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From trickle down to trickle up

A visual model demonstration showing how tax policy impacts economics.

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Shows how, under certain model assumptions, the middle class drives the economy, and how tax policy impacts this dynamic.

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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 most important when dealing with complexity.   Closely related to Decision Engineering, Decision intelligence draws upon  technology such as machine learning, 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 Decision Intelligence discussion group: http://www.linkedin.com/groups?gid=205078.  See http://www.lorienpratt.com/the-decision-intelligence-and-decision-engineering-ecosystem/ to learn about the emerging ecosystem of decision intelligence companies, influencers, and problems solved.
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The World Resources Sim Center: on its way to Silicon Valley

The World Resources Sim Center: on its way to Silicon Valley | Decision Intelligence | Scoop.it
Last month I received an intriguing email inviting me to an event at Kimberly Wiefling's house.  I'd met Kimberly before through Jonathan Trent, as part of the work I've been doing to help out the Omega Global Initiative.  I knew she was an international consultant, but it was great to also learn that she was…
Lorien Pratt's insight:

The SimCenter is the future of in-person, live decision intelligence collaboration.

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Kevin Kelly - Tricks For Predicting The Future

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How Kevin Kelly and his Wired team identified upcoming trends (in order of increasing difficulty):

1.  Look for people doing things for free
2.  Look for things that waste time: either voluntarily or involuntarily
3.  Look for new words
4.  Use extrapolation, especially things that haven't been predicted before.
5.  Look for extrapolations that reinforce each other
6.  Find newly combined terms (e.g. "computational" and "photography") and ask if there's a white space at the intersection with different terms (e.g. "computational" and "cuisine")
7.  "Unthinkables": challenge basic assumptions that "everyone knows". (e.g. Wikipedia: people actually want to do this for free, and work against the vandals. )  Going forward: what if the US border keeps changing?
8.  Scenarios: Make a set of predictions, not a single prediction.  You're trying to find the outer limits of what's plausible about the future.
9.  Use a reverse time machine: how do you get to the certain future?  (e.g. if we know that in 10 years for sure there is widespread human cloning, then how do we get from here to there?)
10.  Listen to technology read the journals, understand the invention pyramid. Don't just rely on "pop magazines"
11.  Don't try to predict a specific way that a technology will play out, rather understand the emerging technology as being worked on by many (e.g. thousands of people were working on the electric lightbulb, but only one could produce one that was a marketing success).
12.  Seek out repeating patterns (e.g. S-curves, hype cycles, commodification, pushback, pivoting) and understand them when making predictions (e.g. lasers are used more for CDs than for weapons).
13.  Understand second-order effects.  (e.g. cars bred suburbia and traffic jams, which created a pervasive impact well beyond the ability to get from here to there, quickly; or the fact that we track ebook reading behavior will change how we write books)
14.  Understand that things aren't disruptive until they're ubiquitous : more is different.
15.  Understand the second-order inversion effect (candles used to be the main source of light, and today they're for wealthy people, at restaurants; and fonts which appear to be hand-written today, to distinguish from computer fonts)
16.  Find the "mega" trends that connect specific trends.  For instance, if you'd known in 1970 that computers would double in speed every year for 40 years, that would have been very helpful. This is the hardest to do, but the most powerful.

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Microsoft’s Project Oxford helps developers build more intelligent apps

Microsoft’s Project Oxford helps developers build more intelligent apps | Decision Intelligence | Scoop.it
You’re a developer with a great idea for an exercise app. In your ideal world, it would do all sorts of cool tricks, like identify individual users’ faces in photos … Read more »
Lorien Pratt's insight:

Microsoft offers  AI APIs to supercharge intelligent apps.  This is an increasingly important way for apps to include AI: It's used by IBM Watson, http://api.ai,  and a number of other companies (http://bit.ly/1MKfvFa).

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Innovation: it's not what you think.

