01
HUMAN + AI
Collective intelligence
Understanding how human and language-model groups reason, deliberate, and reach decisions.
Human–LLM deliberation · Responsible AI learning
Ahead of AI lies risk...
Tengfei Shao
Assistant Professor, Waseda University
AI AGENTS / COMPLEX NETWORKS
Generative AI is becoming a participant in how we think and decide together. It can open new paths to agreement—or create the illusion of it. I test LLM agents against real human deliberation, tracing where they reflect our reasoning and where they drift. Through complex networks and human data, I explore how individual choices become collective outcomes—and what separates shared understanding from a convincing echo.
THE RESEARCH
From interactionto understanding.
AI agents make behavior explorable. Networks make relations visible. Evidence determines what a simulation can explain.
02
RELATIONAL STRUCTURE
Complex networks
Examining how local interactions and network structures reveal patterns in complex systems.
Network motifs · Communities · Attributed graphs
03
SIMULATION + EVIDENCE
Empirical validation
Testing where agent-based simulations align with observed behavior, and where their limits matter.
Holdouts · Null models · Calibration
01 / 05Agents
Intelligence starts with individuals.
Different agents bring different attributes, information, and perspectives. Their differences are the starting point for studying a collective.
How do individual differences shape collective outcomes?
Heterogeneity · Individual behavior
Interactions02 / 05Interactions
A connection changes the system.
An exchange creates a relation. Following who interacts with whom makes the process of collective reasoning visible.
How do interaction patterns influence collective reasoning?
Information exchange · Relational evidence
Networks03 / 05Networks
Local structure. Collective patterns.
Neighborhoods, communities, and recurring local structures offer different views of a complex network. Their interpretation requires careful comparison and analysis.
What can network structure reveal about complex systems?
Network motifs · Communities · Null models
Inspect a conceptual network
Conceptual illustration, not empirical data.
04 / 05Collective behavior
See what emerges between agents.
Coordination, agreement, and differentiation are properties of a collective. I investigate how these patterns relate to the interactions beneath them.
What emerges when many agents interact?
Coordination · Collective reasoning
Validation05 / 05Validation
A convincing simulation needs evidence.
Simulations become scientifically useful when their behavior can be tested against observations. I examine outcomes, interaction processes, and network structure to clarify what a model can support.
How faithfully does a simulation represent the system it studies?
01 / ObserveDefine the empirical reference.
02 / CompareTest behavior and structure.
03 / ClarifyEstablish the model’s limits.
Holdouts · Calibration · Reproducibility
Current workCURRENT WORK
Questionsin progress.
My work connects collective reasoning and social simulation with applications in AI ethics education and sustainable markets.
Collective intelligence
Measuring validity gaps between human and language-model groups
Responsible AI
Evaluating how case-based AI ethics education changes reasoning
Social simulation
Testing generative simulators against emergent network structure
Sustainable markets
Tracing trust, value retention, and recovery in resale systems
SELECTED PUBLICATIONS
Ideas become
evidence.
Research across networks, human behavior, and connected markets.
Full publication record (opens in a new tab)No matching publications. Try another year or search term.
6 selected publications
BEHIND THE RESEARCH
Curiosity.Structure.Evidence.
Tengfei Shao 邵 騰飛
I am an Assistant Professor at Waseda University in Tokyo. My research brings together agent-based simulation, network science, and computational social science.
I develop methods for studying how interactions shape collective patterns, with an emphasis on transparent analysis and empirical validation.
- AFFILIATION
- Waseda UniversityTokyo, Japan
- DEGREE
- PhD in Engineering · 2025
TEACHING & ACADEMIC PRACTICE
Making methodsaccessible.
I teach database systems, data analytics, AI ethics, and computational research practice. My academic work connects methodological rigor with accessible education and reusable research workflows.
- Database systems
- Data analytics
- AI ethics
START A CONVERSATION
What could weunderstand together?
Research collaboration, academic exchange, and questions worth exploring.
tengfei.shao@toki.waseda.jp