Remember, The Innovation Mesh helps innovation marketers to answer some fundamental questions - systematizing the assessment. I've introduced The Quant Innovation Spreadsheet and The Quant Innovation Mesh filled in for UnRisk Capital Manager.
The characteristics of quant innovations
Most of the systems that require quantitative treatment (computation) have a clock, flows, events, transformations, massive information requirements…Most of the problems suggest a function-oriented decomposition and the bottom up composition of computational tasks, based on modeling, control, simulation, optimization, cognition, interplay…characterized by object transformations and events…on each level.
Both schemes are devoted to this quant innovations, assuming that they represent an external type: Thriller (dealing with uncertainty, risk, speculative reality…).
Thrillers, their most important units are tasks, have usually a beginning hook, a middle build and a payoff. Their quantitative approach suggests that they need preprocessors, processors and post processors and all of them need constructors, the management of progressive problems and solutions…quite a nested structure.
Both schemes are coupled and describe special Innovation Meshes in miniature…represented as one page documents. They give us coordinates to map the movements of objects and actors…
Innovation is about change
Innovators want to change the underlying systems that are causing major problems of our lives and time…at a society, sector, institution, individual level…
To manage a change you need to know what the reaction to it is. The first reaction is usually shock and denial, in a next phase a change may even cause a depression, before the change becomes accepted and will be integrated.
Again, I used the metaphor of a thriller to explain what kind of innovations may be compelling and candidates for the classics of the future in their field…
Actors at thrillers work against bad actions and the clock
Consequently, The Innovation Mesh needs a time and event dimension, where changes of the innovation as whole are described in their positive and negative values…If the innovation is flow dominated you may want to describe the changes along a task axes.
Describe the external and internal values (related to events)…it may look like an info-graphic with wave type trajectories showing whether you have interim solutions…and where you have polarity and turning points.
Mapped into one picture
In general, tasks in quant innovations use methodologies, models, solvers…and interpreters that need to be validated…but there are also big principle traps that may accumulate…
A rough graphical representation helps showing whether the flows are logically sound, the timing and synchronization points are right…you'll see possible break points that can sabotage the innovation.
This post concludes the Innovation Mesh episode for a while…
p.s. If the innovation does not work so well…it's easier to fix it, if the objects, models and methods are organized orthogonally, you write in a domain specific, symbolic language, if inherently parallel engines implement it, if you offer a data framework, deployment services…in short, if you are able to make transformative developments atop a technology stack.
Seeking Alpha
In short, "Alpha", as used in financial circles, is a risk-adusted measure of the active return on an investment…In order to consider relative performance often the return of a benchmark is subtracted.
For more about alpha and its mathematics I recommend this article of Keplerian Finance.
Alpha, "average" performer and efficiency
I want to extend the understanding of alpha to "performance relative to a benchmark". Providers want a kind of alpha related to an "average" performer and efficiency…to answer questions like: can we beat the (competitors in a) market?…do we produce more efficient?…
In non financial sectors it's much more difficult to understand risk-adjustment and the many other factors and consequently it's difficult to calculate an alpha…but many quant innovations deal with the search of something like it…it may be a set of information that gives more insight about the positions of products, services…in market segments.
However, quant innovations seeking alpha are thrilling…they need to deal with uncertainty, the duality of the known and unknown, progressive problems…stressors…and offer what-if (across scenario) analytics…optionality….
The two lives of market participants
Market participants live in an external and an internal world. The market and their work. Quant innovation makers' want to serve them in segments to perform better (make better products with better processes or do better marketing).
But quant innovation makers are market participants themselves and The Quant Innovation Mesh is a tool that helps them seeking alpha. Will our quant innovation work and sell well?
To understand, whether it will our point of view is that of the key actions and actors.
External and internal types
Remember that the external life of an innovation is influenced by standards, rules, state of the art, expectations…the what (the effectiveness related to requirements). Outside forces, like "physical products must become programmable" drive markets.
The internal life does not only represent the how, but also a compass…where will the innovation go?…what big questions will it answer?...what barriers will it overcome?…what will the actors get beyond the innovation's fit for purpose?
That's why I'm interested in
External Purpose Type
External Value at Use
Internal Purpose Type
Internal Value at Use
and the details related to them.
UnRisk CM again
I've filled in The Quant Innovation Mesh scheme here and after further thinking, I may change this a little..later.
However, the point of view is fund management, although with a pointed external view the CEO's is important, because she's paying penalties when violating the rules.
So, the drivers of the external world of risk management of investment and capital management firms are regulators and auditors. This is what UnRisk CM is certified for. It exposes risky horrors and helps managing them.
This has nothing to do with any alpha estimations related to funds…It's in compliance with by managing the obligatory tasks…
But the object of desire of the firms go beyond model validation, VaR calculation, back and stress tests…it's seeking alpha and does what Aaron Brown calls "red-blooded risk management".
There are many ways to optimize risk to "beat the market"...in the regulatory framework it could look like this
For more about alpha and its mathematics I recommend this article of Keplerian Finance.
