Can computer science help climate policy making?
summary
Argues that computer science contributes beyond engineering by formalizing concepts and producing verified decision tools: vulnerability as a function (composition of possible, harm, measure) shows common indices like “most likely” violate monotonicity; a monadic framework yields monadic dynamical systems that integrate deterministic, nondeterministic and stochastic scenarios; a verified backward induction engine produces policy sequences when three sufficiency conditions hold; applications include quantifying how uncertainty shifts optimal emissions and ranking which near‑term climate decisions matter most; code and papers sit on GitHub/GitLab.
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chapters (11)
- 0:00Climate policy and computer science
- 1:07Climate science vs policy gap
- 3:13Shaping climate policy with science
- 5:46Vulnerability via functional programming
- 11:49Generating scenarios computationally
- 14:42Human decisions in climate models
- 17:18Backward induction in policy
- 18:45Emission policies and climate decisions
- 19:29Research program challenges
- 20:54Adoption barriers and future work
- 21:36Q&A
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