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How Can the Study of Complexity Transform Our Understanding of the World?

How Can the Study of Complexity Transform Our Understanding of the World? | Decision Intelligence | Scoop.it

The “study of complexity” refers to the attempt to find common principles underlying the behavior of complex systems—systems in which large collections of components interact in nonlinear ways. Here, the term nonlinear implies that the system can’t be understood simply by understanding its individual components; nonlinear interactions cause the whole to be “more than the sum of its parts.”

 

How Can the Study of Complexity Transform Our Understanding of the World?

Melanie Mitchell

https://www.bigquestionsonline.com/content/how-can-study-complexity-transform-our-understanding-world


Via Complexity Digest, Complejidady Economía, NESS
Lorien Pratt's insight:

One of my favorite complexity authors.  An excerpt: "In the past it was widely assumed that such phenomena are hard to predict because the underlying processes are highly complex, and that random factors must play a key role.  However, Complex Systems science—especially the study of dynamics and chaos—have shown that complex behavior and unpredictability can arise in a system even if the underlying rules are extremely simple and completely deterministic.  Often, the key to complexity is the iteration over time of simple, though nonlinear, interaction rules among the system’s components."


This insight is at the core of Decision Intelligence, which adds an understanding of these emergent behaviors to the usual big data/predictive analytics/optimization stack.

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António F Fonseca's curator insight, January 22, 2014 4:45 AM

Wonderful and clarifying text.

Decision Intelligence
Decision intelligence is an interdisciplinary field with a mission to solve the world's most complex problems in an increasingly nonlinear and rapidly changing world. It is based on the premise that "the decision" is the atomic unit of complex problem-solving, along with the observation that a decision requires predicting the future outcome of an action in an uncertain world. 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/
Curated by Lorien Pratt
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Always wanted to learn Decision Intelligence? Start right now.

These six videos are a good way to get started with decision intelligence:



Lorien Pratt's insight:

Everyone who cares about how to understand the complex problems in our world, and who wants to make a difference, should watch these videos. 

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Data from the future

Sally's got a tough decision to make. She's overwhelmed with charts and graphs and spreadsheets. But they don't answer the question: "If I make this decision today, given this data, what will happen?". See how she solves the problem.

Lorien Pratt's insight:

A good quick summary about what's different about decision intelligence: systems models and cause-and-effect give us "Data from the future."

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Non-Equilibrium social science: Feedbacks, Fragility and Failure

Non-Equilibrium social science: Feedbacks, Fragility and Failure | Decision Intelligence | Scoop.it

NESS organised a policy-oriented conference in London on October 2014, where there were discussed practical implications of policy making.

Videos from the conference are now available at NESS:

* Charles Bean, Former Deputy Governor, Bank of England, discussed Macro-prudential Regulation http://www.nessnet.eu/2015/01/19/charlie-bean-at-the-ness-policy-conference-macro-prudential-regulation/
* David Tuckett, Director of the Centre for the Study of Decision-Making Uncertainty Psychoanalysis, University College London, discussed Psychological Foundations of Macro-prudential policy http://www.nessnet.eu/2015/01/19/david-tuckett-at-the-ness-policy-conference-psychological-foundations-of-macro-prudential-policy/
* Mike Batty, Centre for Advanced Spatial Analysis (CASA) at University College London, discussed infrastructure, decision-making and cities http://www.nessnet.eu/2015/01/19/mike-batty-at-the-ness-policy-conference-cities-are-far-from-equilibrium/
* Paul Ormerod, Volterra Partners LLP, London, discussed how are decisions made http://www.nessnet.eu/2015/01/19/paul-ormerod-at-the-ness-policy-conference-how-are-decisions-made/
* Bridget Rosewell, Volterra Partners LLP, London, discussed transport infrastructures and economics http://www.nessnet.eu/2015/01/18/bridget-rosewell-at-the-ness-policy-conference-transport-infrastructures-and-economics/
* Greg Fisher, Chief Executive of Synthesis, discussed uncertainty and policy making http://www.nessnet.eu/2015/01/18/greg-fisher-at-the-ness-policy-conference-uncertainty-and-policy-making/


Via NESS
Lorien Pratt's insight:

Non-equilibrium social sciences model the dynamic nature of things: systems in motion instead of the simplifying assumption of traditional modeling where we only consider equilibrium states.  Non-equilibrium modeling applies to decision making, transportation, economics, policy-making, and more. 

