Morality and computational constraints

It is as if we knew what we were doing

October 2, 2023 — March 20, 2024

economics
mind
sociology
Figure 1

Notes on computational efficiency of morality.

Figure 2: From the Twitter summary of Grosse et al. (2023)

Professor Javen Qinfeng Shi says:

Mind is a choice maker. Choices shape the mind

  • Q learning: do what a good/kind person would do (moment to moment), learn wisdom (V function) and have faith in future and self-growth. It naturally leads to optimal long term accumulative rewards (Bellman equation)
  • Policy gradient: learn from past successes (to repeat or mimic) and mistakes (to avoid). Require complete episodes to reveal the end accumulative reward per episode

This is the first time I have heard of policy gradient as utilitarianism versus Q learning as virtue ethics.

1 References

Awad, Edmond, Sydney Levine, Michael Anderson, Susan Leigh Anderson, Vincent Conitzer, M. J. Crockett, Jim A. C. Everett, et al. 2022. Computational Ethics.” Trends in Cognitive Sciences 26 (5): 388–405.
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Bello, Paul, and Bertram F. Malle. 2023. Computational Approaches to Morality.” In The Cambridge Handbook of Computational Cognitive Sciences, edited by Ron Sun, 2nd ed., 1037–63. Cambridge Handbooks in Psychology. Cambridge: Cambridge University Press.
Crook, Nigel, Selin Nugent, Matthias Rolf, Adam Baimel, and Rebecca Raper. 2021. Computing Morality: Synthetic Ethical Decision Making and Behaviour.” Cognitive Computation and Systems 3 (2): 79–82.
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