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The Bank of England has confirmed that economic forecasting is basically impossible

The Bank of England has confirmed that economic forecasting is basically impossible | Decision Intelligence | Scoop.it
"The economy has been stronger than we thought. That's a good thing, obviously." Bank of England governor Mark Carney's defensive tone as he fielded pointed questions at a press conference today cast some doubt on whether the dilemma he faces could be called "good." As Carney reassured markets that an interest rate rise is not...

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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.   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 .
Curated by Lorien Pratt
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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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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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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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Learning in the 21st century: exploring the edge

Learning in the 21st century: exploring the edge | Decision Intelligence | Scoop.it

His course ... will he held in the game space. In it, students will experience ethical dilemmas that emerge as unintended consequences of the decisions they make in playing the game. Each decision either opens up or closes off later choices in the game. Students can better understand the complexities of ethical judgment and better evaluate the choices they make if they see the effects of those choices.

Lorien Pratt's insight:

This article by John Seely Brown is the most important thing written so far this year.  More than just about education, it addresses the profound difference in the emerging 21st-century workforce, what we've learned about education from World of Warcraft, the importance of experiential learning and simulation, and much more. 


Watch this space: five years from now we'll look back on this moment as the time when complexity theory, visual/spatial learning, simulation, computer gaming, the formal analysis of unintended consequences, and emerging educational best  practices collided for everyone's good, and we won't know how to do it any differently.

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Lorien Pratt's curator insight, April 9, 9:47 PM

Unintended consequences are mentioned explicitly in this report; nice to see some semi-formal attention to this topic.

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Crossing between silos: decision engineering combines CMO, CTO, and more

Crossing between silos: decision engineering combines CMO, CTO, and more | Decision Intelligence | Scoop.it
Instead of traditional analytics, where the analytics is done on a given data set, decision engineering involves working with key decision-makers, such as CMO, CTOs, CXOs to understand what are their pain-points and then figure out what are the various data levers we can apply to help them take the decisions. It is not a canned approach. So for the same problem you would need a combination of data such as market research, big data or may be large data sitting with a company's customer base. Decision engineering brings all the data in the company together to enable decision-making.
Lorien Pratt's insight:

A good article about our friends at AbsolutData, with a focus on using analytics including decision engineering for pricing strategy and brand management.

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Beyond the Professor: Gilligan’s Island and the data science talent search

Beyond the Professor: Gilligan’s Island and the data science talent search | Decision Intelligence | Scoop.it
I am not a data scientist. I repeat, I am not a data scientist. Last week I spoke on a panel with the author of this blog and several other decision intelligence executives. Our topic, “Who is Your Chief Decision Officer”, was a hit. The discussion centered on the fact that the data and technology…
Lorien Pratt's insight:

Just as an airline industry was born after Kitty Hawk, the data science industry is evolving to include many rules, including those responsible for the use of the data in the overall decision. 

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High versus low-level data science

High versus low-level data science | Decision Intelligence | Scoop.it
Here I describe a case study: a solution based on high-level data science. By high level, I mean data science not done by statisticians, but by decision makers…
Lorien Pratt's insight:

This is a good distinction. Very closely related to human-in-the-loop versus fully-automated decision making

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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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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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Who is your company's Chief Decision Officer?

Per a recent PWC report, “Big decisions have big impact on future profitability, with nearly 1 in 3 executives valuing those decisions at least at $1 billion”. Today’s organizations are dealing with complex situations that change “in flight”, technical silos, intellectual property locked in the minds of few, the need to understand how changes will impact future outcomes, and the need for systematic methods for decision making, risk assessment, and measurement. Although organizations are increasingly more focused on leveraging data, what is often missing is the individual who can use decision intelligence to assist the organization to drive Big Decisions.


Via an esteemed panel of technology, industry and talent experts “Who is Your Company’s Chief Decision Officer” will discuss 1) What is Decision Intelligence and how can it drive Big Decisions, and 2) Who should lead the charge for Decision intelligence in your organization and does this role currently exist?

Lorien Pratt's insight:

Thank you, June, Nadine, Neera, Christina for the opportunity to participate in this great panel!

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What’s really frightening about Artificial Intelligence? It’s not what you think.

What’s really frightening about Artificial Intelligence?  It’s not what you think. | Decision Intelligence | Scoop.it
OK, I'll admit it. AI scares me.  But not for the usual reasons: I'm not too concerned about robots taking over the earth or even the Singularity, as are many of my friends.  What does frighten me is the distraction that AI represents from the problems that matter.  The ones that need our judgment, our ethics, our humanity,…
Lorien Pratt's insight:

Please share if you agree.

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hennessy vargas's curator insight, April 8, 4:41 PM

I agree, I'm scared the human race will slowly become dumber and dumber because of all of this artificial intelligence. This can cause us too get to comfortable with machines doing all the thinking and work for us like calculators.

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Giant study finds nine habits that lead to terrible decisions

Giant study finds nine habits that lead to terrible decisions | Decision Intelligence | Scoop.it
And how you can avoid them.
Lorien Pratt's insight:

Top factors from a giant study (>50,000 leaders) : Laziness, not anticipating the unexpected, indecisiveness, remaining locked in the past, over-dependence, isolation, lack of technical depth, failure to communicate the what, where, when, and how associated with decisions. 


I'm delighted that Decision Intelligence addresses many of these :-)

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Intelligence Augmentation (IA) comes of age: World Modeler

Intelligence Augmentation (IA) comes of age: World Modeler | Decision Intelligence | Scoop.it

In anticipation of Mark Zangari's upcoming talk on Agency Theory at MLConf Seattle, a question appeared on Quora yesterday asking  "What is the World Modeler platform and how does it compare to similar platforms?" 

Lorien Pratt's insight:

This article is about why World Modeler is different, and why that matters.

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