The Story Grid Book is Available

The Innovation Mesh has been inspired by Shawn Coyne's great work - The Story Grid.

The Story Grid book is now available: like here.

 It will get a place at my nonfiction book shelf that has space for about 50 books…that I use like stack with reverse gravity…good, "heavy" books sink slower…

It'll be placed next to  NKS, Red-Blooded Risk or Predictably Irrational...

Are You A Consequentialist?

…modeling the consequences of possible actions and select those actions that yield the best outcome? Consequentialism its the class of normative ethical theories (study of ethical actions) that could be simplified by the saying "the ends justify the means"

Adaptive control

I borrow this terminology for a class of pure technical innovations…innovations that want to achieve certain objectives. Say, quality features, a preferred behavior, a return at risk profile…of an outcome of a (production) process - a product, a good…

However you do it, by a mix of analytics or predictions you finally need to control your processes…you do it model based or data driven...its an inverse problem and consequently ill posed…

I've conducted quite a lot projects in this field: the best way to do it is by constant recalibration of control models …adaptive control…thats foundation is the constant estimation of changing parameters…a difficult task with a lot of traps.

We often think, physical modeling in contrast to, say, social or "real". But this is not always true. The prediction of a physical effect is not so easy as we often think. The theory of complex systems come into play…It matters what context we write into our models.

To solve a real problem, you often need to ride the waves of a real behavior and use tricky inversion techniques to recalibrate your models…

The Blank Swan of metal treatment

Let us take a numerically controlled metal treatment machine. Say, a forming machine. It "trades" a shape and the return pays in accuracy of that shape.  To implement the control,  you need to understand the framework of the elastoplasticity theory and the complexity and limits of its mathematics derived from the mathematics of continuum mechanics. 

Deformation is decomposed into elastic and plastic parts and for simplicity decompositions shall determine stress and kinematical quantities. The resulting PDE system can be solved by advanced numerical schemes (say, Finite Elements).

They "only" need to be calibrated related to the physical properties of the material that are dependent on recipes, and properties that are result of the process? And work as predictive models for final shapes, right? No Way.

The metal you buy change their specified properties during the treatment…headache begins.

So, you need to recalibrate your models during the forming process ("continuously"), say, by observing, say, force trajectories. The closer you come to the final shape the more your system knows about the concrete material (including its elastic "memory") and the better it can explain and predict...

Calibration and re-calalibration may need the application of clever machine learning techniques

The Blank Swan is Elie Ayache's original treatise of financial markets…it criticizes the naive treatment of predictability…

It may be surprising, but derivative pricing and metal treatment have much in common. Their adaptive control capabilities need multi-model and multi-method techniques and a lot of clever inverting techniques…

Being a consequentialist needs the full understanding of this framework.

An Internet Of Money?

Making systems universal in the sense of programmable is about taking them to the Edge of Chaos. This is boiled down from the theory of complex systems…if, in terms of the states of matter, a system is solid enough to store and liquid enough to perform transmissions it's universal though programmable…in this metaphor gas is chaos (or randomness), solid is order and liquid is the phase of complexity.

Our money system is programmable

Money (in our fiat money system) is solid enough to store value and liquid enough to be the medium of exchange (and it's a numeraire…a unit of account).

But we know from computer science that programming becomes better with the support of an operating system…a set of programs managing computer resources and provide common services for applications…it hides the complexity of the underlying system from the user. In particular it manages memory dynamically…fixed memory allocation must be an exception and timely limited.

To maximize programmer productivity integrated development environments are built. In their core they interpret programs in a language by translating them into instructions that are formally understood by the underlying system. Programs deal with data and algorithms.

Money programming is still low level

There are many simple abstract machines that are programmable, but I think of a Von Neumann architecture with a Arithmetic/Logic Unit, a Control Unit and a Memory - a register machine.

Money? Money is stored in accounts and the control unit is based on double entry bookkeeping (equity = assets - liabilities  in the accounting equation approach serves as error detection tool) and its rules. And money as a contract of an exchange it simplifies and secures the transformation rules…value, time, interest…

It's important to understand that the market is not a place but the exchange itself...

Monetary policy is the process by which the monetary authority controls the supply of money…its classic equation is Quantity of Money*Velocity = Represented Transactions*Prices or M*V = Price Level*real GDP. it's easy to see that if the money does not circulate (it's put into the fridge) its quantity must go towards infinity and if we think GDP is already close to its full capacity changes in M*V will effect the Price Level…More money meeting rare goods will drive prices…but we have immaterial goods that are available in "infinite" copies…

We understand money as product with a time value…and there are derivative products atop it as well as atop other underlyings. We are able to calculate technical (fair) prices and risk spectra in a risk neutral regime. We've great models and methods, but if we want to understand and avoid systemic risk we don't have enough informative data. Big data turns out to be a big joke when we still struggle with getting the informative small data...

