per Amazon:

‘Fifth Discipline’ is one of the very few approaches to management that has attained position on the International Hall of Fame. Professor Flood’s book explains and critiques the ideas in straight forward terms. This book makes significant and fundamental improvements to the core discipline – systemic thinking. It establishes crucial developments in systemic thinking in the context of the learning organisation, including creativity and organisational transformation. It is therefore a very important text for strategic planners, organisational change agents and consultants.
The main features of the book include:

• a review and critique of ‘Fifth Discipline’ and systemic thinking
• an introduction to the gurus of systemic thinking – Senge, Bertalanffy, Beer, Ackoff, Checkland, and Churchman

*a redefinition of management through systemic thinking
*a guide to choosing, implementing and evaluating improvement strategies
*Practical illustrations.
Robert Flood is a renowned and authoritative expert in the field of management. He has implemented systemic management in a wide range of organisations in many continents and lectured by invitation in 25 countries, including Japan and the USA. Professor Flood has featured on many radio and TV programs. His book Beyond TQM was nominated for the ‘IMC Management Book of the Year 1993’.

Social generativity and complexity

The idea of generativity in the realm of the social world expresses the notion that social phenomena are generated by the actions and thoughts of the individuals who constitute them, and nothing else (link, link). More specifically, the principle of generativity postulates that the properties and dynamic characteristics of social entities like structures, ideologies, knowledge systems, institutions, and economic systems are produced by the actions, thoughts, and dispositions of the set of individuals who make them up. There is no other kind of influence that contributes to the causal and dynamic properties of social entities. Begin with a population of individuals with such-and-so mental and behavioral characteristics; allow them to interact with each other over time; and the structures we observe emerge as a determinate consequence of these interactions.

This view of the social world lends great ontological support to the methods associated with agent-based models (link). Here is how Joshua Epstein puts the idea in Generative Social Science: Studies in Agent-Based Computational Modeling):

Agent-based models provide computational demonstrations that a given microspecification is in fact sufficient to generate a macrostructure of interest…. Rather, the generativist wants an account of the configuration’s attainment by a decentralized system of heterogeneous autonomous agents. Thus, the motto of generative social science, if you will, is: If you didn’t grow it, you didn’t explain its emergence. (42)

Consider an analogy with cooking. The properties of the cake are generated by the properties of the ingredients, their chemical properties, and the sequence of steps that are applied to the assemblage of the mixture from the mixing bowl to the oven to the cooling board. The final characteristics of the cake are simply the consequence of the chemistry of the ingredients and the series of physical influences that were applied in a given sequence.

Now consider the concept of a complex system. A complex system is one in which there is a multiplicity of causal factors contributing to the dynamics of the system, in which there are causal interactions among the underlying causal factors, and in which causal interactions are often non-linear. Non-linearity is important here, because it implies that a small change in one or more factors may lead to very large changes in the outcome. We like to think of causal systems as consisting of causal factors whose effects are independent of each other and whose influence is linear and additive.

A gardener is justified in thinking of growing tomatoes in this way: a little more fertilizer, a little more water, and a little more sunlight each lead to a little more tomato growth. But imagine a garden in which the effect of fertilizer on tomato growth is dependent on the recent gradient of water provision, and the effects of both positive influencers depend substantially on the recent amount of sunlight available. Under these circumstances it is difficult to predict the aggregate size of the tomato given information about the quantities of the inputs.

One of the key insights of complexity science is that generativity is fully compatible with a wicked level of complexity. The tomato’s size is generated by its history of growth, determined by the sequence of inputs over time. But for the reason just mentioned, the complexity of interactions between water, sunlight, and fertilizer in their effects on growth mean that the overall dynamics of tomato growth are difficult to reconstruct.

