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Enabling people to govern themselves – The Hindu

Enabling people to govern themselves

Arun Maira

JULY 14, 2020

With the pandemic showing up flaws in governance institutions, this is a better way for humanity to face new challenges

Governance systems at all levels, i.e. global, national, and local, have experienced stress as a fallout of the COVID-19 pandemic. Architectural flaws have been revealed in their design. Breakdowns in many subsystems had to be managed at the same time — in health care, logistics, business, finance, and administration. The complexity of handling so many subsystems at the same time have overwhelmed governance. Solutions for one subsystem backfired on other subsystems. For example, lockdowns to make it easier to manage the health crisis have made it harder to manage economic distress simultaneously. In fact, the diversion of resources to focus on the threat to life posed by COVID-19 has increased vulnerabilities to death from other diseases, and even from malnutrition in many parts of India.

A mismatch is evident

Human civilisation advances with the evolution of better institutions to manage public affairs. Institutions of parliamentary democracy, for example, and the limited liability business corporation, did not exist 400 years ago. Institutions of global governance, such as the United Nations and the World Trade Organization, did not exist even 100 years ago. These institutions were invented to enable human societies to produce better outcomes for their citizens. They have been put through a severe stress test now by the global health and economic crises. The test has revealed a fundamental flaw in their design. There is a mismatch in the design of governance institutions at the global level (and also in India) with the challenges they are required to manage. Designed like machines for efficiency, they are trying to fit themselves into an organic system of the natural environment coupled with human society. It seems that government institutions are square pegs forcing themselves into round holes.

Interconnected issues

The global challenges listed in the 17 Sustainable Development Goals (SDGs) of the United Nations, which humanity must urgently address now, are systemic challenges. All these systemic problems are interconnected with each other. Environmental, economic, and social issues cannot be separated from each other and solved by experts in silos or by agencies focused only on their own problems. A good solution to one can create more problems for others, as government responses to the novel coronavirus pandemic have revealed.

Even if experts in different disciplines could combine their perspectives and their silo-ed solutions at the global level, they will not be able to solve the systemic problems of the SDGs. Because, their solutions must fit the specific conditions of each country, and of each locality within countries too, to fit the shape of the environment and the condition of society there. Solutions for environmental sustainability along with sustainable livelihoods cannot be the same in Kerala and Ladakh, or in Wisconsin and Tokyo. Solutions must be local. Moreover, for the local people to support the implementation of solutions, they must believe the solution is the right one for them, and not a solution thrust upon them by outside experts. Therefore, they must be active contributors of knowledge for, and active participants in, the creation of the solutions. Moreover, the knowledge of different experts — about the environment, the society, and the economy — must come together to fit realities on the ground.

A case for local systems

Governance of the people must be not only for the people. It must be by the people too. Gandhiji and his economic advisers, J.C. Kumarappa and others, developed their solutions of local enterprises through observations and experiments on the ground (and not in theoretical seminars in capital cities). E.F. Schumacher, founding editor of the journal, Resurgence, and author of Small is Beautiful, had pointed out by the 1970s, the flaws in the economics theories that were driving public policy in capitalist as well as communist countries. He had proposed a new economics, founded on local enterprise, very consistent with Gandhiji’s ideas. Elinor Ostrom, the first woman to win the Nobel Prize in Economics, in 2009, had developed the principles for self-governing communities from research on the ground in many countries, including India.

When there are scientific explanations for why local systems solutions are the best, if not the only way to solve complex systemic problems, and when the Indian Constitution requires this too, then why does not the government devolve power to citizens in villages and towns in India for them to govern their own affairs?

An Indian anthropologist gave me an insight. She said she had observed that several Indian Administrative Service (IAS) officers she knew, who seemed to have more compassion for communities than their colleagues had, were involved at some time in their careers with the evolution of community-based public health and the self-help group movements in Andhra Pradesh. She contrasted their views about how change is brought about with the views of IAS officers who have implemented the Swachh Bharat programme recently. The latter, also very fine officers, saw their role as ‘deliverers of good government’. Whereas the former, through their experience, had begun to see that the role of government is perhaps to ‘enable governance’.

