{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-random-attention-model","title":"A Random Attention Model","arxiv_id":"1712.03448","date":"2019-08-29","proceeding":null,"authors":[],"abstract":"This paper illustrates how one can deduce preference from observed choices\nwhen attention is not only limited but also random. In contrast to earlier\napproaches, we introduce a Random Attention Model (RAM) where we abstain from\nany particular attention formation, and instead consider a large class of\nnonparametric random attention rules. Our model imposes one intuitive\ncondition, termed Monotonic Attention, which captures the idea that each\nconsideration set competes for the decision-maker's attention. We then develop\nrevealed preference theory within RAM and obtain precise testable implications\nfor observable choice probabilities. Based on these theoretical findings, we\npropose econometric methods for identification, estimation, and inference of\nthe decision maker's preferences. To illustrate the applicability of our\nresults and their concrete empirical content in specific settings, we also\ndevelop revealed preference theory and accompanying econometric methods under\nadditional nonparametric assumptions on the consideration set for binary choice\nproblems. Finally, we provide general purpose software implementation of our\nestimation and inference results, and showcase their performance using\nsimulations.","url_abs":"http://arxiv.org/abs/1712.03448v3","url_pdf":"http://arxiv.org/pdf/1712.03448v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-random-attention-model","repo_url":"https://github.com/mdcattaneo/replication-CMMS_2020_JPE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}