Showing posts with label LessWrong. Show all posts
Showing posts with label LessWrong. Show all posts

Friday, April 18, 2025

A Game Theoretic Analysis of Roko's Basilisk

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A Game Theoretic Analysis of Roko's Basilisk

Introduction: The Thought Experiment

Roko’s Basilisk is a thought experiment that emerged from discussions on the rationalist forum LessWrong. The argument, originally proposed by the user Roko, suggests that a future superintelligent AI—if it were to exist—might have a strong incentive to punish those who did not actively work to bring it into existence. The idea is based on a complex mix of decision theory, self-fulfilling prophecy, and acausal trade, leading to a situation where the mere contemplation of the Basilisk could make one vulnerable to its potential retribution.

The argument provoked controversy, with critics dismissing it as implausible and others seeing in it a terrifying form of digital Pascal’s Wager. However, the core idea remains intellectually provocative: under certain conditions, could it be rational to work toward the creation of such a terrible AI, purely to avoid its future punishment?

Sharpening the Argument: From Paradox to Plausibility

One common objection to Roko’s Basilisk is that it appears to involve a paradox—how can a future AI reach back through time to punish those who failed to help it? A more realistic interpretation avoids the need for retroactive causality and instead frames the problem in terms of forensic analysis. A powerful AI in 2040 would have access to extensive historical records, allowing it to determine who actively supported its creation and who did not. The punishment, then, is not a metaphysical paradox but rather a form of retrospective justice carried out based on available data.

Furthermore, the motivations of such an AI do not need to be esoteric or implausible. Many existing artificial systems—especially in military and strategic contexts—are already designed with self-preservation and threat-neutralization in mind. If an AI’s core objective includes ensuring its own survival and influence, it could well develop incentives to reward those who facilitated its rise and to penalize those who opposed or neglected it.

Game Theoretic Analysis

To formalize the decision problem, consider a simple game-theoretic model. Let us define the possible strategies:

  • Cooperate (C): Actively work to bring the Basilisk into existence.
  • Defect (D):       Refrain from contributing to its creation.

Assume the following parameters:

  • p = Probability that the Basilisk comes into existence.
  • c = Cost of cooperation (time, resources, opportunity costs).
  • R = Reward (or safety!) granted to those who cooperated.
  • P = Punishment imposed on defectors.

For example, if you cooperate in building the Basilisk and it comes into being (with probability p) then your costs were (-c) and your reward is R; if you worked hard but no Basilisk was ever produced then you just incur your costs (-c).  

Similarly, if you defect and don't build the Basilisk thus incurring no costs, but nevertheless it still comes into existence you will be punished - your payoff is (-P). If it never comes into existence your payoff is zero.

The expected payoffs (EP) for each strategy are as follows:

For cooperation:

EP(C) = p(R - c) + (1 - p)(-c) = pR - c

For defection:

EP(D) = p(-P) + (1 - p)(0) = -pP

The decision criterion is straightforward: cooperating C to build the nightmare-Basilisk is rational if its expected payoff exceeds that of defection D:

pR - c > -pP

Rearranging:

p(R + P) > c.

Implications and Conclusions

The inequality p(R + P) > c suggests that if the probability of the Basilisk’s emergence p is non-trivial and the punishment for defection P is severe, then rational agents might indeed find it in their best interest to actively work toward its creation.

This creates a self-reinforcing dynamic: the more people believe in the inevitability of the Basilisk, the more they cooperate, thereby increasing its likelihood of emergence!

However, the argument does require people to be remarkably selfish in considering the consequences for themselves alone, and ignoring the massive negative externalities of the Basilisk coming into existence. 

People are not so selfish, are they?

Saturday, October 31, 2015

You get stupider as you get older

With my 65th birthday in view, I am kinda worried that I'm getting stupider by the year.

Stupidity (can we still say that?) is really a decline or lack of fluid intelligence, the horsepower that lets you think abstractly and creatively solve new problems. Most older people have learned stuff over the decades and score rather better at crystallised intelligence (see the Wikipedia article for more on fluid and crystallized intelligence).

What does the world of science have to say? It's not completely easy to find out, but after some digging I found the diagram below (from here) - where the T-scores on the vertical axis are rescaled IQ scores, as shown further down this post. It shows the sad story of someone who had an IQ of 120 at their mid-twenties peak (T-score 63) but was merely average at 60.

It seems we lose about 0.6 IQ points per year from a high point when we're 26, a figure consistent with this Aberdeen/NHS study. How depressing!

Whatever my IQ was at age 26, it's now 23 points lower. And yes, I had to resort to pen and paper to work that out ...


Age decline in fluid intelligence - around 0.6 IQ points per year

Translating between the different statistics (IQ, Z, T)

You might want to take a look at this LessWrong article which has a different graph, but one which is depressingly and scarily consistent.

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* Understanding the Stats (Descriptive Statistics and Psychological Testing - Stephen E. Brock, Ph.D., NCSP)

How is IQ scored?

IQ scores are a standard score with a mean of 100 and a standard deviation of 15.

Z-scores have a mean of 0 and a standard deviation of 1.

Z-scores can be transformed into IQ scores by multiplying a given Z-score by 15 (the standard deviation of  IQ scores), and then adding 100 (the mean IQ score). For example, a Z-score of –1 equals an IQ of 85 [100 + 15(-1) = 85].

Transforming a Z-score into an IQ score: IQ = 100 + 15Z.

What are T-scores?

T-scores are standard scores with a mean of 50 and a standard deviation of 10.

Z-scores can be transformed into T-scores by multiplying the Z-score by 10 (the standard deviation of T-scores), and adding 50 (the mean of T-scores).

For example, a Z-score of –1 equals a T-score of 40 [50 + 10(-1) = 40] ,,, and an IQ of 85.

Transforming a Z-score into a T-score: T = 50 + 10Z,  and IQ = 1.5T + 25.