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Digithead's Lab Notebook: Complex systems

Digithead's Lab Notebook: Complex systems | Decision Intelligence | Scoop.it

One hundred years from now, the role of science and technology will be about becoming part of nature rather than trying to control it. - Joichi Ito, MIT Media Lab director
In complex systems, trade-offs are everywhere. Engineers designing technological artifacts carefully balance competing objectives. An economy allocates resources like land, labor and capital to one use or another. Even evolution faces trade-offs.

 


Via Bernard Ryefield
Lorien Pratt's insight:

Some insights into how to make trade-offs in complex systems when there are multiple competing outcomes.

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Decision Intelligence
Decision intelligence is an interdisciplinary field with a mission to solve the world's most complex problems It is based on the premise that "the decision"; is the atomic unit of complex problem-solving.   Closely related to Decision Engineering, Decision intelligence draws upon  technology such as visual decision modeling, complex systems modeling, big data, predictive analytics, machine learning. UX design, statistical analysis, business intelligence, business process management, causal reasoning, evidence-based analysis, and more. For an overview, see the webinar at http://youtu.be/XRTJt3bVCaEand more videos at http://www.youtube.com/quantellia. Many of these topics are vigorously discussed in the LinkedIn group Effective Decision Making in the Midst of Complexity: http://www.linkedin.com/groups?gid=205078.  Also see http://www.tdi3.org, http://www.quantellia.com, http://www.absolutdata.com/, and http://www.informeddecisions.se/.  I also invite Decision Intelligence pioneers to subscribe to my blog and receive my free eBook: http://forms.aweber.com/form/90/568343590.htm .
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Beyond interactive data visualizations to the causal flow at the NYT!

Beyond interactive data visualizations to the causal flow at the NYT! | Decision Intelligence | Scoop.it
Draw your guess for every income level, then compare it with the actual data on college enrollment and the drawings of other readers.
Lorien Pratt's insight:

I really can't express in words how important this is.  In a world where "interactive data visualization" is *only* about drill-down into static data, the NYT earlier this week showed the roots of an interactive, collaborative cause-and-effect link. 


This is the CORE BUILDING BLOCK of forward models, and is the most important element for the future models we're starting to see more and more, where human expertise, machine learning, complex systems, collaboration, and causal visualization combine to bring us to an ENTIRELY NEW LEVEL in how computers support cognition.  Whether you call it Intelligence Augmentation, Cybernetics, Causal Flow Modeling, or Decision Intelligence, the NYT has nailed it. 


Congratulations to Gregor Aisch, Amanda Cox, and Kevin Quealy.  I don't know you guys, but you've nailed it.

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Are You a Good Decision Maker? Consider This Emerging Field

Are You a Good Decision Maker? Consider This Emerging Field | Decision Intelligence | Scoop.it
Decision engineering (which also goes by the name decision intelligence) is a field that designs decisions themselves as if they were tangible projects, such as building robots or computer systems, using the same engineering methodologies and techniques.
Lorien Pratt's insight:

Delighted that the DE/DI field was covered today on IEEE Roundup!  Congrats to @timvangelder, @SteveBarrager for mentions in this important article. 


This is the week, guys, the inflection point in the hockey stick.  Hold on tight.

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Baby steps: introducing decision intelligence to clients

Baby steps: introducing decision intelligence to clients | Decision Intelligence | Scoop.it
It's not easy selling into an emerging market, no matter how important it is.  We won two projects in the last week or so.  In both cases, the customer hadn't heard  of decision intelligence before talking with us.  In one, they were looking for data analysis to guide a marketing investment; in the other, the question was to determine the…
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In Search Of The True Value In The Internet Of Things

In Search Of The True Value In The Internet Of Things | Decision Intelligence | Scoop.it

The real value lies in being able to capture and analyze the data that comes from the sensors at the endpoints of IoT — and that needs to be the few nuggets of data that are of material value to the business

Lorien Pratt's insight:

More data from IOT devices doesn't necessarily translate into more business value; some data is more useful than others

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A grand tour of decision architecture tools for leaders

A grand tour of decision architecture tools for leaders | Decision Intelligence | Scoop.it
Structure your organization’s work to encourage wise choices.
Lorien Pratt's insight:

A must-read grand tour of tools & techniques for decision leadership, from two Harvard professors  / consultants. My summary:

Leaders can architect decisions through a five-step process:

