It's Quantification Again

How could Donald Trump win with the support of "blue collar workers"? Or will the past decade be remembered as the period when the underclass revolt grew into a movement with political bite? With a cry for change? But which one?

Do we have answers? Can we explain the geopolitical, socioeconomic system without understanding the underlying "physics"?

An attempt at an answer:

A policy that fixes what it eventually broke?

We strive for equilibriums, although we have long since dawned that life, in all possible forms, only happens at the border between chaos and order. Political life above all. Politics benefited from the participation of the people. For this, however, more is needed than direct democracy. Complex concepts must be explained in such a way that they can be understood, comprehended and managed by non-experts. Fueled by explorative learning of people and developed according to the principles of evolution.

To implement such a politics is equivalent to implement a radical innovation. It can't implement a structural and behavioral change simultaneously. It must therefore use existing instruments. The aim is a system that overcomes its fragility and becomes stronger when stressed - by diversification, flexibility and optionality. This is impossible without quantification.

Political engineering

Political engineering, deals with the design of institutions. Institutions are understood as the rules that empower a society to govern itself. It often involves the use of paper decrees, in the form of laws, referenda, ordinances, or otherwise. But those instruments can never replicate the system complexity.

Quantitative policy

If the state, in view of systems of growing complexity, wants to reach its targets, it must quantify. An example of this is provided by Financial Engineering on the basis of Quantitative Finance. Methods of econometrics, finance theory, engineering, physics, mathematics and programming are intelligently combined to design primary financial instrument and their derivatives (secondary financial instruments) to asses their return and risk profiles.

Since politics also supports its primary tasks with secondary instruments, it is far more quantifiable than widely accepted.

Structural changes are complicated, but system changes are complex. International exchange is complicated, but global civilization is complex.

In complex systems, crises are not unavoidable cycles, but operational accidents.

The Agenda of Unser, the book

A river flows from the Industrial Revolution - see here
The complexity of labor and economy - see here, herehere and here
Income or wealth - explaining the essence of the money system, it emphasizes the different effects of income producing investments and speculative investments in existing wealth
The programmability of money - it's a liquid media for economic transformations and a store for value, thus programmable, but then money system lacks an operating system.
A change that works - how to avoid the extremes of the change curve
Free market or capitalism? - explaining, why free markets and capitalism without adjectives cannot co-exits.
Management of a wealthpool - how to deal with the conflicts of individual and collective interests has responsibilities.
It's our turn - why direct democracy helps fixing politics
Centralize or decentralize? - why Marxism 2.0 would't work and why a politics with a left orientation must decentralize.
Income from micro to macro - about the different income types of individuals, enterprises and total economies. Why it's important to understand, where it comes from, where it goes to and how it's used
Control with tax - to manage the tax principles, equality-cetrtainty-convinience.economy, cannot be achieved without quantitative modeling and adaptation.
Universal basic income - why it makes sense, but also why it's required to control is by models
The no-problem problem - about systemic risk management

Unser - in this chapter, I give an answers and a resolution to the questions and progressive problems, I've asked and described in the previous chapters. How to use existing instruments, how to write mid term future and how to change foundations.

The century of complexity

But there's one big thing thats atop the details: there's no significant pay off  of any political change, without quantifying - empowering political engineering. Because the required physics of policy is the physics of complexity. It's why policymakers have lost their abilities to sustain decisions. Because it's impossible, without understanding positive feedback, free agent complexity, the complexity of risk, the limits of probability, the impossibility of predicting future, but the possibility to write it (after having understood patterns)…

Without experience in quantitative fields, risk management, the theory of complex systems and innovation principles, I couldn't have written this book. But the other way around, writing it I've looked into topics that has provided new insights in core business: innovation advisory, focusing on quant innovation. In the century of complexity.

Unser - A Quantitative Politics Book and Blog

I've written the book and decided to share it's ideas and recommendations in a new blog (it's in German):


My Motivation

We strive for equilibriums, although we've long since dawned that life, in all possible forms, oscillates between chaos and order. Political life above all.

Policy benefits from the participation of the population. But this is more necessary than direct democracy: complex concepts must be explained in such a way that they're understood by non-experts.

I therefore designed an adaptive policy that works because it is understandable, comprehensible and manageable. Fanned by the discovery of the populations and developed according to the principles of evolution.

Methodologically, I am guided by the experience that a change, a radical innovation, can not at the same time cope with a radical structural and behavioral change. My concept of an evolutionary left therefore takes bonds in innovation.

The innovation itself is changing, but it uses existing familiar familiar instruments. Propagation of the new policy proceeds concentrically, according to the principle of: (the interchange between the core groups) - convection (the flow over the surfaces of political segments) - diffusion (concentric distribution in all areas)

Order here

A Book The Magazine Style

It's in German and I think it's worth having it. It's a political book written through the lens of an innovator. It's compiling insight that I gained my whole business life having put innovations always in a higher context.
To write it, I've
been working hard for over a year
explained complex concepts as simple as possible
without omitting important details
incorporated recipes that turn ideas into projects
took care of the beauty 
It will be shipping in November, 2016.