Technology companies invest billions in developing new gadgets; business leaders see innovation as the key to a competitive edge; policymakers craft regulations to foster a climate of innovation. And yet businesses report a success rate of only four percent for innovation initiatives. Can we significantly increase our odds of success? 

Lorien Pratt's insight:

I'm about halfway through this book, and it's chock-full of understanding for how we need to innovate DI.  Probably the most important bit so far: innovations are emergent from societies of practices, and innovators cultivate, not create from scratch.

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How to hire a machine learning consultant

How to hire a machine learning consultant | Decision Intelligence | Scoop.it
More and more organizations are realizing the tremendous benefit of machine learning to their bottom line, yet many are not ready to hire a full-time machine learning expert. So a machine learning contractor/consultant/freelancer makes sense. For instance, your business might depend upon "just in time" delivery of some goods, and you need to predict when…
Lorien Pratt's insight:

Nuts and bolts for how to get started.

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Use Big Data Strategy To Get Connected To Your Customers

Use Big Data Strategy To Get Connected To Your Customers | Decision Intelligence | Scoop.it

"With the increasing number of social media platforms, there is great value in investing in Enhanced Social Listening (ESL). ESL can provide real time data on what people do; and say about your brand. If conventional paradigms are to be believed, this will have a very real impact on your brand image. If, say, Net Promotor Score (NPS) is the measure of your choice, then ESL can be leveraged to predict NPS ahead of time - through a healthy mix of text mining and advanced analytics. "

Lorien Pratt's insight:

A good overview of a number of ways that big data and analytics combine to improve a company's ability to understand its customers.

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Machine learning is poised for mass adoption

Machine learning is poised for mass adoption | Decision Intelligence | Scoop.it

Looking backwards, we’ve already been through the AI/ML hype cycle (remember The Fifth Generation of the 1980s?).  This second time around, it’s serious.  From this point of view, I disagree with where Gartner places Machine Learning on its 2015 emerging technology curve, as shown in the graphic above.  We’re not just past the “Peak of Inflated Expectation”: to the contrary, we’re well on the way towards the “Plateau of Productivity”

Lorien Pratt's insight:

An alternative to Gartner's assessment of where machine learning sits on the adoption curve.

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Why we must model.

Slides for my presentation on civic modeling for the Oceanus Conference October 2015 in the Sacramento Delta aboard the Aurora.
Lorien Pratt's insight:

One of the best decks I've seen explaining the imperative for modeling for the planet and all wicked problems.  From @anselm hook at the @oceanus conference last week. 

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What drives the hype cycle? (and how to survive it)

What drives the hype cycle?  (and how to survive it) | Decision Intelligence | Scoop.it

The Internet of Things…Machine Learning…Self-Driving Cars…Artificial Intelligence…Big Data…Smart Cities…Decision Intelligence…my friends talk to me about their excitement about a whole lot of trends.  But which ones are real, and which will fizzle?

Lorien Pratt's insight:

Journalists and analysts hear a lot more from the sell-side than from buy-side decision makers.  This leads to a biased view of the world, which drives hype that goes beyond reality.

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Guest post: Can technology help fix us? | Grok

Guest post: Can technology help fix us? | Grok | Decision Intelligence | Scoop.it

We have Smith using videos and web sites to support his grass roots approach to reforming our systems and we have Pratt’s big systems approach to analyzing the big problems that split the political spectrum. These approaches and, possibly others like them, give me hope that solutions are out there waiting to be found and implemented.

Lorien Pratt's insight:

What is the role of technology in fixing wicked problems, in US national politics and beyond?

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The antifragility of the open source bazaar

The antifragility of the open source bazaar | Decision Intelligence | Scoop.it

"...we were careful and diligent, writing thorough specifications documents, then carefully crafted designs, and only then could code begin.


All good software engineers know this is the way to go.   Or at least that’s what we always thought.


But it turns out we were wrong.  And not in a small way."

Lorien Pratt's insight:

A shift in reality as we know it.

