Thinking About Probability #3

At long last, I am finally returning to the blog. I want to address my earlier point about Hume and review Uwe Saint-Mont’s recent paper Induction: A Logical Analysis to provide some clarification on the application of probability and statistics.

If it seems like I am belaboring the point it’s because I am. When learning probability the inductive approach comes up early and often. Having a sense of the guide posts early on will hopefully lead to better understanding of when and where different methods are applicable.


I mentioned in a previous post that Hume attacked induction with force. He disputed “that inductive conclusions, i.e., the very method of generalizing, can be justified at all.” Since his work induction has been looked at with skepticism even to this day. Popper and Miller went so far as to provide a proof of the impossibility of inductive probability.

As Pillai would point out, we use induction in probability and statistics because in certain – but broad-ranging cases – it works. Supporting Pillai, Jaynes comments (p. 699) on Popper and Millner, their proof is “written for scientists… like trying to prove the impossibility of heavier-than-air flight to an assembly of professional airline pilots.” Understanding where it is induction is applicable is key.

I’d like to go into Uwe Saint-mont’s paper in more detail later, but here I’ll just summarize. Saint-mont seeks to deal with Hume’s objections to induction in a constructive way. The key concepts are boundedness and information.

For bounded problems, induction is a reasonable approach to logic. You can use a deck of cards or a game of dice as examples of bounded problems. The rules of the game are well understood at the outset and outcomes are bounded.

With regards to information Saint-mont considers a basic model where you have tiers going from more general to less general. This introduces the concept of layers of information and the distance between those layers. If the distance is bounded, well-structured inductive leaps between less general and more general can be made in “small” steps. If the distance is unbounded, “a leap of faith from [less general] to [more general] never seems to be well-grounded.” This implies that the less general tier is a subset of the more general tier. In such instances (the realm of “Mediocristan” as Taleb would write), the law of large numbers holds and statistics provides a valid response to Hume’s arguments.

As Saint-Mont writes “without any assumptions, boundary conditions or restrictions – beyond any bounds – there are no grounds for induction.” Thus, when using inductive logic it is important that your boundaries be well defined in order for the “logic funnel” to be applicable.

I can easily imagine constructs where you can create consistent models for induction (games of chance, certain closed financial systems, isolated computer algorithms). The question remains, however, how applicable can these constructs be to real life? Understanding that is key to useful application of probability and statistics since in unbounded systems uncertainty will dominate and induction can rapidly lead you astray.

Thinking About Probability #2

I left off my previous post with Hume’s problem of induction and a way forward coming from Uwe Saint-Mont.

For this post, I was originally planning to do a deep dive on the structure of logic and epistemic uncertainty to help frame future discussions. Fortunately Sean McLure has already done a better job than I could ever do in a recent series on his podcast, NonTrivial.

I highly recommend the listen as a primer for logical analysis to help understand where logic can be useful and where risk management becomes a better proxy, particularly in complex situations.

He spends a lot of time talking about inductive logic and Popperian falsification, which I think will become foundational as this probability series progresses.

Here is the link to the first part of the Facts and Logic Series on NonTrivial:

Here is a link to Sean’s comprehensive list of logical fallacies:

Next post I’ll get back to probability starting with the Uwe Saint-Mont paper.

Thinking About Probability #1

This is my first attempt at adding some of the Notability maths into the blog – we’ll see how it goes.

As I mentioned in my previous post I am starting to chronicle my re-learnings in probability and statistics starting with the textbook “Probability, Random Variables and Stochastic Processes” by Papoulis and Pillai. The writing is pretty engaging for a math book and I’m hope to make it at least a chapter before getting distracted. Joe Norman incorporated parts of the book in his Applied Complexity Science course I took recently and I grabbed it off Amazon where I was pleasantly surprised to find a positive review from Taleb.

On to the learning. It’s late here so I’ll start from the beginning and maybe get through some thoughts on a few ideas/equations.

