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What is Democracy and how do you measure it?

Coppedge, a political science professor currently leading an international research team on the varieties of democracy, says democracy is difficult to break ...
Lorien Pratt's insight:

Yet another terrific data set now available, this one measuring one of the most important aspects of today's world: democracy.  This is not just a data set, but also an ontology: a conceptual map of the dimensions of democracy. Elements of this ontology represent important entities and attributes in many potential decision models. For instance, we might develop an analytic that relates two aspects of democracy: income disparity and access to media.  If these are correlated, then we can go further to try to determine if there is a cause-and-effect relationship.  If we can determine that nature of that relationship, then this becomes part of a decision model that helps us determine if our democracy improvement dollars are best spent on improving access to media, or rule of law, or some other element.  A data model like this one, along with data to populate it, has tremendous value in this setting.

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Lorien Pratt's comment, February 25, 2014 10:52 PM
Also see http://kellogg.nd.edu/varieties.pdf for additional background
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 .
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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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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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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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Small data? Use Deep Learning plus domain expertise for feature engineering.

Small data?  Use Deep Learning plus domain expertise for feature engineering. | Decision Intelligence | Scoop.it

"Since the datasets available in these fields are small, data scientists cannot apply pre-packaged Deep Learning algorithms, but have to artfully determine the features to train and engineer their networks with convolution/dense layers to learn these rather complex features. The data scientists that perform these tasks have to be machine learning engineers who walked in the shoes of a radiologists, internist or a chemical engineer."

Lorien Pratt's insight:

The art of building learning systems requires a collaboration between people and the learning systems, specifically to inject human expertise when data is impoverished.


One way to do this is in feature engineering, where domain knowledge is inserted into systems through preprocessing features to be those that are most likely to lead to good learning results. 


Another source of information: labels on training data. But this can be onerous for large data sets, which is where the magic of deep learning comes in, which can combine supervised with unsupervised training passes.  This article shows a good way to think about all of this. Thanks to Shalini and Yann.

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Artificial Intelligence Powered by Crowdsourcing: The Future of Big Data and Humanitarian Action

Artificial Intelligence Powered by Crowdsourcing: The Future of Big Data and Humanitarian Action | Decision Intelligence | Scoop.it
As far as we know, AIDR is the only Big Data solution out there that combines crowdsourcing with real-time machine learning for disaster response. Why do we use crowdsourcing to train the AI engine? Because speed is of the essence in disasters. You need a crowd of Digital Humanitarians to quickly tag as many tweets/messages as possible so that AIDR can learn as fast as possible. Incidentally, once you’ve created an algorithm that accurately detects tweets relaying urgent needs after a Typhoon in the Philippines, you can use that same algorithm again when the next Typhoon hits (no crowd needed).
Lorien Pratt's insight:

I'm excited to see this book announcement: another data point in the explosion of new use cases we're starting to see for AI/ML/DI.

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Liam Southwell's curator insight, March 31, 1:50 AM

Yet another heavy impact hat AI will have on the world. In this instance, we can see potential for incredible advances in response rates to natural disasters. AI can, and will save lives, it's already beginning to do so in the medical industry, and soon enough it will be facilitating the human response after natural disasters. 

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Who is your Company’s Chief Decision Officer?

Who is your Company’s Chief Decision Officer? | Decision Intelligence | Scoop.it

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, and more.

Lorien Pratt's insight:

Looking forward to this great keynote panel.  Join me!

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Meat and Bones: Machine learning and DI working together for evidence-based management

Meat and Bones: Machine learning and DI working together for evidence-based management | Decision Intelligence | Scoop.it
I had a great call with the CEO of a possible partner company for Quantellia this week, where I found myself saying that Decision Intelligence is the “bones” to the “meat” of machine learning.
Lorien Pratt's insight:

This is one of the most tremendously exciting things in the world, to a geek like me.

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Data data everywhere, but hard to make it think.

Data data everywhere, but hard to make it think. | Decision Intelligence | Scoop.it

Our community of big data providers consists of people with wide range of professional and academic backgrounds: Data Engineers, Data Scientists, Data Mining Experts, Data Analyst/Modelers, Big Data Solutions Architects, Visualization Designers, Statisticians, Applied Physicists, Mathematicians, Econometricians and Bioinformaticians.