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The thinker's toolbox, updated for the 21st century: van Gelder and Monk

The thinker's toolbox, updated for the 21st century: van Gelder and Monk | Decision Intelligence | Scoop.it
We help organisations think more effectively about almost anything by bringing to bear our unique range of methods, tools and insights, and drawing on our extensive experience.
Lorien Pratt's insight:

With deep expertise in argument mapping as well as offerings in critical thinking, process analysis, wicked problem solving, and more, vG&M is an important company in the decision intelligence space.  Worth a look.

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Econometric model for OMEGA: how can #algae systems be profitable? #eaba #decint

Econometric model for OMEGA: how can #algae systems be profitable? #eaba #decint | Decision Intelligence | Scoop.it

Slide from Jonathan Trent's talk at #eaba

Lorien Pratt's insight:

Quantellia built this decision intelligence model to support econometric analysis of an integrated water / food / solar / biofuels system.  Thanks to Informed Decisions for sponsoring this important work!

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Cause And Effect: The Revolutionary New Statistical Test That Can Tease Them Apart

Cause And Effect: The Revolutionary New Statistical Test That Can Tease Them Apart - The Physics arXiv Blog - Medium
Statisticians have always thought it impossible to tell cause and effect apart using observational data. Not any more.
Lorien Pratt's insight:

In the face of overwhelming spurious correlations from really big data sources, this could be very important.

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Stop googling Prince if you want to lose weight #decisionengineering #decint #prince #bigdata @ichartny

Stop googling Prince if you want to lose weight #decisionengineering #decint #prince #bigdata @ichartny | Decision Intelligence | Scoop.it
VIDEO: Mathematically sound but instinctively insane.
Lorien Pratt's insight:

The core building block of Decision Intelligence is the cause-and-effect link, preferably supported by good data.  But without a good understanding of the underlying system, things can go a little nutty. Thanks to Sara Silverstein!

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Landmark study: the new art and science of decision making

Landmark study: the new art and science of decision making | Decision Intelligence | Scoop.it
Businesses make thousands of routine decisions a day. But what happens when the time comes for a business to shift course and how can data and analytics help? Explore our survey findings to learn more.
Lorien Pratt's insight:

PWC is rapidly becoming one of the world's decision intelligence thought leaders, and arguably the most influential of us all.  If you read one report this year, Dan DeFilippo and Paul Blase's "Gut and gigabytes" should be it. 


This report is based on interviews with 1,135 senior executives, over half of which (54%) are C-level executives or board members. Some excerpts:


  • "Nearly 1 in 3 executives value [big] decisions at least at $1 billion:
  • "...this is the time for the C-suite to upgrade the art as well as the science behind their decision making"
  • "using data to test different scenarios before making a decision is becoming increasingly common"
  • Most "big decisions" are made between every month and every 3 months.
  • "Defensive" decision making : overly cautious decisions that no one will get into trouble for making, are common, accounting for between one-third and one-half of all decisions


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How can the field of machine learning expand to solve the world's great problems?

So often, we use machine learning for a limited set of use cases, in customer experience, marketing, advertising, and finance.

In this talk, we step back to understand how big data and machine learning can serve a new class of problems: those for which users need to know "what will be the impact of today's decision, tomorrow?"

Lorien Pratt's insight:

Friends, if you watch one decision intelligence video this year, please make it this one.   Slides for this talk are lower down on the scoop.it page, or at  this link. Note that the talk didn't get through every slide, so that's a good place to go next if you want a "deep dive".  Then call me.

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If big data "flows" then it needs good pipeline design

If big data "flows" then it needs good pipeline design | Decision Intelligence | Scoop.it
Creating an integrated pipeline for big data workflows is complex. Read about several factors to consider.
Lorien Pratt's insight:

 The explosion of big data analytics is creating a need for something to stitch them together: ""The challenges for many organizations working with their own big data pipelines is finding the 'glue' to stick all of these different skills and toolsets together into a cohesive fabric," said Ion Stoica, CEO of Databricks and a University of California Berkeley professor. "When you use Hadoop, Storm, GraphLog, and other big data solutions, each comes with its own set of tools.

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What machine learning experts need to know about Decision Intelligence: Solving the great problems of the world

So often, we use machine learning for a limited set of use cases, in customer experience, marketing, advertising, and finance. In this talk, we step back to …
Lorien Pratt's insight:

Here are my slides from Friday in San Francisco.  Targeted especially at machine learning people, to understand where ML fits into DI.

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AbsolutData: A Decision Engineering (aka Decision Intelligence) Company!