We start with an intro to probability and I think one of the key takeaways is that probability is dealing specifically with mass phenomena. How many events? Well, it depends. More on this later I suppose.

A physical interpretation of probability is provided by the following equation (Notability works great btw):

Where P means probability, A stands for an event, n(A) is the number of times the event occurred and n is the number of experiments run.

In a classic example of flipping a coin, if you flip the coin 10 times and you get heads 4 times out of the 10, then your probability P(A) of getting head (funny undergrad story on this I might tell later) is 4/10 or 0.4.

Easy enough… but is it?

This equation is an approximation of the probability and has a couple key assumptions baked in (assumptions to me are like kernels or rules of an automaton, perhaps more on this later). The first assumption is that the relative frequency of the occurrence of A is close to P(A) provided that n is sufficiently large. Obvious question: how large is large enough? The second assumption is that provided you have meet the first demand then only with a “high degree of certainty” is the equation valid.

Any use of this equation for prediction in the real word takes us down the path of induction. You’ll never be able to run an experiment an infinite amount of times and so to estimate the probability of an event occurring on the n+1 experiment it is going to have to be based on a priori knowledge. Say you flip a coin 100 times and see heads half of the time, then you would induce that the next 100 flips would yield heads half of the time.

David Hume had a problem with inductive logic. Gauche had the following summary of his argument:

“(i)Any verdict on the legitimacy of induction must result from deductive or inductive arguments, because those are the only kinds of reasoning.

(ii)A verdict on induction cannot be reached deductively. No inference from the observed to the unobserved is deductive, specifically because nothing in deductive logic can ensure that the course of nature will not change.

(iii)A verdict cannot be reached inductively. Any appeal to the past successes of inductive logic, such as that bread has continued to be nutritious and that the sun has continued to rise day after day, is but worthless circular reasoning when applied to induction’s future fortunes.

Therefore, because deduction and induction are the only options, and because neither can reach a verdict on induction, the conclusion follows that there is no rational justification for induction.”

Whoops. Now what?

It’s fucking late now and I’m just one page into Papoulis. I’m having fun though so I’ll keep doing this (daily?) and hopefully have time to go back and properly reference things.

Next post, I want to pick this up with a way-out of the inductive reasoning trap which I found in a recent paper by Uwe Saint-Mont. Then maybe I’ll move on to deductive (math-fun) reasoning.

Returning to the Blog

I have had this nagging sensation that I need to write again. Had everything set up to go on my iPad for some late night writing sessions and then my keyboard died. I wasn’t sure what the issue was but finally just decided to replace it.

Lot’s of things going on since my last post. Top of mind is $GME and the recent hedge fund blow ups. I like GME a lot and think they have some long term potential to turn around into a major gaming media company. Also, if we turn the corner on coronavirus and can go back to small public gatherings I can see some huge potential for board gaming as a turnaround strategy for the retail gaming. Nowhere near justifying the $300+ price point, but a good direction for the company nonetheless, I think.

I have also been spending a lot of time circling around complex systems. I think I was originally introduced to the topic through a Coursera class from Scott Page years ago. Some of the recent work from Yaneer Bar-Yam, Nassim Taleb, Joe Norman and Ole Peters has kindled the old fire and I have been reading everything I can get my hands on. I am finding my main limitation so far has been my lack of a deep working knowledge of probability and statistics so I am starting from scratch with a goal of eventually understanding Nassim’s recent writings in his Technical Incerto.

I recently learned that Notability now has an automated handwriting to equation tool so I’ll be posting some of the learning as I go along. It’s mostly for my sake to help express my understanding but feel free to follow along.

I’ll start with Papoulis and see where it goes from there. I have a tendency to bounce around books a bit as I find textbook statistics incredibly boring. I figure by the end of it I’ll have a self-made understanding of probability and stats and hopefully some interesting insights along the way.

Lastly, just before I pop off, I have been digging into Christopher Alexander’s Nature of Order recently. I am currently on Book 1 and its pretty damn interesting already. I’m sensing a deep dive into David Bohm’s physics work on implicate order in the near future.

