A Model for Policy Reform and Automation of Federal Agencies

 

The Trump II Administration's attempt to destroy Federal Agencies (Department of Education, Department of Energy, Environmental Protection Agency, USAID, etc., etc.) brings up the questions of (1) What are all the Federal Agencies actually doing, (2) How do citizens evaluate what they are doing and (3) How do citizens have some input on policy decisions (current voting does not provide enough control and feedback). Intelligent policy reform and automation could help start the process of evaluating Federal Agencies rather than waiting for Presidential wrecking balls.

The graphic above (with some modifications) was generated by ChatGPT in response to the question of whether the US Federal Reserve could be automated. It can be generalized to cover any Federal Agency. In Detail:

• MANDATE Laws passed by Congress define a REACTION FUNCTION that must be minimized.
• DATA The Federal Government currently gathers all the data need for decision making.
• VALIDATION Real time is noisy and has to be validated.
• MULTI MODEL There is no one perfect model of the economy (what ChatGPT calls the "Big Brain"). The multi-model, ensemble approach to forecasting is also used by Hurricane forecasting (see the Boiler Plate). I would recommend using the Akaike Information Criterion (AIC) to evaluate models.
• REGIME CLASS There are different regimes that the agencies operate under in addition to Business-as-Usual (BAU). The different regimes have to be defined in the REACTION FUNCTION.
  
• POLICY ENGINE The Policy Engine would present multiple models predicting actions under different regimes.
• REACTION FUNCTION  would describe the best action under different regimes (here). It describes desired states (Q*) to be compared with an actual states (Q). (Q*-Q) is a multivariable Cybernetic Reaction Function used to control systems.
• VOTING I have allowed for citizen voting using a cell phone app to provide democratic input similar to the Atlanta Fed EconomyNow App (see below in the Notes) but with voting input.
• UNCERTAINTY The main purpose of the Agency Board is to allow human input, accountability and avoid technocratic mistakes under uncertainty. 
• OVERSIGHT Oversight involves identifying regimes that are not BAU.
  
• OVERRIDE Under conditions of uncertainty, the  Agency Board can override the Policy Engine.
• POLICY Ultimately, the Agency decides policy using all available information.
• COMMUNICATE The  Agency Board is charged with transparently communicating the policy decision and explaining their evaluation of existing data and output from the Policy Engine. The communication would include open evaluation of how well the agency has performed under different policy regimes.

So, ultimately and obviously, the Agency Board cannot and should not be replaced by a computer. However, a process can be set up that uses all available multi-model computer input and public voting to allow the Agency Board to make transparent policy decisions.

ChatGPT reports that:

US Executive Agencies (the Department of Defense, HHS, Homeland Security, Dept. of State, Immigration, the White House, Congress, the Supreme Court, etc., etc.) have all been compromised by poor leadership under an authoritarian political regime (the Trump II Administration). Democratic  voting itself has been under threat and is too slow and indirect a process to prevent abuse. Indeed, the entire system seems quite Anachronistic.

The model above provides a general approach to reforming failing institutions. The US Federal Reserve (here) could be used as a model.  

Atlanta FED EconomyNow App

 

The Atlanta Fed EconomyNow App would be extended to allow voting on policy options, as another source of input for the Fed.