I'll announce a pre-order possibility soon. 

Any question? Email to Herbert

Software Makers Should Learn More About Machine Learning

We're thinking of software that develops software. Is machine learning of help?

Remember, machine learning aims on decision making support. Like any learning it is about understanding things better. In a machine learning project, we extract models from data and we hope that those models are understandable and computational.

But the purpose varies from analytics, over predictive modeling to control. And there's no one method that fits all. See A Master Machine Learning Algorithm?

What is the difference between doing a machine learning project and a development project?

In both, you usually deal with data. And data analysis as an exploratory approach should be the begin of anything. But machine learning experts have a different view on code than developers: developers derive programming paradigms from the problem decomposition. Machine learning experts do not think much about data-, function- or object-orieted decompositions - they're in the extracted models...

But monstrous amounts of data are produced by machines and the best way to deal with all that information is by machines.

So, people who do large scale machine learning think like software engineers of large scale, complex systems.

What about software analytics? 

Modern analytic and model-based approaches in software engineering aim on the automatic generation of software from domain models... For this task program code is data. That suggests that machine learning is applicable.

We made some experiments with defect prediction in large industrial software systems (static core analysis) and found out that fuzzy decision tree methods fit well to this purpose.

Software developers may enrich their development methodology with machine learning and apply machine learning in the software quality assurance process - creating a two-sided positive effect.

What Kind Of Innovator Are You?

The first thing I do walking an innovation through The Innovation Mesh: find out about the types. The types classify the innovation in order to manage the expectations of actors and clients.

The Time Type tells you how fast the innovation works related to a real behavior and how well it is synchronized with it

The Reality Type tells you how far the innovation disrupts our believes

The Style Types classifies the appeal of an innovation

The Structure Type tells us how the innovation is going to change our lives

The Matter Type describes the innovation energy…It's divided into Purpose Type and Realization Type

But there's more…innovation types control conventions, obligatory tasks, points of view, needs…and finally methods and technologies.

But working in concrete with innovators I'm also interested in what innovators prefer and what characterizes their work best.

Quant Innovator's Orientation

Watching innovators over years I categorize them in the following way:

The Theorist wants to make groundbreaking inventions and build the constructors proving that the underlying theory is possible

In contrast The Crusader wants to radically change a behavior - she's for an innovation with a conscience

The Modernist wants to modernize the way an innovation is built - this includes system designs, architectures, methods and tools

The Rebel wants to show that the impossible is possible - she solves the most complex problems, applies complicated models and show that this is possible with limited resources

The Naturalist is particularly influenced by natural systems and how humans interact with them - they think in small structures of smart and connected things

In contrast The Worker takes her inspiration from more or less systematized ways of working - thinking in workflows, transformations…

For radical innovations, shaping a new world, it's vital to build innovation teams that are able to co-create by integrating such orientations.


Thinking a little deeper about quantifying the combination with behavioral technologies came to my mind… more general: antidisciplinary...

The Antifragile becomes stronger with added stress. The word defies easy definition and I think NN Taleb introduced it, because the non-fragility is not equal to resilience or robustness.

Antidisciplinary is all about non-elephant animals 

The MIT Media Lab uses "antidisciplinary" as position for doing research and learning. I enjoyed reading about it in Joi Ito's Blog: Antidisciplinary. It is another word that defies easy definition. It is not the same as interdisciplinary. It is a specialization on the non-specialization. Joi Ito says: "it is all about non-elephant animals".

It's against fragmentation of disciplines in thinking, speaking, doing…Thinking mathematically without restricting to special functions, operators…? In combination with computer science it is maybe about knowledge based languages to program everything? Make "everything" computational.

Behavioral theories of finance or quant finance?

Behavior theories of economics haven been accepted in financial circles. But more than as the feedback of financial markets cocktail parties? Quants may think they are all treated in features, like mean reverting…and behavioral economists may not think about turning them into something quantifiable?

Modeling Volatility and Valuing Derivatives under Anchoring

About 2 years ago, Wilmott, Lewis and Duffy published a rare connection between anchoring - a big idea in behavioral theories - and practical quant finance. They introduced a complete-markets model with volatility smiles, tractability, and intuitive appeal as an anchoring or habit-formation model. A derivatives valuation model that has a memory.

Joi Ito may say: instead of impressing a small number of experts, taking the high risk of an unconventional approach.

Does antidisciplinary need disciplines?

Paul Wilmott's approach requires different thinking but also heavy algorithmic mathematics.

The antidisciplinary space needs disciplines and a rich technology stack, but also openness to ideas between disciplines.

