Who is Going to Win Executive Orders in the Supreme Court of Michigan?-Conflict Leads to Definition

Emergency powers has been enacted in a number of states to squelch the spread of COVID. Since that time the political and medical landscape had changed and the essential question of how long can a Governor have emergency management powers. I'm not a big conflict supporter but do sometimes see the benefit of conflict that leads to greater defining of an essential issue of who and when we can and can't use emergency powers and when do those terms end. This article isn't about picking a side or talking about who is right or who is wrong. In a pandemic it is very hard to make perfect choices with imperfect emerging information and thus conflict helps us define essential issues that could be used in the future. 

Let me say I'm taking a bird's eye view of this from an objective viewpoint and not really discussing my personal opinion on when emergency powers are expired. My interest is in the political mode of conflict and how groups disagree but that these disagreements can sometimes be more beneficial than harmful if the parties come to a resolution of important topics. (Trust me...I'm not in favor of most conflicts 😡.)

One wants a 28 day limit and the other one doesn't. Let us just use two denotations to create more objective clarity R FOR 28 day limit and G for NOT 28 days (I couldn't think of a better one😬). R and G are our two logical terms that will be used to denote the opposing sides. I suspect we could build an equation out of this if we really put some effort into it. That is not in the scope of this post either. 

R and G are upset over a contract interpretation and they have a conflict. One says Its OK and the other says its not OK. They try and settle their differences and they can't and still fell upset after talking so they want a third party to help. So we have to introduce factor C (🙈🙉🙊) They have been to court 2X  so we have C1 and C2 and they want to go again so we can introduce C3. C1 and C2 have ruled in favor of G thus far but no one knows what C3 will do. 

How will C3 decide?

Well...I have no idea! 😂 What I can say is that there is a lot of ambiguous information and there are ways in which we might break down and analyze the outcomes. There are some models out there and I will refresh my memory and share a little latter. However, I do know that this is a point in time so I want to use a trajectory so as to look at the very next likely step. 

I'm going to say again that I'm not a legal expert and am only slight familiar with how all the appeals processes work and under what circumstances. What I can say with my labor relations experience is that there are methods to dealing with this ambiguity to help decide what the likely options are. Thus, we need to look at some data so we can create some trends that help us predict the future. 

We might look at C1 and C2 and sort of predict that C3 will be the same just using probability. We might always want to review what courts have typically decisions in order to better understand how appeals courts ruled. We can thus look at prior court cases for trends in information on such things as reverse rates, total amount lost, lawsuits per year, political background, income, education, etc.. etc... etc... Whatever information is available gives us a hint. 

As a side note that is what is scary about cyber crime in that when multiple databases are hit they create lots of information and sometimes this triangulation of hits leads to new knowledge. Other times there may be enough information to create historical trends because past behavior predicts future behavior. The point being, a lot of information creates insight when it is put together well and interpreted by skillful people. 🙈

This isn't a novel idea. The intelligence community and high powered attorneys use these skills from time-to-time. For example a study of past behavior modeling of courts and the supreme court found, "Casting predictions over nearly two centuries, our model achieves 70.2% accuracy at the case outcome level and 71.9% at the justice vote level." (Katz, Bommarito & Blackman, 2017).

In this case you would have to model at the Michigan and Supreme Court level. While 70% is accurate and narrows down the ambiguity it still leaves a lot of room for error. You will want to use other decision matrix to help determine other potential choices. Because we are dealing with real actors and living people with animal spirits we must assume some rationality in decision making. Courts are typically places of higher levels of rational thought (Depends on who you ask and assuming the courts are using both critical thinking and morality in the process 😕.)

To balance this out I could use other tools to help me make decisions and Game Theory and probably another one. I like Game Theory because it interactive with moving players in a fluid situation. that doesn't mean it doesn't have its downfalls. Its just that it allows for presence of the utility of agents that can change and adjust at each of the stages to create possibilities and potentials that can be quantified. So that 30% looks something like perhaps 15%. 

A gap of 15-30% is a still a big open unknown space for people and that can be risk. Because we are dealing with novel situations that will require additional cognitive effort by the judges to fall back and rely more heavily on their strengths and personalities. This is where one would get into deep psychology and how personality impacts choices. It could go into such detail that people might be able to use neuroscience to understand human choice at a granular level.

Its not that simple though as these random "acts of God" that happen that mess it all up. We have no idea where they come from but even the best plans are subject to derailment. New information, environmental changes, political shifts, etc... all impact the context of choices. In all prediction games there is an element of randomness where even if you think your really really sure it will turn out, it doesn't, and you missed something in your calculation such an unknown or random event. Thus you must control for the unknown and randomness in some way (Way beyond the scope of this article). 

So R, G, C1, C2 C3? are all part of the same problem. If we analyzed the situation well we might have narrowed it down to being 87% sure of who will win. There is still enough large variability that one cannot discount the opposite occurring. In business we would try and hedge some risk but ultimately people will put their resources where they are likely to have a win. In business we might analyze cases to understand what new industry and legal landscape changes are occurring. 

If you want to know more about predictive analytics and some of the different models you can look at a pretty good piece entitled What is predictive analytics? Transforming data into future insights that states, " Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning"(Edwards, 2019, para 1).  

While we narrowed down the most likely choices we must still use common sense. We can make predictive modeling of the possibility of reversal, adjustment and confirmation of previous legal decisions. We may find the case won't be heard and we have a vaccine as an act of a changed playing field and thus decisions that might have ended one way ended up another way; while the fundamentals of the case didn't actually change but their context did. So at the upper levels Supreme Court Justices must make decisions that impact a whole different set of expectations. If we knew what those are we can create better decision matrix by reintegrating that knowledge into our existing method. More information continues to adjust and make increasingly accurate the predictions. 

I have a good idea of which side I think will win but that is just my personal opinion. Remember that this case defines certain powers between the executive and legislative branch during an emergency so it is best we just define it and keep moving forward. 

Edwards, J. (August 16, 2019). What is predictive analytics? Transforming data into future insights. CIO. Retrieved 09-11-2020 https://www.cio.com/article/3273114/what-is-predictive-analytics-transforming-data-into-future-insights.html

Katz DM, Bommarito MJ II, Blackman J (2017) A general approach for predicting the behavior of the Supreme Court of the United States. PLoS ONE 12(4): e0174698. https://doi.org/10.1371/journal.pone.0174698

https://www.detroitnews.com/story/news/politics/2020/08/21/whitmers-emergency-actions-upheld-court-appeals/3402687001/