The race is not always to the swift, nor the battle to the strong, but that’s the way to bet.
Damon Runyon

According to Harvard professor Clayton Christensen, nearly 30,000 new products are introduced yearly, and 95% fail.

But there are ways that one can help increase the potential for product success. It involves determining the Value, Demand, and Cost of goods and services before they launch. Buyers reveal how they Value the product features and their Demand. It is up to producers to figure out those parameters, along with their Costs. Not looking at all those variables in advance is a recipe for financial disaster.

You’re bound to fail if you placed a heavy bet on a program with long odds against you– but you went ahead with it, got it into production, and rode it out until it ran out of steam after losing tens of billions of Euros. That is the conclusion I reached for the Airbus A380 in my paper, “CSI EU: Cost Scene Investigation,” for which I won the ICEEA 2023 Best Modeling and Case Studies Track Paper. This step-by-step analysis gives you the framework for creating models that enhance your chances of being that one in 20 product that succeeds. Below is the video of the presentation:

https://hypernomics.com/wp-content/uploads/2023/06/products_800x300.png300800Mdleminer472https://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngMdleminer4722023-06-16 12:51:462023-09-22 19:59:38How to Lose €10B+

To reject one paradigm without simultaneously substituting another
is to reject science itself.
Thomas S. Kuhn

No rejection of science here. But we can dismiss an ineffective paradigm.

Paul Samuelson wrote that the law of supply and demand, the root paradigm of economics, meant that “the equilibrium price, i.e., the only price that can last…must be at this intersection point of supply and demand curves.” That model works for commodities such as gold, silver, or iron.

But what about jets and jet engines? They use iron. You’ll only gain deep insight into these markets by substituting economics with Hypernomics.

Its fundamental principle, The Law of Value and Demand, states that:

https://hypernomics.com/wp-content/uploads/2023/09/Economics_Classroom_800x300.png300800Mdleminer472https://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngMdleminer4722023-04-16 12:20:252023-09-16 12:41:47The Law of Value and Demand: Not Your Grandfather’s Economics

It’s not what you look at that matters; it’s what you see
Henry David Thoreau

Many of you asked about it; now I can tell you: I’ve signed a deal with Wiley to publish my book, Using Hidden Dimensions to Solve Unseen Problems: Hypernomics and Markets.

It studies market phenomena we haven’t been able to examine previously, mainly because no one invented the techniques to do so.

Until now.

The book’s theme of finding the location and direction of market competitors mirrors the development of radar and has a like effect.
In the years between WWI and WWII, many countries sought to discover opposing planes’ positions and headings. Several had acoustic detectors like that in (A) but found they could only provide broad direction of incoming aircraft. It took the development of the Chain Home Radar (B) to reveal the value of having a much finer granularity of approaching enemy warplanes.

Modern economics gives us simple 2D charts such as (C), showing the intersection of iron supply and demand curves. But planes use iron, and to characterize them thoroughly, we need the 4D arrangements the book offers, as (D). The book’s readers will gain ways to see more clearly for themselves, improving bottom lines.

https://hypernomics.com/wp-content/uploads/2023/09/wiley_logo.png300800Mdleminer472https://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngMdleminer4722023-03-16 11:52:572023-09-16 12:02:36My Book Is Coming: What’s In It For You

There are no solutions; there are only trade-offs
Thomas Sowell

Forget the “classic” choice model between guns and butter with its single imaginary frontier. Such notions offer no basis for action. Never settle for heuristics when you can have analytics. To reveal true alternatives, we’ll need to do some heavy lifting.

In 4D.

No, really.

In A, we find an aircraft Demand Frontier in yellow. If we want to make 100 units (Quantity – Dimension (Dim) 1), we find our price limited to $393M (Price – Dim 2). For 55 copies, our price could rise to $610M (purple lines). In A’s Value Space, the sustainable price goes up with range (Dim 3) and velocity (Dim 4) but down with added units; thus, the angled Value Response Surface for 55 units is higher than that for 100 units. They form straight lines in log space where they intersect their respective price ceilings (the horizontal yellow and purple planes in Value Space). In B’s linear space, those intersections form multiple curves revealing the proper trade-offs. The yellow line shows us we could build a plane with 10K in range, with a max V of just over 1400 KPH.

