  As recommendation systems become increasingly standard for online platforms,
simulations provide an avenue for understanding the impacts of these systems on
individuals and society. When constructing a recommendation system simulation,
there are two key challenges: first, defining a model for users selecting or
engaging with recommended items and second, defining a mechanism for users
encountering items that are not recommended to the user directly by the
platform, such as by a friend sharing specific content. This paper will delve
into both of these challenges, reviewing simulation assumptions from existing
research and proposing alternative assumptions. We also include a broader
discussion of the limitations of simulations and outline of open questions in
this area.
