Literature DB >> 28154052

Predicting human behavior: The next frontiers.

V S Subrahmanian1, Srijan Kumar1.   

Abstract

Machine learning has provided researchers with new tools for understanding human behavior. In this article, we briefly describe some successes in predicting behaviors and describe the challenges over the next few years.
Copyright © 2017, American Association for the Advancement of Science.

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Year:  2017        PMID： 28154052     DOI： 10.1126/science.aam7032

Source DB:  PubMed          Journal:  Science        ISSN： 0036-8075            Impact factor:   47.728


  7 in total

1.  Computational assessment of long-term memory structures from SDA-M related to action sequences.

Authors:  Benjamin Strenge; Ludwig Vogel; Thomas Schack
Journal:  PLoS One       Date:  2019-02-22       Impact factor: 3.240

2.  The future of human behaviour research.

Authors:  Janet M Box-Steffensmeier; Jean Burgess; Maurizio Corbetta; Kate Crawford; Esther Duflo; Laurel Fogarty; Alison Gopnik; Sari Hanafi; Mario Herrero; Ying-Yi Hong; Yasuko Kameyama; Tatia M C Lee; Gabriel M Leung; Daniel S Nagin; Anna C Nobre; Merete Nordentoft; Aysu Okbay; Andrew Perfors; Laura M Rival; Cassidy R Sugimoto; Bertil Tungodden; Claudia Wagner
Journal:  Nat Hum Behav       Date:  2022-01

3.  A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments.

Authors:  Aitor Almeida; Unai Bermejo; Aritz Bilbao; Gorka Azkune; Unai Aguilera; Mikel Emaldi; Fadi Dornaika; Ignacio Arganda-Carreras
Journal:  Sensors (Basel)       Date:  2022-01-18       Impact factor: 3.576

4.  Randomly distributed embedding making short-term high-dimensional data predictable.

Authors:  Huanfei Ma; Siyang Leng; Kazuyuki Aihara; Wei Lin; Luonan Chen
Journal:  Proc Natl Acad Sci U S A       Date:  2018-10-08       Impact factor: 11.205

5.  A data driven methodology for social science research with left-behind children as a case study.

Authors:  Chao Wu; Guolong Wang; Simon Hu; Yue Liu; Hong Mi; Ye Zhou; Yi-Ke Guo; Tongtong Song
Journal:  PLoS One       Date:  2020-11-20       Impact factor: 3.240

6.  Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

Authors:  Daniel A Adler; Dror Ben-Zeev; Vincent W-S Tseng; John M Kane; Rachel Brian; Andrew T Campbell; Marta Hauser; Emily A Scherer; Tanzeem Choudhury
Journal:  JMIR Mhealth Uhealth       Date:  2020-08-31       Impact factor: 4.773

7.  Fundamental limitations on efficiently forecasting certain epidemic measures in network models.

Authors:  Daniel J Rosenkrantz; Anil Vullikanti; S S Ravi; Richard E Stearns; Simon Levin; H Vincent Poor; Madhav V Marathe
Journal:  Proc Natl Acad Sci U S A       Date:  2022-01-25       Impact factor: 11.205
  7 in total

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