Me Talking To Myself In The Future

This piece of the Canadian performance artist Marie Brassard inspired me to look playfully at the past from a future perspective.

What have I become and what will I become? I change, and inevitably I move to the person I'll end up. But not only memy brand, my company, my community...

I'll check in the future whether I became more willing to explore, more generous, more confident, a better market risk optimizer? When I'd arrive by time machine, what would I show myself? It's speculative…but worth trying...

It's about my transformation from an innovator and innovation marketer to an innovation marketer and innovation marketing advisor…

What I may have done right technically

Predict the fast move to more quantifying. Quant innovations supports systematic investigations of observable phenomena via computational techniques in fields where this was not so common - social sciences, journalism and even law and politics. 

Defend the multi-model and multi-method strategies against the one-size-fits-all hype of master machine learning and AI algorithms, Big Data…It was my strong belief that connectionists, evolutionaries, Bayesians, symbolists and analogizes must cooperate with modelers and numerical analysts…

Recommend developing technologies products and solutions orthogonally. Develop a technology stack first…technology that isn't devoted to a context at first place. Develop you products transformative and make your solutions by tying things together. It's the only way to make smart connected things

The contextual technologies help recommending develop front-ends that explain complex concepts to non-experts…by contextual technologies and media. 

How my brand promise changed accordingly

My brand promise was helping innovators leverage their businesses by assessing their innovations and help them to connect purpose, position, team and client experience. To deliver a brand that is the meaning that clients attach to their innovations.

As I increasingly convinced innovators to provide not only innovation-use-training, but give full explanation on the methods behind and finally unleash the programming power behind their innovation (to make their innovations a solution and development environment in one)…I've decided to do the same by sharing my methodologies for the assessment of innovations…

What I'm proud of

My diagnosis tool The Innovation Mesh and a paradigm to understand how to optimize market risk of innovations…how to deal with the known and uncertainty, how to find the barrier between them and how to optimize…and how to apply Real Options as one tool to maximize the value of an investment project.

This is what I'd show myself when I'd arrive by time machine…

But there's one more thing

Putting my work into a higher context (like politics), and inspired by a high level process technique…I've introduced the reaction-convection-diffusion metaphor to marketing. It helps to optimize the market risk qualitatively…put (the story of) an innovation into the pot (market)…it will
  • react with worldview, states, solutions…in market segments related to its change potential and requirements
  • circulate by convection at borders of segments with gradients in expectations, knowledge, persuasion
  • diffuse through the market promoted by multiplying actors
Marketers can observe the "heat and speed" and control the process by adjusting access, information, education…avoid that the "pot" freezes the story or boils over with negative reactions.

It works even better as quantifying emerged and conquered new fields, sectors and segmentsWhen a quant innovations work, the reaction-convection-diffusion metaphor in marketing will help optimizing market risk even in new markets. 

Contextual technologies build the perfect platforms…if they're understood.

Development Principles Revisit

The contextual age and technologies, the emergence of smart connected things and processes, the need to quantify more...force innovators finding new abstractions, automation and better user-system interplay…

This is what you should think of:

Develop evolutionary - Evolutionary development cycles will motivate developers and empower constant feedback

Let technology revolutionize workflows - Integrated model and demonstration building will help explaining complex concepts to non experts

Establish a bank of innovation - A lab to design, analyze and fund innovations will boost dynamism for transformative development

Build a technology stack and get the tools required - Enhance your platform and workbench to build a variety of products and solutions atop

Organize a fast path from lab to market - Remove deployment obstacles and close the client feed back loop

Support rule breakers - New approaches, methodologies and technologies will help to solve progressive problems

Think in development cities - Establish teams who do everything from development, deployment to services. Let developers communicate with clients and actors via the underlying theories, models, methods and technologies

This development principles will help you building big things with a comparatively small outfit.

It's Now Two Years...

that I've given my uni software plus into Michael Aichinger's hands - and launched my new business.

What's essential:

Exploit the wu-wei paradox

The wu-wei principle helps by letting the complex stream work for me. It was never so easy to share ideas, concepts and methodologies…understand and influence trends without reacting jittery to short-lived modes.

The continued work with my features partners made it easier to do things that matter for those who care….and having saved some time I could even develop new tools…

Develop technology and products orthogonally

It's becoming increasingly important to develop technologies without context first and develop contextual products atop them - transformative and adaptive. The intelligent combination of modeling with data driven methods (machine learning) is still the key.

Technology stacks may include domain specific languages, engines that implement them, data and communication services…all with hybrid programming features, being platform agnostic and inherently parallel.

You can inherit a lot from Wolfram Technologies - the internal value of building technology stacks easily…and the external value of its declarative nature that let you build functions, tasks and workflows like stories.

