We apply a machine-learning algorithm calibrated from general human vision to predict visual salience of parts of a stock price series. We hypothesize that visual salience of adjacent prices increases decision weights on returns computed from those prices. We analyze the inferred decision impact...

We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are...

Many psychology experiments show that individually judged probabilities of the same event can vary depending on the partition of the state space (a framing effect called partition-dependence). We show that these biases transfer to competitive prediction markets in which multiple informed traders...