chinese AI systems are reported to have collapsed from $C$34 to $C$35, a decline of $C$36 percentage points in $C$37 months. Using

$C$38

with $C$39 and $C$40, the extraction gives

$C$41

implying a half-life of roughly $C$42 months for the initial advantage (Abiri, 17 Aug 2025). It also reports that language-understanding gaps shrank from $C$43 to $C$44, while mathematical-reasoning gaps fell from $C$45 to $C$46 over the same period (Abiri, 17 Aug 2025).

Abiri’s argument is organized around three “false promises.” The durable lead assumption is refuted by rapid diffusion. The low-drag assumption is challenged by examples in which governance accelerates innovation: the extracted text cites California’s Zero Emission Vehicle mandate and Tesla, China’s New Energy Vehicle policies and BYD, meta-analyses of $C$47 environmental regulations under the Porter Hypothesis, NIST’s AI Risk Management Framework pilot with $C$48 of participants reporting streamlined processes, the UK fintech sandbox with a $C$49 increase in participant funding, and the EU AI Act coinciding with an $C$50 jump in European AI investment during 2023–24 (Abiri, 17 Aug 2025). The net strategic benefit assumption is rejected on the ground that deregulation worsens security across near-, medium-, and long-term horizons.

The extracted horizon analysis identifies three threat classes. In the near term, AI-driven misinformation and cyber-intrusion scale rapidly; provenance standards such as C2PA watermarks, algorithmic-audit disclosures, and platform liability are presented as critical levers (Abiri, 17 Aug 2025). In the medium term, deregulation erodes biosecurity safeguards such as export controls, sequence screening, model-risk assessments, and liability regimes (Abiri, 17 Aug 2025). In the long term, AGI first-strike incentives produce a digital arms race in which red-teaming, compute caps, and weight escrow become necessary predeployment guardrails (Abiri, 17 Aug 2025).

The extracted policy frameworks include NIST AI RMF with its Govern, Map, Measure, and Manage functions; a regulatory sandbox model; a compute-registry and model-weight escrow proposal with

$C$51

and enhanced auditing when $C$52; and multilateral treaty elements such as verifiable weight escrow, joint red-team exercises, and coordinated export controls on frontier compute hardware (Abiri, 17 Aug 2025). In this account, mutually assured deregulation is explicitly treated as a pathology to be reversed by stronger, well-designed governance.

7. Comparative interpretation, mechanisms of inhibition, and major points of contention

Across these literatures, mutually assured deregulation is not a single theory but a family of strategic mechanisms. The main variants can be summarized as follows.

Domain	Mechanism	Trigger condition
Regulatory capture	Bilateral collusion between corporation and regulator	$C$53 (Albino et al., 2013)
Many-player technology governance	No stable fixed point under learning dynamics	$C$54 (Kusnezov et al., 2017)
Federalism and regional policy	State-level offsetting of federal burdens	Positive moderation of regulation by economic freedom (Lucas et al., 2019)
Financial regulation	Gaming of objective risk metrics and systemic concentration	Common optimization on noisy $C$55 under capital rule (Maymin et al., 2010)
AI geopolitics	Competitive dismantling of guardrails	Rapid capability convergence and race incentives (Abiri, 17 Aug 2025)

Several inhibiting strategies recur, although their normative direction differs by paper. In the capture model, inhibition comes from decreasing influence $C$56 or increasing regulator cost $C$57: strict separation, independent expertise, transparency, distributed decision-making, stronger enforcement, whistleblower protection, ethics training, public shaming, and accountability (Albino et al., 2013). In the chaotic many-player setting, the extracted synthesis suggests “control of chaos” rather than static treaty-style regulation, using targeted feedback interventions (Kusnezov et al., 2017). In the federalism literature, state economic freedom serves as a countervailing mechanism that attenuates national regulation’s negative employment effect (Lucas et al., 2019). In the financial-regulation argument, inhibition means abolishing the algorithmic rule itself and restoring market discipline (Maymin et al., 2010). In Abiri’s AI-governance essay, by contrast, the antidote is not deregulation but stronger governance architectures, including risk-management frameworks, sandboxes, compute registries, weight escrow, and multilateral controls (Abiri, 17 Aug 2025).

The principal controversy lies in whether mutually assured deregulation is descriptive, predictive, or prescriptive. In (Albino et al., 2013), it is descriptive and predictive: a formal condition under which capture should occur. In (Kusnezov et al., 2017), it is an inferred strategic response to equilibrium-free dynamics. In (Lucas et al., 2019), it describes institutional offsetting within a federation. In (Maymin et al., 2010), it becomes prescriptive: complete deregulation is argued to be the only stable equilibrium. In (Abiri, 17 Aug 2025), it is again descriptive but normatively negative: a collective-action failure generated by geopolitical rivalry.

This divergence is substantive rather than terminological. Some uses treat deregulation as the endogenous consequence of private rationality under flawed institutions; others treat it as a decentralized adaptation to multilevel governance; still others frame it as a dangerous race-to-the-bottom. A plausible implication is that the phrase is best understood as an umbrella label for reciprocal weakening, neutralization, or abandonment of constraints under strategic interdependence, with the welfare implications determined by the underlying mechanism rather than by the term itself.