Alpha, "average" performer and efficiency
I want to extend the understanding of alpha to "performance relative to a benchmark". Providers want a kind of alpha related to an "average" performer and efficiency…to answer questions like: can we beat the (competitors in a) market?…do we produce more efficient?…
In non financial sectors it's much more difficult to understand risk-adjustment and the many other factors and consequently it's difficult to calculate an alpha…but many quant innovations deal with the search of something like it…it may be a set of information that gives more insight about the positions of products, services…in market segments.
However, quant innovations seeking alpha are thrilling…they need to deal with uncertainty, the duality of the known and unknown, progressive problems…stressors…and offer what-if (across scenario) analytics…optionality….
The two lives of market participants
Market participants live in an external and an internal world. The market and their work. Quant innovation makers' want to serve them in segments to perform better (make better products with better processes or do better marketing).
But quant innovation makers are market participants themselves and The Quant Innovation Mesh is a tool that helps them seeking alpha. Will our quant innovation work and sell well?
To understand, whether it will our point of view is that of the key actions and actors.
External and internal types
Remember that the external life of an innovation is influenced by standards, rules, state of the art, expectations…the what (the effectiveness related to requirements). Outside forces, like "physical products must become programmable" drive markets.
The internal life does not only represent the how, but also a compass…where will the innovation go?…what big questions will it answer?...what barriers will it overcome?…what will the actors get beyond the innovation's fit for purpose?
That's why I'm interested in
External Purpose Type
External Value at Use
Internal Purpose Type
Internal Value at Use
and the details related to them.
UnRisk CM again
I've filled in The Quant Innovation Mesh scheme here and after further thinking, I may change this a little..later.
However, the point of view is fund management, although with a pointed external view the CEO's is important, because she's paying penalties when violating the rules.
So, the drivers of the external world of risk management of investment and capital management firms are regulators and auditors. This is what UnRisk CM is certified for. It exposes risky horrors and helps managing them.
This has nothing to do with any alpha estimations related to funds…It's in compliance with by managing the obligatory tasks…
But the object of desire of the firms go beyond model validation, VaR calculation, back and stress tests…it's seeking alpha and does what Aaron Brown calls "red-blooded risk management".
There are many ways to optimize risk to "beat the market"...in the regulatory framework it could look like this
- estimate x%, n day VaRs each day before trading…
- compare actual daily P&L against VaR prediction…
- test for the correct number and distribution of VaR breaks…
- collect data within the limit of the reliable VaR…
- investigate days when you lose more then VaR...calculate expected shortfalls…
- analyse tail risk further…by, say, power law analysis
- analyze other risk factors (a kind of Var "cube")
- try to determine the optimal Kelly bet base on informative data within the VaR limit…
This is very simplified, but more than the regulatory bodies require. UnRisk CM does not only calculate tons of useful numbers automatically, but also provide high-level programming and hundreds of useful functions helping to evaluate this results further…
Play games or define game rules?
And this is a very general requirement. Quant innovations are especially attractive if they are two-sided in the sense that they analyze and control what is but also what-if and what could be. In short, does the innovation help playing the competitive game or does it even help to define game rules...by business scenario analytics.
In general, a quant innovation that helps conserving capital, setting the stage for its application field, building skills and leveraging technology has a principle potential for attractiveness…seeking alpha is exciting.
Such an innovation is compelling, because it serves both sides of marketer and maker lives. But, be aware, in detail, in their possible win/lose outcomes, they're not identical.
External and internal values
Red-blooded is different from cold-blooded (fulfilling regulatory requirements) risk management. So, the external value may be positive…the deal is in a regulation-compliant stage, but what-if analysis has shown some possible dark clouds and the internal worldview of trading says "stop!"…
UnRisk CM wouldn't work if it just supported compliance with regulation…but there are more difficulties...
Fund managers usually start with an illusion (possible alpha) and they may run through stages of confusion, and even disillusion, but using UnRisk CM it's not blind belief…Risk managers are skeptics they may start with disillusion and may run through clearness and positive views due to justified belief.
BTW, Arming David, means that UnRisk CM does support quant work in a way that doesn't require rocket science skills. And this includes quant work that's not directly related to risk management.
Remember, what I want: understand whether a quant innovation is not only economically feasible, but attractive, compelling…Consequently, my diagnosis tool The Quant Innovation Mesh adds more emotional criteria than other methodologies I've seen and used in my life as an evaluator and reviewer of innovation projects for the European Commission.
It was inspired by( and I couldn't have done it without the details in) Shawn Cone's great blog the Story Grid.
I've spent now many days developing, carefully choosing attributes and methods, backtesting…but it's still improvable. However, its clear about the positive payoff through better decisions in projects I'm working for.
And I'll continue applying, (hopefully) improving and describing it…for the benefit of Storydoers...
And this is a very general requirement. Quant innovations are especially attractive if they are two-sided in the sense that they analyze and control what is but also what-if and what could be. In short, does the innovation help playing the competitive game or does it even help to define game rules...by business scenario analytics.
