There is this book: Anitfragile. Its conclusion: the antifragile is immune to prediction errors and it becomes stronger with added stress.
There is an ongoing thread at Wilmott General Forum - Can antifragility exists without fragility?
"Traden4Alpha" (an outstanding knowledgeable senior member) points out that it depends on the assessment criteria. At the transaction level, antifragility needs fragility: hedgers need speculators, the counter party that accepts to take the fragile side of a contract (but speculators themselves can buy antifragility).
The difficult thing about this at the global level is transparency: who has what fragile/antifragile position and how do those positions cross-connect to fragility concentration or fragility diffusion and buffering.
This led the two of us to
Local and systemic risk management
In heterogeneous, decentralized systems the failure of one agent is for the benefit of another. Diffusion and buffering helps to gain overall antifragility.
The danger of tightly coupled complex systems
The fear of systemic risk may lead to tightly coupled complex (security) systems that may have unintended consequences (complexity) but do not give enough time to react to them (tight coupling).
The metaphor: a nuclear power plant. Their tightly coupled complexity is a inevitable system characteristic, but their safety mechanism does not always make them safer (too poor instruments, wrong interpretation, unexpected connections, false alarms, …)
In search of the perfect antifragility may lead to "normal accidents".
A sufficiently complex tightly coupled system will fail sooner or later
Learning from turbulences leads to antifragility
Avoiding risk is a poor way to manage risk. You never learn and all events stay unexpected, hidden and more or less catastrophic.
The safety system in the time when the subprime crisis emerged with all the toxic CDOs blew the danger up instead of making the system safer. Market participants - thinking the system was safe - "forgot" the convex risk of a put-option integrated into a mortgage backed security.
If such a system fails, we have not gained anything.
So, a diversified, decentralized, limited purpose, less tightly coupled system of financial institutions, less overall constraints and rules, … may be a better way. Gain from disorder - as NN Taleb says.
Showing posts sorted by relevance for query tightly coupled. Sort by date Show all posts
Showing posts sorted by relevance for query tightly coupled. Sort by date Show all posts
An Antifragile System Concept
It took me a while to get my breath under control again. A page view explosion to 40.000 responding to Sascha's programming comparison. A little related to our hybrid programming concept:
Aaron Brown wrote another brilliant article in the Mar-14 Wilmott Magazine. A review of Nate Silver's book, the signal and the noise. A great book with one blind spot: tail events (when discussing the financial crisis). Not surprisingly, its is about Black Swans, and the trap of modeling uncertainty as casino game. (NN Taleb called it lucid fallacy)
Recently, I wrote about antifragilty and the problem of tightly coupled complex systems and the cage of strategic planning.
Today I read towards an antifragile IT strategy - in Kailash Awati's Eight to Late.
Take advantage from options we do not know yet
It is about the impossibility to predict the future in detail. How can you make a strategic plan without that?
The trick is to plan with options that we we do not know now (like real options relating to project size, life and timing, as well as operation). (Real) options usually increase the value of a project - they capture the value of managerial flexibility in a world of uncertainty.
Suggestions for an antifragile IT strategy
Decentralization - give structural units authority
Agility - be prepared to change regimes
Diversification - diversify system elements that may be effected by uncertainty
Creating an environment of trust - if you have multi-strategy, multi-methods and multi-skills you need trust
What is true for the process is true for the system
In a tightly coupled organization you do not plan for possibilities but industrialization.
A tightly coupled complex system, usually result of such a process, does also not increase the number of choices and the emergence of an unexpected event can become horrible.
So, diversify and decentralize your system, make it agile by increasing its adaptability. Make intelligent independent system components that are accurate and robust, but flexible. Let them co-exist and co-evolute.
This may raise the eyebrows of the system centralizers, integrators, top-downers … but it is correct.
There is no such horrible operational risk than a tightly coupled organization with tightly coupled complex systems. It never becomes stronger when stressed.
Picture from sehfelder
The Tight-Integration Trap
This goes beyond our core business interests and positioning. It is about complex, tightly coupled systems. A dangerous mix.
A complex system can have unintended consequences and tightly coupled in most of the cases means there is not enough time to react to them.