Lastly, lastly, I had the pleasure of running across a recent blog post from Jerry Neumann on LinkedIn. The first piece I read was called Strategy Under Uncertainty and it is a wonderful combination of Christiansen, Taleb and his personal experiences in Angel and VC startup investing. His blog is called the Reaction Wheel (after some kind of satellite part) and I slogged through all 270 posts over the Christmas holidays. It was time well spent.

Let’s Talk About Your Quantifiable Value Proposition

If you’re developing a product or service you’ve probably come across the term Quantifiable Value Proposition (QVP). What is QVP and why is it important?

Your QVP is a measure of the value that you provide to your customers and clients through your products and services. As the name implies, the key concept of QVP is that the value you provide is quantifiable (ad hoc) – and it’s best if your QVP is also measurable (post hoc).

Your QVP should be aligned with your customer’s priorities and aspirations. If your customer’s #1 priority is increasing their sales conversions and your QVP is increasing sales conversions then your offering benefits are aligned with your customer priorities. If, on the other hand, your QVP was focused on increasing the number of leads, the conversation with the customer will be an uphill struggle. In this case, there’s misalignment between the customer’s priorities (sales conversion) and the QVP that you are promoting (lead gen). 

Quantifying your value proposition helps you paint a vision of the future for your client. You are saying “through the use of our offering, you can expect XYZ benefits.” Measuring is important because it allows the customer to reflect on the purchase to determine if it met expectations.

Sometimes quantifying and measuring your value proposition is easy. As an example, let’s say your customer is a product development department in a medical device company. Your offering has the ability to help them save 3 weeks of time in the product development process through increased efficiencies. The QVP is that through the use of your product the customer will be saving 3 weeks in product development time. The QVP is also easily measurable by the client. At the end of their product development they compare to a previous estimate or similar project and see if they indeed saved time using your offering.

Sometimes your value proposition is not so easy to quantify – particularly when dealing with subjective experiences like taste, feeling, or satisfaction. In these cases, it is helpful to consider how your offering improves the customer’s condition. Are there simple ways to show they are better off with your product or service than without it? Does your product or service help your customer deal or cope with a problem and reduce its negative impacts?

Your QVP begins with understanding your clients top priorities and aspirations. Once you have a clear understanding of what matters most to your client you can then craft a QVP that helps them paint a vision of the future with your product or service. Your QVP will help you in determining your pricing and once you have settled on your business model you can Calculate your Customer Lifetime Value.

How to Determine your Customer Lifetime Value (LTV)

Customer lifetime value (LTV) is an important measure of unit economics for MedTech and HealthTech companies. LTV is a measure of your product or service offering’s profitability over a length of 3 to 5 years. Since the LTV is calculated over a time period, the profits are discounted to come to a net present value for each additional customer.  Note that LTV does not take into account the costs associated with acquiring a customer (CAC), which is an important consideration that helps determine your sales and marketing expense.

The first step to determining your LTV is to decide on your business model. Do you plan to sell your product or service offering as a one-time fee? Do you have a razor-blade model where you sell a device with ongoing consumables? Will you have a subscription and ongoing service? Or maybe you have some combination all of the above? There are many business models to choose from and your choice will depend on your product or service offering and the core strengths of your team. Your choice of business model will also have an important effect on your LTV calculations.

Once you have decided on your business model, calculating the LTV requires a few key pieces of information:

1. Your product pricing and revenue streams: What is the price of your product? How much product do you expect your average customer to buy? Is there a one-time revenue stream, a recurring revenue stream or both? Do you have any upselling opportunities once you have landed a customer that do not require much work on behalf of the sales and marketing team?

2. Product repurchase rate: For one-time revenue streams, how frequently you think your customers will be repurchasing your product over a 3 to 5 year window?

3. Your customer retention rate: For recurring revenue streams, what percentage of customers will continue paying a recurring fee for use of the product or service?