In general, a quant innovation that helps conserving capital, setting the stage for its application field, building skills and leveraging technology has a principle potential for attractiveness…seeking alpha is exciting.
Such an innovation is compelling, because it serves both sides of marketer and maker lives. But, be aware, in detail, in their possible win/lose outcomes, they're not identical.
External and internal values
Red-blooded is different from cold-blooded (fulfilling regulatory requirements) risk management. So, the external value may be positive…the deal is in a regulation-compliant stage, but what-if analysis has shown some possible dark clouds and the internal worldview of trading says "stop!"…
UnRisk CM wouldn't work if it just supported compliance with regulation…but there are more difficulties...
Fund managers usually start with an illusion (possible alpha) and they may run through stages of confusion, and even disillusion, but using UnRisk CM it's not blind belief…Risk managers are skeptics they may start with disillusion and may run through clearness and positive views due to justified belief.
BTW, Arming David, means that UnRisk CM does support quant work in a way that doesn't require rocket science skills. And this includes quant work that's not directly related to risk management.
Remember, what I want: understand whether a quant innovation is not only economically feasible, but attractive, compelling…Consequently, my diagnosis tool The Quant Innovation Mesh adds more emotional criteria than other methodologies I've seen and used in my life as an evaluator and reviewer of innovation projects for the European Commission.
It was inspired by( and I couldn't have done it without the details in) Shawn Cone's great blog the Story Grid.
I've spent now many days developing, carefully choosing attributes and methods, backtesting…but it's still improvable. However, its clear about the positive payoff through better decisions in projects I'm working for.
And I'll continue applying, (hopefully) improving and describing it…for the benefit of Storydoers...
Back To Type
Inspired by The Story Grid, I've set up a methodology for the diagnosis of (quant) innovations.
To explain it, I followed Shawn's approach of microscopic and macroscopic diagnosis…but I've used UnRisk CM as an example before I described the microscopic methodology to lay out the elements of a quant innovation…for didactic reasons (the motivation room).
Moving from the frog's to the bird's view is not so easy…and it may unmask that my quick typology of UnRisk CM requires a correction?
The Quant Innovation Spreadsheet represent a first view...it's motivating, but t's not answering the core questions of innovation marketers.
To explain it, I followed Shawn's approach of microscopic and macroscopic diagnosis…but I've used UnRisk CM as an example before I described the microscopic methodology to lay out the elements of a quant innovation…for didactic reasons (the motivation room).
Moving from the frog's to the bird's view is not so easy…and it may unmask that my quick typology of UnRisk CM requires a correction?
The Quant Innovation Spreadsheet represent a first view...it's motivating, but t's not answering the core questions of innovation marketers.
- What's the type?
- What are the conventions, rules, state of the art for the type?
- What's the point of view?
- What are the desires, requirements, needs?
- What are the controlling ideas, methods and technologies?
- What are the Constructor, Build and Payoff?
Types will save us. They'll manage clients and actors expectations…so looking at the big picture of or innovation we'll know how to position it. This needs a scheme, a checklist…The Quant Innovation Mesh for quant innovations is that checklist.
Let's run it down for UnRisk CM (now based in microscopic views)
Time: it's fast enough, event driven and synchronized…real-time
Reality: its models are calibrated and re-calibrated to market data…realistic
Style: it provides evidence of all valuations, valuators and valuation events in a document-centered way…documentary
Structure: instruments and portfolio flows and are valued across scenarios…multi flow
Matter-Purpose: it's to expose market risk horrors and manage them in order to optimize them…market risk optimization thriller
Matter Realization: capabilities for fundamental quant work and automated risk services…an open valuation and risk management factory (implemented in a multi paradigm-hybrid programming environment)
Knowing the types help to fill in The Quant Innovation Mesh (scheme)
I don't want to look at UnRisk CM through the lens of a potential marketer- It's a results, it works and it sells….I check how it looks through my Quant Innovation Mesh….searching for improvements.
At the macroscopic level, I look into the innovation as a whole, the beginning hook, usual the general constructor, the middle build with progressive problems and critical moments and the ending payoff the solution and the capabilities to adaptations, extensions....
Whatever the details are UnRisk CM is a risk thriller, it provides life or death "probabilities" of financial deals…it's taking actors beyond experience…it's actors must face dramatic downturns…and UnRisk CM helps those market participants who don't have the most sophisticated quant teams to conserve capital, set the stage of successful risk management, build quant finance skills and leverage technologies.
We look through the functions, tasks, workflows and (sub)system(s), and their building blocks…
Tasks of the "risky horror innovation" UnRisk CM may turn on other values, but the system as a whole helps detecting and quantifying deal killers, toxic instruments and portfolios, impact of market regime anomalies…to change that would lead to a disaster.
It's what it's for. It's not a portfolio composition, not a front office trading, not a limit management, not a reporting...not an accounting system.
In a future post, i'll try to dive deeper into the internal matters. How what's it and what's it for influences how it's built and what it needs.
