Our Natural Law is a Game Theoretic System Expressed in Operational and Evolutionary Form
Much of Curt Doolittle and Brad Werrell’s system is implicitly game-theoretic even though it is expressed in operational and evolutionary rather than mathematical form.
Here’s how the correspondences map out:
The foundational causal chain—
maximization of evolutionary computation → maximization of cooperation → production of self-determination → insurance of sovereignty and reciprocity → proscription of truth, excellence, and beauty—
is a hierarchical game structure.
maximization of evolutionary computation → maximization of cooperation → production of self-determination → insurance of sovereignty and reciprocity → proscription of truth, excellence, and beauty—
is a hierarchical game structure.
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Each actor’s strategy is the pursuit of self-determination.
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Payoffs are measured in demonstrated interests (capital, time, sovereignty).
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Equilibria arise when reciprocal cooperation outcompetes predation and boycott.
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The rules of the game are your reciprocity and sovereignty constraints.
This makes Natural Law a generalized cooperative game, where the equilibrium is the Pareto frontier of maximal reciprocity under bounded liability.
In their framework:
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Truth = minimization of information asymmetry (epistemic equilibrium).
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Reciprocity = minimization of externalities (moral equilibrium).
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Liability/Warranty = enforcement of incentive compatibility.
In formal game-theory terms, these correspond to:
Their “truth-constrained cooperation” is a mechanism design problem: create institutions that make reciprocity the dominant strategy by pricing deceit and parasitism.
Their “maximization of evolutionary computation” is equivalent to an evolutionary game dynamic:
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Strategies that increase aggregate returns on cooperation survive.
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Non-reciprocal strategies (free riders, parasites) are selected against.
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The system evolves toward higher computability (predictability of reciprocity).
So their law of cooperation is the replicator dynamic under moral constraints.
Your applied work (closure, constraint, governance layers) parallels mechanism design and repeated games:
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The Closure Layer = rules of the repeated game (enforced consistency).
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The Constraint Layer = incentive compatibility filter.
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The Governance Layer = adjudication of deviations (dispute resolution).
Together they define an iterated reciprocal game with liability enforcement—essentially a dynamic constitution that preserves equilibrium across time and population.
They treat uncertainty as priced, which is the core of Bayesian game theory:
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Agents hold private beliefs (priors) about others’ reciprocity.
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Communication updates these priors (posterior belief revision).
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The market (or polity) prices uncertainty through reputation, trust, or warranty.
Hence, your system models knowledge exchange as Bayesian updating under liability.
Their Science as a Moral Discipline reframes science as a truth-production game:
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Scientists are players.
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Testifiability is the rule set.
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The Nash equilibrium is truthful testimony under reciprocal warranty.
Deceit, bias, and pseudoscience become forms of strategic defection.
Summary Table
In short:
Their system operationalizes game theory without invoking its mathematics—it embodies it.
Where conventional game theory predicts equilibria, their Natural Law constructs them by enforcing truth, reciprocity, and liability as first principles rather than derived constraints.
Their system operationalizes game theory without invoking its mathematics—it embodies it.
Where conventional game theory predicts equilibria, their Natural Law constructs them by enforcing truth, reciprocity, and liability as first principles rather than derived constraints.
Source date (UTC): 2025-10-14 23:39:50 UTC
Original post: https://x.com/i/articles/1978244385159721320
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