4. Your cost of goods sold (COGS): How much does it take to produce or make each of your individual products? Note that this does not include costs associated with sales & marketing, R&D or administration.

5. Weighted average cost of capital (WACC): The WACC an estimate of how much of a premium investors place on investing today in your solution. The number is highly variable, but generally somewhere between 35 and 75 percent depending on the offering, financial markets and experience on the team. A high WACC means that revenues in later years become less important and this is one of the reasons you typically don’t see LTV calculations go beyond 5 years.

A simple spreadsheet can help you easily calculate your LTV once you have all of the above information. I have included a simple template based on a widget that you can use for LTV calculations here. The widget has a one-time revenue stream upon purchase, a recurring revenue stream for consumables, and a service upsell included in the calculations.

A positive LTV means that you are contributing to your gross profit with each additional customer. A negative LTV means that your unit economics aren’t sustainable and you may need to reconsider your offering’s value proposition, pricing, or business model. It could also mean that your offering is not profitable at lower volumes, however, at higher volumes your offering becomes profitable. 

Whatever the case, LTV is an important metric to help understand your business’ unit economics and will be a key consideration for potential investors and savvy board members.

Determining your product pricing

OK, so you’ve got the idea and maybe you even have a prototype of the product or service your are planning to offer. How do you determine what the price of your offering should be?

This question is often flummoxing for early stage entrepreneurs. After all, you are running an innovation-driven enterprise — your idea is new and meant to change the way people are currently doing things! How do you price something that has no direct comparison?

The first method of pricing is cost-plus whereby you determine the cost inputs that go into the delivery of your offering and charge a flat markup, say 20%, to come up with the final price. Cost-plus pricing is commonly requested from contractors by military and government agencies. 

While cost-plus is the simplest way to determine your pricing — and often a good exercise as it forces the entrepreneur to understand the cost structure of their offering and the lowest price possible to remain profitable — it often leaves a lot of value on the table. In the early stages of your product or service lifecycle there are early adopters who are willing to pay a premium to have access to the product or service before anybody else. Also remember: your costs are irrelevant to your customer. They just care that your product is providing value at an amount they can afford!

Understanding pricing of your offering begins with understanding the Quantifiable Value Proposition (QVP) to your customer. In a B2B setting, the QVP can often be determined as the revenue gains or cost savings the business will obtain through the delivery of your offering. A good rule of thumb is to target your pricing to be about 20% of your customer’s QVP.

Let’s assume that you have done your research and are confident that through the use of your offering a company can reasonably expect to earn an additional $100K in revenues. As a first approximation, an offering price of $20K would be a good place to start — the client obtains plenty of value from having your offering and you extract a modest amount of the benefits. You have set up a win-win for both sides provided your cost structure allows for profitability at that price point.

The 20% figure is just a goal post in determining the pricing of your offering. There are other important factors to consider when you are looking to decide your pricing. 

Does your company have a monopoly? If so, you may be able to command higher pricing. 

Does the price come within your decision-maker’s budget? If not, your offering may require additional approvals and lengthen your sales cycle. 

Does the price exceed the reimbursement the customer receives? Even if your customer receives benefits from your offering outside of reimbursement, it can be difficult to craft an appeasing story to the acquisition committee who may have a few metrics they make decisions on.

There are several other methods for determining pricing that can help you triangulate on your target customer’s willingness to pay. One method is looking at comparables: what are people already paying for similar products and services? If a customer has previously paid for a similar offering then it’s probably already in their budget. The friction to replace an existing offering is often a lot lower than bringing in something completely new. Another method is substitution: if a customer isn’t already using something similar to your offering what are they using instead? Understanding how much a customer is spending on substitutes can often lead to interesting dialogues with the customer about how your offering can better serve their needs and position them for future success.

Different customers will derive different QVPs from your offering so be sure to segment appropriately! Segmenting in this way can help to determine beachhead markets and branding opportunities within your offering. As you scale and are able to lower your cost structure, opportunities may open at the lower ends of the market where you can sell to customers and maintain similar profitability.