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Society for Judgment and Decision Making annual meeting to feature Danny Kahneman, Women in SJDM, more

Society for Judgment and Decision Making annual meeting to feature Danny Kahneman, Women in SJDM, more | Decision Intelligence | Scoop.it
The 2015 SJDM (Society for Judgment and Decision Making) preliminary conference program is now online
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Looks like a good show.  Anyone going?

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In-depth interview with Decision Intelligence pioneer Lorien Pratt on Johnmac Radio

In-depth interview with Decision Intelligence pioneer Lorien Pratt on Johnmac Radio | Decision Intelligence | Scoop.it

"My guest this week on the johnmac Radio Show is Lorien Pratt, Phd, founder and Chief Scientist at Quantellia, which offers data, machine learning, and decision intelligence software and services worldwide."

Lorien Pratt's insight:

This pretty much traverses my whole resume, including learning to code in high school, Dartmouth, my time at IBM, and more.

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How World of Warcraft Could Save Your Business and The Economy

Learning guru John Seely Brown is not being even slightly ironic when he says that he'd hire an expert player of World of Warcraft hire an expert player of World of Warcraft (the massive multiplayer online fantasy videogame) over an MBA from Harvard.

Lorien Pratt's insight:

Twenty-first management is about shifting from text-based linear management to a workspace that supports interactive global collaborative measurement-based guilds.  When we pursue our passion, our curiosity, and enjoy exponential learning, it is not a world of books, meetings, and reports, but rather a rich (offline) world of balls and fast games and a rich (online) world of video games.  This is the world that we are seeing, for now, in WoW, and the world that Decision Intelligence tools and practices are meant to support, through integrating our smartest technologies, including machine learning, data, AI, and more.

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Why 2015 Was a Breakthrough Year in Artificial Intelligence

Why 2015 Was a Breakthrough Year in Artificial Intelligence | Decision Intelligence | Scoop.it
Computers are “starting to open their eyes,” said a senior fellow at Google.
Lorien Pratt's insight:

It was quite a year! Important AI news at @google, @facebook, @microsoft.

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MS researchers win ImageNet challenge, uses 152-layer deep residual learning, and transfer learning, beats humans

MS researchers win ImageNet challenge, uses 152-layer deep residual learning, and transfer learning, beats humans | Decision Intelligence | Scoop.it

"Microsoft researchers on Thursday announced a major advance in technology designed to identify the objects in a photograph or video, showcasing a system whose accuracy meets and sometimes exceeds human-level … "

Lorien Pratt's insight:

Years ago, my PhD committee set a high bar for my graduation: "You must invent something new". (Thank you: Jack, Haym, Mick, Cas, and Sebastian). The result was neural network inductive transfer. 


So today I'm delighted to see that transfer was part of what won Microsoft this important challenge, for the second year in a row, beating human performance. Jian Sun, shown here, is the team lead. "After researchers used the system for the classification tasks in the ImageNet challenge, they found that it was significantly better at the three other metrics: detection, localization and segmentation."


These days it seems pretty obvious: of course we're going to get better performance with multiple networks from multiple problem domains, because that's how we ferret out the underlying system dynamics from the noise.


Yee har!  Go MS!  Go Jian!

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2015 in ML: Miracles and wonders: deep dreams, label machines, and my new super-super-super computer

2015 in ML: Miracles and wonders: deep dreams, label machines, and my new super-super-super computer | Decision Intelligence | Scoop.it

2015 was a turning point year for machine learning.  Check it out: https://www.youtube.com/watch?v=8BFzu9m52sc Based on Andrej Karpathy's "NeuralTalk2" code, as modified by Kyle McDonald: a Brooklyn-based artist who "works in code".  Neuraltalk uses GPUs - another breakthrough for 2015, where my (and probably your!) laptop took a big leap in performance.

Lorien Pratt's insight:

This is where it gets interesting...

