Novel dose-finding designs, using estimation to assign the best estimated
maximum- tolerated-dose (MTD) at each point in the experiment, most commonly
via Bayesian techniques, have recently entered large-scale implementation in
Phase I cancer clinical trials. We examine the small-sample behavior of these
"Bayesian Phase I" (BP1) designs, and also of non-Bayesian designs sharing the
same main "long-memory" traits (hereafter: LMP1s).

The focus of this paper is an approach to the modeling of longitudinal social
relational or network data. Such data arise from measurements on pairs of
objects or actors made at regular temporal intervals, resulting in a social
network for each point in time. In this article we represent the network and
temporal dependencies with a random effects model, resulting in a stochastic
process defined by a set of stationary covariance matrices.

In this article we evaluate the statistical evidence that a population of
students learn about the sub-game perfect Nash equilibrium of the centipede
game via repeated play of the game. This is done by formulating a model in
which a player's error in assessing the utility of decisions changes as they
gain experience with the game. We first estimate parameters in a statistical
model where the probabilities of choices of the players are given by a Quantal
Response Equilibrium (QRE) (McKelvey and Palfrey, 1995, 1996, 1998), but are
allowed to change with repeated play.