Now consider the idea of strong emergence — the idea that some aggregates possess properties that cannot in principle be explained by reference to the causal properties of the constituents of the aggregate. This means that the properties of the aggregate are not generated by the workings of the constituents; otherwise we would be able in principle to explain the properties of the aggregate by demonstrating how they derive from the (complex) pathways leading from the constituents to the aggregate. This version of the absolute autonomy of some higher-level properties is inherently mysterious. It implies that the aggregate does not supervene upon the properties of the constituents; there could be different aggregate properties with identical constituent properties. And this seems ontological untenable.

The idea of ontological individualism captures this intuition in the setting of social phenomena: social entities are ultimately composed of and constituted by the properties of the individuals who make them up, and nothing else. This does not imply methodological individualism; for reasons of complexity or computational limitations it may be practically impossible to reconstruct the pathways through which the social entity is generated out of the properties of individuals. But ontological individualism places an ontological constraint on the way that we conceptualize the social world. And it gives a concrete meaning to the idea of the microfoundations for a social entity. The microfoundations of a social entity are the pathways and mechanisms, known or unknown, through which the social entity is generated by the actions and intentionality of the individuals who constitute it.

Source: Understanding Society: Social generativity and complexity

[I love when David Ing shares his students’ infographics – truly rich sources of overview on systems thinking]

Eight infographics on Systems Methods (UToronto iSchool 2018)

Learning only a single systems method is reductive.  A course that exposes breadth in a variety of systems methods encourages students to reflect on their circumstances-at-hand, and their explicit and implicit influences on guiding others in projects espousing systems thinking.  This was a premise behind the structuring of “Systems Thinking, Systems Design“, an Information Workshop (i.e. 6-week elective quarter course) offered to master’s students at the University of Toronto Faculty of Information (iSchool).

The first class day had a short course introduction focused on the history of the systems sciences, and a minimal orientation to the most basic concept in systems theory.  Then, for the four class days that followed, student groups led 8 presentation-facilitations on a research reference cluster (with the instructor on standby as a subject matter expert on the content).  The topics included:

1. Object Process Methodology
2. Dialogue Mapping
3. Idealized Design
4. Soft Systems Methodology
5. Viable System Model
6. Resilience in Socio-Ecological Systems
7. Service Systems
8. Generative Pattern Language

After each of the four days, students wrote Personal Appreciation Diary Logs (blog posts), mostly on the open web.  These provided feedback to the instructor for commentary (and some remediation) at the beginning of the subsequent class meeting.  We could review common understandings, difficulties and misconceptions about systems methods.

For the last (sixth) class meeting, each student group was asked to “prepare and present an infographic poster on their impressions about the system approaches most relevant to their research”.  The conclusions reflected different interests, experiences and orientations amongst the iSchool students.

Continues in source: Eight infographics on Systems Methods (UToronto iSchool 2018) – Coevolving Innovations

Studies Show That People Who Have High “Integrative Complexity” Are More Likely To Be Successful

A self-made billionaire studied Bill Gates, Steve Jobs, and Elon Musk. An eminent researcher interviewed Nobel Laureates. They each came to the same conclusion.

My 6’5” dad was black and grew up in one of the most dangerous cities in America. He sported a huge afro into the early ’90s, when he died at the age of 35 from lung cancer, one year younger than I am now.

My mother, a Jewish refugee from Poland, arrived in Brooklyn when she was 17 with no money and no English. She was essentially a single mother for most of my childhood.

That makes me a half-black, half-white, 6’5” man born into a half-Christian, half-Jewish family, and raised by a refugee.

So I watch the daily culture wars unfold with mixed feelings. Recently, I listened to a podcast about race in which my people were described as “the victims.” Then I listened to another podcast, and this one cast me on the side of “the oppressor.” The result is that I tend to feel like a chameleon and see both sides of many of the issues currently being debated. I used to feel like I should pretend to strongly take one side or the other. But as I’ve gotten older, I’ve come to embrace this ability to appreciate contrasting viewpoints without labeling one right and the other wrong.

And then I found four studies, independently conducted by four of the greatest thinkers of our time, that basically came to the same surprising conclusion: Many of the world’s top entrepreneurs — like Bill Gates, Steve Jobs, and Elon Musk — along with Nobel Laureates have a common, rare skill called “integrative complexity.” Integrative complexity is the ability to develop and hold opposing traits, values, and ideas and then integrate them into larger ones.

