Morality and computational constraints
It is as if we knew what we were doing
October 2, 2023 — March 20, 2024
economics
mind
sociology
Notes on computational efficiency of morality.
- Joscha Bach, From elementary computation to ethics? (Hear also his interview)
- APXHARD, Ethical Systems as Computational Optimizations
- https://podcast.clearerthinking.org/episode/177/jeffery-martin-experiments-for-enlightenment-and-fundamental-wellbeing/
- Karl Friston is implicitly involved via his predictive coding
- Junior Okoroafor | Computational Morality
- Grosse et al. (2023) is a magisterial study of LLMs which looks at how they reason from examples. Why I think this is significant TBD.
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
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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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