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Enabling people to govern themselves – The Hindu

site: https://systemicapproachtoarchitecturalperformance.wordpress.com/

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The Long Time. Beatrice Pembroke & Ella Saltmarshe | by The Long Time Project | Medium

The Long Time

The Long Time Project

Oct 29, 2018 · 9 min read

Beatrice Pembroke & Ella Saltmarshe

A few weeks ago the IPCC released a report about climate change so devastating that some of its authors were in tears at the launch. It highlighted how our actions now will determine the kinds of lives future inhabitants of the planet will have, and ultimately whether they will have lives at all. We hold immense responsibility for the future; yet in these times of apocalyptic news cycles, it can feel that everything is extremely urgent but happening too fast to change. We hold immense power, yet feel impotent. In the face of global anxiety, we put our heads down and our horizons get closer and closer. The problem is that the tunnel vision of short-term thinking is leading to decisions that might mean we are only left with a short term as a species.

We’ve started the Long Time project as we believe that (1) Our capacity to care about the future is crucial to our ability to preserve it (2) Developing longer perspectives on our existence will change the way we behave in the short term and (3)Art and culture will be crucial to making the much needed transformative shift in attitudes and behaviours. Here we explain both why and how, proposing five paths to safeguarding the long-term…

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The Long Time. Beatrice Pembroke & Ella Saltmarshe | by The Long Time Project | Medium

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Spooky Wisdom: What Lessons Should We Be Learning from How Our Ancestors Built Cities?

The Strong Towns Podcast

http://podcast.strongtowns.org/feed.xml

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Spooky Wisdom: What Lessons Should We Be Learning from How Our Ancestors Built Cities?

Other Strong Towns and related source:

The spooky wisdom of cities – extract from Charles’ book: https://www.strongtowns.org/journal/2019/9/19/the-spooky-wisdom-of-cities-satbook

The spooky wisdom of incremental – Rachel Quednau https://www.strongtowns.org/journal/2018/4/13/the-spooky-wisdom-of-incremental

link to the wonderful @wrathofgnon https://twitter.com/wrathofgnon/status/988403388047024129?s=20

the article referenced in the above: https://www.theamericanconservative.com/articles/remembering-the-spooky-wisdom-of-our-agrarian-past/

The book: https://books.google.co.uk/books?id=w0WyDwAAQBAJ&pg=PT12&lpg=PT12&dq=%22spooky+wisdom%22&source=bl&ots=ank8Fmgg5z&sig=ACfU3U0aA-yf3HM–MyJKpVYKUyPjhdPfQ&hl=en&sa=X&ved=2ahUKEwj33cDr2snqAhXTSBUIHYipCr4Q6AEwBnoECAoQAQ#v=onepage&q=%22spooky%20wisdom%22&f=false

More from strong towns: https://www.strongtowns.org/journal/2020/1/21/spooky-wisdom-as-seen-in-your-community

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Taxis – Wikipedia

Taxis

From Wikipedia, the free encyclopediaJump to navigationJump to searchThis article is about the behavioural response. For the vehicle, see taxi. For other uses, see Taxi (disambiguation).

A taxis (plural taxes^[1][2][3] /ˈtæksiːz/, from Ancient Greek τάξις (taxis), meaning ‘arrangement’^[4]) is the movement of an organism in response to a stimulus such as light or the presence of food. Taxes are innate behavioural responses. A taxis differs from a tropism (turning response, often growth towards or away from a stimulus) in that in the case of taxis, the organism has motility and demonstrates guided movement towards or away from the stimulus source.^[5][6] It is sometimes distinguished from a kinesis, a non-directional change in activity in response to a stimulus.

e.g.