AbsolutData: A Decision Engineering (aka Decision Intelligence) Company! | Decision Intelligence | Scoop.it

Gaurav Rastogi: "We were all doing it wrong— the bridge from 'data' to 'decisions' should be built decision first. "

Lorien Pratt's insight:

I'm thrilled that AbsolutData is positioning around Decision Engineering!  "Decision Engineering" is what we called Decision Intelligence in its previous incarnation. 

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11 DVDs of Jay Forrester's System Dynamics

11 DVDs of Jay Forrester's System Dynamics | Decision Intelligence | Scoop.it

Seminar Information
Professor Jay Forrester, in the fall of 1999, conducted a seminar series for Ph.D. students in system dynamics. Each of the eleven three-hour sessions is devoted to a different stand-alone topic, which should be of benefit to any student or practitioner in the field. The series is not a progressive tutorial for learning system dynamics, but covers related concepts and philosophy.

Lorien Pratt's insight:

This looks like a great resource!

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Absolutdata Introduces First 'Decision Engineering' Product

Absolutdata Introduces First 'Decision Engineering' Product | Decision Intelligence | Scoop.it

The first 'Decision Engineering' product from Absolutdata, NAVIK Converter identifies users who are primed for conversion and recommends how and when to target them with marketing messages and/or product changes.

Lorien Pratt's insight:

Congratulations to Absolutdata for its first Decision Engineering product!    Truly a milestone.

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Decisions based on copying others + huge network effects = nonlinear emergent decision dynamics. Even Keynes knew it.

Decisions based on copying others + huge network effects = nonlinear emergent decision dynamics. Even Keynes knew it. | Decision Intelligence | Scoop.it
Paul Ormerod, from Volterra Partners (London), discussed how are decisions made, at the NESS Policy-Oriented Conference in London, October 2014.Source: www.youtube.comSee on Scoop.it - Non-Equilibr...
Lorien Pratt's insight:

What a terrific talk by Paul Ormerod at the NESS conference last October.  Check out this image from the talk: even Keynes knew that, when the future was not like the past, we tended to fall back on conventional wisdom.  The only solution imho, is an explicit systems model of the future, so that we get away from superficial copycat modeling, which gets us into trouble again and again.  Please check out a free evaluation copy of World Modeler (http://www.quantellia.com); in honor of Omerod's work, I've told our support staff to triple the usual evaluation period for any economists interested in giving it a spin, who signs up by 1/26.

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What’s next after Big Data? It’s Decision Engineering

What’s next after Big Data? It’s Decision Engineering | Decision Intelligence | Scoop.it
To take power out of Big Data and use it to make better engineered business decisions will be the next big thing in 2015…And there will be plenty of high paying jobs too!! Big Data is currently at the peak of the hype cycle. A lot is being talked, written, researched and reported around it. …
Lorien Pratt's insight:

An excellent short article from the IndiaTimes people about Decision Engineering (aka Decision Intelligence). 

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Following the money trail using unstructured data for document analysis

Following the money trail using unstructured data for document analysis | Decision Intelligence | Scoop.it

Big data analysis is great if your information is in formats that are easy for computers to read, such as spreadsheets with numbers, or responses on a scale from one to five. But a lot of information isn't organized like that. Instead, it's in presentations, memos, reports, comments or just plain e-mail. Analysis of that kind of information -- often called "unstructured" or "dark" data -- is really tough to do by computer

Lorien Pratt's insight:

A good quick read on new technology for unstructured data analysis, including case study about following ARRA money

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Causality Workbench

Causality Workbench | Decision Intelligence | Scoop.it
Datasets for challenges on causality
Lorien Pratt's insight:

Challenges every year to discover causation in data.

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Data science without statistics is possible, even desirable

Data science without statistics is possible, even desirable | Decision Intelligence | Scoop.it
The purpose of this article is to clarify a few misconceptions about data and statistical science.
I will start with a controversial statement: data science ba…
Lorien Pratt's insight:

Very important to read.  There's a lot of confusion around statistical learning, traditional statistics, data science.  This article is part of the untangling.

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▶ Is it better to rent or buy a home? Crunching the numbers using DI.

The decision as to whether to rent or buy a house used to be straightforward: homes appreciated in value, and renting was a "black hole". But in many world e...
Lorien Pratt's insight:

Here's a Decision Intelligence analysis of this important question.  I hope you find it valuable.