I don't need to repeat that complexity and low level system programming put us in tailspin.

For many years now I ask: where is the initiative for an internet of finance? It's too much spread interest: the massive-data sellers, the market makers…

But here it comes (through the back door)?:

An internet of money

Envisioning a world where we can trade money as we trade data…and program it? In a relative short period I read two articles that I take as a hope: An algorithm to make online currency as trustworthy as cash, WIRED and Why Bitcoin is and isn't like the internet, Joi Ito's web.

Remember, in our fiat money system a modern, sovereign state can make anything it chooses a money…provided it accepts the proposed money in payment of taxes…why not cryptocurrency?

With a kind of symbolic programming of money we could construct things like option, future…or other derivative special purpose money and check, say, debt webs in order to understand how wide would the spread of economies distress be, if a market participant failed.

The internet of money will help to achieve the antifragile...a system becoming stronger when stress is decentralization (of money creation), diversification (a portfolio of moneys for a purpose), agility (prepared for money regime changes) and trust (see the WIRED article).

Centralizers, integrators, top-downers…may raise their eyebrows…but innovators get the chance to fix a system they haven't broke.

There is one more little thing: understanding money, we need to think of properties and exchange first and try to understand why prices and values are not equal. Think what you can buy for a barrel of oil in Switzerland, compared to, say, South Africa?

Make trading a fair play needs programming...

Minimum Viability and Maximum Optionality

I've used a quite high "ink factor" recommending a systematic diagnosis tool for innovations: The Innovation Mesh. It was inspired by the great book editor Shawn Coyne.

A diagnosis tool…not more?

It shall enable innovators and innovation marketers to understand better, whether an innovation works and sells. I emphasize on innovations that support quantitative aspects of a real behavior. They are very similar to an genre of literature - the thriller.

It uses a few schemes mapped into a picture…The whole assessment task should not consume too much time and effort.

The big picture is about change

Innovators want to change the underlying systems that are causing major problem...but the usual reaction to change is a wave of shock-denial-depression-acceptance-integration.

Most systems that require computation have a clock, flows, events, transformations, massive information requirements…suggesting a function-oriented decomposition and consequently a bottom-up recomposition…

This external and internal aspects suggest developing and marketing the innovation the evolutionary prototyping fashion.

It has a great business advantage:

Minimum viable innovation and minimum viable audience

A minimum viable product is the product with the highest return at risk…minimum viable audience is the smallest possible audience for this product with a maximum of multiplication power. In short, a product that matters for those who care.

Minimum Viable Audience, has been originally coined by Brian Clark…The liked article (addressing digital media entrepreneurs in particular) shows that it's the innovators' job to find out what their users want and that it has great benefits to choose this explorative, constructive model. It's leading to the right sequence of insight-consensus sales.

But don't forget to look into the numbers:

Option valuation to optimize market risk

The approach gives you a lot of options and there's still uncertainty…what options will most probably provide the highest return at risk? There's great technology to support innovators finding them: Real Options Valuation tools.

I partner with experts, who know how to apply them…in in joint projects, I focus on innovators.
Interestingly enough, most real options projects are conducted in traditional sectors, like commodities…

But it's the innovators, who need to deal with uncertainty, because they usually can't rely on "game rules"...

How I help innovators leverage their businesses

An innovator has told me the story of her innovation. We've walked it through The Innovation Mesh...we know the type, conventions, rules and states, the points of view, desires, requirements, needs, controlling ideas, coverage, methods and technologies, the constructors, builds and solutions...and after a few fixes of the Prototype we know it works and will sell, what it's for, for whom, how actors will experience it…we roughly know the qualitative external and internal values…

With that information it's easier to build the release 1.0 following the principle of a minimum viability. We want feed back from a distinguished audience and again The Innovation Mesh helps defining the minimum viability segment and audience.

Now, we need to outline the licensing and pricing, a development plan and strategic marketing with a tree of options. It's the right time to use a real options valuator for what-if simulations…to understand return at risk probabilities and funding requirements better.

Believe me, it's an exciting task with amazing insight…and because of the instant methodologies and technologies I can position its high/low at the value/cost map.

Get a proof…here's the contact

p.s. I'll support you in any stage of your business development…real option valuation tools, for example can be also applied to define the game rules of a multi-unit business with products and services in different life cycle phases.