• Thigmotaxis is the response of an organism to physical contact or to the proximity of a physical discontinuity in the environment (e.g. rats preferring to swim near the edge of a water maze). Codling moth larvae are believed to used thigmotatic sense to locate fruits to feed on.^[23]

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Taxis – Wikipedia

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Liquid or solid: what are the boundaries of a system? | by noelito | Jul, 2020 | Medium

Liquid or solid: what are the boundaries of a system?

noelito

Jul 2 · 4 min read

I’ve always been struck as to how certain people need to see the world through a single lens, sometimes through opportunism to get funding and sometimes just through sheer evangelical belief in a particular way of thinking. At the moment, it seems that everyone’s talking about systems change. As @aliceevans highlights, we need to avoid the temptation that “systems change” becomes the latest bandwagon that everyone jumps on, after social innovation, agile or design thinking, without forgetting government branded buzzwords, like Big Society, Total Place or Neighbourhood Renewal.

And we know what happens with that, as the experience of New Public Management has shown. More importantly, it’s those people at the very frontline of navigating very complex situations and even more contrived systems who are the often the pioneers of these new ways of doing before they get packaged up and institutionalised.

The leadership of Lankelly Chase, Forum for the Future, Collaborate and others on this shows that systems change isn’t a linear process or a highly professionalised way of doing things that only certain people can do. On the contrary, examples like Systems Changers by @lankellychase show that it’s not just about conceptual frameworks by policy wonks like me, it’s about how people navigate complex situations and often very contrived systems together.

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Liquid or solid: what are the boundaries of a system? | by noelito | Jul, 2020 | Medium

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The Invented History of ‘The Factory Model of Education’

The Invented History of ‘The Factory Model of Education’

Audrey Watters

 on 25 Apr 2015

“What do I mean when I talk about transformational productivity reforms that can also boost student outcomes? Our K–12 system largely still adheres to the century-old, industrial-age factory model of education. A century ago, maybe it made sense to adopt seat-time requirements for graduation and pay teachers based on their educational credentials and seniority. Educators were right to fear the large class sizes that prevailed in many schools. But the factory model of education is the wrong model for the 21st century.” – US Secretary of Education Arne Duncan (2010)

One of the most common ways to criticize our current system of education is to suggest that it’s based on a “factory model.” An alternative condemnation: “industrial era.” The implication is the same: schools are woefully outmoded.

As edX CEO Anant Agarwal puts it, “It is pathetic that the education system has not changed in hundreds of years.” The Clayton Christensen Institute’s Michael Horn and Meg Evan argue something similar: “a factory model for schools no longer works.” “How to Break Free of Our 19th-Century Factory-Model Education System,” advises Joel Rose, the co-founder of the New Classrooms Innovation Partners. Education Next’s Joanne Jacobs points us “Beyond the Factory Model.” “The single best idea for reforming K–12 education,” writes Forbes contributor Steve Denning, ending the “factory model of management.” “There’s Nothing Especially Educational About Factory-Style Management,” according to the American Enterprise Institute’s Rick Hess.

I’d like to add: there’s nothing especially historical about these diagnoses either.

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The Invented History of ‘The Factory Model of Education’

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Otto Scharmer

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(PDF) Disruptive Innovation Ecosystems: Reconceptualising Innovation Ecosystems

Disruptive Innovation Ecosystems: Reconceptualising Innovation Ecosystems

Conference Paper (PDF Available) · June 2019 ·Conference: Disruptive Innovation Ecosystems: Reconceptualising Innovation Ecosystems, At Academy for Design Innovation Management Conference, London, UK

• Badziili Nthubu
  • Lancaster University

AbstractEcosystems are valuable in creating diverse and collaborative environments that enable businesses to innovate in ways that are much more difficult without them. However, business managers can be reluctant to participate in building ecosystems mainly due to lack of understanding. Specifically, businesses can be uncomfortable sharing resources, data, intellectual property and secrets with other ecosystem actors. Drawing on inter-disciplinary perspectives from literature, we use a ‘design focused ecosystem thinking’ to propose a new type of Disruptive Innovation Ecosystem (DIE). Firstly, we discuss the significance of adopting innovation ecosystems to create shared value. Secondly, we conceptualize a new type of DIE and propose steps on how DIEs can be created and fostered. Finally, we discuss DIE roles in relation to Amazon, Apple, Uber, and Siemens ecosystem cases. This paper offers a new type of DIE design process which may be leveraged by businesses towards building sustainable innovation ecosystem

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(PDF) Disruptive Innovation Ecosystems: Reconceptualising Innovation Ecosystems

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A loop theory of wisdom – how do we respond to foolish times?