Finally, at the early stages of your company it is often necessary to offer a discount to early customers in order to build the customer base and win referrals. While discounts are a great way to drive sales, take care not to erode the perceived value of your offering and make sure there is a timeline on them so that the customer knows they are receiving a discount because they are taking an early risk. Hardware discounts are often much easier to remove than software discounts as people more readily recognize the tangible value of hardware.

Pricing your startup’s offering depends on several levers and requires an in-depth understanding of your customers needs and their willingness to pay. Cost-plus pricing should be avoided if possible as it tends to leave a lot of value on the table and entrepreneurs should focus on delivering and extracting value through their quantitative value proposition. Once you have settled on your business model and pricing you can now look at calculating your customer lifetime value (LTV)!

End Qualified Immunity

Below is the transcript of a speech I gave on June 9, 2020.

“I pledge allegiance
To my flag
And the Republic
For which it stands,
One nation
With liberty
And justice
for all.

Liberty and justice for all.

When Francis Bellamy published the Pledge of Allegiance back in 1892 he planted a seed. This seed has since gone viral and it is hard to find a person in America who is not familiar with the Pledge of Allegiance since revised.

Liberty and justice are terms we are all familiar with.

Liberty (noun)

The state of being free within society from oppressive restrictions imposed by authority on one’s way of life, behavior, or political views.

Justice (noun)

Just behavior or treatment.

Just (adjective)

Based on or behaving according to what is morally right and fair.

Injustice (noun)

Lack of fairness and justice.

The Doctrine of Qualified immunity (noun)

One Definition: A Supreme Court doctrine, which often protects law enforcement officers from being sued, even in cases where they demonstrably violate civil rights. Let me repeat, Law enforcement officers are entitled to qualified immunity when their actions do not violate a clearly established statutory or constitutional right.

My definition: A seed of injustice.

Qualified immunity prevents American citizens from obtaining justice if there hasn’t been a similar case in their jurisdiction where the court already decided that was a constitutional violation.

The following real-life example is taken from a recent FOX News article and highlights the qualified immunity doctrine in action:

“The simple fact is the majority of this time this situation happens to anyone, they have no recourse,” James King told FOX 17.

The Grand Rapids native says he was assaulted by plain cloth officers in a case of mistaken identity in 2014.

The incident was caught on camera, now 6 years later, the case will be reviewed by the U.S. Supreme Court.

King’s lawyer says they have not been able to get the officers in court because of qualified immunity.

“The officers claimed that yes, even if we violated Mr. King’s rights, we can’t be held accountable under this doctrine created in 1982 because there wasn’t an exact case that mirrored the factual situation with the case we had with James,” King’s Attorney Patrick Jaicomo said.

The Ending Qualified Immunity Act proposed by Rep Justin Amash Libertarian-MI (White) and Rep. Ayanna Pressley, Democrat-MA (Black) seeks to end this source of injustice.

I summarize the bill as follows:

In 1871, to help realize the promise of equality protected in the Fourteenth Amendment, Congress passed the Civil Rights Act of 1871, also known as the Ku Klux Klan Act, granting individuals the right to sue state and local officials who violate their rights, including police officers, under Section 1983.

Section 1983 never included a defense or immunity for government officials who act in good faith when violating rights, nor has it ever had a defense or immunity based on whether the right was “clearly established at the time of the violation.

From 1871 to 1967 government actors were not afforded qualified immunity for violating civil rights.

Since 1967, the Supreme Court has issued several decisions gutting this protection by inventing the qualified immunity doctrine, preventing police officers from being successfully sued for abuse of power or misconduct unless a prior case has “clearly established” that the abuse or misconduct is illegal-a unique protection that no other profession holds.

The Court’s broad interpretation of this doctrine allows police to violate constitutional rights with impunity, immunizing them for everything from unlawful traffic stops to brutality and murder. Qualified immunity shields police from accountability, impedes true justice, and undermines the constitutional rights of every person in this country.