The Bigger's Opera
Too big to fail, too big to fall, too big to jail…I never sang this song. Because the market dynamics is more complex and in debt webs (who owns whom what) with a DebtRank the bank size is not as important as we might have thought. It's more the effect of transition: how wide would the spread of economies distress be, if a bank failed.
Centralization
However, the regulatory bodies decided to force central counter party and clearing regimes…and who else as "the big players" will be able to be a central clearing house for OTC derivatives?
Margin compression
This may have unintended consequences: on the higher-level view it's reducing counter party exposure but may be resulting in an increase in liquidity risk. Such kind of centralization may drive a marginal cost regime with margin compression…for several market participants.
Financial advisors are suggesting that banks shall find new services to compensate…
New Services
RBS describes such an offer here: cutting the clutter of corporate banking…they offer integrated systems for corporate treasuries, after they have explained why those need it here: why new regulations will hit companies hard…and (my compilation) internationally acting banks will be able to provide this service in a regime of Babylonian (rules syntax and semantics).
A big one? Clearing houses are often operated by major banks…
So, will "the big" act as central counter parties and offer services to other market participants that promise to master the complexity of the new regulatory regime that they are an important part of?
David in Bondage?
I'm not looking into this through the lens of finance (micro- vs macro-prudential…wrong-way risks..) but the lens of innovation. In any market, if a few market participants, the Goliaths, figure out how to become the champion-in-all-areneas the effective marketing hypothesis is (definitely) broken.
Who will arm the David...now working hard to serve the corporate treasuries…individually, whilst driving long term innovations?
Centralization
However, the regulatory bodies decided to force central counter party and clearing regimes…and who else as "the big players" will be able to be a central clearing house for OTC derivatives?
Margin compression
This may have unintended consequences: on the higher-level view it's reducing counter party exposure but may be resulting in an increase in liquidity risk. Such kind of centralization may drive a marginal cost regime with margin compression…for several market participants.
Financial advisors are suggesting that banks shall find new services to compensate…
New Services
RBS describes such an offer here: cutting the clutter of corporate banking…they offer integrated systems for corporate treasuries, after they have explained why those need it here: why new regulations will hit companies hard…and (my compilation) internationally acting banks will be able to provide this service in a regime of Babylonian (rules syntax and semantics).
A big one? Clearing houses are often operated by major banks…
So, will "the big" act as central counter parties and offer services to other market participants that promise to master the complexity of the new regulatory regime that they are an important part of?
David in Bondage?
I'm not looking into this through the lens of finance (micro- vs macro-prudential…wrong-way risks..) but the lens of innovation. In any market, if a few market participants, the Goliaths, figure out how to become the champion-in-all-areneas the effective marketing hypothesis is (definitely) broken.
Who will arm the David...now working hard to serve the corporate treasuries…individually, whilst driving long term innovations?
Tracking Flows
The workflow is created by tasks and the system/subsystems are created by workflows. In The Quant Innovation Spreadsheet. The columns Point of View | Timing | Actors concern the innovation flow and continuity.
The workflows are usually dealing with "object" changes by internal "actors", whilst the innovation as a whole usually needs user guidance. This is why I distinguish between Point of View (who is it for and how do this "see" the innovation) and actor (who drives it through the processes).
From a more detailed description I can see whether the flows are logically sound, the timing and synchronization points are right…
All in the light of the Time Type.
The Quant Innovation Spreadsheet is the place to keep track of the flows and look for possible break points that can sabotage the innovation.
If we take UnRisk CM the risk management workflows are often scheduled for automated over night calculation…market data import…model recalibration…VaR calculations…back tests... and the system must be fast enough to repeated the process if something goes wrong and enable the managers to make some extra tests before they, say, report anomalies to the regulatory bodies…
Tracking flows is about deconstruction the movements and relate them to object and data flows and timing.
As mentioned earlier, I use The Quant Innovation Spreadsheet to walk through The Quant Innovation Mesh on a macro level. The birds view.
This will lead to a (slightly changed) release of The Quant Innovation Mesh for UnRisk Capital Manager.
But most important, I see the much more detailed picture behind and understand much better that this is a great innovation that works and sells.
And I know that I don't need quantitative information, like number of functions, tasks…or lines of code.
We've recently released a brand-new UnRisk Quant product.
This is how we promote it
The workflows are usually dealing with "object" changes by internal "actors", whilst the innovation as a whole usually needs user guidance. This is why I distinguish between Point of View (who is it for and how do this "see" the innovation) and actor (who drives it through the processes).
From a more detailed description I can see whether the flows are logically sound, the timing and synchronization points are right…
All in the light of the Time Type.
The Quant Innovation Spreadsheet is the place to keep track of the flows and look for possible break points that can sabotage the innovation.
If we take UnRisk CM the risk management workflows are often scheduled for automated over night calculation…market data import…model recalibration…VaR calculations…back tests... and the system must be fast enough to repeated the process if something goes wrong and enable the managers to make some extra tests before they, say, report anomalies to the regulatory bodies…
Tracking flows is about deconstruction the movements and relate them to object and data flows and timing.