These findings go against conventional wisdom in the business world, which is that we should double down on our strengths and mitigate everything else. They are also opposed to conventional tribal wisdom that says we should pick one side of every polarity and vehemently fight for it.

Here are the four breakthrough studies on why integrative complexity is a key to success, personal growth, and cultural polarization.

Continues in source:  Studies Show That People Who Have High “Integrative Complexity” Are More Likely To Be Successful

The Critical Thinker’s OODA Loop

Summary: The Critical Thinker’s OODA Loop is a high-speed decision making and feedback process using simple rules to upgrade your critical thinking skills for a sharper mind.

As humans, we typically operate on cognitive autopilot. We rarely stop and reflect on how we interpret information and create mental models which replicate our perception of reality.

However, what do we do when our mental models fail to match reality?

Instead of changing our mental models, we simply ignore reality and operate throughout the day on implicit assumptions. These are hidden assumptions and not conscious choices. Our mental models allow us a simple way to cope with reality, yet we fail to confront reality when it is different than our mental model. Essentially, we have unknowingly created a ready-made default mechanism.[1]

So, what can we do?

We must first take time to reflect. By simply understanding how you interpret and perceive information differently than everyone else is a great first step. However, to truly upgrade your critical thinking skills, you must examine how thoughts arise in your mind and how they got there. Critical thinking is about asking yourself how you make choices. We can choose to believe something we hear or see; however, why do we choose to believe something we hear or see?

As a Red Team Member in the U.S. Army, I will explain how I upgrade my critical thinking skills using Colonel John Boyd’s OODA Loop as a framework for critical thinking. I will then demonstrate practical ways to upgrade your critical thinking skills for a sharper mind using tools and techniques from the University of Foreign Military and Cultural Studies (UFMCS) Center for Applied Critical Thinking (also known as the Red Team school) and The Applied Critical Thinking Handbook (also known as The Red Team Handbook).

What is Critical Thinking?

Critical thinking can be explained in a number of ways. Let’s quickly examine a few definitions.

• “Critical thinking is a process, the goal of which is to make reasonable decisions about what to believe and what to do.” — Robert Enis
• “Critical thinking means developing an ever better worldview and using it well in all aspects of your life. The essence of critical thinking is questioning and arguing logically.” — Gary Jason
• “Critical thinking is searching for hidden assumptions, noticing various facets, unraveling different strands, and evaluating what is most significant. It implies conscious, deliberate inquiry, and especially it implies adopting a skeptical state of mind.” — Sylvan Barnet and Hugo Bedau

To me, critical thinking is as follows:

“Critical thinking is observing the world with an open and skeptical mindset with the goal of exploring all alternatives objectively (as much as possible). It is our ability to orient our mental models to view reality through an emotionless lens seeking the truth by questioning our own assumptions and deconstructing arguments logically. It is our ability to identify gaps and uncover what is missing to improve our quality of decisions. Finally, it is our ability to unravel different strands of significant information through a continuous stream of feedback so that we continuously destroy and create new mental models allowing us to act closer to reality.” — Dr. Jamie Schwandt

What is the OODA Loop?

I use John Boyd’s OODA Loop as a framework for critical thinking. It is similar to Swarm Intelligence, where we use simple rules to allow the collective intelligence to emerge. The simple rules are Observe, Orient, Decide, and Act.

Continues in source: The Critical Thinker’s OODA Loop – Dr. Jamie Schwandt – Medium

Source: SoSE 2018 – Paris

Drawbacks to visual thinking

 Mark Nicoll

Continues in source: Drawbacks to visual thinking • Meaning Guide

How to bridge the complexity gap

My colleagues and I have been studying the complexity gap for about 15 years now.

It all started when we were hired in 2002 by a U. S. federal agency to conduct research on what it takes for leaders to make good decisions under VUCA (volatile, uncertain, complex, and ambiguous) conditions.

