In-Depth Behavior Understanding and Use: The Behavior Informatics Approach (2007.15516v1)

Abstract: The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. The so-called behavior analysis is actually based on human demographic and business usage data, where 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 and impact on complex problems and business issues. We propose the approach of Behavior Informatics (BI), in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific means by finding deeper and more informative patterns leading to greater in-depth behavior understanding. BI creates new directions and means to enhance the quantitative, formal and systematic modeling and analysis of behaviors in both physical and virtual organizations.

• The paper introduces the BI framework which explicitly models behaviors by converting transactional records into detailed behavior vectors.
• Its methodology enables conversion of data into a behavior feature space, facilitating simulation and impact analysis across various sectors.
• Empirical case studies in market microstructure and social security demonstrate BI's ability to uncover and analyze hidden behavioral patterns.

An Analytical Overview of Behavior Informatics Approach

The paper "In-Depth Behavior Understanding and Use: The Behavior Informatics Approach" by Longbing Cao presents a methodological framework termed Behavior Informatics (BI), which aims to overcome the limitations of traditional behavior analysis approaches. These traditional methodologies predominantly rely on qualitative analyses and transactional data. BI introduces a more dynamic framework that focuses on converting these transactional records into behavior-oriented datasets, enabling explicit and quantitative scrutiny of behavior patterns and impacts.

Key Components of Behavior Informatics

BI is introduced as a multifaceted approach to model, analyze, simulate, and utilize behaviors more effectively. It includes several critical components:

• Behavior Representation and Modeling: BI emphasizes the need for systematic representation of behaviors. Instead of being hidden as in typical transactional data, behaviors are explicitly formulated as vectors that encapsulate various attributes like subject, context, action, and impact.
• Behavioral Data Construction: This involves converting standard transactional data into a behavior feature space, thereby explicating hidden behavioral elements.
• Behavior Impact and Pattern Analysis: BI frameworks allow for modeling the impact of behavior on business processes, enabling the discovery of more nuanced patterns compared to traditional demographic-focused analyses.
• Behavior Simulation and Presentation: This involves using simulations to understand and predict behavior outcomes and presenting these insights in a manner that is actionable for decision-making processes.

Case Studies and Empirical Evidence

The paper illustrates the application of BI through two case studies. The first one focuses on market microstructure behavior within capital markets, where trading actions are analyzed to detect exceptional trading behaviors. The second case study examines debt-related interactions within social security systems, uncovering high-impact patterns that contribute to governmental debt prevention.

Substantial experiments presented in these studies show that BI's approach to uncovering hidden behavioral patterns contributes significantly to understanding business dynamics that were previously opaque using traditional methods. For example, identifying patterns in trading can aid in market surveillance, while discerning social security behavior patterns can preemptively address overpayment issues.

Implications and Future Directions

The BI approach proposes novel directions in the systematic modeling and analysis of behaviors. The frameworks introduced can extend to various domains, offering more detailed insights than those provided by classic analyses based solely on transactional data. This shift has practical implications not only for business intelligence but also for security, marketing, and organizational behavior assessment.

Looking forward, BI presents several open research avenues. There is a need for further validation of its methodologies across diverse datasets and contexts. Additionally, the integration of advanced data mining techniques and machine learning within the BI framework could enhance the extraction and utilization of complex behavior patterns.

In summary, the Behavior Informatics approach detailed by Longbing Cao emerges as a substantive refinement over traditional behavior analysis methodologies, presenting a paradigm wherein behavior data is both explicit and scrutinizable, thus allowing for more profound and actionable interpretations across various sectors.