It takes work to find Demand and Value, but in the end, we get insight available nowhere else.

https://hypernomics.com/wp-content/uploads/2023/09/analytics_800x300.jpg300800Mdleminer472https://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngMdleminer4722023-02-16 11:49:012023-09-16 11:50:07Proper Production Possibility Curves

One should concentrate on getting interesting mathematics.
Paul Dirac

Let’s examine how markets work together across 7 dimensions.

Far from being some exotic mathematical anomaly, such arrangements occur daily across many markets. Please feel free to offer some feedback.

To make a pencil, given wood, you’ll need graphite. Making a bike takes a frame and tires. These markets are bonded—you can’t make a final product without some key pieces. How do bonded markets such as jets and their engines interact across 7 dimensions?

Let’s look.

In the 7D diagram below (with log scaling in all directions), turbofan engines use Dimensions (Dims) 1-4. As Specific Fuel Consumption (SFC, Dim 1) goes down and Max Thrust goes up (Dim 2), turbofan prices, reflecting their Value, moves up as well (Dim 3), with Quantities sold (Dim 4) limited by the market’s demand frontier (yellow line on the red, right-hand Demand Plane). Making a new engine with a specified level of SFC and Max Thrust yields a value of the large green sphere, marked by “T,” at left.

That engine supports a new business aircraft model and accounts for a portion of the plane’s cost, marked by the large green sphere labeled “B.” Aircraft Value goes up (Dim 3, shared with the engines) with Max MPH (Dim 5) and Cabin Volume (Dim 6), as limited by their Demand Frontier (Dim 7). Such entanglements exist in all bonded markets. They must be studied thoroughly to be optimized.

#hypernomics #markets #innovation #economics

https://hypernomics.com/wp-content/uploads/2023/01/2023-01-27_circle.png300800Mdleminer472https://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngMdleminer4722023-01-27 10:05:412023-09-16 11:41:09Markets Across Seven Dimensions

I sing my heart out to the wide open spaces
Pet Townshend

This month, we’ll study the difference between cost and price, why it matters, and how knowing how both behave in tandem is the key to success.

I know from our analytics that most of you are in the business of working out costs and prices. For many, it is hard to separate the two, especially if you work in or with the government. Today’s analysis directs itself to commercial operations. In a future newsletter, we’ll look at how to adapt this framework to the public sector.

All too often in business, someone comes up with a seemingly great idea and gets fellow workers excited about it. It gets pushed into production. Producers then wait to see what the market will bear for it, often falling short of projections.

What if you could change the paradigm?

Suppose you could see market openings and limits and test sample specifications and sales targets before you commit resources to a configuration. That would improve your chances of success.

You’ll have to work to enable this vision, but you will find it worthwhile.

When we at Hypernomics look at a market, we begin with Demand. As shown below as the red plane, that means finding the ordered pairs for Quantity and Price. We create a series of price bins (either equally spaced or binned by geometric or Fibonacci methods) and determine the ordered pairs (as the purple hexagons) representing each bin’s average price and total Quantity. Then we run a regression curve through them, which represents Aggregate Market Demand.

To the left of that curve, we find the Demand Frontier, a regression through the outermost points on the Demand Plane. This curve shows the limit of the products this market can absorb over time. As markets mature, the Aggregate Market Demand and Demand Frontier slopes often approximate one another.

If we examine the points closely, we’ll notice a price gap. Using its midpoint, we would find the 1) Quantity limit the market will support at that price (the vertical red line coming down from the Demand Frontier) and our 2) Target Price (the horizontal red line originating from the Demand Frontier).

To support that price, we’ll need to offer our customers something they like, here as Features A and B, which show up as the green Value Space at left, with the target Price as the horizontal red plane. We’ll need to figure out the Value Surface that the combinations of Features A and B command (the points for which we excluded from this view, for clarity). As seen on the left, there are Cost Surfaces for one or 500 units below the Value Surface. If we further bound our potential offering with Constraints (the vertical orange planes), we now have a region restricted on all sides. Conceptually, this expanse is not different than a like delimited region, such as your head.

Now, if you suspected that you had a deviated septum, your ear, nose, and throat doctor might order a CT scan, in which the doctor would develop section cut views of your head.