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Participate in a brief survey studying what matters the most in decision making #decint @tedmarra

Participate in a brief survey studying what matters the most in decision making #decint @tedmarra | Decision Intelligence | Scoop.it

"The purpose with this study is to get an understanding of what the respondents perceive as challenges around decision making and /or consequences of decisions (both good or bad). "

Lorien Pratt's insight:

Looks like an interesting global survey.  Looking forward to hearing the results!

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Rebels at Work: How to rock the boat without falling out

Rebels at Work: How to rock the boat without falling out | Decision Intelligence | Scoop.it
Providence, RI (PRWEB) November 19, 2014 -- Former CIA executive Carmen Medina and marketing strategist Lois Kelly – both lifelong rebels at work – have written the book to help people learn how to cut through corporate politics and bureaucracy to create positive change at work. While most business books are written for the 10% of the workforce who are managers, Rebels at Work, the first business book with a CIA Disclaimer, is written for the 90% that can make the biggest difference.
Lorien Pratt's insight:

Anyone trying to get Decision Intelligence going will have to rock the boat from time to time. 


A few years back, I sat down at a conference lunch table full of strangers, and Carmen Medina was next to me. So some of the early Decision Intelligence ideas came from her - like the importance of a visual map that goes beyond text.  She's awesome, and her book looks like it is, too. 


If you read it, please post something in the scoop.it comments, thanks!

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Collaborative Design Process and Decision Engineering: Steve Barrager

Slides from INFORMS Presentation, Nov. 11, 2014
Lorien Pratt's insight:

The number of decision engineering talks around the world seems to be taking off lately.  Here's a great one from Steve Barrager, a decision engineering long-time expert with comprehensive experience in many industries.

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A quick video about Decision Engineering (aka Decision Intelligence)

A quick video about Decision Engineering (aka Decision Intelligence) | Decision Intelligence | Scoop.it
Lorien Pratt's insight:

A great way to learn the essence of DI from Absolutdata

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How do agents make decisions? Great comprehensive review article of this important topic.

How do agents make decisions? Great comprehensive review article of this important topic. | Decision Intelligence | Scoop.it

When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically- and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.by Tina Balke and Nigel Gilbert


Via NESS
Lorien Pratt's insight:

For those note familiar with agent-based social simulation, the idea is to simulate the individual decisions of many "agents", each representing a person, in some situation.  For example, we might model decisions that people make as they move through a train station or amusement park, in order to design those places to best handle traffic flow. 


This is a terrific, comprehensive review article, covering a number of ways of approaching the task of modeling those individual decisions made by simulated agents.  Each is designed with somewhat different constraints in mind.  For instance, some models like SOAR, are meant to mimic certain cognitive processes.


From a Decision Intelligence point of view, these approaches are complementary.  If we want a sophisticated model of the emergent behavior of a large number of people, as one building block in a decision model for what to do about it, an agent-based simulation can be very helpful. For instance, we might be trying to decide which new medicine to launch, taking into account our competitors, our price to produce the medicine, and its impact on disease.  Part of that decision model might be based on an agent-based simulation of the decisions that people make to take the medicine, and their behavior that leads to contracting the disease. 


To continue the example, we could either use an agent-based simulation to learn from the agent-based simulation, or have it run in the same simulation as a decision model. For an example of the first, an agent-based simulation might show that the disease is expected to grow geographically according to a certain pattern. That spread pattern would then be an external input to a decision model. 


The second way to use this is to use the decision model to interactively move certain assumptions about the decision-making process, and to immediately observe the impact on agents. For instance, we may adjust the number of dollars invested in a public health campaign and observe how that impacts the emergent properties of an agent-based simulation.  There are a lot of possibilities here!



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Decision Intelligence supercharges machine learning, so it applies in 1000s of new use cases

Decision Intelligence supercharges machine learning, so it applies in 1000s of new use cases | Decision Intelligence | Scoop.it

Here the abstract for my talk on Friday in SF: Decision Intelligence is an emerging discipline that unifies machine learning, complex systems, predictive analytics, causal reasoning, optimization, and more into a unified framework that overcomes limitations of the current data stack that are faced by organizations worldwide. Just as the Unified Modeling Language (UML), along with associated tool companies like Rational, brought the discipline of design to software development, decision intelligence is a methodology, supported by software, that overcomes a number of barriers that have limited the practical use cases of the analytic / data stack. In particular, Decision Intelligence brings engineering practices to decision making, treating the “decision” as an engineered artifact. This means that best practices from design, agile development, and more can now be used to evolve decisions over time, creating a continuous “organizational learning” framework in diverse settings such as the US government and transnational corporations.

Lorien Pratt's insight:

I hope to see you there!

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