Integrating explanation and prediction in computational social science

cxdig's avatarComplexity Digest

Jake M. Hofman, Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik, Simine Vazire, Alessandro Vespignani & Tal Yarkoni
Nature (2021)

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete…

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‘Red Hamlet: The Life and Ideas of Alexander Bogdanov’ by James D White,’Empiriomonism: Essays in Philosophy, Books 1-3′ by Alexander Bogdanov reviewed by Nicholas Bujalski – Marx & Philosophy Society

Marx & Philosophy Review of BooksReviews‘Red Hamlet: The Life and Ideas of Alexander Bogdanov’ by James D White,’Empiriomonism: Essays in Philosophy, Books 1-3′ by Alexander Bogdanov reviewed by Nicholas Bujalski

‘Red Hamlet: The Life and Ideas of Alexander Bogdanov’ by James D White,’Empiriomonism: Essays in Philosophy, Books 1-3′ by Alexander Bogdanov reviewed by Nicholas Bujalski – Marx & Philosophy Society

James D White
Red Hamlet: The Life and Ideas of Alexander Bogdanov

Brill, Leiden and Boston, 2018. 494 pp., €170.00 hb
ISBN 9789004268906

Alexander Bogdanov
Empiriomonism: Essays in Philosophy, Books 1-3

David G. Rowley (ed. and trans.), Brill, Leiden and Boston, 2020. 450 pp., €165.00 hb
ISBN 9789004300315

Reviewed by Nicholas Bujalski

An essay about cybernetics and slime molds courtesy TektologicⒶl҉ – Serendipity on Twitter

Macy conferences – Alchetron, The Free Social Encyclopedia

Macy conferences

Macy conferences – Alchetron, The Free Social Encyclopedia

The Power of Systems – How Policy Sciences Opened Up the Cold War World – Egle Rindzeviciute (2016) – full book

The Power of Systems

The Power of Systems

The Power of Systems

How Policy Sciences Opened Up the Cold War World

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Rindzeviciute, Egle
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EnglishShow full item recordThe International Institute of Applied Systems Analysis (IIASA), an international think tank established jointly by the United States and Soviet Union in Austria in 1972, was intended to advance scientific collaboration. Until the late 1980s, the IIASA was one of the very few permanent sites where policy scientists from both sides of the Iron Curtain could work together to articulate and solve world problems, most notably global climate change. One of the best-kept secrets of the Cold War, this think tank was a rare zone of freedom, communication, and negotiation, where leading Soviet scientists could try out their innovative ideas, benefit from access to Western literature, and develop social networks, thus paving the way for some of the key science and policy breakthroughs of the twentieth century.

CA habitat restoration used beavers to restore Placer | The Sacramento Bee

A dry California creek bed looked like a wildfire risk. Then the beavers went to work BY ISABELLA BLOOM JULY 02, 2021 07:03 AM, UPDATED JULY 04, 2021 04:09 PM

CA habitat restoration used beavers to restore Placer | The Sacramento Bee

courtesy of the Systems Changes group

Complex Adaptive Systems Group

Complex Adaptive Systems Group

Complex Adaptive Systems Group

University of Michigan LSA Complex Systems

https://lsa.umich.edu/cscs

CAS Group Wiki

http://wiki.cas-group.net/index.php?title=Main_Page

CAS-Group blog

http://blog.cas-group.net/

Observing Systems | Heinz von Foerster (1984) | download

Observing Systems Heinz von Foerster

Observing Systems | Heinz von Foerster | download

Cybernetics’s Reflexive Turns – Klaus Krippendorff (2008)

Cybernetics’s Reflexive Turns

“Cybernetics’s Reflexive Turns” by Klaus Krippendorff

Louis H. Kauffman

Louis H. Kauffman

Louis H. Kauffman

e.g. EigenForm (2003) http://homepages.math.uic.edu/~kauffman/Eigen.pdf

Cracking Complexity | Leading Blog: A Leadership Blog – book by Benjamin and Komlos (2019)

