I am keenly interested in notions of causation, and specifically how they relate to models of complex phenomena. One reason is that complexity (i.e., feedback, multiplier effects, evolution, etc.) blurs the lines of clear causal relations. I have made the point elsewhere that causation is not something in the world that we match and/or uncover, but rather only something in our models (including mental models) that structures and orders the actions and reactions. I am therefore especially focused on the methodology of mechanisms in models and how to make them clean, and conform to how we think things happen (which is why cross-level, e.g., downward or upward, causation is something to avoid). Within a level of organization a model's causal relationships follows the rules generating behavior in that model, but the higher-order (emergent) phenomena generated by that mechanism may reveal causal relationships that violate the ones generating said phenomena. Here I outline a project that can reveal such an emergent causal inversion.

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