Tom Everitt, Ryan Carey, Chris Van Merwijk
We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| causalincentives/pycid | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| bernoulli | Ran | causalincentives/pycid/pycid/core/cpd.py code served (permissive licence) · get_code("5282a851cb47edd3") |
| discrete_uniform | Ran | causalincentives/pycid/pycid/core/cpd.py code served (permissive licence) · get_code("98237261af389bd6") |
| noisy_copy | Ran | causalincentives/pycid/pycid/core/cpd.py code served (permissive licence) · get_code("42d8919281eb9192") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Influence diagrams have recently been used to analyse the safety and fairness properties of AI systems. A key building block for this analysis is a graphical criterion for value of information (VoI). This paper establishes the first complete graphical criterion for VoI in influence diagrams with multiple decisions. Along the way, we establish two techniques for proving properties of multi-decision influence diagrams: ID homomorphisms are structure-preserving transformations of influence diagrams, while a Tree of Systems is a collection of paths that captures how information and control can flow in an influence diagram.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2202.11629")
get_code_for_paper("2202.11629")
have("2202.11629")
Connect an agent — have() is free.