As mentioned earlier, I use The Quant Innovation Spreadsheet to walk through The Quant Innovation Mesh on a macro level. The birds view.
This will lead to a (slightly changed) release of The Quant Innovation Mesh for UnRisk Capital Manager.
But most important, I see the much more detailed picture behind and understand much better that this is a great innovation that works and sells.
And I know that I don't need quantitative information, like number of functions, tasks…or lines of code.
We've recently released a brand-new UnRisk Quant product.
This is how we promote it
LESS THAN ZERO
What if interest rates stay negative?
How low can they go?
Low enough to make widely used models
unreliable
The majority isn’t always right - but
there’s no magic
behind creative thinking
VALUE SMARTER
A good time to change to UnRisk
It’s multi-model suite manages all
interest rate regimes
It maps all practical details from
pricing to risk management
calculating all types of VaR and xVA
High-end financial language for maximum productivity
Go wider and drill deeper with
UnRisk Quant
after we've applied The Innovation Mesh diagnosis tools.
Tracking The Tasks
I've introduced The Quant Innovation Spreadsheet as a kind of one view tool assessing an innovation. The posted UnRisk CM example is made concise allowing one view in one page.
But I split the Changes column into 3 sub columns: Change Values | Polarity | Turning Point
If we take the VaR Calculations task of UnRisk CM…
Change Values: VaR numbers quantify the border between the known and uncertain
Polarity: VaRs alone don't say much abut the breaks
Turning Point: Back and stress tests validate VaRs (across instruments and risk factors)
Regulators often just want VaRs, but for fund management decisions it's vital to know if the number of VaR break days, their distribution…are plausible…because the methodologies may be too "optimistic/pessimistic" in certain regimes.
In general tasks in quant innovations use methodologies, models, solvers…and interpreters that need to be validated. Especially of you need to deal with ill-posed problems.
Remember, in mathematical problem solving we 1. transform a textual problem description (an instrument term sheet) into a model (a general stochastic model), 2. transform it into a model that is of good nature for reality near calculations (an interest rate PDE with calibrated parameters), 3. solve the PDE (by Finite Element techniques…), 4. interpret the results (by dynamic visualization, cross model validation…)
In this process you need to avoid quite a lot of traps and only if you've organized your objects, models, methods...orthogonally, you'll be able to detect (Polarity) and avoid (Turning Point) them. Otherwise the Change Value of the tasks is in question.
I've outlined the difficulties in Model Validation - First defense Front of Risk Management (2012)...
And tasks are the most important units of a Quant Innovation. Finding problems in them is the (my) first evaluation job.
But tracking the tasks is not enough…there are the big principle traps…like The Problems With Computing "Expected" Returns in Finance.
We talk about principles, methodologies, models…technologies, but applying them is like "writing a story". And tracking it's continuation…Point of View | Timing | Actors is indispensable.
I'll come to this in one of the next posts.
But I split the Changes column into 3 sub columns: Change Values | Polarity | Turning Point
If we take the VaR Calculations task of UnRisk CM…
Change Values: VaR numbers quantify the border between the known and uncertain
Polarity: VaRs alone don't say much abut the breaks
Turning Point: Back and stress tests validate VaRs (across instruments and risk factors)
Regulators often just want VaRs, but for fund management decisions it's vital to know if the number of VaR break days, their distribution…are plausible…because the methodologies may be too "optimistic/pessimistic" in certain regimes.
In general tasks in quant innovations use methodologies, models, solvers…and interpreters that need to be validated. Especially of you need to deal with ill-posed problems.
Remember, in mathematical problem solving we 1. transform a textual problem description (an instrument term sheet) into a model (a general stochastic model), 2. transform it into a model that is of good nature for reality near calculations (an interest rate PDE with calibrated parameters), 3. solve the PDE (by Finite Element techniques…), 4. interpret the results (by dynamic visualization, cross model validation…)
In this process you need to avoid quite a lot of traps and only if you've organized your objects, models, methods...orthogonally, you'll be able to detect (Polarity) and avoid (Turning Point) them. Otherwise the Change Value of the tasks is in question.
I've outlined the difficulties in Model Validation - First defense Front of Risk Management (2012)...
And tasks are the most important units of a Quant Innovation. Finding problems in them is the (my) first evaluation job.
But tracking the tasks is not enough…there are the big principle traps…like The Problems With Computing "Expected" Returns in Finance.
We talk about principles, methodologies, models…technologies, but applying them is like "writing a story". And tracking it's continuation…Point of View | Timing | Actors is indispensable.
I'll come to this in one of the next posts.
How Long Can Faster, Lighter, Thinner…Be Innovative?
Early this week, I experienced the Apple keynote on my iMac. It's amazing, how perfect its was staged again.
Apple wants to make things for those, who see things differently. Again and again and again…
In search of perfection?
And this made me brooding. Industrial work does what it did yesterday tomorrow, but faster, preciser, thinner, smaller, bigger, ... cheaper. Only at the lab we are striving to find a breakthrough, new ways to solve new problems, and do new things.
