Behavior informatics
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Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights.[1] BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inference of future possible behaviors. This occurs through pattern recognition.[2]
Different from applied behavior analysis [3] from the psychological perspective, BI builds computational theories, systems and tools to qualitatively and quantitatively model, represent, analyze, and manage behaviors of individuals, groups and/or organizations [1].
BI is built on classic study of behavioral science,[4] including behavior modeling, applied behavior analysis, behavior analysis, behavioral economics, and organizational behavior. Typical BI tasks consist of individual and group behavior formation, representation,[5] computational modeling,[6] analysis,[7] learning,[8] simulation,[9] and understanding of behavior impact, utility, non-occurring behaviors etc. for behavior intervention and management. The Behavior Informatics approach to data utilizes cognitive as well as behavioral data. By combining the data, BI has the potential to effectively illustrate the big picture when it comes to behavioral decisions and patterns. One of the goals of BI is also to be able to study human behavior while eliminating issues like self-report bias. This creates more reliable and valid information for research studies. [10]
Behavior analytics
Behavior informatics covers behavior analytics which focuses on analysis and learning of behavioral data.
Behavior
From an Informatics perspective, a behavior consists of four key elements: actors (behavioral subjects and objects), operations (actions, activities) and interactions (relationships), and their properties. A behavior can be represented as a behavior vector, all behaviors of an actor or an actor group can be represented as behavior sequences and multi-dimensional behavior matrix.
See also
References
- ^ Cao, Longbing (2010). "In-depth Behavior Understanding and Use: the Behavior Informatics Approach". Information Science. 180 (17): 3067–3085. arXiv:2007.15516. doi:10.1016/j.ins.2010.03.025. S2CID 7400761.
- ^ Pavel, Misha (2015). "Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care". IEEE Transactions on Biomedical Engineering. 62 (12): 2763–2775 – via PubMed.
- ^ Fisher, Wayne W.; Piazza, Cathleen C.; Roane, Henry S. (eds.) (2011). Handbook of Applied Behavior Analysis. The Guilford Press.
{{cite book}}:|first3=has generic name (help) - ^ Hinkle, D.E.; Wiersma, W.; Jurs, S.G. (2002). Applied Statistics for the Behavioral Sciences: Applying Statistical Concepts. Wadsworth Publishing.
- ^ Wang, Can; et al. (2015). "Formalization and Verification of Group Behavior Interactions". IEEE Transactions on Systems, Man, and Cybernetics: Systems. 45 (8): 1109–1124. doi:10.1109/TSMC.2015.2399862. S2CID 18274342.
- ^ Ilgen, D.R.; Hulin., C.L. (Eds.) (2000). Computational Modeling of Behavior in Organizations: The Third Scientific Discipline. American Psychological Association.
- ^ Pierce, W.D.; Cheney, C.D. (2008). Behavior Analysis and Learning. Psychology Press.
- ^ Xu, Y.S.; Lee, K.C. (2005). Human Behavior Learning and Transfer. CRC Press.
- ^ Zacharias, G.L.; MacMillan, J. (Eds.) (2008). Behavioral Modeling and Simulation: From Individuals to Societies. National Academies Press.
- ^ Gellman, Marc D., ed. (2020). Encyclopedia of Behavioral Medicine. New York, NY: Springer New York. doi:10.1007/978-1-4614-6439-6. ISBN 978-1-4614-6439-6.