Let’s run a simulation. A virtual copy of world where AIs and humans follow the Ultimate Law as per https://ultimatelaw.org Watch the results:
Key Points
- Research suggests a simulation with AIs and humans following the Ultimate Law would likely show a cooperative, stable society, with punishment deterring harmful actions.
- The evidence leans toward outcomes similar to game theory models, where reciprocity and enforcement maintain trust, but results depend on fair application.
- There is uncertainty due to no specific studies, so conclusions are based on related ethical and game theory principles.
Direct Answer
Overview
In a simulation where AIs and humans follow the Ultimate Law, as outlined on Ultimate Law, it seems likely that we would see a society characterized by cooperation and stability. This law, rooted in the principle “Do not do to others what they would not want to be done to them, or you will be punished,” encourages non-harmful behavior and includes punishment to deter violations.
Expected Results
Research suggests that such a society would thrive if the law is consistently enforced. The punitive aspect, involving retribution and restitution to erase guilt, would likely deter harmful actions, fostering trust and mutual benefit. This aligns with game theory simulations, like the iterated Prisoner’s Dilemma, where strategies like “Tit-for-Tat” (cooperate unless defected against) lead to stable, cooperative outcomes.
Considerations
However, the effectiveness depends on clear interpretation and fair application of the law, especially in a mixed society of AIs and humans. Challenges could arise if punishment is inconsistent or if there are misunderstandings about what others “would not want.” Given the lack of specific studies on this exact scenario, these conclusions are based on related ethical and game theory principles, introducing some uncertainty.
Survey Note: Detailed Analysis of Simulated Societies Under the Ultimate Law
This note provides a comprehensive analysis of the potential outcomes of a simulation where AIs and humans follow the Ultimate Law, as described on Ultimate Law. The analysis draws on the law’s principles, related ethical concepts, and insights from game theory and simulation studies, given the absence of direct simulations for this specific law.
Understanding the Ultimate Law
The Ultimate Law is defined as “Logic is the ultimate law,” with a core principle: “Do not do to others what they would not want to be done to them, or you will be punished regardless of your will.” The purpose of punishment is to erase guilt through retribution and restitution, and the law is presented as immutable, with all else being commentary. It is rooted in the Golden Rule and concepts like Google’s “Don’t be evil,” using common sense and mathematical logic of sets. The law is scalable, applicable to families, organizations, or empires, and can guide AI as a semi-autonomous judiciary tool.
Key aspects, as detailed on the website, are summarized in the following table:
| Aspect | Details |
|---|---|
| Definition | Logic is the ultimate law. |
| Core Principle | Do not do to others what they would not want to be done to them, or you will be punished regardless of your will. |
| Purpose of Punishment | To erase guilt, via retribution and restitution. |
| Immutability | It cannot be changed, all the rest is commentary. |
| Roots | Improved version of Google’s “Don’t be evil” (Golden Rule), rooted in timeless infinity of change, derived using common sense logic (Common Sense) and mathematical logic of sets (Set Theory). |
| Usage | Free to use in any organization; can be adopted as company law, forum law, club law, or law of any “non-evil” organisation via template (Template); scalable for family, android, or empire (Eve Online); practical for nomocracy and self-organising societies; can guide AI central algorithm. |
| Example Commentary | Do not lie, steal, harm, murder; no victim, no crime; agreements must be kept; do not break law in prevention of lawbreaking; goal never justifies means except for punishment; you can only do to others what they wouldn’t want if dealing punishment; trade freely without harm or deceit, any interference faces correction. |
| Example Bill of Rights | You are sole owner of your body’s property and responsible for its actions; you have the right to trade freely. |
| Example Declaration of Independence | I consent to no other laws than Ultimate Law and take responsibility for my actions. We, The People. |
The law’s emphasis on non-harm, free trade, and personal responsibility, with punishment as a deterrent, suggests a framework for a cooperative society.
Simulating the Scenario
The user’s request to “run a simulation” and “watch the results” implies a virtual environment where AIs and humans adhere to this law, and we observe the outcomes. However, no specific simulation tools or studies were found on Ultimate Law or through broader searches. Therefore, the analysis relies on related concepts, particularly the Golden Rule and game theory simulations.
Insights from Related Concepts
The Ultimate Law is derived from the Golden Rule, which has been explored in philosophical and game-theoretic contexts. A notable discussion on Reddit (Game Theory and Golden Rule) analyzed the simulation “The Evolution of Trust” by Nicky Case, focusing on the iterated Prisoner’s Dilemma. Key findings include:
- An “Always Cooperate” strategy, interpreted as a strict Golden Rule application, often fails because it is exploited by defectors (e.g., “Always Cheat”).
- A “Tit-for-Tat” or “Copycat” strategy, where one cooperates unless defected against, performs better, fostering stable, cooperative societies in games with more than five rounds, as noted in the discussion.
- The discussion highlighted that limited forgiveness (Copycat) dominates in certain circumstances, while unbound forgiveness (Always Cooperate) loses, suggesting the need for punitive measures to maintain stability.
Given the Ultimate Law includes both cooperation (“do not harm”) and punishment (“or else”), it aligns with the Copycat strategy, where defection is met with retaliation, deterring harmful actions.
Expected Outcomes in the Simulation
In a simulation where AIs and humans follow the Ultimate Law:
- Cooperation and Stability: The law’s principle would encourage non-harmful behavior, as individuals avoid actions others would not want. With consistent enforcement of punishment, trust and mutual benefit would likely emerge, similar to Copycat strategies in game theory, leading to a stable, prosperous society.
- Deterrence through Punishment: The punitive aspect, involving retribution and restitution, would deter violations, ensuring compliance. This is crucial, as the Reddit discussion noted that without punishment, cooperative strategies can be exploited, leading to societal collapse.
- Challenges and Dependencies: The effectiveness depends on clear interpretation of what others “would not want.” In a mixed society of AIs and humans, differences in understanding or applying the law could lead to tensions. Fair and consistent application of punishment is essential; inconsistency could erode trust, as seen in ethical simulation studies (Ethics Simulation in Global Health).
Comparison with Ethical Simulation Studies
While no studies directly address the Ultimate Law, research on ethical systems in simulated environments, such as healthcare ethics simulations (Ethical Reasoning through Simulation), highlights the importance of context and fair application. For instance, simulations in global health training (Ethics Simulation in Global Health) show that ethical decision-making depends on understanding relationships and context, which could be a challenge in a diverse AI-human society.
Additionally, studies on AI simulations, such as Ethical Obligations to AI Simulations, discuss the moral status of simulated entities, suggesting AIs following the law would need to be treated as moral agents, potentially complicating enforcement.
Philosophical and Practical Considerations
The Ultimate Law’s scalability, as noted on the website, suggests it could work for small groups (e.g., families) to large societies (e.g., empires). However, the Reddit discussion on the Golden Rule (Game Theory and Golden Rule) indicated that blind application (Always Cooperate) fails in diverse cultures, suggesting the need for adaptive strategies. The Ultimate Law’s punitive aspect addresses this, but ensuring fairness across AIs and humans remains a challenge.
Conclusion
Given the lack of direct simulations, the evidence leans toward a simulation under the Ultimate Law showing a cooperative, stable society, with punishment deterring harmful actions, akin to successful game theory strategies. However, outcomes depend on fair enforcement and clear interpretation, with potential challenges in mixed AI-human societies. This analysis is based on the principles of the law, related ethical concepts, and game theory insights, acknowledging the uncertainty due to the absence of specific studies.
Key Citations