Protecting bad cops from liability is bad for harmed parties, its bad for good cops, and its bad for the values we are committed to in this experiment we call America.

Many years ago, qualified immunity started off much narrower and it’s become much broader over the years. It is a seed of injustice that has gone viral. It’s past time to end qualified immunity.

Support the Ending Qualified Immunity Act. Let’s bring an end to this source of injustice.

Let us focus again,
On liberty
And justice
For all.

Han Zero

To be accompanied with the music video “Han Zero (lofi)”

“Han!” she yelled.

“What?” He replied in a tired tone.

“We’re moving.” She said, pointing out the window.

Han sat up rubbing his eyes. He got out of the bed and moved over to where Agatha was standing. Peering out the window he could see the telltale signs of motion as the station they had been docked at slowly became smaller and smaller.

Sure enough, they were on a trajectory, but when had he put in the coordinates? He drifted off into his mind trying to piece together events in the last few hours. As he re-entered that mental space between sleep and wakefulness he remembered that he was startled earlier by that easily forgotten clattering of the Bernoulli Drive.

Snapping to attention he reached down to grab his pants and got up to walk over to the Bernoulli Drive. It was a surprisingly simple machine that liked to play games – always leaving the pilot guessing as to whether it would work or not. They had gotten enough thrust from the previous burn that they were still moving, but without a proper kick their orbit around the station would decay and they would collide back on the Rebel station.

He smacked the drive with his hand and it sputtered and wheezed a breathy mechanical note. They waited. There was nothing, no lights not even a hum or a hiss. It had been doing this more often recently. 

“Shit.” He said. 

The Bernoulli Drive’s function was always discontinuous, bursting between fantastic possibilities of one moment and eery silence in another.  He much preferred the predictability of the Ensemble drives. While they were impossible to understand – complex beyond interpretability – at least they produced something! Han knew that was a cop-out though. Ensemble drives were prone to overfitting and you could easily run off the Graph if you didn’t have a team of technicians constantly tuning the device and retroactively explaining deviations.

Ensemble drive aside he still would have liked to retool the Bernoulli Drive at the last docking. The unpredictability was really starting to get on his nerves! Unfortunately his handler for this trip, an unpleasant fellow by the name of Darth was eager to get his cargo to the Axis. They would have to put up with the finicky drive and hope the input parameters didn’t drift any further than they already had.

He slapped the drive again, this time with a noise so loud Agatha’s attenuators whizzed to life. 

“Easy!” Agatha demanded, putting her hands on her ears with a languishing scoff as she adjusted her settings.

The Bernoulli Drive was silent. He tried to think of nothing. Let the drive give him clues. Dealing with pure randomness was like trying to peer into an additional dimension beyond your senses. He caught the Moment and renormalized. With a pinch he started the drive and it sputtered into life in discrete bursts. Chop! Chop chop! Then, with an instantaneous jolt and a loud Wham! they were actively accelerating. He felt satisfied with himself even though he intimately knew that controlling randomness was a rouse.

“I fucking hate that thing,” Han said, exasperated “it’s never predictable when you need it to be.” He leaned back into his pilot seat giving a cursory glance at the Graph monitor. The trajectory was set, there was no changing that now. All they could do was wait.

He adjusted the seat setting to lift his feet up and relaxed. With the last output from the Bernoulli Drive they had all the momentum they needed to make it to their destination.

“Han?” Agatha said after several minutes.

He snapped back to attention and looked over at her. As he acknowledged her with a dumb smile, he gazed past her trying to remember where he had found her again. Was it on the Edge during one of his cargo missions? Or was it the inner worlds of the Axis on the last delivery? Trips down the Graph always left the short-term memory a little hazy. It didn’t matter any more. 

There was one thing that was clear: Agatha was an AI babe he couldn’t live without.