Geoff Mulgan

• Jul 1

A loop theory of wisdom – how do we respond to foolish times?

Is it possible for an organisation, a system or a society, to become wiser? If so, how could we make this real and not just a vague invocation – like wishing people would be kinder or more loving?

In this draft paper (a more developed version of which will be published in a couple of months) I share some answers. I suggest what might be missing in much writing about wisdom and I suggest an alternative framework that cuts across different disciplines, including philosophy, psychology, computer science and organisational design.

I argue that progress in this field is badly needed, and not just because of the very visible lack of wisdom amongst many leaders and institutions, but also because rapid progress in use of data and artificial intelligence has not led to obviously wiser actions, in part because these fields lack a coherent view of the relationship between data, knowledge and wisdom.

I also argue that wisdom, and thought about wisdom matters, because it should sit above other types of knowledge, including scientific knowledge, or the insights of particular disciplines or professions.

Wisdom depends on expertise, but sits above it – and, as I argue, this should shape how we design institutions and laws, as well as science advice and governance, the design of digital technologies, and the crucial institutions that help the world make wiser decisions about complex long-term challenges – such as the IPCC and others around climate change, or IPBES concerned with biodiversity and ecosystems.

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The paper challenges some conventional views of this topic which see wisdom as static rather than dynamic, individual rather than collective, introspective rather than involving argument and open learning, and general rather than domain specific.

All of these assumptions may be misleading. I argue that instead of thinking of wisdom as an attribute we should understand it as a series of loops – loops linking thought, action and results; loops involving feedback from others; and loops involving argument and decision.

The paper shows why attempts at definition and taxonomy have been unsatisfactory and why wisdom is not a single thing but rather a shifting assembly of elements linked by what I call integrative judgement, that is in turn guided by reflection on experience. I suggest how institutions could be designed in ways that partly mimic the sometimes competing and sometimes cooperating parts of the individual brain to come closer to a capacity for wisdom.

I present wisdom as an inherently looped concept. I question the idea that wisdom is an attribute of particular people or institutions, presenting it more in terms of processes and actions. What is wise is what in the long run turns out to be wise. We can only truly recognise wisdom in retrospect, or from a distance. Words alone cannot be wise (and putting too much weight on the declarative, verbal side of wisdom opens up greater risks of hypocrisy and error, and greater risks of taking at face value the traditional hierarchical associations of wisdom – age, status, gender etc).

But if wisdom is looped, as I suggest, this also means that it can be learned, whether by individuals or organisations, through habits that partly mirror those of Bayesian inference. Moreover it is possible to address head-on processes that run counter to wisdom –algorithms that circulate lies, media dynamics that tend to amplify attention to people with vivid but misleading ideas, or legal processes that fuel discord.

I also suggest that wisdom is to some extent collective – dependent on others and their feedback – and that it is contextual; we can only judge it from a vantage point. There is no such thing as universal wisdom and wisdom is unstable because the environment that makes up its context is fluid, meaning that what is wise at one point may not be at another point. Wisdom is also looped in another sense. To think wisely we have to learn both to go out, and then to come back: to go out in the sense of exploring other perspectives, ways of seeing and thinking; and to come back in the sense of returning to an action or decision that will always be simpler than the thoughts that guide it.

Drawing on this idea I show how it is possible to cultivate wisdom; to build it into institutions and systems, usually through a division of labour; how to embed it into physical objects and into a further evolution of knowledge management and search tools, as well as artificial intelligence. I also address how wisdom can be cultivated in making sense of new fields of science and technology, bringing with them uncertain risks and benefits.

By making the pursuit of wisdom more explicit with claims, predictions and formal processes that allow for shared reflection and learning, along with a constant iteration of questions and answers, I argue that we can improve the quality of thought not only of individuals but also of organisations and whole systems. By removing some of the mystique surrounding wisdom we can do more to promote it.

