Should The Random, Drunkards Walk Policy Model be Taken More Seriously?

It's hard to look at the current policy environment in the US or in Argentina and seriously think about Rational Policy Models. In the current Trump II Administration, events seem to happen randomly: Tariffs being imposed and withdrawn; Immigrants being expelled and then brought back by the courts; Universities having research funding withdrawn for political reasons and then given back after a shake-down; National Guard Troops being deployed to the streets of states and cities with democratic Governors and Mayors and then being withdrawn; the Supreme Court supporting some radical policy measures and not others, etc. etc. What Rational Policy Models can explain all of this?

Q(t) = Q(t-1) + E(t-1)

The Random Walk model is really simple (equation above) and easy to estimate from a statistical standpoint (there is only one parameter in the model and its value is given as 1.0): Tomorrow is like today except for Random Error or History is One Damned Thing After Another or The Drunkards Walk: How Randomness Rules Our Lives.

When using Multimodel Inference (MMI) and selecting models based on the Akaike Information Criterion (AIC), the Random Walk (RW) model should always be one of the competitors. Any model I present will always be tested against the RW. For many countries and time periods, especially using year-to-year data, the RW is often the best short-term model. Yet, we continue to hold on to the belief that there is (rational?) causal structure in macro-social systems.

In Systems Models, there are many kinds of RW models: RW with drift, RW with Feedback, Partial RW (some diagonal elements of the System Matrix are 1.0), in addition to the pure RW model:

The pure RW model above is from Argentina. The only distinguishing feature of the model (from any other country) is the initial state:

If the on-diagonal elements of F take on values other than 1.0 or the off-diagonal elements of F take on values other than 0.0 or if there is a fourth column to F (a constant vector capturing drift) we have the other RW models. To decide which one is most appropriate in a given historical period, I use the AIC criterion.

To supplement Universal Growth Theory in Evolutionary Economics (Random Walk -> Malthusian Growth Model -> Neoclassical Growth Model), I would add an initial RW stage (above) that can be revised at any time in history when the Environment changes and a random search for new approaches is needed.

Or, in Marxist Economics, as the initial stage prior to Feudalism.

Or in Post-Modernist variants (above) with a feedback loop returning to RW when the Environment Changes.

Below in the Notes are some concrete examples. 

Notes

Examples:

• Is the US Presidential Election a Random Walk?
• Trump Takes the US On a Random Walk
• Mexico Takes a Random Walk
• Argentina Takes a Random Walk
• What We've Learned about Economic Bubbles
• A Random Walk Among the Undead
• The Drunkards Walk and the Moment of Truth
• What Caused the Financial Crisis
• Policy Models, Randomness and Free Will
• The Deficit and the 2012 Federal Budget Overview
• The Edge of Chaos in Switzerland