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Complexity Economics: A Different Framework for Economic Thought - W. Brian Arthur

Abstract
This paper provides a logical framework for complexity economics. Complexity economics builds from the proposition that the economy is not necessarily in equilibrium: economic agents (firms, consumers, investors) constantly change their actions and strategies in response to the outcome they mutually create. This further changes the outcome, which requires them to adjust afresh. Agents thus live in a world where their beliefs and strategies are constantly being “tested” for survival within an outcome or “ecology” these beliefs and strategies together create. Economics has largely avoided this nonequilibrium view in the past, but if we allow it, we see patterns or phenomena not visible to
equilibrium analysis. These emerge probabilistically, last for some time and dissipate, and they correspond to complex structures in other fields. We also see the economy not as something given and existing but forming from a constantly developing set of technological innovations, institutions, and arrangements that draw forth further innovations, institutions and arrangements.
Complexity economics sees the economy as in motion, perpetually “computing” itself—perpetually constructing itself anew. Where equilibrium economics emphasizes order, determinacy, deduction, and stasis, complexity economics emphasizes contingency, indeterminacy, sense-making, and openness to change. In this framework time, in the sense of real historical time, becomes important, and a solution is no longer necessarily a set of mathematical conditions but a pattern, a set
of emergent phenomena, a set of changes that may induce further changes, a set of existing entities creating novel entities. Equilibrium economics is a special case of nonequilibrium and hence complexity economics, therefore complexity economics is economics done in a more general way. It shows us an economy perpetually inventing itself, creating novel structures and possibilities for exploitation, and perpetually open to response.


Via Alessandro Cerboni
Lorien Pratt's insight:

From the body of the paper:

Complexity economics is not a special case of neoclassical economics. On the contrary, equilibrium economics is a special case of nonequilibrium and hence complexity economics. Complexity economics, we can say, is economics done in a more general way. Equilibrium of course will remain a useful first-order approximation, useful for situations in economics that are well-defined, rationalizable, and reasonably static, but it can no longer claim to be the

center of economics.

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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.
Curated by Lorien Pratt
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From Systems Thinking to Systems Being

From Systems Thinking to Systems Being | Decision Intelligence | Scoop.it

Systems being and systems living brings it all together: linking head, heart and hands. The expression of systems being is an integration of our full human capacities. It involves rationality with reverence to the mystery of life, listening beyond words, sensing with our whole being, and expressing our authentic self in every moment of our life. "

Lorien Pratt's insight:

Beyond systems thinking.  I think those of us doing this have been feeling something along this lines.  Nice to hear it articulated.

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Bugs, Scopes, Decisions, and Regulations

Bugs, Scopes, Decisions, and Regulations | Decision Intelligence | Scoop.it

Recent headlines about “superbug” transmission by endoscopes highlight the importance of high-quality decision making when information about adverse consequences surfaces after medical devices are marketed. Existing practice (ISO 14971) emphasizes the use of the Risk Matrix, which lacks foundation in decision science and may lead to arbitrary decision making.

Lorien Pratt's insight:

The SDP webinars are excellent.  Highly recommended.

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Boatload of new US government data now available

Boatload of new US government data now available | Decision Intelligence | Scoop.it
  • Promoting employment by unlocking workforce data, including training, skill, job, and wage listings.
  • Enhancing transparency and participation by expanding available Federal services to the Open311 platform currently available to cities, giving the public a seamless way to report problems and request assistance.
  • Releasing public information from the electronically filed tax forms of nonprofit and charitable organizations (990 forms) as open, machine-readable data.
  • Expanding access to justice through the White House Legal Aid Interagency Roundtable.
  • Promoting open and accountable implementation of the Sustainable Development Goals.


Lorien Pratt's insight:

Important new data to support your favorite DI projects:


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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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Sandy Gilchrist's curator insight, Today, 9:45 AM

1% of the world's data is compatible with another 1% of the rest of the 99% available data.  This is due to the abundance of data standards, and importantly the accessibility of that data.  For example, there are many proprietary systems, build as proprietary systems so that they would have competitive value.  We see this in the way that institutions have developed or bought software over the past 50 years from banks to governments to telcos, to retailers to mines to manufacturers, er, etc.  Then the systems were upgraded, often without linkage to old legacy data.  Then someone made an attempt to make one system 'interoperate' with another system, creating a whole new data set, with a new data schema, data library, data definitions, etc - all of the, of course, complete incompatible with any other system of it's kind.  Standards like ODBC appeared, which helped 1%, so thanks for that.  The point is that data these days includes multimedia data like video and voice, not to mention all sorts of sensor data... less than 1%  of it compatible with anything else.  So, the big data industry is growing and that means loads of money being thrown at it.  Reality check - be careful what you expect.  On the other hand, we could give everybody their own data, on an open standard, for example, on a platform like LifeBank and make it really easy for LifeBank to interoperate with any other system... I say "we could", but I should've said, "we already are."  Enough said

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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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Talking Sustainability with Gil Friend

"On this week's Behind the Headlines editor Jocelyn Dong leads a discussion with guest Gil Friend, chief sustainability officer of Palo Alto and reporters Gennady Sheyner and Sue Dremann about Palo Alto's goals to reduce carbon emissions and promote alternative modes of transportation."

Lorien Pratt's insight:

Municipal sustainability planning is one of the most important applications for decision intelligence. 

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Bucky, Bucky, Everywhere

Bucky, Bucky, Everywhere | Decision Intelligence | Scoop.it
This popped up today: (thanks, Ray Podder!) Of course!  It's a Bucky Ball! ...which is probably the root meme here, driving such new developments as Kimberly Wiefling and Peter Meisen's initiative to bring a Fuller-inspired SIMCenter to Silicon Valley (more on this soon). And here's the graphic I've been using (from the Millennium Project) in…
Lorien Pratt's insight:

Please send along your own buckyball sightings.  I'll add them to the article

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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

Lorien Pratt's insight:

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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