A complex system can have unintended consequences and tightly coupled in most of the cases means there is not enough time to react to them.
Is It Enough To Be Good In Not Being Bad?
When I made my first tumbling steps in computing it was already agreed: "No one ever has got fired for buying IBM". But at that time, the 70s, only a few understood that decisions are about optimizing risk.
The fear of regret
It is a wonderful example of understanding loss aversion - defensive decision making. Traditional marketers of brands codify growth promotion: value & benefit, standard & importance, recognition & programming, identity & promotion or emotion & love
And we know it but often suppress it: value is the weakest and love it the strongest growth code - but most people do not love risk management systems, nor find them ideologic. But they could be mainstream. Risk is often mistaken with danger - on all levels, from the managerial to the operational.
So, in enterprise level technologies the "IBM example" goes on and on. Standard&importance wins the game.
Please do not misunderstand, I do not complain - I want to point out that this is is our power source: do things that matter for those who care. Is this arrogant? Not thought to ….
Better or less likely to be bad?
My experience as a seller and buyer: people quite often pay a fairly large premium for brands, not because they are objectively better, but they do not expect them to be bad.
There is no metric for assessing a technology.
Stochastic tinkering?
As NN Taleb calls trial and error insight gain. Yes, people make progress using things without knowing how they work. And this is great - if somebody comes along and explains (a little later). Especially if traps and side effects need to be avoided. I call this the black box - white box principle of using (in contrast to the white box - place box principle of learning). This is the risk engineering part of risk management,
The tightly coupled complex systems trap
We have pointed on many detailed model-method traps here. And uncovered principles that were common sense for a long time but do not work a expected.
Here's a real dangerous one: a complex system may have unintended consequences and tightly coupled means there is not enough time to react to them. And it is paired with the "sunk cost bias". If a system is expensive, you don't change them.
Serving costumers individually by an integrated, but loosely coupled system
UnRisk offers highly automated, adaptable, decision support risk management systems that are development systems in one. It works on a generic portfolio across scenario valuation principle and because of its data base concept it is integrated, its orthogonal organization makes it loosely coupled.
Our growth code is individualization & innovation.
This post is inspired by a Rory Sutherland at Edge contribution-
Picture from sehfelder
5 New Insights Helping To Avoid The Risky Horror in 2014
Newer insights have unmasked established concepts as unreliable in certain cases. As a reference, I take the worst type of optimizer - Markowitz' concept of diversification. But also in times where the market shifts into new regimes established models do not work properly - Black vs. Bachelier revisited.
The Web Became 25 - Will It Become 50?
Cover story of the Wired UK, March-14 Edition: Web@25.25 years ago, Tim Berners-Lee published a proposal for what became the WWW.
It took off because it is universal, decentralized, open, free, built on collaboration, ….
But what of the next 25 years?
Less about searching and more about getting?
Why Is It That Socks Always Get Lost in The Laundry?
From Ask Ariely .. I really like his answer - in short: you have many socks, socks are in pairs and if you miss one and find it later elsewhere, but forgot that you missed it once, you miss its partner ... You count as lost each sock in a pair - even if non of them is really lost (in the laundry).
VaR Is Dead - Long Live VaR
The US Senate Permanent Subcommittee on Investigations released a comprehensive Report on JPMorgan Chase Whale Trade.
To read the whole report was not easy for me, being not a practitioner, and consequently I cannot seriously summarize - not about questionable trading practices.
To read the whole report was not easy for me, being not a practitioner, and consequently I cannot seriously summarize - not about questionable trading practices.
What Will I Be Writing in 2024
This post has been motivated by a session of the WIRED UK June-14 issue.
The UnRisk team always looks a little into the future. What about new mathematical schemes, programming techniques, computing platforms and tools for better development and project management tools …. and media and communication platforms …. socio-economic and financial systems. In short, new environments for innovation and marketing?
Look a little further ahead?
The future as it was? It's hard to believe, but back in 2014 people propagated fragile, tightly coupled complex systems for front-to-back investment and risk management and not so few developers believed in one-technology-fits-all approaches.