Avoid the fear of regret - but be aware of the eager seller - stony buyer principle

To remove this kind of fear is one of the big challenges in innovation marketing. As innovator you know that at first you make things for somebody, not everybody. You need to understand from the first prototype whether your innovation will work and sell. To understand this better, I've developed and shared The Innovation Mesh. It adds some emotional criteria to the assessment of your innovation.

Because, loss aversion of buyers can make sales figures disappointing, The Innovation Mesh also helps to get actors hooked to your innovation.

Get the bigger picture…

...of marketing, selling and branding. If buyers will not spend less but buy less expecting more quality, the inner story of your innovation will become more important than the outer struggle for existence.

And his will happen when products will become smarter and more connected. Consequently, you need to invest more in the development than in promotion. 

How did I move?

I've continued working on principles that help optimizing market risk of innovations. And I've applied my recommendation to innovators - pushing their innovations into a higher context - myself.

I've worked on my Quant Politics concept and decided to release it in book-form - self published in an evolutionary prototyping fashion.

I speak for the project

I'm a marketer at heart. But my trick is not to present the innovator, the maker, the client or the re-marketing partner...I represent the project. And I want to get paid for success…not for work.

If you're a startup or an established innovator…get the proof. Contact  me.


We had UnMarketing and UnSelling and now we have UnBranding (Fast Company calls it Debranding). It's all about the big pictures of marketing, selling and branding.

Brand Promise

I think, it's all about this: a brand promise connects purpose, positioning, team and client experience…It enables a brand delivery connecting with your clients (by head and heart) and differentiate your brand. In short, it's a promise about expectations, stories and relations…all related atop your innovations.

Even shorter, brand is the meaning that clients attach to your innovation.

Not Overdo

This is what I understand from the article: if buyers will not spend less but buy less expecting more quality, it'll become more important to combine the what and who. Products will not only be chosen because of its differential advantages, but if there's enough trust in your promise.

Yours worlds view, personality, values, visions...your communication, voice…your actions…how you build user experiences…will become more important related to your your design, imagery, messaging

Make clients feel good about their decisions.

Branding is Communication

It has never been so easy to communicate, but the media also suggest that everything is interconnected.  But innovations are special products that change more...So, the inner story of you innovation - how it works and integrates will most probably become more important then the outer struggles for existence.

Consequently, you may choose investing more in the development than in promotion?! Let your innovation do most of the story. It will strip branding to its core…and The Innovation Mesh will help making your innovation work and sell…in the sense of UnBranding.

Multiple Choice Buying?

Regulations shift revenues from banks to Central Counterparties.  Shall banks adopt a kind of "collaborative filtering algorithms"...successfully used by the online retail sector…to pitch new products to clients?

What's a collaborative filtering algorithm?

It's a method of making automated predictions about the interests of a user by collecting preferences from many users. If one shares a taste with others its more likely that she shares another taste too.

If you listen too much to main stream, you create mainstream

Collaborative filtering needs enough samples, it looks into the past…so, although collaborate filtering can claim to achieve good diversity and independence, it may work the other way around in unpredictable cases - if you listen too much to mainstream, you create mainstream.

Good for gadget buyers is not necessarily good for corporate treasurers

The traditional role of, say, a corporate treasury embraces core functions…corporate finance, cash management, liquidity planning and control, procurement of financial investments…and increasingly important: management of interest, currency and commodity risk…and marginal functions that are extremely company specific.

Senior management and board seek greater visibility to liquidity and risk exposures and better monitoring financial metrics on critical projects…this requires finding new ways to leverage treasury skills and technologies…maybe use things that are not in stock.

Quant finance

Without using algorithms, you can't understand values and risk and engineer new financial instruments. But the success of treasury departments may need also quantitative methods to validate the major instruments they need…far beyond collaborative filtering.

A treasurer's role may need to shift from being an asset guardian to a value creator…set the stage for successful investment and risk management, leverage technology and build quant finance skills.

It's one example how indispensable quant innovation can become...

Turn Techies Into Founders

The usual scenario: investors look for startups with business plans, designs…and teams in place. There are other ways of supporting startups that all have in common: they need to understand whether an innovation will work and sell.

Don't find them - build them

Today, I read here (Wired UK, July issue) about building startups in-house. It's putting innovation into higher context. Tech will help leverage the developments and deployments, but in its core it's a bottom up approach of developing businesses around people.

A company builder for innovation builders

A company builder begins pre-idea, pre-team…Their approach is evolutionary in the sense that they encourage and support people to do groundbreaking things. They finance the try at a low level already. The ability to support cross-overs, selection and mutation enable the company builder to finance along a profile of optimal market risk for the startups and of their own.

I like this approach, because it creates real options for both the company and the innovation builder…and it provides constructive learning on a higher level…also for both.

This is what I wrote two years ago about startups. Don't do it alone gets another dimension when working with a company builder…

You're A Genius

Is there a magic behind creative thinking?