We can do the same thing in markets, using Financial CAT scans. Thus, after carefully setting up a 4D arrangement and taking cuts in both the Sections A and B directions, we can predict the 1) maximum Quantity Sold (reducing the 4D problem to one in 3D). Then we selected 2) the Price (dropping the remainder of undetermined dimensions to 2), 3) Feature A (the distance of the black plane from the origin, reducing the problem to 1 dimension), and 4) Feature B (the Vertical Profit Line, the final dimension). The per-unit profit line on the left times the number of units on the Demand Plane gives the projected profit.

In the process, we reduced a 4D problem to a single objective of maximum potential profit.

To complete the analysis, we’d examine all open price points and all viable combinations of the Features considering risk as well, searching for the best potential configuration.

Watch this video to see the analytical steps in action:

Together we stand, divided we fall; come on now, people, let’s get on the ball and work together.
Canned Heat, Let’s Work Together

Upper Demand Frontiers form in every market outside of commodities. You can prove this by plotting shares for all S&P 500 companies on the horizontal axis against their sales prices on the vertical (best seen in log-log space). For any given day, you’ll find an Upper Demand Frontier takes shape.

Frontiers limit sales. How do you work around them?

Hypernomics enables us to see how interconnected markets work. In (A), the Boeing 787 Business Jet was, for a period, pushed up against its Demand Frontier (the dashed blue line). One of its compatible engines, the GE Genx-1B, found itself in a like condition, hard up against its Demand Frontier (in B, the dashed orange line). What to do?

If GE, whose engines make up about a quarter of the B787 cost, finds their Learning Curve (recurring costs, as the solid orange line) below their price limit, they could drop their prices and make more profits. That would enable Boeing to lower their B787 business jet price and do the same. Knowing your partner’s place in the market is key to making them and you more profits.

#hypernomics #profits #markets #partner

https://hypernomics.com/wp-content/uploads/2022/05/ge-turbine-detail21-e1668687544111.jpg6451291Mdleminer472https://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngMdleminer4722022-11-17 04:21:012023-01-27 13:19:51Help Your Partner; Help Yourself

It is the obvious which is so difficult to see most of the time.
Isaac Asimov, I, Robot

Here’s a question with a seemingly obvious answer: How many stocks are part of the S&P 500? If you guessed 500, you’d be close, as there are 504 companies listed there today.

You likely know that not all S&P companies have issued the same number of shares, nor do all share price match. Too obvious? Not really.

Consider what you were undoubtedly told if you ever took an economics class. According to Paul Samuelson (Economics, 9th Ed., p. 63), “the equilibrium price, i.e., the only price that can last…must be at the intersection point of supply and demand curves.” Samuelson would have you believe markets have but one equilibrium point.

But we know that is nonsense: 504 stocks in the S&P 500 form 504 quantity and price pairs. While they are viable, all, in the language of Hypernomics, enjoy sustainable disequilibrium as their stock prices exceed their costs.

What’s really going on? It turns out the value of products goes up as producers add features customers like. At the same time, as prices go up, quantities sold fall. To see this phenomenon, one must employ Hypernomics.

To find out how this works with as many as 8 dimensions, go to our new Hypernomics YouTube channel here:

Assumptions are what we don’t know we are making.
Douglas Adams

Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?

Assumptions are what we don’t know we are making – Douglas Adams

Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?

Many pundits claim they knew it wouldn’t make its target. Most appeared when the program floundered late in its lifespan. What would it take to predict its future in advance?

Projects often use 1) business case analyses and 2) customer polls to “verify it pencils out.” That works if 1) analysts conceive those cases fairly and 2) buyers convert at or above a target sales figure.

What if we don’t have to rely on those techniques?

To forecast the next 20 years, study the last 20. As B reveals (summing all model types to base versions), the airliner market had a poorly correlated (Adj R^2 0.458) yet statistically significant (P-Value 0.035) Demand Frontier over that period. Airbus’s target was nearly ten standard deviations past it.

The A380 took €25B to develop. It didn’t recoup its investment. Take time to model markets in advance. See what a market did to bound what it will do. You may not like the answers, but it beats losing billions.

https://hypernomics.com/wp-content/uploads/2022/08/a380-1.jpg9001600Doug Howarthhttps://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngDoug Howarth2022-08-15 16:13:132023-09-22 19:34:02Assumptions vs. Observations: The A380

It’s not that I’m so smart, it’s just that I stay with problems longer.
Albert Einstein

There’s something deeply affecting about staying with a problem for over 30 years. Once you get some resolution, part of you wonders why it took so long to get answers. A more forgiving part of you thanks Einstein for the inspiration to carry on. One can only be happy when that ah-ha moment finally arrives.

