Dedomenocratic Party principle that we can not discover what we prevent ourselves from seeing.

One of the points I made in my last post is that long-lasting laws that forbid some behavior will inevitably prevent us to observe the modern consequences if we allowed that behavior today.   In the specific example of minimum wage, the law was in response to labor conditions of nearly 100 years ago but the law remains in place though with even higher inflation-adjusted amounts.   The law prevents us from observing what will happen if the law was not in place.

I made two predictions for what might happen if we suspended minimum wage laws.  First, because minimum wage is now so culturally accepted and with abundant studies supporting the benefits to business, there will be a natural floor in wages as a matter of good business practice.   Secondly, the elimination of minimum wages will allow new business start-ups involving larger teams voluntarily working for very low pay with the motivation of creating some business that will pay off later.   The second consequence can help many communities with high unemployment rates as well as the nation as a whole by growing the economy faster.

The minimum wage laws are just one of many laws that addressed some historical issue that may no longer apply to the modern culture or the modern circumstances.    These laws remain in place because in a democracy, the laws are perpetual and can only be removed by new legislation that repeals the old.   Repeal efforts are virtually impossible because of the only hard data for what will happen is the ancient data of what happened before the law was created in the first place.

We are reluctant to retire laws because it would be a show of disrespect for the wisdom of our ancestors who made the laws in the first place.   I concede the point of wise ancestors, but they were addressing economic and human conditions that existed at their time.    Those conditions may not exist any more.  The entire economy is much different today.   Also the culture is much different in terms of how human nature can express itself today compared to their day.   Removing a law will not suddenly return us to the conditions that existed before the law was in place.

We give human laws a status of eternal truth that we seek to understand about nature.   When we want to know about laws governing human nature, the first resource we consult is the body of written laws for society.  These written laws become the truth that we can use instead of seeking out fresh observations.  These observations are made impossible because the laws prevent them from happening.    This preconceived truth can prevent us from new discoveries.   Our data projects merely confirm the laws instead of discovering something new.

This discussion suggests a platform principle for the Dedomenocratic Party (a political party acting in the current democracy) to temporarily suspend old laws and institutions (bureaucracies) in order to have a better opportunity to observe new data about what will happen today if these were not in place.   Such suspensions will permit discovery of new opportunities and that old hazards will not return.

Data-driven decision making is less effective when it is biased by our preconceptions.  The motivation for big data projects is to discover new hypotheses.   With governing laws forcing a preconceived behavior, the only thing that these projects will discover are confirmations of our old (and probably obsolete) biases.   Big data does not study nature, but instead it studies what humans imagine nature to be.

The example of minimum wage laws biases our data so that we see many jobs being paid at or near minimum wage.   The wages are at those levels because of the minimum wage laws.   Without the laws, the wages may change in either direction because they will no longer be biased by a notion of a federal standard for wage.  My guess is that the changes will establish de facto minimum wages without a need for legislation.   The law is not necessary to have a minimum wage, it only sets the value of the minimum wage.

Big data analytic projects are already impacting daily life.   The example I have in mind are large grocery store chains that have customer loyalty cards that provide discounts at checkout.   Among the many uses of these cards, one is to  allow the company to better stock their stores based on purchasing habits of their frequent customers.

Yesterday, I went to the store with the specific intent of buying a specific type of fresh bread that gets stocked daily.  The bakery section has a lot of different options of fresh baked bread, but there is only one that I like.   The daily restocking typically includes only a couple of these loaves and they disappear quickly.   Yesterday was one of those days where I arrived too late.   As I stood there in disappointment, I noticed the abundance of my second-choice loaf of bread that I would buy if I were desperate.   I recalled a time before when both of these loaves were stocked to approximately similar quantities.   It seems that they they are finding that my second-choice type of bread sells more reliably than my first choice.  Then it occurred to me that I’m contributing to this conclusion because I indeed will buy this second-choice if the first choice is unavailable.   The store learns not only that this type of bread sells, but also that I’m one of their repeat customers who will reliably buy it.

The two choices are a round loaf that I prefer and a long loaf that is now stocked more abundantly.   Although I like the round loaf, I can imagine that it is impractical for most people.  The problem with the round loaf is that the slices have a wide variety of different sizes, making it harder to serve in groups.   Although I do not object to this inconvenience I can imagine that many others will.   Among the population of store patrons, I am probably among a minority whose first choice is the round loaf but will choose the long loaf if I am desperate.   My purchasing habits are competing with other patrons who prefer the long loaf but will never consider the round loaf as a suitable substitute.

