What is behavior informatics?
Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights.
Different from applied behavior analysis from the psychological perspective, BI builds computational theories, systems and tools to qualitatively and quantitatively model, represent, analyze, and manage behaviors of individuals, groups, or organizations.
BI is built on a classic study of behavioral science, including:
- Behavior Modeling,
- Applied behavior analysis,
- Behavior analysis,
- Behavioral economics,
- And organizational behavior.
Typical BI tasks consist of individual and group behavior formation, representation, computational modeling, analysis, learning, simulation, and understanding of behavior impact, utility, non-occurring behaviors, etc. for behavior intervention and management.
In essence, Behavior Informatics seeks to deliver quantitative and computational technologies and tools to deeply understand behaviors, social behavior networks, their evolution, effect and impact. In this sense, we also call it behavioral computing. As a research issue, BI consists of many research directions that are worthy of systematic research and conducting case studies from aspects such as behavioral data construction, behavior modeling and representation, behavior impact modeling, behavior pattern analysis, behavior network analysis, non-occurring behavior analysis, and behavior intervention and management. Additionally, behavior measurement and evaluation, behavior presentation, and behavior use are very important topics.
Why do we need behavior informatics?
Behavior forms a critical ingredient and engine of human beings, artificial systems, business, society, environment, and economy. Behavior is ubiquitous, and behavior intelligence drives the development, consequence, and evolution of our human beings, artificial systems, business, society, environment, and economy.
Complex behaviors are widely seen in artificial and natural intelligent systems, on the internet, physical and virtual systems, social and online networks, multi-agent systems, and brain systems. The in-depth understanding of complex behaviors has been increasingly recognized as a crucial means for disclosing interior driving forces, causes, and impact on businesses in handling many challenging issues.
However, traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. The so-called behavior analysis in behavior science, behavioural analytics and learning often focuses on human demographic and business usage data, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle, dynamics and impact on complex problems and business issues.
Behavior informatics aims for an in-depth analysis of behaviors and their interior driving forces, causes and impact. Behavior informatics consists of theoretical and applied studies on high impact behavior sequence analysis, impact-oriented combined behavior analysis, high utility behavior analysis, nonoccurring behavior analysis, coupled, group and collective behavior analysis, statistical modeling of coupled behaviors, probabilistic modeling of sparse rating behaviors, understanding behavior choice and attraction, behavior analysis with recurrent networks, behavior analysis in visual data, behavior learning from demonstrations.