Han had first met Agatha in the Machine Learning Academy where she had been studying irreducible systems, one of the last subjects that AI’s had not completely overturned since the Awakening. He had completed his pilot apprenticeship a few years before and had been back at the Academy’s cantina for a reunion. Agatha had spotted him in one of the smoky corners of the establishment. Han wasn’t your usual smuggler shooting holes cargo containers like the Talebian fixers busting holes through shaky logic. No, Han understood the real world and had learned his trade through experiences that only a proper apprenticeship could provide. It was the deviation from her training that attracted Agatha to Han. He just wasn’t like other things she had seen before. It was galactic romance at first sight.

Han recalled giving up a gig to spend an extra month with her on the Academy world before finally getting back on the graph with a cargo bounty he couldn’t refuse. That was the last time he saw her until he picked her up at the Foundry on one the Edge worlds – that’s where he had found her!

“What happens when we get there?” She asked.

His thought interrupted,, Han reflected on that question. How many times had he been to the Axis since he had gotten his ship? Nine? Ten? Every trip was the same. Grab some illicit cargo in one of the Edge worlds and travel down the Graph to make a delivery to one of the stations or dock worlds near the Axis. Nobody had ever actually been to the Axis before, that was just a saying. People living near the Axis were comfortable living at the limit, never daring to crossover to the Indeterminate. Once the cargo was delivered he would head towards the Axis getting flung out to the Edge again by the Torus-like geometry of space there.

“I guess we do it again.” Han said. He cracked open a beer and sat down reflecting on the unknown lying in wait for him at the Axis…

Travel Restrictions for Limiting Community Disease Spread (Version 1)

Aaron Green, Chen Shen and Yaneer Bar-Yam, Travel restrictions for limiting community disease spread, New England Complex Systems Institute (May 9, 2020). link to NECSI site here

Travel restrictions (Cordon Sanitaire) are a vital tool in the fight against COVID-19 to reduce transmission to zero (#CrushTheCurve) and restore normal activity. By limiting travel, gains from local efforts to stop the disease can be preserved because new outbreaks arising from travelers can be prevented. Without such restrictions any effort to stop the outbreak in one place will be undermined by imported cases. Waiting for all areas to achieve low enough case counts would substantially delay restoring economic activity. Travel restrictions should apply to travelers from a high risk area to any other area, including other high risk areas. 

The status of a designated area (zone) should be identified as being in one of three colors: Green, Yellow and Red. 

  • Green zone—no new local (within community) transmission for 14 consecutive days. All new cases, if any, occur in individuals who were effectively isolated from the moment they entered the zone (imported travelers);
  • Yellow zone—no new local transmission for 14 consecutive days, but there are new cases identified using contact tracing, or the zone is adjacent to red zones;
  • Red zone—community transmission identified within the last 14 days.

A zone should be a district that is naturally or artificially separated from its neighboring districts. A zone should only have controllable traffic transitions with neighboring zones. If two geographical regions have a shared border that cannot be effectively controlled, they should be considered as one zone. Zones can have a nested structure where the largest unit is a country or state. Zones should reduce the number of border crossings as necessary to ensure proper transport guidelines and quarantine are followed.

The most basic zone border protocol includes: 

  • No unnecessary travel into any zone from a yellow or red zone.
  • 14 day quarantine for any individual arriving with permission into any zone from a yellow or red zone.
  • Provision for transit travel if complete isolation protocols (air port transit isolation, not leaving a land vehicle, not leaving a designated road area) during transit across a zone. Where feasible, services should be provided for transit travel with extreme precautions.
  • Import of goods by delivery to a designated cargo border location and transfer of the goods to internal control. 

A more complete zone land border control infrastructure enables a wider range of travel options, with appropriate control to prevent transmission.

Temporary border control locations are necessary right before entering and exiting zones. These may be newly formed or converted service areas or weigh stations. To these areas add hygienic and clean air self-service facilities that protect both the traveler and the community. For freight/cargo drivers, they should be required to perform essential activities (like dining) in these service areas to minimize exposure in the zone. Such self-service facilities will also help travelers from one green zone to another green zone if their route has to cut through yellow/red zones. Negative pressure ambulances should also be deployed at such service areas for medical emergencies, and for out-of-zone individuals seeking medical care in in-zone hospitals. 