I missed this book when it first came out, but I saw I did blog one (https://stream.syscoi.com/2019/09/06/why-highly-diverse-work-teams-are-better-at-untangling-complexity/) of a series of articles that came out around this time which talked mostly about Requisite Variety at a team/problem-solving level. The book has been recommended to me by Kevin Gillick who saw refence to Beer and to Ashby’s Law. Come to see, it’s written by a couple of people out of the Malik school (and gives credit up front), and from what can be seen from Google Books it offers another structured large-group process (comparable to FutureSearch, MG Taylor method, Team Syntegrity etc). Looks more interesting than I would originally have thought.

source:

Cracking Complexity | Leading Blog: A Leadership Blog

06.13.19

Cracking Complexity

Cracking Complexity

THERE ARE complicated problems, and there are complex problems. Complicated problems are technical in nature. They are linear, orderly, and predictable. Complex problems are adaptive challenges. They are messy, unstable, and unpredictable. “Having a wedding is complicated; having a happy marriage is complex.”

If you want to crack a complex problem, you need the code. David Benjamin and David Komlos provide the code in Cracking Complexity.

We can master highly sophisticated technical and technological challenges because we’re quite skilled at making linear connections from one technical feat to the next. But complex, multidimensional challenges are categorically different. They are not linear. They are not solved or even solvable through technical prowess. They don’t stand still. They don’t patiently await solutions. Complexity is a whole different ball game.

The question is, “How can we best deal with something we’ve never dealt with before, without foreknowledge of what’s going to work?” Conventional approaches to problem-solving typically rely on small groups of smart people cloistered away tasked with deciding the best way forward. We need a new approach to complex problems that allow us to cocreate in large groups.

The Complexity Formula

A foundational idea behind the formula is Ashby’s Law or the Law of Requisite Variety which states: Only variety destroys variety. “Ashby’s Law says you need to bring a matching amount of variety to the solving process.” In other words, a high-variety group that can collectively address the variety inherent in the issue to be solved. The Complexity Formula helps you to unlock the skills, knowledge, experience, and expertise of the people around you.

All the steps in the Formula are complementary and build one upon the next to deliver rapid leaps on complex issues.

The first five steps set things up. Steps six through nine are where a requisite-variety of people can spend a short amount of time—typically two days—to sense, absorb, think, decide, and then in Step ten to act on the complex problem.

Listed below are the ten steps with some key thoughts on each:

1. Acknowledge the Complexity

The first step is to determine exactly what kind of problem you are faced with. A complicated problem or a problem that is truly complex. The first step is “recognizing that there are no known answers, that no outsourced provider is going to figure it out for you—at least fast enough—and that the old way of figuring things out isn’t going to work anymore.”

2. Construct A Really, Really Good Question

Frame the issue with a good question. A complex issue needs a question that addresses the complexity. “A good gut check on the question is how people react to it. Are they uncomfortable with it because it challenges the status quo, sets the bar high, or suggests a lot of work needs to be done? Conversely, are they completely comfortable with it because it’s easy to answer? Don’t necessarily retreat from what you think is a good question because people are reacting negatively, and don’t be satisfied if people aren’t pushing back.” Example: “What must we do in the next 12 months to drive necessary changes in mind-set, action, and behavior to fully realize the benefits of…?”

3. Target A Requisite Variety of Solvers

Involve the right people. Identify the requisite variety of people needed to match and absorb the complexity. “Your goal is to include the necessary perspectives, characteristics, roles, functions, hierarchical levels, and so on. If you shortchange requisite variety, you’re setting yourself up for no or partial solution and weak execution.” The authors provide a system to be sure you’re getting the right people together.

4. Localize the Solvers

Get everyone together face-to-face. It allows for neural synchronization. In Google’s team study, they found what distinguished high-performing teams from low-performing teams is not team cohesion, motivation, or average IQ, but rather frequent turn-taking in conversations and high social sensitivity toward what team members are thinking and feeling.

5. Eliminate the Noise

Noise takes all forms: “too much information all at once; too much wrong or inaccurate information; and too much missing, ambiguous, unreliable, or fragmented information.” They recommend that we “Err on the side of too little research, too little data, information, and knowledge—invest the effort instead in the requisite variety of people who carry the tacit data banks and the powerful processors around between their two ears.”