Innovation makes our species different

Behavioral neurologists may say: yes, there are talents, but the neurological principles of creative behavior are the same among us - we all have creative minds. I agree. Creativity is a special class of problem-solving…characterized by difficulty, unconventionality, novelty…but it needs competency. Can you buy competency? Yes.

Wheels aren't hard to reinvent, are they? 

Original thinkers often look for adventure and start thinking not reading (eschewing algorithmic and technological fruits available). This is great for explorative learning, but it may reduce the value of the innovation?

The innovative spiral drives faster if you push new, validated theorems, models...into the knowledge base and use them in a next turn.

What makes quant innovations work?

What are the units of quant innovations and what are their generic building blocks? Remember, the basic units are functions, the most important units are tasks. Functions create tasks, tasks create workflows, workflows create subsystems and subsystems create the quant innovation.

A quant innovation does only work, if it's developed the bottom up fashion and each unit has building blocks that are constructors, progressive problem managers and solvers.

Functions tune the mechanics of tasks, workflows, subsystems...the system. They are the media of actors. They define the coverage and the depth of the system. Their programming style shall be symbolic, functional…but their implementations shall combine symbolic and numerical computation.

Tasks have usually a time dimension and they move objects and actors. Tasks shall offer a clear shift throughout their flows. Tasks may be: data selection, curation and import...model validation…model across scenario evaluations…back and stress tests…result analytics and aggregation.

Workflows may deal with the analysis, prediction or control of processes…in workflows we may use generic tasks like, "Create", "Select", "Apply"…Data, Objects, Models, Parameters, Valuation Methods, Scenarios, Factors

Subsystems and the system are created by workflows…subsystems are add-ons if they're built atop another subsystem (a platform).

Build your technology stack (and share it?)

Develop a cascade of innovations that work and empower yourself unfolding creativity based on a growing stack of technologies. A Task-oriented Language, Engines that implement it, Communication Services, Data Services, A Computation Factory…Provide the same stack to other innovators. Build insight partnerships.

Clouds and contextual front-ends help partners to develop and deploy jointly…innovations that may become the classic of the future. Work of geniuses.

How Is Tech Changing...?

One innovation: clouds helping cooperation between authors and producers, say, in Music. Writing. Educating. Building...Or in Software itself. New contextual front-ends will support the cloud controlling a kind of creation-experimentation-creation…spiral.

We know this from maths, where creation uses abstraction that is driven by the evaluation of examples…

Tech has changed…

integrated and put into workflows: writing, capturing, refining, adapting, marketing, deploying, sharing…and exploring. Good tech offers rich functionality but lets creativity lead…and it shall at the same time lower the barrier to making and deploying...

Creators can find producers and fans..and define projects that they want to work on together…Sharing isn't restricted to ideas, resources…it's related to projects, businesses…

If you can't get purpose and profit at the same time - it doesn't matter much. It's connecting smart things for creation, deployment and services under the framework of multi-level sharing.

And it will work especially well in fields that we find the most difficult: like quant innovations...

How To Win

I've worked on the diagnosis tool for quant innovations and thinking more about it, I became more interested in a higher context: politics.  And it became even clearer to me: innovators need to push their innovations into a higher context…

The long breath of contextual thinking

But even developing or writing, interacting with my core partners frequently is the driver leveraging new inspirations…

Today, I update your information about how UnRisk continued transforming ideas and knowledge into margins. The story-of-being-lucky began with the decision to develop technologies without context in order to develop contextual products fit for purposes swiftly and adaptive. If we were a taylor we'd say: made-to-measure.

And then we decided to unleash the programming power behind UnRisk. And partners helped us understanding new uses and users…and consequently contexts.

This is my reference: UnRiskOmega. It has one controlling idea: enable experts and non experts to manage complex financial deals. In different environments but with the identical cores.

Everything we ever had in mind…its cumulated in the reference. It's context technology in the core and in the front ends…and consequently, it's made-to-measure itself. It has the many faces of a navigator through the regulatory cliffs, a client profiler, a portfolio optimizer, a product risk classifier…

It's the amazing result of a great cooperation: MathConsult-Multilateral-uni software plus…


You win developing technologies and products orthogonally, rely on open innovation and collaborate. Sounds easy? It's not. It has a few traps...


I haven't posted for a while. I've worked on my Quant Politics concept and decided to release it in book-form. Everything is written, but it's still much work to do getting it into the form I've in mind. It will be in German (first).

Learning by writing

I always learned when writing. But there's this autodidact's dilemma:

I've never learned any political theories. I've no theoretical background in social and economic sciences…I analyzed various socio-economic and political systems, but not too much…there're great books and the blogosphere is really rich of great ideas…see my As Read At list.

But where to begin is a personal choice and the emergence of progressive problems force you to dive deeper into a particular discipline….along the writing.