The "new MacBook" (presented at the beginning of the show) is lighter and more compact…consequently thinner…its better? Is this important for those who see things differently?
Then the Apple Watch...
A Watch is not a tool
Apple Watch. I don't wear a watch, because there are watches all around.
I own the WATCH by Femming Bo Hansen. It's minimalist. It's represented at the Museum of Modern Art, NY. And, what a shame, I have put it into a drawer...
A watch is not a tool - it clocks our life.
A computer is not a tool either
Hold on, Apple Watch is a computer on a wrist, and a computer is not a tool either - it is a universal machine to build tools for many usages.
When computational things become smaller, connectivity become more important. Years ago, Sun said: the network is the computer. And now we have entered the "cloudy" days.
But still, front-end devices become smaller and more intelligent.
However, one of the challenges for a "wrist computer" is the interaction paradigm…that at the other hand - if solved adequately - enables
New uses
It seams Apple wants to take the watch to occupy new market segments, like Health, Fitness...By making things computational they can provide better real time advice. If you are running, bicycling, climbing…you don't want to wear a tablet computer o a bigger than bigger smartphone, in a race you want to optimize your risk: go to the limit but not across…based on dynamic information…
Summarizing, I think, this Apple announcement is still underestimated. It may be the begin of making massive information computational on a very small scale.
Wear a watch again?
No, I don't want to do all the things at Apple Watch that I do at my iP…s. I want computational support on my wrist…it will not even change my office life much…maybe I just buy it to wear a watch again, after years…a watch that's style I can adapt…and use it's computational support as added value?
It will polarize the watch market more than any other...
Apple wants to make things for those, who see things differently. Again and again and again…
In search of perfection?
And this made me brooding. Industrial work does what it did yesterday tomorrow, but faster, preciser, thinner, smaller, bigger, ... cheaper. Only at the lab we are striving to find a breakthrough, new ways to solve new problems, and do new things.
The "new MacBook" (presented at the beginning of the show) is lighter and more compact…consequently thinner…its better? Is this important for those who see things differently?
Then the Apple Watch...
A Watch is not a tool
Apple Watch. I don't wear a watch, because there are watches all around.
I own the WATCH by Femming Bo Hansen. It's minimalist. It's represented at the Museum of Modern Art, NY. And, what a shame, I have put it into a drawer...
A watch is not a tool - it clocks our life.
A computer is not a tool either
Hold on, Apple Watch is a computer on a wrist, and a computer is not a tool either - it is a universal machine to build tools for many usages.
When computational things become smaller, connectivity become more important. Years ago, Sun said: the network is the computer. And now we have entered the "cloudy" days.
But still, front-end devices become smaller and more intelligent.
However, one of the challenges for a "wrist computer" is the interaction paradigm…that at the other hand - if solved adequately - enables
New uses
It seams Apple wants to take the watch to occupy new market segments, like Health, Fitness...By making things computational they can provide better real time advice. If you are running, bicycling, climbing…you don't want to wear a tablet computer o a bigger than bigger smartphone, in a race you want to optimize your risk: go to the limit but not across…based on dynamic information…
Summarizing, I think, this Apple announcement is still underestimated. It may be the begin of making massive information computational on a very small scale.
Wear a watch again?
No, I don't want to do all the things at Apple Watch that I do at my iP…s. I want computational support on my wrist…it will not even change my office life much…maybe I just buy it to wear a watch again, after years…a watch that's style I can adapt…and use it's computational support as added value?
It will polarize the watch market more than any other...
An Assorted Link: Death Is Optional
I visit Edge.org frequently.
My experience in factory automation and some AI knowledge point me (in particular) to articles about the replacement of work (on technical, socio-economic or cultural levels).
This is an exciting conversation on this topic, Yuval Noah Harari, Daniel Kahneman: Death Is Optional
We don't know the consequences, but we must not ignore them…it's really different, whether we automate hand work or head work.
BTW, it may not look so, but I find this conversation with Chiara Marletto related: Formulating Science in Terms of Possible Tasks (Constructor Theory) has some relation to the above…it's about regularities in nature that allow the existence of information…there is no such thing as an abstract program.
Remember, tasks are the most important units of a quant innovation...
My experience in factory automation and some AI knowledge point me (in particular) to articles about the replacement of work (on technical, socio-economic or cultural levels).
This is an exciting conversation on this topic, Yuval Noah Harari, Daniel Kahneman: Death Is Optional
We don't know the consequences, but we must not ignore them…it's really different, whether we automate hand work or head work.
BTW, it may not look so, but I find this conversation with Chiara Marletto related: Formulating Science in Terms of Possible Tasks (Constructor Theory) has some relation to the above…it's about regularities in nature that allow the existence of information…there is no such thing as an abstract program.
Remember, tasks are the most important units of a quant innovation...
Can We Learn From Vampires? - a Revisit
I've given quite a lot of thoughts to The Innovation Mesh. It seems it's all about structuring and analytics?…but I want to involve, when not emphasize on, the emotional aspects of quant innovation. Sometimes I get the feeling it needs muscles in my brain, I haven't trained much. Shall I remove my mathematical ladder?...