None of this would matter if the world was replete with wisdom. But it’s not. Wisdom is fragile, elusive and often undervalued. In a world where data and information have become ever more ubiquitous and cheap, wisdom may have become even rarer.

………………….

I am sharing this (quite long) paper in a draft form in the spirit of its contents – to encourage critical comment and feedback.

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A loop theory of wisdom – how do we respond to foolish times?

not-actually-wrong from McKinsey!

source: https://www.mckinsey.com/business-functions/risk/our-insights/demystifying-modeling-how-quantitative-models-can-and-cant-explain-the-world

Demystifying modeling: How quantitative models can—and can’t—explain the world

June 25, 2020 | Article By Sarun Charumilind, Anas El Turabi, Patrick Finn, and Ophelia Usher Open interactive popup Demystifying modeling: How quantitative models can—and can’t—explain the world Open interactive popup The COVID-19 crisis has brought quantitative models to the forefront. Here are some ways that modeling helps us—as long as we avoid its pitfalls.

One of the many impacts of the COVID-19 crisis has been to highlight the role of quantitative models in our lives. Ideas associated with modeling, such as flattening the curve of disease transmission, are now regularly discussed in the media and among families and friends. Across the globe, we are trying to understand the numbers and what they mean for us.

Forward-looking models aren’t new. They have long played an important but unseen role in day-to-day life—for instance, in pricing homeowners’ insurance, anticipating the weather, and deciding how many iPhones to manufacture. However, in the COVID-19 pandemic, the scale of impact and the level of uncertainty have introduced new challenges—and notoriety—for modelers.

Used properly, models provide information that can present a framework for understanding a situation. But they aren’t crystal balls that state with certainty what will happen, and they don’t in themselves answer the difficult question of what to do. The eminent British statistician George Box summarized the point with his famous aphorism: “All models are wrong, but some are useful.” And he refined it by saying, “Since all models are wrong, the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad.” Sidebar

What is a model?

This article explains how models can help us make sense of the world and why they behave the way they do (see sidebar “What is a model?”). We also discuss the most common modeling pitfalls and how to avoid them.

The power of models

Making decisions in the face of uncertainty is challenging, particularly during a pandemic. Quantitative models can help us understand systems and behaviors in a number of useful ways that help navigate this ambiguous environment.

Clarifying which drivers matter

Models structure data in support of reasoned decision making by restricting variables to those that matter for a particular question. For example, when developing a demographic model to help civic leaders plan future community needs, key drivers could be birth rates, death rates, and new-job creation. Models can help users understand what is known about each element and identify the areas of continuing uncertainty. https://view.ceros.com/mckinsey/coronavirus-promo-video-desktop

Determining how much an input can matter

Models are well suited to exposing sensitivities: they show how even small changes in key assumptions can produce large variations in outcomes, helping decision makers establish priorities. An obvious case in point related to the COVID-19 pandemic is the massive impact of even small adjustments in the transmission rate of infection. By establishing sensitivities, models pinpoint areas for investment of effort or money to reduce uncertainty.

Facilitating discussions about the future

Models expose how different assumptions lead to different outcomes. Through discussion of modeling results, decision makers can form a collective judgement on scenarios to plan for, based on the multiple variables considered, and thus reach practical decisions (see sidebar “Building a quantitative model while using it”). For example, models were used to enable policy makers to weigh the benefits of requiring seatbelts against the moral hazard of encouraging people to drive faster. Not only do models trigger discussion, but they may force a more nuanced and evidence-based approach to decision making. In many cases, that is more important than the specific output itself. Sidebar

Building a quantitative model while using it

Pitfalls to avoid when using models

Overlooking the fact that a model can’t fix bad data

Taking assumptions and simplifications for granted

The risks of bias in modeling

Expecting too much certainty

Modeling philosophy for the COVID-19 pandemic

continues in source: https://www.mckinsey.com/business-functions/risk/our-insights/demystifying-modeling-how-quantitative-models-can-and-cant-explain-the-world