  1. Understand biases
    1. Kahneman, thinking fast and slow, system 1 and system 2
  1. Understand behavioral issues at the heart of poor decisions
    1. Burnout?
    2. Technology / knowledge issues?
    3. Narrow down the problem
  1. Determine causes
    1. Insufficient motivation?
      1. e.g. an onboarding process that failed to create an emotional connection
    1. Cognitive bias?
      1. Action-oriented
      2. Bias from perception and judgment of alternatives
      3. Biases from framing of alternatives
      4. Stability bias: prefer the status quo
    1. Seek to shift from System 1 to System 2
  1. Redesign the decision-making context
    1. Use choice architectures and nudges e.g. Thaler
      1. e.g. sign near big plates at google about portion size
      1. e.g. opt-in vs. opt-out retirement planning
    1. Trigger system 1: tap into emotions
    2. Harness biases, e.g. loss aversion: "pay your tax or lose your car"
    3. Simplify processes
    4. Engage system 2
      1. Reflection, broaden thinking, increase accountability, use reminders, and more
    1. Skirt systems 1 and 2 altogether using default and automatic systems
  1. Rigorously test
    1. Identify outcome
    1. Focus on one solution at a time
    1. Think in terms of treatment and control groups
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Learning to See Data

Learning to See Data | Decision Intelligence | Scoop.it

Scientists working in a little-known branch of psychology called perceptual learning have shown that it is possible to fast-forward a person’s gut instincts both in physical fields, like flying an airplane, and more academic ones, like deciphering advanced chemical notation. The idea is to train specific visual skills, usually with computer-game-like modules that require split-second decisions. Over time, a person develops a “good eye” for the material, and with it an ability to extract meaningful patterns instantaneously.

Lorien Pratt's insight:

This is probably the most important Decision Intelligence-related article I've read in a while; apparently there is a new field called Perceptual Learning that studies how we can train specific visual skills, much like my co-founder Mark's STATPLAY research. 

 

Summary:

 

  • A Brooklyn-based painter and conceptual artist is helping genetic scientists to visualize data
  • The goal: reduce big data overload
  • "perceptual  learning" is the little-known field that fast-forwards gut instincts by training specific visual skills, often with computer game modules
  • Eleanor J. Gibson founded  this field in 1969
  • UCLA medical school has adopted perceptual learning modules as part of its standard curriculum, to train on skills like reading electrocardiograms, identifying rashes, and      interpreting biopsy samples
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Showing up: #Hack4Congress goes to Washington

Showing up: #Hack4Congress goes to Washington | Decision Intelligence | Scoop.it
Earlier this year, the newly appointed White House CTO told Wired Magazine that the tech industry needs to "show up" in DC.  So this week, award-winning teams from around the nation flew to Washington to attend the #hack4congress finalist presentation, which you can watch below. http://www.youtube.com/watch?v=Xf3ZBkipVsM Commented Abby Alger, from the Office of the House Republican Conference Chair,…
Lorien Pratt's insight:
It's great to see data and analytics being taken seriously in DC, in creative and innovative ways. Also a little plug for causal modeling on the hill this week :-)
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Catch the ball: shifting from text to visual/motor thinking for complex problems

Catch the ball: shifting from text to visual/motor thinking for complex problems | Decision Intelligence | Scoop.it
We are shifting our thinking style to accommodate a much more complex and interdependent world than we have faced in the past. Consider: In 2014, Google purchased Deep Mind, formerly a UK-based startup with a focus on "Neural Turing Machines".       Deep Mind's unique approach: to use video game play as a learning environment,…
Lorien Pratt's insight:

A shift to visual/motor thinking is essential to better decision intelligence

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What's broken in international development, and some good hints on how to fix it.

What's broken in international development, and some good hints on how to fix it. | Decision Intelligence | Scoop.it
According to Ben Ramalingam’s Aid on the Edge of Chaos, international development is just such an invasive species. Why Dertu doesn’t have a vaccination clinic, why Kenyan schoolkids can’t read, it’s a combination of culture, politics, history, laws, infrastructure, individuals—all of a society’s component parts, their harmony and their discord, working as one organism. Introducing something foreign into that system—millions in donor cash, dozens of trained personnel and equipment, U.N. Land Rovers—causes it to adapt in ways you can’t predict.
Lorien Pratt's insight:

A must-read.  One unintended consequence after another, from well-meaning, but ultimately misguided initiatives.  But my conclusion is a bit less bleak than the author's.  I think that with better understanding of complex adaptive systems (including adjacent areas like knowledge transfer between domains, real-time systems modeling with adaptive learning, integrated human-in-the-loop, evidence-based analysis, and automated decision making), we can still do good.  We must.

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Lorien Pratt's curator insight, April 26, 11:19 PM

A must-read.  One unintended consequence after another, from well-meaning, but ultimately misguided initiatives.  But my conclusion is a bit less bleak than the author's.  I think that with better understanding of complex adaptive systems (including adjacent areas like knowledge transfer between domains, real-time systems modeling with adaptive learning, integrated human, evidence-based analysis, and automated decision making), we can still do good.  We must.

