We “See” What We Believe
I’m not saying we are wrong. And I’m certainly not saying we should plan to be wrong. But what if . . . ? [I think this may well be an ongoing series of posts!] I’ve been thinking more and more about how we’ve gotten so much wrong. From the political realm to the business realm to the social realm, things seem even more unpredictable than ever.
The whole problem with the world is that fools and fanatics are always so certain of themselves, and wiser people so full of doubts.
What brought this to mind was a recent article in the business/economic news that, “The Kansas Experiment Is Bad News For Trump’s Tax Cuts.” (Here is one article of many.) Now I’m not taking sides here. And it doesn’t matter to me who is proposing the tax cuts. What bothers me is that I’ve always lived with the notion that reducing taxes on businesses and individuals would spur economic growth. The Kansas experiment shows that assumption to be faulty in practice (perhaps not in theory).
Admit Mistake and Correct
In the Kansas case, the legislature recently voted to override Governor Brownback’s veto of a bill to repeal the $1.2 Billion of tax cuts put in place over two years prior. That’s interesting. The legislature supported putting in the tax cuts and then watched as the budget became a disaster. The results were not at all in line with expectations.
Their analysis showed that the problem was that while their constituents were quite happy with the tax reductions, they were not about to let their favorite programs get cut to balance the budget. So a piece of the experiment is missing — cutting government services along with the cut in tax revenue. And, more importantly, the tax cuts did not generate the predicted growth in jobs or in revenue.
However, that missing piece is, in the end, irrelevant. If the electorate will not allow cutting expenses, then the legislature cannot cut taxes unless they are willing to run a deficit. Which most legislators claim they are not willing to do to any great extent or for any significant length of time.
Where Are We Then?
Citizens and legislators are on the wrong side of the complexity curve on this one. The theory doesn’t matter if the model is incomplete. Kansas learned that they rushed to implement before vetting their assumptions. In hindsight, they would have been better off if they surveyed their citizens to see what services they were willing to forego in return for tax relief.
Politicians are re-learning that citizens can be pretty fickle. According to what I’m reading, the ACA (Affordable Care Act, aka “Obama Care”) is somehow now quite popular with the voters. And 70% do not want major changes. At least, they don’t want what the proposed AHCA (Affordable Health Care Act, aka “Trump Care”) is offering. Go figure. Seems that as the voters work through that complexity curve, they are modifying their “simplistic views” on this topic.
Doubts and Dangers
I’m not sure, but it seems that the more we work through a complex subject, the less likely we are to be definitive. This of course must be balanced with making a decision to act and then following through. A hazard of being too aware of and concerned about the complexities is that we fall into analysis paralysis. That is to be avoided if we wish to build high-functioning teams.
The doubts created by fully understanding the complexity of the tasks in front of us help to make sure we continuously question our assumptions. Admitting that we could be wrong is a powerful tool to enable our staying on track to achieving the larger vision. There is no sense and no joy in spending time and treasure to “take the hill” only to find that our assumptions about which hill to take were wrong!
The next time someone says to you, “Look. It’s simple,” close your ears. Or, at least, ask questions to determine if the person making that assertion has actually been through the complexity curve. We can’t all be experts on every topic. We can all be wrong (even the experts). Choose your experts carefully. And then remain in doubt — but not paralyzed.