aomModel: Population Analysis for the Homogeneous Attention Overload...

In ramchoice: Revealed Preference and Attention Analysis in Random Limited Attention Models

Population Analysis for the Homogeneous Attention Overload Model

Description

'aomModel' evaluates the population choice-probability inequalities implied by a collection of candidate preference orderings under the homogeneous Attention Overload Model (AOM). It provides a model-specific interface to the AOM restrictions implemented by [revealPrefModel()].

Usage

aomModel(
  menu,
  prob,
  pref_list = NULL,
  tolerance = sqrt(.Machine$double.eps),
  attBinary = 1
)

Arguments

menu	Numeric matrix of zeros and ones. Each row identifies an observed menu.
prob	Numeric matrix of choice probabilities with the same dimensions as 'menu'.
pref_list	Numeric matrix whose rows are candidate strict preference orderings. The default is '1, 2, ...'.
tolerance	Nonnegative numerical tolerance used when classifying a population inequality as violated.
attBinary	Numeric value between one half and one. Values below one impose the attentive-at-binaries restriction used by the legacy API.

Value

An object of class 'ramchoiceAOMModel'. Its 'results' component has one row per candidate preference, including compatibility, inequality counts, and violation magnitudes. The object also contains 'preferences', candidate-specific 'inequalities', the classification 'tolerance', and the complete legacy [revealPrefModel()] result.

References

M. D. Cattaneo, P. H. Y. Cheung, X. Ma, and Y. Masatlioglu (2026). Attention Overload. Working paper.

Examples

menu <- prob <- matrix(c(
  1, 1, 1,
  1, 1, 0,
  1, 0, 1,
  0, 1, 1
), ncol = 3, byrow = TRUE)
for (i in seq_len(nrow(prob))) {
  prob[i, menu[i, ] == 1] <- logitAtte(sum(menu[i, ]), 2)$choiceProb
}
aomModel(menu, prob, pref_list = rbind(1:3, 3:1))

ramchoice documentation built on Sept. 4, 2026, 9:07 a.m.