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- How much does such a closed system tell us about the real world?
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Classroom Contents
Spurious Normativity Enhances Learning of Compliance and Enforcement Behavior in Artificial Agents
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- 1 - Intro
- 2 - Paper Overview
- 3 - Why are some social norms arbitrary?
- 4 - Reinforcement learning environment setup
- 5 - What happens if we introduce a "silly" rule?
- 6 - Experimental Results: how silly rules help society
- 7 - Isolated probing experiments
- 8 - Discussion of the results
- 9 - Start of Interview
- 10 - Where does the research idea come from?
- 11 - What is the purpose behind this research?
- 12 - Short recap of the mechanics of the environment
- 13 - How much does such a closed system tell us about the real world?
- 14 - What do the results tell us about silly rules?
- 15 - What are these agents really learning?
- 16 - How many silly rules are optimal?
- 17 - Why do you have separate weights for each agent?
- 18 - What features could be added next?
- 19 - How sensitive is the system to hyperparameters?
- 20 - How to avoid confirmation bias?
- 21 - How does this play into progress towards AGI?
- 22 - Can we make real-world recommendations based on this?
- 23 - Where do we go from here?
