Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data
H. S. Moat, C. Y. Olivola, N. Chater & T. Preis. Searching Choices: Quantifying Decision‐Making Processes Using Search Engine Data. Topics in Cognitive Science 8, 685-696 (2016).
12 Pages Posted: 9 Apr 2019
Date Written: June 1, 2016
When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process.
Keywords: Search Engine Data, Google, Wikipedia, Decision Making, Behavioral Science, Computational Social Science
JEL Classification: A10, B40, C10, C20, C22, C53, C90, D70, D79, D83, J10, J11, O40, O47
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