Lorien Pratt's insight:

Experfy is my hero.  And they're solving one of the most important issues in a world of galloping big data.  Watch the video at the top of their page, it's excellent.

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Google has developed a technology to tell whether ‘facts’ on the Internet are true

Google has developed a technology to tell whether ‘facts’ on the Internet are true | Decision Intelligence | Scoop.it
the fact that a search engine could effectively evaluate truth, and that Google is actively contemplating that technology, should boggle the brain. After all, truth is a slippery, malleable thing — and grappling with it has traditionally been an exclusively human domain.
Lorien Pratt's insight:

It's a method that's ingenious in its simplicity: spider for knowledge "triples", e.g. (Barack Obama, nationality, American).  In other words: (object, attribute, value).  Yet another example, imo, of taking the oo paradigm out of code and into reality.  A good idea.

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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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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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First Issue of the Journal of Network Theory in Finance is Published

First Issue of the Journal of Network Theory in Finance is Published | Decision Intelligence | Scoop.it
Financial institutions and markets are highly interconnected, but only recently has literature begun to emerge that maps these interconnections and assesses their impact on financial risks and returns.
Lorien Pratt's insight:

I've been following Kimmo's work for a few years.  This emerging discipline represents a very important new perspective on financial analysis.

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Time for big data and machine learning to take a bold step forward…into 1946

Time for big data and machine learning to take a bold step forward…into 1946 | Decision Intelligence | Scoop.it

We've been lost in data for half a century. Time to move on, friends. Robert McNamara, secretary of defense from 1961 through 1968, came to the post from Ford Motor company, where he was ultimately appointed president.  His success in both positions came from bringing the discipline of statistical control from the Air Force into Ford"

Lorien Pratt's insight:

This is a quick read (lots of pictures :-) ) and a link to Mark's talk at USF last year for a deeper dive.

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Qstride's curator insight, March 26, 1:35 PM

Technology

Mitchell Ryan's curator insight, March 26, 10:35 PM

A Really interesting take on how big data is useful

Pedro Ruiz Aldasoro's curator insight, March 28, 1:19 PM

This is a very good point of view. Interesting way of begining understanding Big Data..

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Beautiful interconnection patterns in organizations mimic biology

my vision is a collective human intelligence assisted by a technology substrate like a membrane of technology that augments human capacity and ability to perceive, reason, think, act (experience collectively). I am interested to understand real human behaviours as evidenced by how we relate and communicate and provide an understanding of the individual vs group, the continuous spectrum and widening of the self-concept and the malleable and porous nature of human identity, the individual vs the collective, we are all one.
Lorien Pratt's insight:

I've seen this so may times, and it's lovely that we're now drawing pictures of new previously invisible things.

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Welcome to the age of unintended consequences (and what to do about them)

Welcome to the age of unintended consequences (and what to do about them) | Decision Intelligence | Scoop.it
So one of my favorite things about decision intelligence is its promise to help to overcome unintended consequences. As a way to capture both mental models, as well as providing an ongoing infrastructure to gather evidence to support and refine what start out as mental models and end up as sophisticated systems models, I’m tremendously excited about the future of what we can do.
Lorien Pratt's insight:

Unintended consequence design patterns?  Yet another "reality engineering" post. 

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Google wants to rank websites based on facts not links

Google wants to rank websites based on facts not links | Decision Intelligence | Scoop.it
Being trustworthy and accurate could help a web page rise up Google rankings if the search engine giant starts to measure quality by facts, not just links
Lorien Pratt's insight:

When you do a google search, you can receive millions of results, so their order matters a lot.  Google's current approach orders the results based on the number of incoming links to a site.  It is looking at ranking, instead, by truthfulness. 


This article also describes a few other truth-detection initiatives.  Important work, and it's just getting started.

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Are Machine Learning and Big Data all about just advertising and marketing?

Are Machine Learning and Big Data all about just advertising and marketing? | Decision Intelligence | Scoop.it

"use cases seemed to have focused on just a few, very lucrative ones, and that many other applications of this great technology had been left by the wayside. In what appeared to be a bit of winner-take-all"

Lorien Pratt's insight:

Are we seeing impoverished ML use cases in recent years?  And if so, is the tide turning?

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