Using machine-learning to predict sudden gains in treatment for major depressive disorder.

Objective: Sudden gains during psychotherapy have been found to consistently predict treatment outcome but evidence on predictors of sudden gains has been equivocal. To address this gap, the present study utilized three machine learning algorithms to predict sudden gains during treatment for major depressive disorder.

Method: We examined predictors of sudden gains in two large samples of individuals receiving treatment in a partial hospital setting (n = 726 and n = 788; total N = 1514).

On the relation between economic bubbles and effort gaps between sellers and buyers: An experimental study.

Economic bubbles are an empirical puzzle because they do not readily fit the notion of an efficient market. We argue that bubbles are associated with a conflict and a gap in the allocation of effort during negotiation by sellers and buyers. We examined 21 experimental asset markets where in one condition players could buy and sell and in the other they could either buy or sell.