Grok on the status of Forrester's National Model: Status and Legacy The full National Model was never completed and released as a single, polished, publicly available model or definitive book (Forrester joked for years that it was “two years from completion”). Intermediate results appeared in papers (notably a 1976 introduction in Technological Forecasting and Social Change by Forrester, Nathaniel J. Mass, and Charles J. Ryan, and Forrester’s 1989 chapter “The System Dynamics National Model: Macrobehavior from Microstructure”), PhD theses, internal MIT memos, and conference papers. Simplified models derived from it, especially on the long wave, have been more widely studied and published. Efforts have continued sporadically to reconstruct, modernize, and document portions of the work (including software migrations and curated collections related to economic dynamics). The project remains influential within system dynamics for demonstrating how macro patterns can emerge from micro decision policies and for providing one of the most developed endogenous theories of the economic long wave. It stands in contrast to equilibrium-oriented or purely statistical approaches in mainstream economics by emphasizing structure, feedback, delays, and the often counterintuitive consequences of ordinary policies.
On Tue, Aug 4, 2026 at 12:01 PM James Bowery <[email protected]> wrote: > > > On Mon, Aug 3, 2026 at 10:25 AM Matt Mahoney <[email protected]> > wrote: > >> ...How do we negotiate the data set? How do we prove causation? >> > > Trees: Negotiate the data set. > Forest: "Prove" causation. > > It's hard to overemphasize how important it is to see the forest. Let's > not palaver about the trees. Let's focus on what qualifies a causal model. > > A causal model of the world starts with two things: > > 1. The state of the world at t_0 that contains within it some notion > of change. This is what Newton did when he incorporated the first and > second derivatives into his notion of state. > 2. Invariant rules of state transformation from t_n -> t_n+1 > > If you are missing #1, then you are reduced to kinematics rather than > dynamics. > > If you are missing #2, then all you have are statistics -- and that's true > even if you have done a curve fit that has a time parameter. > > Another critical aspect of the "forest" is that it is naive as to which of > the trees affect which other trees. Therefore, the architecture is a dense > RNN (i.e., it can provide a pseudo-UTM since the number of trees is > unbounded finite). By "dense," I mean simply that all "weights" are > unconstrained. And I'm only using the dense RNN as a metaphor for the "no > priors" of the "There's a forest," perspective. > > Once we have settled that, we can palaver a bit about the fact that the > laws of motion are reversible, etc., but this is pedantry when we are > dealing with neighbors arming themselves to kill each other over who gets > to tell everyone else to abide by "narrative" of social causation. > > > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T5b58bcc51c493d41-M4c4b70b60f2d28f41fb9257e Delivery options: https://agi.topicbox.com/groups/agi/subscription