It was not so difficult to predict that the financial security systems did not make the system safer - regulatory bodies forced banks do "go nuke" (do as the nuclear power plants needed to do, because of the technology)
Why the atifragility argument led to re-decentralization
For some it may be surprising, but ….
Big players providing information technologies did voluntarily split themselves into independent units. Buffers, diffusion and agility will fix what did not work: hierarchies, strategic plans and industrialized work in an economy of scales - flexibility, innovation and enthusiasm drive better offerings and consequently returns - and are safer for the economies.
I finance this led to a retraction of the strict central collateral management and clearing system. Market systems are too complicated to be centrally managed.
In combination with the extreme xVA treatment it broke to a marginal cost regime. Large banks have identified collateral transformation and central clearing as lucrative service, but that created the old problems of market dominance through another doorway.
How to program the money system
A system is universal, if it is solid enough to store and liquid enough to transform. A universal system is programmable. Our money can store values and it is a media for economic transactions, consequently, the money system is programmable. We now understand that this needed an operating system and development tools.
System architects, computer scientists and quants are now indispensable in new jobs dealing with money policy and creation as well as providing the operational semantics of the programs. Symbolic, declarative programs that needed a lot of clever algorithms to implement them.
Following the decentralization logic money creation became even more decentralized and for better programming we invented new money types, like derivative money - futures, options, …
Technologies Do It Yourself investors should use
Structure me this is not longer in disrepute. There are technologies that help to make complex
financial concepts to non experts. Decision support system will use new media and dynamic visualizations techniques. Systems will adapt to a financial "aura" of a market participant describing objectives, risk appetites, ….
High level, simple, interactive, knowledge-based, domain-specific programming will allow them to simulate deals with portfolios of various deal types.
The internet of finance is reality
We all, dealing with financial applications, knew that it was a joke to worry about big data, when still struggling with getting informative small (market)data with the possibility to turn this data into something that supports decisions.
Informations like debt and profit webs came into play to let agents understand feedback loop better ….
Those data are now available and widely accessible. They allow us technology providers to intelligently combine modeling with more data driven adaptability - making data meaningful.
Reasons, why quant work is now more polarized
10 years ago quants whee so overwhelmed by the operational requirements meeting the regulatory system. If they did xVA they did the same thing (valuations) 100 million times an hour instead of 5 million times an hour when doing portfolio-across.scenario simulation. This needed a lot of plumbing, especial if their banks suffered from the was-not-invented-here syndrome.
Now, the new technologies and tools give them time to optimize their business. They can design new financial systems. How they do it will not make a big difference, but what they do will.
Adding value still means: doing the hard work.
Programming is knowledge-based
Programming is symbolic, multi-paradigm and uses algorithmic knowledge-bases. There are development environments that support high level programming in multi-languages, that are platform agnostic. We do not longer care about versions and releases and underlying programming languages. All algorithms for high performance computing are inherently parallel and support all architectures of the new computing muscles.
The most complex applications go where the users go
Write once, deploy anywhere.
Clouds went private and crunch and store all the (risk) numbers blazingly fast.
Even smart devices will have enough power and display-quality to do massive pre-processing creating insight about incoming data flows and prepare them for processing.
Financial objects and their risk spectra can be post-processed and insightfully arranged on the smart mobile devices.
Program everything
The blog posts are interactive, some stories are created by and act like programs. 70% of the news are created by computers. Ask a question and you will receive a little program that answers all questions of that type.
More quants do more meaningful things
and turned them into businesses
And
it is late June, the sky has scattered clouds, the birds are singing, …..
Quantsourcing - Managing the Polarity of Risk Management
Let me start with the dilemma of enterprise risk management. Is it a cost for securing business or a strategic asset? Shall it be centralized or autonomous? Shall it be system driven or business driven?
Managing Polarity
This are the poles of the dilemma of (enterprise-wide) risk management: predictability vs adaptability, control vs agility on the positive side and bureaucracy vs lack of control on the negative side. Organizations tend to oscillate between poles.
The shock of the crisis has given the pole of cost, centralization and system magnetic power. Regulators want tightly coupled systems.