However, avoiding to cling to it and streamline my thinking, I'll put it into the "jewelry box" for a little while.
Only the imperfect diversify…and live?
I recalled that I posted my view on can we learn from vampires a few weeks ago.
In short, absolute wisdom means: it's done before…but many groundbreaking products offer completely before unknown, inexistent solutions.
Oh, has The Innovation Mesh caught me again?
However, avoiding to cling to it and streamline my thinking, I'll put it into the "jewelry box" for a little while.
Only the imperfect diversify…and live?
I recalled that I posted my view on can we learn from vampires a few weeks ago.
In short, absolute wisdom means: it's done before…but many groundbreaking products offer completely before unknown, inexistent solutions.
Oh, has The Innovation Mesh caught me again?
The Quant Innovation Spreadsheet
This is a spreadsheet filled in for UnRisk Capital Manager. In order to show everything in one page I've put in only the, IMO, most important info. The real thing is much more comprehensive (info in cells).
The Innovation Life Cycle
And it's import to say that I do use this sheets in stages of a review process. When the innovation gets on my desk for further analysis I pull up a new Quant Innovation Spreadsheet and fill it in.
It's my understanding of the work of an innovation marketer: evaluate the pre release (the potential to work and sell). Let's assume that the innovation pre release does not perfectly work (it may technically…). It's close but I do not have a quick idea to fix the disappointing approaches. This is where it helps to have the first Spreadsheet.
If the makers and I come to an arrangement I offer to review the releases…by walking them through The Quant Innovation Mesh and refine or aggregate the Spreadsheet in cycles. But this does only work if the first release does it already right in principle
In a few cases I do the business development for the Innovation. UnRisk. The current UnRisk Capital Manager version is the 12th release since it has been launched (2008). It has quadrupled in "size" since then and released new deal types, models, methodologies…tasks, workflows, add-on subsystems…and most important: a few years ago we have integrated UnRisk-Q the programing power behind UnRisk.
That's a turning point!
A little suite of diagnosis tools
As we're leading up to the concise Innovation Mesh for UnRisk CM and look into the cells of the Quant Innovation Spreadsheet for UnRisk CM combined with The Quant Innovation Mesh for UnRisk CM (scheme) we'll come to quite dynamic view of the innovation. What it does. What it's for. How it flows. Whether it hooks. How it builds. What the pay-offs are…
To see this better, it helps to understand the type and its conventions and rules, the points of view, the desires, needs and requests, the controlling ideas, methods and technologies, what the constructors, builders, solvers are…helping to design marketing mixes and move to insight selling.
The suite helps me but it also helps the makers to fix little (non-technical) problems.
This is where I am now. I am sure that "evolutionary prototyping" is the right way to go in quant innovations.
But, are there more general principles? Automated model-method selection? More automated direction in general?...
If you look into the concise spreadsheet of the current version, you may recognize that UnRisk CM has been developed the bottom-up and inside-out fashion and that it follows the nested constructor-managegement of progressive problems-solution principle (technically it uses preprocessing-processing-postprocessing solvers).
It's fruit of transformative development. It needs a comprehensive technology stack in the right design.
If the foundation is right you can concentrate on solution needs-access-values-education marketing mixes…want an "UnRisk Bank"? Look into the above and add a subsystem that manages Credit/Fund/Debt Adjustments and central counterparts/clearing…margin rules…
Make things that matter for those who care…in a thrilling way (I add now).
Having inspected each unit…tracking the (quant) innovation as a whole will be the final step. Maybe there's more about this…? Does quantitative information help? Like number of tasks...lines of code per programming paradigm…?
The Lens of The Innovation Marketer
We've reviewed the fundamentals of quant innovations (constructors, progressive problems, solutions) and its units (functions, tasks, workflows, subsystems and the innovation as a whole).
Now i go back to the very beginning - the motivation why I want the help of The Innovation Mesh scheme: What I Do.
Provided, I'll review, advise marketing…sell (for) a quant innovation...
With the background of The Quant Innovation Mesh I start with a table…in order to provide the innovator with a quick feedback.
Its columns are
Tasks | Events | Changes | Points of View | Timing | Actors | Worldview | Realization
Having filled out that table I can walk through The Quant Innovation Mesh from left to right and the top to the bottom explaining the details and extending…
If I put the table into a spreadsheet and weight the criteria, I could use the information of The Innovation Mesh for a special innovation to rate them, I'd get a total score?! I prefer the qualitative....
In the near future, I'll fill out the table for UnRisk Capital Manager.
Now i go back to the very beginning - the motivation why I want the help of The Innovation Mesh scheme: What I Do.
Provided, I'll review, advise marketing…sell (for) a quant innovation...
With the background of The Quant Innovation Mesh I start with a table…in order to provide the innovator with a quick feedback.
