38. Keno Juechems: Where does value (in RL) come from, optimality with finite computational resources, and learning as a PhD student

Keno Juechems is a Junior Research Fellow at St John's College in Oxford. He studies how humans make decisions, using computational modelling, behavioural tasks, and fMRI. In this conversation, we talk about his papers  "Optimal utility and probability functions for agents with finite computational precision" and  "Where does value come from?", and various related topics.

BJKS Podcast is a podcast about neuroscience, psychology, and anything vaguely related, hosted by Benjamin James Kuper-Smith. New episodes every Friday. You can find the podcast on all podcasting platforms (e.g., Spotify, Apple/Google Podcasts, etc.).

Timestamps
0:00:05: Where does the name "Keno" come from?
0:01:47: How Keno got into his current research area
0:14:09: Start discussing Keno's paper "Optimal utility and probability functions for agents with finite computational precision"
0:26:46: Rationality and optimality
0:38:58: Losses, gains, and how much does a paper need to include?
0:51:04: Start discussing Keno's paper "Where does value come from?"
1:10:28: How does a PhD student learn all this stuff?
1:19:56: Resources for learning behavioural economics and reinforcement learning
1:25:42: What's next for Keno Juechems?

Podcast links

• Website: https://bjks.buzzsprout.com/
• Twitter: https://twitter.com/BjksPodcast

Keno's links

• Website: https://www.sjc.ox.ac.uk/discover/people/keno-juchems/
• Google Scholar: https://scholar.google.de/citations?user=tereY1oAAAAJ
• Twitter: https://twitter.com/kjuechems

Ben's links

• Website: www.bjks.blog/
• Google Scholar: https://scholar.google.co.uk/citations?user=-nWNfvcAAAAJ
• Twitter:  https://twitter.com/bjks_tweets

References
Juechems, K., & Summerfield, C. (2019). Where does value come from?. Trends in cognitive sciences.
Juechems, K., Balaguer, J., Spitzer, B., & Summerfield, C. (2021). Optimal utility and probability functions for agents with finite computational precision. Proceedings of the National Academy of Sciences.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica.
Keramati, M., & Gutkin, B. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. Elife.
Lewis, M. (2016). The undoing project: A friendship that changed the world. Penguin UK.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Thaler, R. H. (2015). Misbehaving: The making of behavioral economics.
Trepte, S., Reinecke, L., & Juechems, K. (2012). The social side of gaming: How playing online computer games creates online and offline social support. Computers in Human behavior.

https://en.wikipedia.org/wiki/Indifference_curve
David Silver's reinforcement learning course on YouTube: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
Chris Summerfield's course How to Build a Brain: https://humaninformationprocessing.com/teaching/