A Random Attention Model

This paper illustrates how one can deduce preference from observed choices when attention is not only limited but also random. In contrast to earlier approaches, we introduce a Random Attention Model (RAM) where we abstain from any particular attention formation, and instead consider a large class of nonparametric random attention rules. Our model imposes one intuitive condition, termed Monotonic Attention, which captures the idea that each consideration set competes for the decision-maker's attention. We then develop revealed preference theory within RAM and obtain precise testable implications for observable choice probabilities. Based on these theoretical findings, we propose econometric methods for identification, estimation, and inference of the decision maker's preferences. To illustrate the applicability of our results and their concrete empirical content in specific settings, we also develop revealed preference theory and accompanying econometric methods under additional nonparametric assumptions on the consideration set for binary choice problems. Finally, we provide general purpose software implementation of our estimation and inference results, and showcase their performance using simulations

Persuasion bias, social influence, and uni-dimensional opinions.

We propose a boundedly rational model of opinion formation in which individuals are subject to persuasion bias; that is, they fail to account for possible repetition in the information they receive. We show that persuasion bias implies the phenomenon of social influence, whereby one’s influence on group opinions depends not only on accuracy, but also on how well-connected one is in the social network that determines communication. Persuasion bias also implies the phenomenon of unidimensional opinions; that is, individuals’ opinions over a multidimensional set of issues converge to a single “left-right” spectrum. We explore the implications of our model in several natural settings, including political science and marketing, and we obtain a number of novel empirical implications.

Three Puzzles on Mathematics, Computation, and Games

In this lecture I will talk about three mathematical puzzles involving mathematics and computation that have preoccupied me over the years. The first puzzle is to understand the amazing success of the simplex algorithm for linear programming. The second puzzle is about errors made when votes are counted during elections. The third puzzle is: are quantum computers possible?Comment: ICM 2018 plenary lecture, Rio de Janeiro, 36 pages, 7 Figure

Explorations Using Extensions and Modifications to the Oppenheim et al. Model for Cumulative Semantic Interference

This thesis discusses extensions and modifications to a model of semantic interference originally introduced by Oppenheim et al. The first of the two networks presented extends the original toy model to be able to operate over realistic feature-norm datasets. The second of the two networks presented modifies the operation of this extended network in order to artificially activate non-shared features of competitor words during the selection process. Both networks were extensively tested over a wide range of possible simulation configurations. Metrics were developed to aid in predicting the behavior of these networks given the structure of the data used in the simulations. The networks were also tested for noise tolerance and duration of interference retention over time. The results of these experiments show resultant semantic interference behavior consistent with predictions over the parameter space tested, as well as high noise tolerance and the expected reductions in semantic interference effects as the networks were artificially aged. The new network models could be used as simulation platforms for experiments that wish to examine the emergence of semantic interference over complex or large datasets