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A blog post that writes itself, using a decision model. Wow!

A blog post that writes itself, using a decision model.  Wow! | Decision Intelligence | Scoop.it

"The model ... mixes ingredients from machine learning, big data, intelligence augmentation, predictive analytics, dashboards, complex systems analysis, causal modeling, agency theory, statistical analysis, and more in a way that answers the most practical of all questions: If I make this decision today, how will it impact my future business objectives?"

Lorien Pratt's insight:

This is the most important thing I've written / built so far this year.  If you have any interest in decision intelligence at all, you owe it to yourself to check this out; scroll to the bottom to play with the model.

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Decision models are the requirements and design language for decision intelligence

Decision models are the requirements and design language for decision intelligence | Decision Intelligence | Scoop.it
The software engineering revolution is happening again. I was a coder before software engineering, and it wasn't pretty.  When we needed to build a new program, we'd get together with the end customer, and ask a lot of questions, then go back to the office to write code.  It didn't go very well.  It was…
Lorien Pratt's insight:

SO important. A new way to think about requirements and design, when the result is a great decision, instead of just software. 

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Australia: a complex system of systems

It is easier to make one's way in the world if one has some sort of expectation of the world's future behaviour. Even when facing a very complex problem, we are rarely in a state of full ignorance: some expectations of system behaviour and the level of risk arising from uncertainty are usually available and it is on the basis of these expectations that most decisions are taken. Humans use models, which are mental or formal representations of reality, to generate these expectations, employing an ability that is shared more or less by all forms of life. Whether it is a tree responding to shortening day length by dropping its leaves and preparing its metabolism for the winter ahead or a naked Pleistocene ape storing food in advance of winter for the same reasons, both are using models. This view leads to two outcomes. The first is that predictions, seen as an expectation of ranges of future behaviours, are not just desirable, but necessary for decision-making. The often-asked question 'do models provide reliable predictions?' then shifts to 'given a certain problem, what type of models provide the most useful and reliable prediction?' The second outcome is that modelling is no longer a scientist's activity but is instead a social process. Different types of models can be employed to ensure that all available information is included in model building and that model results are understood, trusted and acted upon.

Lorien Pratt's insight:

Rich and valuable material here and in volume 1, which you can find at https://www.science.org.au/publications/negotiating-our-future-living-scenarios-australia-2050

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If Algorithms Know All, How Much Should Humans Help?

If Algorithms Know All, How Much Should Humans Help? | Decision Intelligence | Scoop.it

"...the stakes are rising as the methods and mind-set of data science spread across the economy and society. Big companies and start-ups are beginning to use the technology in decisions like medical diagnosis, crime prevention and loan approvals. The application of data science to such fields raises questions of when close human supervision of an algorithm’s results is needed."

Lorien Pratt's insight:

We're starting to see some new challenges around the question of how human and computer knowledge interacts. Good that there's starting to be a dialogue about fully automated versus computer-in-the-loop use cases. 

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Philippe Vallat's curator insight, April 15, 5:42 AM

The Laplace's demon is not dead... Machines can compute and deduce, but certainly not decide - as decision always includes some uncertainty. True is also that some fact finding and calculation can reduce human biases - as long as enough time and quality and availability of the data are given.


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Experfy Secures Seed Round to Scale Data Scientist Marketplace

Experfy Secures Seed Round to Scale Data Scientist Marketplace | Decision Intelligence | Scoop.it
Founded last year, Experfy Inc. has built a marketplace for data scientists who, like designers using 99Designs Inc. or business consultants using HourlyNerd Inc., respond to requests from individuals and businesses for help with specific projects.
Lorien Pratt's insight:

Experfy provides world-class, expert resources for data science.  Including decision intelligence / decision engineering, I'm delighted to say :-)  (see https://www.experfy.com/practice-areas/visualizations ). Yup, this is the week.

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Gongos: a Decision Intelligence company

Gongos: a Decision Intelligence company | Decision Intelligence | Scoop.it

Gongos is a decision intelligence company.  We partner with outcome-driven, visionary organizations exposing them to the future as seen through consumers' eyes.

Lorien Pratt's insight:

It's tremendously exciting to see decision intelligence taking off.  Here's yet another company in this space! Based in Michigan, Gongos offers solutions to multiple verticals. Clients include Whole Foods, Chase, Fiat Chrysler, and more.  Beyond dashboards, the company offers "infographical journeys, video documentaries, insight portals, animated vignettes, and insight integration initiatives".   Looking forward to getting to know you, @_Gongos!

