(a) A trial (with evidence and a verdict) is a good way to show how to update beliefs as new evidence comes to light, if there is room in the game for that. It’s such a natural thing to use Bayes nets in this context that lawyers invented an early version called ‘Wigmore charts’.
(b) It would be neat to demonstrate confounding bias somehow (e.g. a common cause cancelling out an existing relationship, or explaining it away entirely).
I was intending on having something like confounding bias appear in the form of the protagonist’s model of the world being gradually updated to contain larger and more detailed networks, so e.g. two variables that appeared to have a causal relationship in an early network would turn out to have a common cause in a later, more accurate one. (The player can acquire more accurate networks either by allocating time to studies and learning from the work of others, or by experimenting themselves. Not sure of the exact mechanics for these yet.)
Neat! Two suggestions:
(a) A trial (with evidence and a verdict) is a good way to show how to update beliefs as new evidence comes to light, if there is room in the game for that. It’s such a natural thing to use Bayes nets in this context that lawyers invented an early version called ‘Wigmore charts’.
(b) It would be neat to demonstrate confounding bias somehow (e.g. a common cause cancelling out an existing relationship, or explaining it away entirely).
Thanks, I have to look up Wigmore charts!
I was intending on having something like confounding bias appear in the form of the protagonist’s model of the world being gradually updated to contain larger and more detailed networks, so e.g. two variables that appeared to have a causal relationship in an early network would turn out to have a common cause in a later, more accurate one. (The player can acquire more accurate networks either by allocating time to studies and learning from the work of others, or by experimenting themselves. Not sure of the exact mechanics for these yet.)
Pointer on W. charts:
https://www.ucl.ac.uk/jdi/research/evidence-network/docs/BURGLARY.PDF
Thanks!