About a political system that may fix what it eventually broke - Innovation

That is an usual objective of innovation. And my simple idea is: it must be adaptive - more computational. Quant Politics. So, one of the controlling idea is to apply my knowledge on how to make innovation not only working but attractive enough to be integrated…

It must be adaptive because of the complexity of the economy and labor. They must develop in a coevolution. It's radical innovation, but to make it acceptable it needs to use existing tools and instruments. Our money system. Taxes. Transfer Payments. Hard- and Soft-Infrastructure. Financial Instruments…but differently set up and applied.

It's required to be much more quantitative

Its foundation is the philosophy of Speculative Realism, where new contexts are written into blank socioeconomic spaces. To understand whether a theory is possible we need Constructors. And politics need to surf at the waves of reality and adapt its decisions by recalibrating its models…

It shall help avoiding the no-problem problem

That is what politics impresses: the constant peeking for danger allows no no-problem phases and the soporific we-are-doing-so-well often leads to a crisis.
Hiding the chances of risk breeds fear - hiding the dangers of risk creates disasters and crises
We need policies against exaggerated fear and recklessness.  Consequently, a new political system needs to work with quantitative support. It must be interactive. Evolutionary. It has to work with simple models and tools. It must support explorative and experimental political learning enabling much more direct democratic elements.

To integrate it quickly we need to accelerate the change process

I recommend to run through the following phases of the promotion process: Reaction according to the principle of chemical reactions between groups with different philosophical beliefs, cultures, social systems, economies, stages of development, educational standards…Circulation by a kind convection at the boundaries of different political segments with different expectations, knowledge and skills...Diffusion by concentric dissemination of information and frameworks.

Integrate it by projects

It needs projects to understand the required behavior changes, how much it contradicts world views, culture, social systems, economy. Projects shall tell us how it is accepted, how much explanatory support it needs and how quickly it gets stuck without direct intervention and how it spreads with the help of early adopters and multipliers.

A politics of open innovation and sharing

Taking the complexity economy and labor of the future and the technological evolution and combine them, I come to a few conclusions:
  • Our money system should be programmable and implementable in a kind of Internet of Money. Implementing a book that is not owned by anybody, but everybody. It would most probably enable the implementation of a much more dynamic book keeping system
  • New methodologies and technology could empower a politics with much more direct democratic elements
  • A better understanding of property rights, use rights and copyrights could open the possibilities of privately offered semi-properties enhancing the usual hard- ind soft-infrastructures provided by the public sector…emphasizing on the stimulation of innovation
  • The Main Street could benefit more from the innovations of the Wall Street without suffering from the downsides
  • To help turning automation into augmentation the progressive tax system shall be extended from wages to total income and a basic income should be implemented stepwise according to the future of work…tax systems and transfer payments shall be adaptive
  • The general direction of the system design: as decentralized as possible - as centralized as required
The big picture: make a political system that becomes stronger when stressed

I'm not megalomanic…thinking that my book will convince authorities to make the changes...but I hope that one or the other idea will attract experts to transform them into concrete projects.

I've looked into politics through the lens of an innovator and innovation marketer.

A Master Machine Learning Algorithm?

It's all about extracting knowledge from data. And there's hope that this knowledge is understandable and computational. I worked with machine learning for over 25 years and I've learned the lesson: working in the data salt mines make us (too) often breaking a sweat.

A short story of being unlucky

The idea has a long tradition: computerized systems are people and there's a strong relation between algorithms and life.

First…if systems replicate all or knowledge, expertise…they are intelligent. This top down expert system thinking "died".

Then...Artificial Life, in contrast, named a discipline that examines systems related to life, its processes and solutions. Genetic programming would emerge intelligent artificial creatures? They did not..

Now…because we've neurons intelligent machines need to have them too…our brain has an enormous capacity…to make AIs we only need to combine massive inherent parallelism, massive data management and deep neural nets…?

All this phases have brought us a fantastic technology stack for machine learning…but we still wait for the breakthrough.

The race for the single algorithm

This race is motivated by the idea: if we understand how our brain learns, we can develop learning machines…and the belief of the schools of imitating biology seems to be that the way brains learn can be captured in a single algorithm.

The connectionists belief that all our knowledge is encoded in the neural nets and "backpropagation" is the "master algorithm".

The evolutionaries think the ideas of genetic programming further

But there're schools that find imitating evolution or the brain will not lead to an master algorithm. Its better to solve the problem using principles from logic, statistics and computer science.

The Bayesians think creating the master algorithm boils down to implementing the mathematical rules of Bayes's theory.

The symbolists closest to "classic"knowledge-based AI thinking believe in general-purpose learning in extraction and combining rules that can be evaluated by inference engines.

The analogisers work on forms of analogical reasoning based on massive data.

One size doesn't fit all?