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Decision Intelligence building blocks: about levers

Decision Intelligence building blocks: about levers | Decision Intelligence | Scoop.it
I'm going to write a series of posts about the core elements of a decision model.  This one's about Levers: simulations of things you can change as you make the decision.   We might also have called levers choices. It can be confusing: you'd think that a decision model would produce the choices as output, not as input.  Because…
Lorien Pratt's insight:

Every complex decision is made of just a few kinds of building blocks.  Levers represent the choices you can make,

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Counterfactual blindness and the invisible things

Counterfactual blindness and the invisible things | Decision Intelligence | Scoop.it

This is an article about invisible things that matter.  Because these things are invisible, it's going to be harder going than usual.   Because they matter, it's worth it. There's a story a friend told me last week about a plumber.  A women calls him in an emergency; there's water everywhere. ..."

Lorien Pratt's insight:

There are a lot of invisible things that need to be made visible.  The "but for" reality is one of them.

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Mark Zangari speaks on Agency and Machine Learning: from data to actions

Mark Zangari speaks on Agency and Machine Learning: from data to actions | Decision Intelligence | Scoop.it
Once upon a time there were programmers, but not software engineers.  As businesses and other organizations learned the value of this new technology, software engineering emerged as a discipline to derive maximum business value from it. There is a similar need emerging in data science today.  This means that machine learning is underutilized compared to…
Lorien Pratt's insight:

Great talk by Quantellia CEO Mark Zangari in Seattle a couple of weeks ago.

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Decision Engineering 3.0: Twilight of the gurus

Decision Engineering 3.0: Twilight of the gurus | Decision Intelligence | Scoop.it
Decision Engineering 3.0:The guru industry seems ripe for disruptive innovation.
Lorien Pratt's insight:

An excellent perspective on the past and future of Decision Engineering from one of *my* favorite Gurus, Steve Barrager at Baker Street Publishing.

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Countries are complex systems: decision intelligence in conflict and disaster recovery

Countries are complex systems: decision intelligence in conflict and disaster recovery | Decision Intelligence | Scoop.it

In our years building decision intelligence models for domains like banking, telecom, and more, the project that I am most proud of is the work that we did for Liberia in collaboration with The Carter Center. The basic idea: countries are complex systems. "

Lorien Pratt's insight:

Another one including an interactive decision model, this time with time-based simulation.

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A new Decision Intelligence company: Busigence

A new Decision Intelligence company: Busigence | Decision Intelligence | Scoop.it
Busigence is a Decision Intelligence Company. An IIT-NSIT Alumni Organization. We create decision intelligence products for real people by combining data, technology, business, and behaviour enabling strengthened decisions.
Lorien Pratt's insight:

It's exciting to see the ecosystem growing!

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The critical missing ingredient in predictive analytics

The critical missing ingredient in predictive analytics | Decision Intelligence | Scoop.it

...for a widespread class of situations, predictive analytics alone aren’t enough. Consider the decision model below, which I introduced in my last post. The blue graphs on the right-hand side are based on predictive analytics, but they are only building blocks in the full model. They are not enough on their own."

Lorien Pratt's insight:

This article explains the important distinction between predictive analytics and decision intelligence.

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Towards two-marshmallow government

Towards two-marshmallow government | Decision Intelligence | Scoop.it

"...What’s less well-known about the marshmallow study is that delayed gratification can be taught. Says this article, “‘Impulsivity,’ [the authors] concluded, ‘is not a purely maladaptive trait, but one whose consequences hinge on the structure of the decision-making environment.'"

Lorien Pratt's insight:

This post contains an interactive decision model, to aid your intuition as to how a simple dynamic works.  Looking forward to hearing your thoughts!

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Upon acquisition by Microsoft last week, Revo CEO Dave Rich promises decision (re)engineering

Upon acquisition by Microsoft last week, Revo CEO Dave Rich promises decision (re)engineering | Decision Intelligence | Scoop.it
Delivery of big data analytics to all roles within a company — no matter the size — will make "Decision Process Reengineering" to the next decade what "Business Process Reengineering" has been to the previous two decades. It’s as simple as that.
Lorien Pratt's insight:

Now it gets interesting...

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Amazon announces machine learning for AWS

Amazon announces machine learning for AWS | Decision Intelligence | Scoop.it
Throughout the history of machine learning, there have been tools that have tried to make it easier to build a learner, with a focus on the "practitioner" rather than the research scientist or professional software developer.  These systems have come and gone, but none seem to have really taken off. This time, it's is different.…
Lorien Pratt's insight:

I think Granny could use this one.  But behind the scenes: a sophisticated engine.  In this article I do a bit of comparison to the Azure ML engine as well.

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