Zones must set up a system for travelers from yellow/red zones to notify zone local authorities in advance about their arrival, indicated by signs along the transit road and online, including registering their name, license plate number, and ETA. Upon arrival at the entry service area, travelers are redirected directly to isolation facilities. Ideally, the isolation facility should be close to the border control service area, and they can also serve as the temporary lodging space for officials working at the service area, to minimize their contact with the community. Symptomatic testing (fever and questionnaire) should be standard at all crossing points. Where available, RT-PCR rapid nasal swab tests should be performed. Test results should be added to official travel documents when available. Arrivals without registration are denied entry.

Travel restrictions should be based on local needs. There are four aspects of travel: (1) Arrivals into a zone, (2) essential worker travel, (3) transit across one zone to get to another, (4) delivery of goods.

I. Arrivals into a Zone

In general, travelers arriving from yellow or red zones to any other zone (green, yellow or red) should be quarantined or self-isolate with supervision for 14 days. Travelers originating in green zones may not need to be quarantined if they are coming from another low risk area. In order to avoid the need for more stringent restrictions, travelers should have official or otherwise reliable itinerary records (origin, intended destination, zones they have traveled through) and tests with results (if possible) from zones they have passed through on their journey. A system for official travel documents should be developed. 

Patients from a yellow or red zone seeking medical services should be encouraged to find treatment in their home zone. If they are unable to obtain services in their home zone they may seek treatment in specifically designated clinics or hospitals that have the proper protocols in place to receive inbound patients who may be infected. Travel from the zone boundary to the service center should be supervised and documented for tracing purposes.

II. Essential worker travel

In order to separate transmission in different zones, individuals who regularly travel between two zones for work and living quarters must do one of the following:

  • Temporarily change work or living quarters so that they are within the same zone;
  • Follow strict guidelines for isolation from local population in one of the two zones. This may include living alone or working without contact with other employees;
  • Change roles so that remote work is possible;
  • Take a temporary leave of absence with pay.

Where this is not possible, frequent screening tests are necessary. In a case where individuals who live or work in boundary regions between two zones that are both of reduced risk, exceptions may be made for essential workers. Essential workers who are COVID-19 positive should quarantine in their home zone.

Under exceptional circumstances essential workers may arrive or be imported into a zone with zone authority approved quarantine and work plans that ensure extremely low risk for transmission employment conditions. 

III. Transit or thru traffic

Transit airport, highway and major road traffic should not be blocked. However, precautions must be taken at entrance into a zone and pit stop locations. If a zone decides to allow an individual from a higher risk zone to travel across to get to another zone it would be prudent to track the vehicle for enforcement of regulations and tracing purposes. Designate controlled routes and pit stops to ensure through-zone transit is safe for residents.

Through-zone travelers should avoid pit stops if possible. When necessary, wear a mask, practice distancing and thoroughly wash hands. Add pit stop to travel itinerary for tracing purposes.

IV. Delivery of goods

Freight should have clear destinations and itinerary. Set up quarantine areas for freight drivers to go once they have delivered their goods. They either return to their origin the same day or wait in a quarantine area until their next job.

Packages should undergo sanitation or quarantine for a period of time to deactivate any virus on fomites and prevent transmission.

In general, for transport into a yellow or red zone, freight drivers must quarantine upon return to their home zone. Freight and cargo transport without quarantine upon return is possible, provided the following conditions are met:

  1. There are no symptoms of illnesses;
  2. Vehicle operators do not leave the vehicle, with exceptions for designated and reliably managed pit stop locations;
  3. Loading and unloading is carried out by the customer;
  4. The vehicle operators leave the zone again within 24 hours.

For trips that last longer than 24 hours, upon return they should quarantine while they wait for the next transport job.  If they want to reenter their zone without restrictions they must quarantine or self-isolate for 14 days.