Such is the case with Hypernomics. After first entertaining the idea at 14, somewhere around 49, I saw the first hints of the practical applications of Hypernomics. 18 years later, we have evidence of its practicability in one of the most complicated markets, that of securities.

In Feb 2020, we made our first investments based entirely on Hypernomics. Far from being perfect, tests suggested that given a market downturn, we would suffer losses. Figure A shows we’ve endured setbacks in 2022. But backtesting supported the idea we would lose less than the competition.

In the longer run, in Figure B, the theory has had a chance to shine. Note the Hypernomics fund is doing more than 2X as well as Berkshire Hathaway and over 3X what the other major indices are doing.

## How to Lose €10B+

According to Harvard professor Clayton Christensen, nearly 30,000 new products are introduced yearly, and 95% fail.

But there are ways that one can help increase the potential for product success. It involves determining the Value, Demand, and Cost of goods and services before they launch. Buyers reveal how they Value the product features and their Demand. It is up to producers to figure out those parameters, along with their Costs. Not looking at all those variables in advance is a recipe for financial disaster.

You’re bound to fail if you placed a heavy bet on a program with long odds against you– but you went ahead with it, got it into production, and rode it out until it ran out of steam after losing tens of billions of Euros. That is the conclusion I reached for the Airbus A380 in my paper, “CSI EU: Cost Scene Investigation,” for which I won the ICEEA 2023 Best Modeling and Case Studies Track Paper. This step-by-step analysis gives you the framework for creating models that enhance your chances of being that one in 20 product that succeeds. Below is the video of the presentation:

## The Law of Value and Demand: Not Your Grandfather’s Economics

No rejection of science here. But we can dismiss an ineffective paradigm.

Paul Samuelson wrote that the law of supply and demand, the root paradigm of economics, meant that “the equilibrium price, i.e., the only price that can last…must be at this intersection point of supply and demand curves.” That model works for commodities such as gold, silver, or iron.

But what about jets and jet engines? They use iron. You’ll only gain deep insight into these markets by substituting economics with Hypernomics.

Its fundamental principle, The Law of Value and Demand, states that:

You can study this new field in my upcoming book with Wiley, entitled Hypernomics: Using Hidden Dimensions to Solve Unseen Problems, in January 2024. In the meantime, have a look at my paper called “8D Cost Trades with Entanglement,” published in the April 2023 edition of the Journal of Cost Analysis and Parametrics,” to see how markets work.

## My Book Is Coming: What’s In It For You

Many of you asked about it; now I can tell you: I’ve signed a deal with Wiley to publish my book, Using Hidden Dimensions to Solve Unseen Problems: Hypernomics and Markets.

It studies market phenomena we haven’t been able to examine previously, mainly because no one invented the techniques to do so.

Until now.

The book’s theme of finding the location and direction of market competitors mirrors the development of radar and has a like effect.

In the years between WWI and WWII, many countries sought to discover opposing planes’ positions and headings. Several had acoustic detectors like that in (A) but found they could only provide broad direction of incoming aircraft. It took the development of the Chain Home Radar (B) to reveal the value of having a much finer granularity of approaching enemy warplanes.

Modern economics gives us simple 2D charts such as (C), showing the intersection of iron supply and demand curves. But planes use iron, and to characterize them thoroughly, we need the 4D arrangements the book offers, as (D). The book’s readers will gain ways to see more clearly for themselves, improving bottom lines.

## Proper Production Possibility Curves

Forget the “classic” choice model between guns and butter with its single imaginary frontier. Such notions offer no basis for action. Never settle for heuristics when you can have analytics. To reveal true alternatives, we’ll need to do some heavy lifting.

In 4D.

No, really.

In A, we find an aircraft Demand Frontier in yellow. If we want to make 100 units (Quantity – Dimension (Dim) 1), we find our price limited to $393M (Price – Dim 2). For 55 copies, our price could rise to $610M (purple lines). In A’s Value Space, the sustainable price goes up with range (Dim 3) and velocity (Dim 4) but down with added units; thus, the angled Value Response Surface for 55 units is higher than that for 100 units. They form straight lines in log space where they intersect their respective price ceilings (the horizontal yellow and purple planes in Value Space). In B’s linear space, those intersections form multiple curves revealing the proper trade-offs. The yellow line shows us we could build a plane with 10K in range, with a max V of just over 1400 KPH.