But what about optimizing risk?
Managing polarity is listening to supporters and detractors of the poles and view the positives and negatives of each pole, create awareness of them and try to avoid the negatives. Optimizing risk needs learning from turbulences, adaptability and agility.
Make or buy? One size fits all? Is it enough to being good in not being bad?
Quants would usually have an important role in understanding the the right oscillation trajectory through a pole map (oscillation is inevitable - antifragility needs fragility).
Quantsourcing?
Let me jump to the roles. When we say to a (large) group: everybody-does-this, than it is easy to let someone else do it. Everybody doesn't make things, they may use things.
It's a bit like with crowdsourcing and crowdfunding. Crowds don't make things. It's people, who make things.
Ann, Paul, .. make it. Individual quants make things that diffuse into valuation and risk management - it's Annsourcing and Paulfunding. One person, one contribution, urgent, necessary and indispensable.
Our quantsourcing promise is two-sided - because we believe in quant-powered innovation, we offer quant work for small market participants who do not have quant teams and we serve quants with our technologies enabling them doing things better and swifter.
Expanded Curation
The consequence of unleashing the programming power behind UnRisk, is transparency,. We deliver know-how packages, not black boxes. To optimize the knowledge transfer we always look for new formats. Hybrid learning arrangements between lectures and live presentations. Events that bridge science and usage.
A crowd of quants is not a crowd, but rather a number of individuals gathered in a space, not listening, like in an opera, but interacting.
We want to perfect such formats and try new ways, like this one. In response we hope quants will embed the event experiences into their products and services.
We want quants understanding their role in managing the polarization. Modern risk management needs innovators, not only administrators of black boxes.
Managing Polarity
This are the poles of the dilemma of (enterprise-wide) risk management: predictability vs adaptability, control vs agility on the positive side and bureaucracy vs lack of control on the negative side. Organizations tend to oscillate between poles.
The shock of the crisis has given the pole of cost, centralization and system magnetic power. Regulators want tightly coupled systems.
But what about optimizing risk?
Managing polarity is listening to supporters and detractors of the poles and view the positives and negatives of each pole, create awareness of them and try to avoid the negatives. Optimizing risk needs learning from turbulences, adaptability and agility.
Make or buy? One size fits all? Is it enough to being good in not being bad?
Quants would usually have an important role in understanding the the right oscillation trajectory through a pole map (oscillation is inevitable - antifragility needs fragility).
Quantsourcing?
Let me jump to the roles. When we say to a (large) group: everybody-does-this, than it is easy to let someone else do it. Everybody doesn't make things, they may use things.
It's a bit like with crowdsourcing and crowdfunding. Crowds don't make things. It's people, who make things.
Ann, Paul, .. make it. Individual quants make things that diffuse into valuation and risk management - it's Annsourcing and Paulfunding. One person, one contribution, urgent, necessary and indispensable.
Our quantsourcing promise is two-sided - because we believe in quant-powered innovation, we offer quant work for small market participants who do not have quant teams and we serve quants with our technologies enabling them doing things better and swifter.
Expanded Curation
The consequence of unleashing the programming power behind UnRisk, is transparency,. We deliver know-how packages, not black boxes. To optimize the knowledge transfer we always look for new formats. Hybrid learning arrangements between lectures and live presentations. Events that bridge science and usage.
A crowd of quants is not a crowd, but rather a number of individuals gathered in a space, not listening, like in an opera, but interacting.
We want to perfect such formats and try new ways, like this one. In response we hope quants will embed the event experiences into their products and services.
We want quants understanding their role in managing the polarization. Modern risk management needs innovators, not only administrators of black boxes.
When Three Rights Make A Wrong
Yesterday I posted about model abuses, today I read in HBR, Apr 2013, Robert C. Merton - Innovation Risk. Both about complicated models. Whilst the former emphasizes on lying the latter summarizes: a more-complete but more-complicated model may carry greater risk than a cruder one if the user is not qualified for the job.
11 Years of UnRisk Event - an Inside Summary
In Do I Remember? I summarized remarkable experiences of 2012 of which our event celebrating 11years of UnRisk were one of the most insightful.
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