  Vehicle-to-Vehicle (V2V) communication is intended to improve road safety
through distributed information sharing; however, this type of system faces a
design challenge: it is difficult to predict and optimize how human agents will
respond to the introduction of this information. Bayesian games are a standard
approach for modeling such scenarios; in a Bayesian game, agents
probabilistically adopt various types on the basis of a fixed, known
distribution. Agents in such models ostensibly perform Bayesian inference,
which may not be a reasonable cognitive demand for most humans. To complicate
matters, the information provided to agents is often implicitly dependent on
agent behavior, meaning that the distribution of agent types is a function of
the behavior of agents (i.e., the type distribution is endogenous). In this
paper, we study an existing model of V2V communication, but relax it along two
dimensions: first, we pose a behavior model which does not require human agents
to perform Bayesian inference; second, we pose an equilibrium model which
avoids the challenging endogenous recursion. Surprisingly, we show that the
simplified non-Bayesian behavior model yields the exact same equilibrium
behavior as the original Bayesian model, which may lend credibility to Bayesian
models. However, we also show that the original endogenous equilibrium model is
strictly necessary to obtain certain informational paradoxes; these paradoxes
do not appear in the simpler exogenous model. This suggests that standard
Bayesian game models with fixed type distributions are not sufficient to
express certain important phenomena.
