SB 30-15: Comptroller Reform Act

[Fmr. Representative Encke]

Saw this thread and will chime in.

This Laffer Curve + Wealth Tax stuff is all fine and well; I used something similar to lfromnj's sheet when I was CG/DGM (I also tied the numbers to unemployment). And for a time I had hopes of being able to 'automate' a large part of the budgeting process by having spreadsheets that would do most of the heavy lifting. Unfortunately, in practice, this almost never works as a labor-saving technique because many of the more detailed tax proposals change certain tax brackets or incentives in ways that require significant amounts of manual intervention. In addition, the variety of bills that the CG is required to analyze is so enormous that any simple simulator is only an effective time-saver in very narrow and specific situations that comprise a small fraction of the total body of expected work.

The other issue is that a purely mathematical economic simulator will necessarily depend on a set of fairly simple inputs and therefore can be easily gamed by people who are aware of the structure of the simulator (particularly if the spreadsheets are made public). Wouldn't it be nice if IRL we could know exactly what policies to pass because we could just predict the outcomes on a spreadsheet?  Of course, in practice, the Comptroller General would have plenty of room for adding additional, more subjective elements at his discretion, but would still have to deal with the burden of 'realism' as well as the burden of having to create a system that is reasonably consistent with the work of prior CGs (no hockey-stick unemployment graphs, Clyde!!).

Earlier in the thread, Yankee mentioned the economic simulators found in video games. The problem with taking these games as a blueprint for a similar economic simulator in Atlasia is that participants in Atlasia seem to have a much higher standard for strict realism that they would when playing a video game. I remember when I did the federal budget, Sestak questioned me over discrepancies between the estimates I presented and the RL numbers for that year. The discrepancy was something seemingly insignificant like 0.6% but was apparently important in the context of growth from the previous year's budget (that had been completed by a different DGM). In other cases, the changes made by certain pieces of legislation are too granular for simple economic simulators, and require the use of specific RL data for detailed cost analyses. The use of this RL data then can later come into conflict with the simulated data in sometimes unexpected ways.

The difficulties often arise in the management of the interconnectivity of various parts of the simulator; one is expected to find a way to connect unemployment, GDP, population growth/immigration, trade, a whole slew of tax statistics, etc. in a way that continues to produce semi-realistic numbers for each when changes are made via legislation, both at the federal level and at the (rather opaque) in-game state/regional level. IMO this is practically impossible to do because it would require a much greater deal of complexity to the simulator than is really feasible, or would rely heavily on manual intervention from the CG, which defeats the point of a simulator and is no better than the situation we have now. IMO in order to successfully implement a 'videogame-style' economic simulator, the expectation for realism (in the form of even a loose adherence to RL data) would need to be lowered significantly.

Despite my reservations, I am and have always been interested in the general idea and will follow further developments with interest.

[Fmr. Representative Encke]

In terms of what you mean by realism, do you mean what would actually happen as a consequence of actions being done or a copy paste from real life data without considering the in game differences on various factors (policies, history etc)? It seems from the Sestak example, to be the latter scenario in which case, I wouldn't necessarily consider that to be an example of "realism" as I have applied the term for a good while.

Historically speaking, the "quest for realism" meant that in game actions had "realistic consequences or effects" not that the data matched real life for the sake of matching real life regardless of what might make it not be so.

Determining whether a simulated consequence or effect is realistic is only easy when speaking in very general terms: "X should result in an increase in Y and a decrease in Z" or "X policy will result in growth in Y sector of the economy." It is much more difficult to determine if something is quantitatively realistic.  In-game income tax revenues increased by 41 billion dollars between FY2019 and FY2020. Is this realistic or is it unrealistic? Is there an acceptable range of values that should be considered realistic? If the calculated value is too close to the RL value despite the numerous policy differences between game and RL, is this a coincidence born from a balancing out of several competing factors or should we be concerned about the model itself? Usually the only way to initially test if something is realistic (that is, if it would happen in a RL scenario) is to use RL numbers as a guide.

Perhaps the Sestak example was poorly written; it was intended to illustrate the fact that others' preconceptions of what is and isn't realistic are drawn from looking at what happens IRL. Sestak wasn't necessarily concerned that the numbers weren't a copy-paste of RL, he was wondering why the revenues were X amount lower than they were IRL. The answer, of course, was that I was trying to keep the year-to-year growth realistic in relation to the previous year's budget, which was done by a different DGM and had revenues that were much lower than RL. And my assessment of whether the quantitative year-to-year growth was 'realistic' was very generally based on current and historical RL growth statistics, combined with subjective assessments of how the past year's legislation would affect growth (based, once again, on my own RL knowledge and/or research). Oftentimes, comparison to RL numbers is the only way to determine if something is realistic or not. If I publish an income tax revenue estimate that is $200 billion dollars lower than the RL value, people will want to know why. And it is often difficult to isolate the 'why;' are the numbers lower because of some unforeseen quirk of the model (say, something fundamental to the way that I am calculating the numbers), or are the numbers lower due to the implementation of actual in-game consequences arising from the past year's legislation? The only way to establish a concrete starting point is to use your model to try to recreate (approximately) the RL numbers. But this reverse-engineering process can lead to plenty of problems down the road.