On few occasions, I arrived at the store just as this bread is being stocked.   They remove the bread from the previous day and replace with the freshly arrived bread.   I don’t know what happens to this expired (but still good) bread, but I can imagine that they will record what loaves failed to sell.   Since I only buy bread every 3-4 days, there are bound to be many days when one round loaf would not get sold.

Inevitably and logically the stocking choices will be to stock more long loaves and perhaps only provide round loaves on certain days.  In fact, I’ve begun to recognize what days have better chances.   Sunday trips are usually successful and Monday trips are always unsuccessful.

I had a similar experience when it comes to meat.  My favorite store is a smaller grocery store and some time ago it did a great job catering to single people who make up most of the local population.  In particular, it offered small portions of meat suitable for a single serving or sliced for modest servings where there is no intention to impress guests.   One of the reasons it became my favorite store was because it often had these prepackaged for picking up while other stores featured their “family packs” or family size portions.   Times change and the larger portions started to outsell the smaller ones so that now it is hard to find portions suitable for someone living alone.   It is the same kind of conflict between patrons: I’ll substitute a larger portion if the smaller portion is unavailable, but they would never choose multiple smaller portions to substitute for the lack of a larger portion.

Instead of complaining, I go along with the game.   First of all that is just my personality to just go along without being pushy about my desires.   Secondly, I like to find ways to exploit the scenario.   I’ll buy the long loaves and slice it diagonally for longer slices.   Or I’ll buy the bigger cuts of meat and slice it at home.

If I would complain, I doubt it would do any good.   The butcher may go back and repackage something for me, but he is not going to change the stocking plans for the next time.   The bakery department will probably just shrug because they can’t make a round loaf appear if it isn’t stocked.

To make the change I want, I need some way to generate the data that will remind the algorithms that I still exist.   When I compromise by buying the second-choice selection, this gets recorded a successful sale.  To the extent that it is tracked to my loyalty card, the algorithms will recognize that my preferences at least can be satisfied with this choice.   There is no way I can generate data to tell them that I was dissatisfied.   I thought of this the other day when they had neither the bread nor the meat cut I wanted and I left the store empty handed.   There was no opportunity to check out an empty shopping cart and ring up zero quantities for the items I wanted.   Normally, if one of the items were available I would buy other items to stock up my pantry but since nothing was urgently low I saw no point of buying them on this trip so I didn’t check out at all.   From their loyalty-card data, I never visited the store at all.

I mention this shopping scenario as an illustration of how data-driven analytics can be blind to hidden variables that policies prevent their observation.   In these scenarios, there is no record that I bought zero items of what I sought.  Alternatively, there are non-zero items of items I bought as a compromise for what was lacking.   The algorithms will never know what it was that was lacking.

I’m reminded of a very old story someone told me about optimizing the stocking of bread on shelves (referring to packaged sliced bread) where the optimal quantity is to have a single loaf left unsold when the new stock arrived.   Intuitively, this represents a waste and a loss: a better option would be that the shelf would be completely empty when the new stock arrived.  But, the optimization came from the confirmation that no potential customer was missed due to lack of stock.   If the stock is not available, then you will never know the number of potential customers who would have wanted it if it were present.  This story came from operations research lore that predates the modern big data analytics.

In order to have algorithms make better choices, we need to avoid missing observations that are prevented by our policies.  We need to find ways to observe the hidden variables of unsatisfied needs.  This is one reason why I insist that the dedomenocracy must have short-lived rules.   The rules forbid certain things from happening so that there is no data to confirm that such things are still possible.  Allowing the rule to expire will permit fresh measurements that can inform us of unmet and legitimate needs.

I started with the example of minimum wage laws that prevent exploitative low wages.  As long as the law is enforced, there will be no data of desires for sub-minimum wages.   We assume this is inevitable but this assumption is a theoretic model instead of an observed fact.   The theoretic model is that the exploitative labor market of 80 years ago still applies today.   We assume that without a law to force a minimum wage, the lowest wages will instantly collapse.   Because we have no data for this, we have to justify the law through democratic deliberations that rely on this preconception of how wage markets work.

A dedomenocracy approach will not extend a minimum wage law because it lacks any data to justify the need or urgency for the law.

Dedomenocratic Party needs the principle that permits obtaining measurements that otherwise would not be available.   For governance, this involves reverting to the original liberties to measure how much of a problem remains after the rule has had its chance to have an effect.  To be able to manage something, we need to be able to measure it.   This is why a dedomenocracy has short-lived rules.   Permanent laws denies the possibility of collecting data that the problem still exists or how bad it remains.


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