It takes work to find Demand and Value, but in the end, we get insight available nowhere else.

## Markets Across Seven Dimensions

Let’s examine how markets work together across

7dimensions.Far from being some exotic mathematical anomaly, such arrangements occur daily across many markets. Please feel free to offer some feedback.

To make a pencil, given wood, you’ll need graphite. Making a bike takes a frame and tires. These markets are bonded—you can’t make a final product without some key pieces. How do bonded markets such as jets and their engines interact across 7 dimensions?

Let’s look.

In the 7D diagram below (with log scaling in all directions), turbofan engines use Dimensions (Dims) 1-4. As Specific Fuel Consumption (SFC, Dim 1) goes down and Max Thrust goes up (Dim 2), turbofan prices, reflecting their Value, moves up as well (Dim 3), with Quantities sold (Dim 4) limited by the market’s demand frontier (yellow line on the red, right-hand Demand Plane). Making a new engine with a specified level of SFC and Max Thrust yields a value of the large green sphere, marked by “T,” at left.

That engine supports a new business aircraft model and accounts for a portion of the plane’s cost, marked by the large green sphere labeled “B.” Aircraft Value goes up (Dim 3, shared with the engines) with Max MPH (Dim 5) and Cabin Volume (Dim 6), as limited by their Demand Frontier (Dim 7). Such entanglements exist in all bonded markets. They must be studied thoroughly to be optimized.

#hypernomics #markets #innovation #economics

## Financial Cat Scans

## Cost, Price, and The Space Between

This month, we’ll study the difference between cost and price, why it matters, and how knowing how both behave in tandem is the key to success.

I know from our analytics that most of you are in the business of working out costs and prices. For many, it is hard to separate the two, especially if you work in or with the government. Today’s analysis directs itself to commercial operations. In a future newsletter, we’ll look at how to adapt this framework to the public sector.

All too often in business, someone comes up with a seemingly great idea and gets fellow workers excited about it. It gets pushed into production. Producers then wait to see what the market will bear for it, often falling short of projections.

What if you could change the paradigm?

Suppose you could see market openings and limits and test sample specifications and sales targets before you commit resources to a configuration. That would improve your chances of success.

You’ll have to work to enable this vision, but you will find it worthwhile.

When we at Hypernomics look at a market, we begin with Demand. As shown below as the red plane, that means finding the ordered pairs for Quantity and Price. We create a series of price bins (either equally spaced or binned by geometric or Fibonacci methods) and determine the ordered pairs (as the purple hexagons) representing each bin’s average price and total Quantity. Then we run a regression curve through them, which represents Aggregate Market Demand.

To the left of that curve, we find the Demand Frontier, a regression through the outermost points on the Demand Plane. This curve shows the limit of the products this market can absorb over time. As markets mature, the Aggregate Market Demand and Demand Frontier slopes often approximate one another.

If we examine the points closely, we’ll notice a price gap. Using its midpoint, we would find the 1) Quantity limit the market will support at that price (the vertical red line coming down from the Demand Frontier) and our 2) Target Price (the horizontal red line originating from the Demand Frontier).

To support that price, we’ll need to offer our customers something they like, here as Features A and B, which show up as the green Value Space at left, with the target Price as the horizontal red plane. We’ll need to figure out the Value Surface that the combinations of Features A and B command (the points for which we excluded from this view, for clarity). As seen on the left, there are Cost Surfaces for one or 500 units below the Value Surface. If we further bound our potential offering with Constraints (the vertical orange planes), we now have a region restricted on all sides. Conceptually, this expanse is not different than a like delimited region, such as your head.

Now, if you suspected that you had a deviated septum, your ear, nose, and throat doctor might order a CT scan, in which the doctor would develop section cut views of your head.

We can do the same thing in markets, using Financial CAT scans. Thus, after carefully setting up a 4D arrangement and taking cuts in both the Sections A and B directions, we can predict the 1) maximum Quantity Sold (reducing the 4D problem to one in 3D). Then we selected 2) the Price (dropping the remainder of undetermined dimensions to 2), 3) Feature A (the distance of the black plane from the origin, reducing the problem to 1 dimension), and 4) Feature B (the Vertical Profit Line, the final dimension). The per-unit profit line on the left times the number of units on the Demand Plane gives the projected profit.

