| P50 | author | Tobias Preis | Q7811656 |
| | Nick Chater | Q22035664 |
| | Helen Susannah Moat | Q57414185 |
| | Christopher Y Olivola | Q58584989 |
| P2860 | cites work | Early prediction of movie box office success based on Wikipedia activity big data | Q21559574 |
| | Quantifying the advantage of looking forward | Q24604429 |
| | Quantifying Wikipedia Usage Patterns Before Stock Market Moves | Q24629584 |
| | Quantifying trading behavior in financial markets using Google Trends | Q24631411 |
| | Detecting influenza epidemics using search engine query data | Q27700189 |
| | Taking time seriously. A theory of socioemotional selectivity | Q28141265 |
| | Quantifying the semantics of search behavior before stock market moves | Q28244846 |
| | The framing of decisions and the psychology of choice | Q28283469 |
| | Complex dynamics of our economic life on different scales: insights from search engine query data | Q28298406 |
| | Adaptive nowcasting of influenza outbreaks using Google searches | Q28646474 |
| | Characterizing the time-perspective of nations with search engine query data | Q28660019 |
| | Quantifying the relationship between financial news and the stock market | Q28661027 |
| | Quantifying the digital traces of Hurricane Sandy on Flickr | Q28661772 |
| | A Behavioral Model of Rational Choice | Q29300980 |
| | Digital Disease Detection — Harnessing the Web for Public Health Surveillance | Q29302361 |
| | Prospect Theory: An Analysis of Decision under Risk | Q29540774 |
| | Mapping collective behavior in the big-data era. | Q30765239 |
| | Using big data to predict collective behavior in the real world | Q30765251 |
| | Distributions of observed death tolls govern sensitivity to human fatalities | Q33564343 |
| | Predicting consumer behavior with Web search | Q34200094 |
| | Scale invariance in biology: coincidence or footprint of a universal mechanism? | Q34276653 |
| | Novelty and collective attention | Q34706943 |
| | The Economic Lives of the Poor | Q37088969 |
| | Decision by sampling: the role of the decision environment in risky choice. | Q37420548 |
| | Scaling laws in cognitive sciences. | Q37723747 |
| | On forgetting the historical past | Q38502793 |
| | Discounting of delayed rewards: Models of individual choice | Q43184925 |
| | Twitter mood predicts the stock market | Q43662959 |
| | Decision by sampling. | Q51985230 |
| | Loss Aversion in Riskless Choice: A Reference-Dependent Model | Q55879700 |
| | Power laws, Pareto distributions and Zipf's law | Q55898849 |
| | Loss aversion, diminishing sensitivity, and the effect of experience on repeated decisions | Q56336856 |
| | Exploration and Exploitation in Organizational Learning | Q56387222 |
| | SNIF-ACT: A Model of Information Foraging on the World Wide Web | Q56504945 |
| | Manifesto of computational social science | Q57400736 |
| | Scale-invariance as a unifying psychological principle | Q77230414 |
| P433 | issue | 3 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | human–computer interaction | Q207434 |
| | decision making | Q1331926 |
| P304 | page(s) | 685-96 | |
| P577 | publication date | 2016-07-01 | |
| P1433 | published in | Topics in Cognitive Science | Q15817100 |
| P1476 | title | Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data | |
| P478 | volume | 8 | |