most citedAutomated Diagram Generation to Build Understanding and Usability

1 citations · 3 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI20201 cited

Finding the Loops that Matter

Robert Eberlein, William Schoenberg

The Loops that Matter method (Schoenberg et. al, 2019) for understanding model behavior provides metrics showing the contribution of the feedback loops in a model to behavior at ea…

cs.AI20201 cited

Automated Diagram Generation to Build Understanding and Usability

William Schoenberg

Causal loop and stock and flow diagrams are broadly used in System Dynamics because they help organize relationships and convey meaning. Using the analytical work of Schoenberg (20…

cs.SE20201 cited

Seamlessly Integrating Loops That Matter into Model Development and Analysis

William Schoenberg, Robert Eberlein

Understanding why models behave the way they do is critical to learning from them, and to conveying the insights they offer to a broad audience. The Loops that Matter methodology a…

cs.SI2019

LoopX: Visualizing and understanding the origins of dynamic model behavior

William Schoenberg

It is a fundamental precept of System Dynamics that structure leads to behavior. Clearly relating the two is one of the roadblocks in the widespread use of feedback models as it no…

physics.soc-ph2019

Understanding model behavior using loops that matter

William Schoenberg, Pål Davidsen, Robert Eberlein

The link between structure and behavior is central to System Dynamics, but effective tools for understanding that relationship still elude us. The current state of the art in the f…

cs.LG2019

Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs

William Schoenberg

This paper presents a new causal network learning algorithm (FSNN, Feedback System Neural Network) based on the construction and analysis of a non-linear system of Ordinary Differe…