By experience from practical machine learning projects, I'm infected by the multi-strategy and multi-method approach and the combination of modeling and machine learning.

This doesn't mean that I do not believe that a master machine learning algorithm isn't possible…but why should we make machines that think like us, if they possibly could extract things that we don't recognize…helping us to gain fundamentally new insights?

Applying machine learning as for the analysis, prediction and control of industrial processes, we've used fuzzy logic based methods to reduce the problem to a size that makes it fit for automation and identified parameters of the models for constant recalibration…with a set of methods from statistics and neural nets…

We shall not think like machine so that machines can think like us…but shall machines think like us in order to replace us or think differently to augment our capabilities?

This post has been inspired by an article of the April-16 issue of the Wired UK Edition (Pedro Domingos).

An Innovator's Reputation


When I present an innovation, I trust that it's good. No, I trust that it provides differential advantages over competitive offers. I trust that it'll be accepted at least by those who I want to care. Maybe I entrust even that they remain loyal to me and my innovation. I also trust that my team and I are capable of shipping the required quality and to respond appropriately to future needs. I trust that we will do the right stories to stretch and expand to new buyers. I trust that we have found the right position on the value / price Map. I trust that I know the competitive arena well enough...I trust that we will control our costs and financial obligations and not the other way around...

This is quite a lot of trust. Too much to keep it under competitiveness?


When I've developed my innovation atop a partner's products and and consequently, I act as a reseller of their products, I trust that the originators have considered all aspects of above (I'll check all factors through my lens). I trust that they're interested in my market analysis and experience. I've confidence in my own abilities as a reselling partner, but I need to trust that they do not let me develop a market and then change licensing and reselling agreements….to my disadvantage.

This is quite a lot of trust. Too much to keep it under cooperation?

Cooperate to become competitive?

Provided I've been lucky and introduced a bestseller innovation for a specific market and I offer my know-how, my technology and my special marketing knowledge…to other developers? I empower them to become creative copiers. What do I trust? I trust that they help me to stretch and expand to markets that I can't reach alone. I trust that I can continue determining the rules of the game…that I'm bursting with ideas and that the pioneer always keeps an advantage over the creative copier.

Does this describe the paradox that businesses may be competitive and cooperative simultaneously?

It's not an easy game, but an innovator's reputation rises with the reputation of the technology it uses and the reputation of the innovations that are built atop it.

It's important that you understand the game of Open Innovation very well…its options and market risk...

Income and Wealth - a Complication?

It's so simple: Income is what flows into the "pocket" and wealth is what's already there. But, when it comes to determining the precise terms in social, labor or tax laws it can be hellishly complicated. So complicated that specialists make a living, to interpret them correctly.

An archaic water-dependent community

Once upon a time, the people of "Water" lived in a barren, hilly countryside from mixed farming.

It didn't rain enough, to grow fields and pastures without irrigation. In particular, the amount was not fairly distributed.

The ways of the hills, managed to pump water from the depths, stored it in large underground caverns and distribute it via an ingenious canal system to the remote settlements. They used the slope and locks to supply the quantity…

Water is treated as carefully as money in Water. It's used in cereals, vegetables and fruits grow and to water their herds. This water is their income. Their wealth is the drinking water, which they store (as well as their crops and animal products).

The ways reserve the right to keep a portion of the water to use it for their privileged activities. For example, for the production of rose oil (which they exchanged for food) ...

There was peace in Water, all lived strictly compliant to the rules...come to two events: after a long drought the central water supply could not deliver enough and the farmers in the plains wanted to build their own fountains...and an innovative cereal farmer, who lived on a canal with a particularly large slope, wanted to build a water mill.

"But we can grind for you," said the ways. "But your slope is far too low, I can produce much more flour per day," said the engaged miller.

"And what will we get back from the local water producers" the wise men put pressure on the innovators...and it happened a complicated discussion about taxes and the use of infrastructure

History shows how easy it can be to organize income. Headache starts with the proper use of wealth, because this references analysis: who does benefit from what…

And this is where reason is often suppressed by ideology…and this is one of the reasons why I think politics need to use much more models and simulate much more and test much more in order of find better explanations…

I better let David Deutsch explain explanation...

Types of Work

Remember, once upon a time there was a profession and a job for a lifetime. We've accepted this hoping that augmentation will compensate automation….I wrote about this here.

When we take a quick look at the highly specialized profession of a quant (again). we'll see that it's in danger to become replaced by quant finance technology.

What career steps can a quant consider, when this will happen?

Climb - become head of risk management, dive in - become a structurer of innovative financial instruments, surf - become a financial advisor, leap forward - build a next generation of quant finance technology.

