Inference Dynamics
Making Complex Reasoning Explicit
Complex challenges are not only difficult because of data—they are difficult because of how different perspectives interact, conflict, and evolve over time.
Inference Dynamics is a research program and analytical method that makes these processes visible.
At its core, it combines:
- qualitative research on expert dialogue and decision-making
- AI-based analysis of language and meaning structures
- system-oriented modeling of interactions and dynamics
Implemented in our system Stratify, this approach enables organizations to move from fragmented discussions to structured, evidence-based decision models.
What We Do
We analyze how stakeholders:
- frame problems
- make implicit assumptions
- generate (often unarticulated) causal inferences
AI allows us to identify patterns in these processes that remain invisible in conventional analysis—revealing hidden structures of reasoning and coordination.
What This Enables
- Clarity in complex decision environments
- Alignment across conflicting perspectives
- Explicit modeling of system dynamics and trade-offs
- Development of actionable strategies and policies
Typical Use Cases
- Strategy development in complex organizations
- Multi-stakeholder governance processes
- Innovation and transformation programs
- Policy design under uncertainty
The Role of AI (Stratify)
Our system Stratify operationalizes Inference Dynamics by:
- structuring qualitative data into dynamic models
- identifying patterns across perspectives
- supporting the design of intervention strategies
This creates a new type of AI-supported research and decision infrastructure.
Partnership
We collaborate with organizations facing:
- high complexity
- multiple stakeholders
- unclear or contested problem structures
Typical partners include corporations, public institutions, and research consortia.
Invitation
If your organization is navigating complex challenges and seeks a structured, AI-supported approach to decision-making, we invite you to connect with us.
Read About Our Research
Collective Intelligence as Communication & Inference
Rehm, S.-V. (2026). The promise of collective intelligence: Communicative–inferential coordination across collective systems. Collective Intelligence Perspectives, 1(1), 13–22.
https://doi.org/10.57958/cip.2026.0003
Turning Tacit Knowledge into Dynamic Strategies
Rehm, S.-V. (2025). Turning tacit knowledge into dynamic strategies through collective inferencing. MIS Quarterly Executive, 24(4), Article 7, 375–382.
https://aisel.aisnet.org/misqe/vol24/iss4/7
🎧 https://ais-insights.ai/insights/678
Observing Artifacts Through Distinctions
Rehm, S.-V., Goel, L., & Junglas, I. (2023). Observing artifacts: How drawing distinctions creates agency and identity. In R. D. Galliers & B. Simeonova (Eds.), The Cambridge Handbook of Qualitative Digital Research, 193–210. Cambridge University Press.
https://doi.org/10.1017/9781009106436.017
Researching Digitalized Work Arrangements
Rehm, S.-V., Goel, L., & Junglas, I. (2022). Researching digitalized work arrangements: A Laws of Form perspective, Information & Organization, 32(2), June 2022, 100391.
https://doi.org/10.1016/j.infoandorg.2022.100391