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Machine learning tools of the trade

Machine learning tools of the trade | Decision Intelligence | Scoop.it

Like any maturing discipline, machine learning is splitting into specialties.   And just as a surgeon uses a scalpel, and a general practitioner prefers a stethoscope, different tools are appropriate for different use cases within these subfields. In the last few months, I've run projects that have used tools in at least five categories.    Here's a survey"

Lorien Pratt's insight:

It's exciting to see an explosion of new machine learning tools.  But the choices can be overwhelming.  This article helps to sort things out.

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What is machine learning, and why you should care (in 500 words)

What is machine learning, and why you should care (in 500 words) | Decision Intelligence | Scoop.it

It's critical that you understand machine learning, even if just a little bit. Why? Machine learning is at the heart of the most common artificial intelligence systems today. It's an important new technology that's moved beyond hype to the brink of an exponential explosion..."

Lorien Pratt's insight:

For my friends who would like to know what this Machine Learning thing is all about.  It's a very sophisticated technology, but surprisingly it's not at all hard to understand the essence of what's happening.  Since ML is how we bridge from data to value in many situations (it gives us some cause-and-effect links in most decision models), ML is also essential.

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Artificial intelligence will replace traditional software approaches, Alphabet's Eric Schmidt says

For some enterprise users, it's difficult to separate real artificial intelligence from the sci-fi depictions we've seen. But according to Eric Schmidt, executive chairman at Alphabet, the next big evolution for software is machine learning, and the business world will follow suit – think less R2-D2 and more ROI.
Lorien Pratt's insight:

We'll look back at 2015 as the transition year: between when AI / ML was a cool, edgy thing that only the likes of Facebook and Google used, to where it started to be ubiquitous.

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10% of the data holds 90% of the value: four reasons why

10% of the data holds 90% of the value: four reasons why | Decision Intelligence | Scoop.it
Lorien Pratt's insight:

There's a tremendous amount of resource waste in "gratuitous data management": of fields and tables that matter less than others.  All data is not created equal when it comes to solving business problems.

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Machine Learning basics for a newbie

Machine Learning basics for a newbie | Decision Intelligence | Scoop.it
Machine learning involved supervised, unsupervised & reinforcement learning. This article tells basics of ML & its difference from data mining
Lorien Pratt's insight:

A great little article for everyone wondering "what is this machine learning thing all about, anyway?"

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Data from the future in the presidential race

Data from the future in the presidential race | Decision Intelligence | Scoop.it

Looking back on the presidential election of 2012, one view of the Obama win is to attribute it to his team’s understanding of a phase shift in electoral dynamics: Democrats looked at historical turnout numbers and perceived a systemic change; in contrast many believed that Republican certainty in a Romney win was based on a reasonably expected regression to the mean.  This is the essential idea behind “data from the future“.   We ignore these principles in this system, as in many others, at our peril.


In light of this history, it’s worth asking if the fundamental dynamics of how elections are won is shifting this year again.

Lorien Pratt's insight:

The role of forward systems models  - which can go beyond data - in presidential elections.

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Guest Post: A knowledge management system capable of blinking red

Guest Post: A knowledge management system capable of blinking red | Decision Intelligence | Scoop.it
Inattention to critical knowledge is an old problem. Lessons are forgotten, near misses are ignored, caution is dismissed, disasters result. Titanic. Bhopal. AIG. Katrina. Fukushima. And on and on. Knowledge Management (KM) is supposed to make the right information available to the right people at the right time in the right form—and to the best…
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The connection between risk,  knowledge management, and decision intelligence.  Thanks @lindalarsonkemp!

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Decision modeling to understand energy transformation decisions

Decision modeling to understand energy transformation decisions | Decision Intelligence | Scoop.it
Many companies are facing the prospect of steep increases in the cost of energy in the coming years.  In response, many are looking at alternative energy sources. However, navigating the transition to this new world contains hidden dangers, so an evidence-based modeling approach can make a big difference.  This article looks at this decision-making process…
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A demonstration of Quantellia's World Modeler tool for a complex corporate decision.

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