Making such a decision it helped to know in which type of work you are in. And, I recommend a typology that's principles are not so different from the innovation types (of The Innovation Mesh)

Time Type - high performance jobs, real-time jobs and off-time jobs
Reality Type - factual (collecting facts), real (solving problems of a real behavior), ideal (provide methodologies or technologies), fancy (stimulate imagination or present things in a virtual reality)
Style Type - what interaction patterns does the job owner mainly use: manual processes, documents, interactive systems or al kinds of presentation.
Structure Type - this most important type describes how the job is integrated into the institution. How it works internally and externally. It describes, whether the job will survive (determined by the stable integration into linear workflows), how it will live in the inner competitive arena (determined by its application in multiple, parallel workflows), how it will be requested (determined by agility). The structure type provides information about how connected the development of the job and the institution are.
Content Type -  it determines the classical job description. They provide information on market segments, sectors, areas of knowledge, ways of working, hierarchy levels...sometimes controlling ideas, conventions and obligations. I tend to distinguish Purpose Types (analyst) and Realization Types (statistician).


Innovators change systems that have caused problems (or create new things). Their innovation can even help that our systems become stronger when stressed. That is difficult work. To do it they need freedom and a high degree of self-organisatiuon and responsibility.

Make money or have fun?

Evaluating any activities, we run into difficulties when pressing emotional and rational criteria into a linear scheme. It provides much more insight to create a map where hygiene factors and motivation factors are orthogonal.

The bigger picture of work

Disputes about labor are often occupied by a broad dispute about the hygiene factors (time and money) - income. This is because they are metrical…motivation factors are difficult to quantify. Parts of the motivation factors are quantitative: margin to name one

In the financial system "Alpha" measures how much an economic bet has beaten the market - more precise, how much a risk-adjusted return beats a the return of a benchmark.

I "generalize" Alpha, denoting a qualitative and quantitative criteria for how much a work result is better than a benchmark. Alpha is then one of the motivation factors. But there are much more, let's summarize them under Self-realization

Simplifying, I identify 4 regions on a Self-realization / Income map of work

Slavery - in the low / low corner
Hobby - in the high / low corner
Factory in the low / high corner
Lab - in the high / high corner

Through the lens of innovators we're interested in factory vs lab work, but politicians should look  deeper…some of them preach still "the dignity of slave work"...

Innovators do lab work, striving today for a breakthrough for tomorrow. The more this work is aided by connected AIs the more it may become factory work. Make the same things better and cheaper. We need answers before this will happen…and they're not found in "better marketing".

The Future of Work

Three people can make and install a classical kitchen. A carpenter, a plumber and an electrician. But we need only two to install an almost automatic kitchen lab. One, who lifts up all the machines and aggregates and one who integrates them all into a system. "The Biceps" and "The Brain".

Some experts see three worlds of labor.

Working with intelligent machines

Working in the factories of the big companies whose headquarters are in the skyscrapers of the "world trade centers"... They'll become even bigger and more profitable and they'll advertise long-term job contracts asking for engagement and flexibility.

Working around the quality of life

Working for the companies whose social responsibility dominates the corporate culture. They'll offer ethical values ​​and balanced principles of work and demand loyalty, green sense of responsibility.

Working on a short-term contract basis

Working for companies that want flexibility by minimal fixed costs. Often small businesses that want to achieve rapid growth trough innovation. They promise varied challenge and autonomy but on a short-term contractual basis.

The polarization of work

But striving for a better politics, I'm more interested in the polarization. The biceps versus the brain polarization, the system makers vs the users…Especially, what will happen, when technology becomes capable of self reorganization and replication.

Think of a skilled lathe operator, who has produced complicated parts on a lathe manually. A marvel of mechanics, hydraulics and electrics. Then, she became trained as programmer of computerized lathes and managed three of them in parallel. She got an off-line programming system that provided animation of the operations, making her quite comfortable that things will go right…But after thousands of computerized machines were connected and centrally programmed and monitored…the headache began. Will she understand parametric programming, the theory of optimal operation plans…?

Once upon a time there was a profession and a job for a lifetime

OK, we can accept that this time is gone. The change created the chance that the new job was more exiting and generated more income.

But, the threatening polarization, conveyed by smart connected systems, rises a few questions:

Can education win the race against technology? IMO, in the age of quantification and context, the innovation spiral turns faster.  This does not mean we shall give up emphasizing on education. No, we need to think of new methodologies and use clever tools to educate better…but this may not be enough.

Will technology be more responsible than economic and politics rules? That question brings to my mind that it was never so easy to start a business, but in many industrial countries start-ups are in decline. So, IMO, it seems political rules do not fit well to technological capabilities.


Will technology describe a new nature of work that changes everything? This may happen, if technology becomes an underlying for cultural, socio-economi and political systems…supporting more quantitative treatment and adaptation.

New technology - new work - new economics - new politics.

p.s. Think of the quants. They contribute to the replacement of quant work by systems. What will they do? Develop even better quant replacing systems? Design a fundamentally new financial system? Reinvent risk management? Become a quant trainer?...