I’m using the term leader here, fully aware that some people might use managerinstead.

We began the project with a study of leader decision making, ultimately interviewing several hundred employees representing every level in a large federal agency’s employee hierarchy. The only group we were unable to interview were leaders at the very top.

Thinking complexity

The interviews were wrapped around a set of “wicked” workplace problems — complex problems with VUCA features and no “correct” solutions. We studied these interviews, scoring them for their complexity level with the Lectical Assessment System, while thoroughly documenting the ideas and skills demonstrated in responses (so we could construct descriptions of how ideas and skills changed from level to level).

Role complexity

As we completed the first phase of the project, which focused on thinkingcomplexity, we began developing an approach to determining role complexity, again using the Lectical Assessment System. This was an interesting challenge. We settled on an approach that started with a basic Lectical Analysis of easily observable increases in the number and complexity of stakeholder interests associated with decision making ateach higher level in the agency’s hierarchy. Then we zoomed in, examining the complexity associated with the work of different departments within the organization, and finally, the complexity associated with work in specific roles.

The figure on the left shows the results of the thinking and role complexity analyses. The basic story here is that the complexity of roles increases in a linear fashion as we move up the hierarchy, but the average complexity of leaders’ thinking does not.

However, that’s not the whole story. From semi-skilled roles to entry level roles, role complexity and thinking complexity are pretty well aligned. Then there’s a flattening out of the curve from the mid- to upper-levels, and a return to growth in higher work levels that’s parallel to, but well below, the role complexity curve.

Our client wasn’t surprised by this pattern. Our client reported that the agency routinely hired for senior roles by going outside the agency — because existing employees were not developing the skills required in higher roles. We weren’t surprised either. The agency had a command and control culture. Nothing stifles development like command and control, because there is generally no role for critical reflection — essential for development — at lower levels in a command and control hierarchy.

Continued in source: How to bridge the complexity gap – Theo Dawson – Medium

Wednesday, May 23, 2012

The social world as morphogenesis

Critical realism has progressed far since Roy Bhaskar’s early writings on the subject in A Realist Theory of Science.  One of the most important thinkers to have introduced new ideas into the debate is Margaret Archer. Several books in the mid-1990s represented genuinely original contributions to issues about the nature of social ontology and methodology, including especially Realist Social Theory: The Morphogenetic Approach and Culture and Agency: The Place of Culture in Social Theory.

Archer’s work addresses several topics of interest to me, including especially the agent-structure dichotomy. This is key to the twin concerns I have for “actor-centered social science” and “autonomous meso-level explanations”.  Anthony Giddens offers one way of thinking about the relationship between agents and structures (link).  Archer takes issue with the most fundamental aspect of Giddens’s view — his argument that agents and structures are conceptually inseparable. Archer argues instead for a form of “dualism” about agents and structures — that each pole needs to be treated separately and in its own terms.   (Chapter 5 provides a detailed discussion of both Bhaskar and Giddens on levels of the social.) She acknowledges, of course, that social structures depend on the individuals who make them up; but she doesn’t believe that this basic fact tells us anything about how to analyze or explain facts about either agents or structures.  Here are the opening paragraphs of Realist Social Theory.

Social reality is unlike any other because of its human constitution. It is different from natural reality whose defining feature is self-subsistence: for its existence does not depend upon us, a fact which is not compromised by our human ability to intervene in the world of nature and change it. Society is more different still from transcendental reality, where divinity is both self-subsistent and unalterable at our behest; qualities which are not contravened by responsiveness to human intercession. The nascent ‘social sciences’ had to confront this entity, society, and deal conceptually with its three unique characteristics.

Firstly, that it is inseparable from its human components because the very existence of society depends in some way upon our activities. Secondly, that society is characteristically transformable; it has not immutable form or even preferred state.  It is like nothing but itself, and what precisely it is like at any time depends upon human doings and their consequences.  Thirdly, however, neither are we immutable as social agents, for what we are and what we do as social beings are also affected by the society in which we live and by our very efforts to transform it. (1)

Continues in source: Understanding Society: The social world as morphogenesis

As if on cue, via CX Digest, mathematics and underlying patterns.

