01
Human–AI collective intelligence
When do AI agents strengthen collective reasoning, and when do they only make agreement look easier than it is?
- Human–LLM deliberation
- Responsible AI learning
- Generative-agent validity
Assistant Professor · Waseda University
Structural decision
intelligence for
human–AI–market systems.
I develop validated relational methods for understanding how people, artificial agents, and markets interact, and for turning complex structural evidence into decisions that are interpretable, reproducible, and actionable.
Based inTokyo, Japan
FieldComputational social science
Core lensNetworks & complex systems
Network scienceHuman–AI collaborationDecision analyticsAgent-based simulationSustainable marketsResponsible AI
01 / Research architecture
One question, two connected programs.
How can relational evidence make complex systems more understandable, valid, and actionable?
02
Sustainable markets & resilient operations
How can relational evidence reveal value formation, trust, and recovery pathways across physical and digital markets?
- Second-hand & circular markets
- Consumer networks
- Robust operations
Method spineA repeatable path from complex data to defensible decisions.
Represent
Model actors, objects, attributes, and time as relational systems.
Network motifs · attributed graphs · NLPValidate
Separate a model that fits from one that can support a claim.
Null models · holdouts · calibrationDesign
Turn structural evidence into decisions, tools, and interventions.
Simulation · decision analytics · open pipelines02 / Current work
Validity before strong claims.
My current projects test whether computational patterns can survive the checks needed for explanation and decision-making.
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
03 / Selected publications
Research across systems, methods, and applications.
2025Dissecting the Second-hand Luxury Market Dynamics: Insights from E-commerce versus Brick-and-MortarJournal of Information Processing 33, 219–230Lead & corresponding author2024Multiple Clusters Discovery Utilizing Network Motifs for Community Improvement: Insights from Tourism and Goods’ TransactionsJournal of Information Processing 32, 308–318Lead & corresponding author2024Time Series Network Analysis for Profit Dynamics in Pre-owned Luxury Goods Market Based on Network MotifsNew Frontiers in Artificial Intelligence, 5–20Lead & corresponding author2024A Study of AI Ethics Education in the Context of Japanese Job-Hunting Based on Case Method Using Network AnalysisACM International Conference Proceeding Series, 92–100Lead & corresponding author2022Value Creation Framework for Tourist Destinations Based on Designable Evaluation NetworkSocial Networks 71, 1–11Co-author2021Discovering Multiple Clusters of Sightseeing Spots to Improve Tourist Satisfaction Using Network MotifsIEICE Transactions on Information and Systems E104.D, 1640–1650Lead & corresponding author
04 / Academic practice
Research, teaching, and open infrastructure.
My academic practice connects methodological rigor with accessible education and reusable tools. I teach data and database subjects while developing research workflows that make validation visible and reproducible.
Building data literacy from foundations to decisions
Database systems, data analytics, AI ethics, and computational research practice.
Connecting structural methods to real systems
I welcome collaborations on human–AI interaction, complex networks, sustainable markets, and rigorous social simulation.
05 / Contact
Let's make complex systems
more understandable.
For research collaboration, student supervision, invited talks, or academic exchange:
tengfei.shao@toki.waseda.jp