A Common Core for Active Conceptual Modeling for Learning from Surprises

The new field of active conceptual modeling for learning from surprises (ACM-L) may be helpful in preserving life, protecting property, and improving quality of life. The conceptual modeling community has developed sound theory and practices for conceptual modeling that, if properly applied, could help analysts model and predict more accurately. In particular, we need to associate more semantics with links, and we need fully reified high-level objects and relationships that have a clear, formal underlying semantics that follows a natural, ontological approach. We also need to capture more dynamic aspects in our conceptual models to more accurately model complex, dynamic systems. These concepts already exist, and the theory is well developed; what remains is to link them with the ideas needed to predict system evolution, thus enabling risk assessment and response planning. No single researcher or research group will be able to achieve this ambitious vision alone. As a starting point, we recommend that the nascent ACM-L community agree on a common core model that supports all aspects--static and dynamic--needed for active conceptual modeling in support of learning from surprises. A common core will more likely gain the traction needed to sustain the extended ACM-L research effort that will yield the advertised benefits of learning from surprises.