A re-watch of ‘All Watch OVer by Machines of Loving Grace part 2 – the Use and Abuse of Vegetational Concepts’ raises some interesting questions.

(Two openly available versions below)

Wikipedia summary from All Watched Over by Machines of Loving Grace (TV series) – Wikipedia

Part 2. ‘The Use and Abuse of Vegetational Concepts’

This episode investigates how machine ideas such as cybernetics and systems theory were applied to natural ecosystems, and how this relates to the false idea that there is a balance of nature. Cybernetics has been applied to human beings in an attempt to build societies without central control, self organising networks built of people, based on a fantasy view of nature.

Arthur Tansley had a dream where he shot his wife. He wanted to know what it meant, so he studied Sigmund Freud. However, one part of Freud’s theory was that the human brain is an electrical machine. Tansley became convinced that, as the brain was interconnected, so was the whole of the natural world, in networks he called ecosystems, which he believed were inherently stable and self-correcting, and which regulated nature as if it were a machine.

Jay Forrester was an early pioneer in cybernetic systems who believed that brains, cities and even societies live in networks of feedback loops that control them, and he thought computers could determine the effects of the feedback loops. Cybernetics therefore viewed humans as nodes in networks, as machines.

The ecology movement also adopted this idea and viewed the natural world as systems, as it explained how the natural system could stabilise the natural world, via natural feedback loops.

Norbert Wiener laid out the position that humans, machines and ecology are simply nodes in a network in his book Cybernetics, or Control and Communication in the Animal and the Machine, and this book became the bible of cybernetics.

Howard T. Odum

Howard T. Odum and Eugene Odum were brothers, and both of them ecologists. Howard collected data from ecological systems and built electronic networks to simulate them. His brother Eugene then took these ideas to make them the heart of ecology, and the hypothesis then became a certainty. However, they had distorted the idea and simplified the data to an extraordinary degree. That ecology was balanced became conventional wisdom among scientists.

Meanwhile, in the 1960s, Buckminster Fuller invented a radically new kind of structure, the geodesic dome, which emulated ecosystems in being made of highly connected, relatively weak parts. It was applied to the radomes covering early warning systems in the Arctic. His other system-based ideas inspired the counterculture movement. Communes of people who saw themselves as nodes in a network, without hierarchy, and applied feedback to try to control and stabilise their societies, and used his geodesic domes as habitats. These societies mostly broke up within a few years.

Also in the 1960s, Stewart Brand filmed a demonstration of a networked computer system with a graphics display, mouse and keyboardthat he believed would save the world by empowering people, in a similar way to the communes, to be free as individuals.

In 1967, Richard Brautigan published the poetry work All Watched Over by machines of Loving Grace. The title poem called for a cybernetic ecological utopia consisting of a fusion of computers and mammals living in perfect harmony and stability. The arguments in this part of the documentary closely echo Andrew Kirk’s 2007 environmental history of the California-appropriate technology movement, Counterculture Green: The Whole Earth Catalog and American Environmentalism.

By the 1970s, new problems such as overpopulation, limited natural resources and pollution that could not be solved by normal hierarchical systems had arrived. Jay Forrester stated that he knew how to solve this problem. He applied systems theory to the problem and drew a cybernetic system diagram for the world. This was turned into a computer model, which predicted population collapse. This became the basis of the model that was used by the Club of Rome, and the findings from this were published in The Limits to Growth. Forrester then argued for zero growth in order to maintain a steady equilibrium within the capacity of the Earth.

Jan Smuts

However, this was opposed by many people within the environmental movement, since the model did not allow for people to change their values to stabilise the world, and they argued that the model tried to maintain and enforce the current political hierarchy. Arthur Tansleywho had invented the term ecosystem, had once accused Field Marshal Jan Smuts of the “abuse of vegetational concepts”. Smuts had invented a philosophy called holism, where everyone had a ‘rightful place’, which was to be managed by the white race. The 70s protestors claimed that the same conceptual abuse of the supposed natural order was occurring, that it was really being used for political control.