Its columns are
Tasks | Events | Changes | Points of View | Timing | Actors | Worldview | Realization
Having filled out that table I can walk through The Quant Innovation Mesh from left to right and the top to the bottom explaining the details and extending…
If I put the table into a spreadsheet and weight the criteria, I could use the information of The Innovation Mesh for a special innovation to rate them, I'd get a total score?! I prefer the qualitative....
In the near future, I'll fill out the table for UnRisk Capital Manager.
When Uncertainty Is Good
In theory decision making under uncertainty goes as follows (provided the problem is defined): identify options-->evaluate options across (defined) criteria-->select the top rated option.
But this is too simplified for decisions related to business development. It's difficult to identify all options…it's difficult to rate them because of information asymmetry (the known vs the unknown)…even when ratings are possible their rating isn't objective.
The standard view of uncertainty
It can be summarized as follows:
The standard approach to managing uncertainty assumes that all possible states, actions and consequences can be determined (or boiled down into single probability distributions). But this is rarely so.
This part was inspired by this post of the Eight to Late blog - that I read frequently.
It explains why enterprise risk management is a management risk. The poles of the dilemma are predictability vs adaptability or control vs agility on the positive side and bureaucracy vs lack of control on the negative side.
But what about optimizing risk? It needs learning from turbulences, adaptability and agility.
Stochastic tinkering?
Is a kind of trial and error insight gain possible? Yes, people make progress using things without knowing exactly how they work. And this is great.
Manage an enterprise under uncertainty?
In risk of the jungle I pointed out that uncertainty is not risk and that you may still be able to optimize risk in the "uncertain" zone - by using (real) options to name one…
Changing the game rules of competition
In real options enable antifragility I've outlined that real options can maximize the value of a project. Real option valuation tools apply proven mathematical, statistical and corporate finance concepts based on option theory to value future decisions…emphasizing on investment projects they give answers on the Net Present Value, odds on special losses and wins…prices of call or put options and their exercise probabilities…informations that are definitely valuable for top managers.
But there's one more thing beyond making better decisions to become better: use the what-if capabilities of a good real-option valuation tool to simulate businesses in order to get insight into possible new market behaviors in order to define the game rules of competition.
Remember, by 1987 the option traders played the (simple) Black-Scholes formula game. But then the far-out-of-the-money options were introduced and the game rules changed drastically (models needed the volatility smile and stochastic volatility extension…dynamic hedging became much more difficult...).
I know, this is simple, compared to real businesses, but still…top managers, using the simulation capabilities of a comprehensive real options valuation tool (rov) may get the insight to apply new types of in- and out licensing, business development partnerships…game rules...
IMO, this is the killer application of rov.
But this is too simplified for decisions related to business development. It's difficult to identify all options…it's difficult to rate them because of information asymmetry (the known vs the unknown)…even when ratings are possible their rating isn't objective.
The standard view of uncertainty
It can be summarized as follows:
- Figure out all possible states
- Enumerate possible actions
- Figure out the consequences of actions for all possible states
- Attach values to consequences
- Select actions that maximize values
is unrealistic
Problems with this approach are easy to see: state space uncertainty - if you identify risk as cost you may not see a possible growth…option uncertainty - it's rarely that we can predict consequences of enterprise transformations programs (especially if innovations is involved)…preference uncertainty - we usually lack factual information of possible future outcomes and consequences.
The standard approach to managing uncertainty assumes that all possible states, actions and consequences can be determined (or boiled down into single probability distributions). But this is rarely so.
This part was inspired by this post of the Eight to Late blog - that I read frequently.
It explains why enterprise risk management is a management risk. The poles of the dilemma are predictability vs adaptability or control vs agility on the positive side and bureaucracy vs lack of control on the negative side.
But what about optimizing risk? It needs learning from turbulences, adaptability and agility.
Stochastic tinkering?
Is a kind of trial and error insight gain possible? Yes, people make progress using things without knowing exactly how they work. And this is great.
Manage an enterprise under uncertainty?
In risk of the jungle I pointed out that uncertainty is not risk and that you may still be able to optimize risk in the "uncertain" zone - by using (real) options to name one…
Changing the game rules of competition
In real options enable antifragility I've outlined that real options can maximize the value of a project. Real option valuation tools apply proven mathematical, statistical and corporate finance concepts based on option theory to value future decisions…emphasizing on investment projects they give answers on the Net Present Value, odds on special losses and wins…prices of call or put options and their exercise probabilities…informations that are definitely valuable for top managers.
But there's one more thing beyond making better decisions to become better: use the what-if capabilities of a good real-option valuation tool to simulate businesses in order to get insight into possible new market behaviors in order to define the game rules of competition.
Remember, by 1987 the option traders played the (simple) Black-Scholes formula game. But then the far-out-of-the-money options were introduced and the game rules changed drastically (models needed the volatility smile and stochastic volatility extension…dynamic hedging became much more difficult...).
I know, this is simple, compared to real businesses, but still…top managers, using the simulation capabilities of a comprehensive real options valuation tool (rov) may get the insight to apply new types of in- and out licensing, business development partnerships…game rules...
IMO, this is the killer application of rov.
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