6. Agree on the Right Agenda

Do not preset the agenda. Once you get everyone together, begin by deciding what to talk about. “Let the group decide what they have to talk about in order to answer the question. Their first task together is agreeing on how to deconstruct the question into the right component parts to discuss.”

7. Put people On A Collision Course

A highly engineered conversation—engineered serendipity. “Serendipity often happens where people, domains, and/or systems collide. And collisions can be engineered. When we talk about domains and systems colliding, we mean people from one domain or system bumping into people from another domain or system.”

8. Advance Iteratively and Emergently

You must trust that the answers will emerge. “Your requisite variety group needs to operate with energy and an expectation that the right answers will arise from the right kinds of interactions together.” Also, “Having set their agenda, your group needs to go through that entire agenda once, then again, then again.” Three times is the number—more yields diminishing returns.

9. Change How People Interact

Nothing will happen if the interactions between your group members are not productive ones. To be effective, they need to be “candid, incisive, unconstrained, unguarded, transparent, fierce, and focused.” That requires, “discipline and structure, right-sized teams (no more than 8), effective conversation roles, and environment where productive friction is expected and not frowned upon, and have a neutral note taker.”

10. Translate Clarity and Insights into Action

“The actions that result from the use of the Complexity Formula fall into three categories: Things to do, things to try, and the newly revealed complexities.” The job in step ten is to categorize the solutions in the three categories and then to attack “each pile in the right way to make progress, to continue learning, and to get after the next big challenge.” Sometimes working on one complexity reveals yet another complexity that needs to be resolved.

source:

Cracking Complexity | Leading Blog: A Leadership Blog

Owtcome – Senseframing Model – Daiana Zavate

linkedin update:

Post | Feed | LinkedIn

How to weave systems out of our stories? And what is our default learning mode?

The honest answer is: I don’t know. But that is a good starting point.

In the pursuit to grow our understanding of the world and its relationship to us, it might be worth looking at how we make our message coherent.
The concept of Frame has become popular and easy to appeal to because it gives us space to connect context, people and minds. It helps us share entire systems, rather than just isolated bits of knowledge.
Sense-Framing is a model that can act as an inclusive and transformative network of processing intelligible content.

The ‘Senses’ are the nodes that connect the network and help information circulate.

The Insights/Breakthroughs are the Knots of the network where change and transformation can happen. 

Learning to learn has been a personal struggle of mine. Instead of complaining about the system, I set on a journey to discover how do I learn and how can I share it with others. I think Sense-Framing is an important milestone in this journey.

Link to ebook (signup required):

Adaptability is set to be the key skill for the future

Adaptability is set to be the key skill for the future Jessica Schueller and Hugo Figueiredo  03 July 2021

Adaptability is set to be the key skill for the future

Concept Map — New England Complex Systems Institute

CONCEPT MAP

Concept Map — New England Complex Systems Institute

Webinar: System Dynamics for Climate Change Mitigation – and other webinars from the Systems Dynamics Society

Webinar:
System Dynamics for Climate Change Mitigation
Wednesday, July 7 @ 11 am New York | 4 pm London | 11 pm Beijing
System Dynamics models have long been used used for research and decision support at the intersection of the economy and the environment.  

Since the seminal work of World Dynamics
and Limits to Growth, many others in the field have contributed to this important area of work.  

Join us on Wednesday as we explore how System Dynamics models continue to support decision-making and public engagement to further efforts toward global sustainability.
Join us next Wednesday, July 7 to: Discuss how System Dynamics models support decision-making and public engagement in climate change and sustainabilityReflect on existing models such as Climate Interactive’s En-ROADS and Millennium Institute’s Integrated SDG Simulation (iSDG) toolLearn how the Climate Change Initiative at UMass Lowell uses System Dynamics to raise awareness on climate change.

Our expert panelists:
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Join people from all over the world to learn, connect, and develop skills at the largest Conference in the field.
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Introduction to Modeling Process
In this FREE seminar, you will be able to build your very first System Dynamics model! This will be a small, quantitative model of Romeo and Juliet’s love for each other.