At the time, there was a general belief in the stability of natural systems. However, cracks started to appear when a study was made of the predator-prey relationship of wolf and elks. It was found that wild population swings had occurred over centuries. Other studies then found huge variations, and a significant lack of homeostasis in natural systems. George Van Dyne then tried to build a computer model to try to simulate a complete ecosystem based on extensive real-world data, to show how the stability of natural systems actually worked. To his surprise, the computer model did not stabilize like the Odums’ electrical model had. The reason for this lack of stabilization was that he had used extensive data which more accurately reflected reality, whereas the Odums and other ecologists had “ruthlessly simplified nature.” The scientific idea had thus been shown to fail, but the popular idea remained in currency, and even grew as it apparently offered the possibility of a new egalitarian world order.

In 2003, a wave of spontaneous revolutions swept through Asia and Europe. Coordinated only via the internet, nobody seemed to be in overall charge, and no overall aims except self-determination and freedom were apparent. This seemed to justify the beliefs of the computer utopians.

However, the freedom from these revolutions lasted for only a short time. Curtis compares them with the hippie communes, all of which had been broken up within a few years by, “the very thing that was supposed to have been banished: power.” Aggressive members of the group began to bully the weaker ones, who were unable to band together in their own defence because formal power structures were prohibited by the commune’s rules, and even intervention against bullying by benevolent individuals was discouraged.

Curtis closes the episode by stating that it has become apparent that while the self-organising network is good at organising change, it is much less good at what comes next; networks leave people helpless in the face of people already in power in the world.

Critically, the wikipedia for https://en.wikipedia.org/wiki/Balance_of_nature includes:

Despite being discredited among ecologists, the theory is widely held to be true by the general public, with one authority calling it an “enduring myth”.^[2] At least in Midwestern America, the “balance of nature” idea was shown to be widely held by both science majors and the general student population.^[1] In a study at the University of Patras, educational sciences students were asked to reason about the future of ecosystems which suffered human-driven disturbances. Subjects agreed that it was very likely for the ecosystems to fully recover their initial state, referring to either a ‘recovery process’ which restores the initial ‘balance’, or specific ‘recovery mechanisms’ as an ecosystem’s inherent characteristic.^[7] In a 2017 study, Ampatzidis and Ergazaki discuss the learning objectives and design criteria that a learning environment for non-biology major students should meet to support them challenge the “balance of nature” idea.^[8]

The concessions to ‘superseded by chaos theory and catastrophe theory’ seem to me to provide an immediate way out (being, it seems to me, entirely systems theory and demonstrating maintenance of whole-system equilibrium in some meaningful sense), and also the demonstrations of homeostasis are still valid, even if demonstrations of non-homeostasis are also valid. And systems thinking and cybernetics, in any non-trivially dumb form, do not need any global, holistic or metaphysical ‘tendency to stability’ to justify their existence! And even if such a fundamental Hegelian-like grounding were needed, I’d plump for entropy and emergence any day…

BUT – is this something important? Useful? Have I finally understood some point of where complexity theory and ‘living systems’ people talk dismissively of ‘mechanistic’ systems thinking and cybernetics?

I don’t actually think so – just see my recent Andrew Pickering post for a much richer reading of cybernetics (I’m putting Adam Curtis in the ‘George Monbiot’ annoying, occasionally interesting, usually ideological and sometimes willingly lying box for now – though like the Jan de Bont Award for Worst Screenplay, the crown is unlikely to be taken from the title-holder in a rush ;-)) – but it would be interesting if people could respond….

I mean ‘there was no difference between humans and machines’, ‘both were just nodes in networks’ – that kind of characterisation actually makes me quite angry – I can open any page of the transcript of the Macy Conferences at random and find something to definitively refute that! Grr….