In the process, we reduced a 4D problem to a single objective of maximum potential profit.

To complete the analysis, we’d examine all open price points and all viable combinations of the Features considering risk as well, searching for the best potential configuration.

Watch this video to see the analytical steps in action:

## Help Your Partner; Help Yourself

Upper Demand Frontiers form in every market outside of commodities. You can prove this by plotting shares for all S&P 500 companies on the horizontal axis against their sales prices on the vertical (best seen in log-log space). For any given day, you’ll find an Upper Demand Frontier takes shape.

Frontiers limit sales. How do you work around them?

Hypernomics enables us to see how interconnected markets work. In (A), the Boeing 787 Business Jet was, for a period, pushed up against its Demand Frontier (the dashed blue line). One of its compatible engines, the GE Genx-1B, found itself in a like condition, hard up against its Demand Frontier (in B, the dashed orange line). What to do?

If GE, whose engines make up about a quarter of the B787 cost, finds their Learning Curve (recurring costs, as the solid orange line) below their price limit, they could drop their prices and make more profits. That would enable Boeing to lower their B787 business jet price and do the same. Knowing your partner’s place in the market is key to making them and you more profits.

#hypernomics #profits #markets #partner

## Announcing The Hypernomics YouTube Channel

Here’s a question with a seemingly obvious answer: How many stocks are part of the S&P 500? If you guessed 500, you’d be close, as there are 504 companies listed there today.

You likely know that not all S&P companies have issued the same number of shares, nor do all share price match. Too obvious? Not really.

Consider what you were undoubtedly told if you ever took an economics class. According to Paul Samuelson (Economics, 9th Ed., p. 63),

“the equilibrium price, i.e., the only price that can last…must be at the intersection point of supply and demand curves.”Samuelson would have you believe markets have but one equilibrium point.But we know that is nonsense: 504 stocks in the S&P 500 form 504 quantity and price pairs. While they are viable, all, in the language of Hypernomics, enjoy sustainable disequilibrium as their stock prices exceed their costs.

What’s really going on? It turns out the value of products goes up as producers add features customers like. At the same time, as prices go up, quantities sold fall. To see this phenomenon, one must employ Hypernomics.

To find out how this works with as many as 8 dimensions, go to our new Hypernomics YouTube channel here:

https://www.youtube.com/channel/UCYsso5Yf0OFY3k78u5c30LQ#hypernomics #marketanalysis #prices #demand

## Assumptions vs. Observations: The A380

Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?

Assumptions are what we don’t know we are making – Douglas Adams

Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?

Many pundits claim they knew it wouldn’t make its target. Most appeared when the program floundered late in its lifespan. What would it take to predict its future in advance?

Projects often use 1) business case analyses and 2) customer polls to “verify it pencils out.” That works if 1) analysts conceive those cases fairly and 2) buyers convert at or above a target sales figure.

What if we don’t have to rely on those techniques?

To forecast the next 20 years, study the last 20. As B reveals (summing all model types to base versions), the airliner market had a poorly correlated (Adj R^2 0.458) yet statistically significant (P-Value 0.035) Demand Frontier over that period. Airbus’s target was nearly ten standard deviations past it.

The A380 took €25B to develop. It didn’t recoup its investment. Take time to model markets in advance. See what a market did to bound what it will do. You may not like the answers, but it beats losing billions.

#A380 #demandfrontier #hypernomics

## Long Time Coming

There’s something deeply affecting about staying with a problem for over 30 years. Once you get some resolution, part of you wonders why it took so long to get answers. A more forgiving part of you thanks Einstein for the inspiration to carry on. One can only be happy when that ah-ha moment finally arrives.

Such is the case with Hypernomics. After first entertaining the idea at 14, somewhere around 49, I saw the first hints of the practical applications of Hypernomics. 18 years later, we have evidence of its practicability in one of the most complicated markets, that of securities.

In Feb 2020, we made our first investments based entirely on Hypernomics. Far from being perfect, tests suggested that given a market downturn, we would suffer losses. Figure A shows we’ve endured setbacks in 2022. But backtesting supported the idea we would lose less than the competition.

In the longer run, in Figure B, the theory has had a chance to shine. Note the Hypernomics fund is doing more than 2X as well as Berkshire Hathaway and over 3X what the other major indices are doing.

#innovation #markets #investments #hypernomics