Murray Gell-Mann passes away at 89
Closing the gaps
This paper traces the origins of the educational system’s widespread inability (despite outstanding exceptions) to achieve its most widely agreed goal – i.e. to nurture and recognise the wide range of talents pupils possess. Among the reasons for this neglect is the absence of a shared framework for thinking about the nature, development, and assessment of high-level competencies. Unfortunately, more basic reasons include (i) the absence of a governance system which would facilitate experimentation, innovation, and learning, and (ii) a network of social forces which lead the system to concentrate on manufacturing and legitimising hierarchy in society. Evolving a new governance system and finding ways of harnessing the forces contributing to hierarchy thus become our top priorities.
Keywords: Systems roots of gross deficits in educational system; Nature, development, and assessment of competence; Requisite developments in governance; Hegemony of hierarchical thinking; Conceptualising, mapping, measuring, and harnessing social forces.
The second, which I will term ‘Bookchin’s law’ is an extension of Parkinson’s law – which asserts that work expands to fill the time allotted to it. Bookchin’s law, which I will discuss more fully later, asserts that, in a situation of surplus labour, a network of social processes result in the creation of huge amounts of hierarchically-organised work which delivers few benefits other than those delivered directly through participating in it
A new social law that may rank alongside Parkinson’s Law on the pro liferation of bureaucracy is gaining ac ceptance in some technological quar ters, Called Forrester’s Law, this maxim holds that in complicated situa tions efforts to improve things often tend to make them worse, sometimes much worse, on occasion calamitous.
Fit the people around an organization; or an organization around the people? Working backwards, say @MitroffCrisis + #HaroldLinstone, from current concrete choices to uncertain futures, surfaces strategic assumptions in a collective decision, better than starting with an abstract scorecard to rank candidates. The Unbounded Mind is an easier-reading follow-on to The Design of Inquiry Systems by C. West Churchman.
This scorecard metaphor shows up in the second of five ways of knowing (i.e. inquiring systems)
Chapter 3 is “The World as a Formula: The Second Way of Knowing”. A case study commonly used in business school education is described.
To illustrate the use and meaning of the Analytic-Deductive IS in a social realm, we’ll apply it to a situation that on the surface at least is as “simple” as the question that occupied us in the last chapter. There is a somewhat dated yet classic case in the Harvard Business Review that provides a perfect depiction of the Analytic-Deductive IS.  Four men are running for the presidency of a fictitious life insurance company, Zenith Life. Background information on their strengths and weaknesses, families, career history, skills, and so on, is given for all four, although we do not receive the same information for each of them. Thus, we know more about one candidate in one category than we do about another. Also, the history and current nature of Zenith Life itself, its prospects and problems, its opportunities as well as threats, are described. The central question of the case is, “Which of the four candidates is best qualified to head Zenith Life, given both its past history and its current condition?” [pp. 41-42]
-  Abraham T. Collier, “Decision at Zenith Life,” Harvard Business Review, January-February 1962, Vol. 40, No. 1, pp. 139-157
In all the years that we have given this seemingly “simple case” to scores of students and executives, the typical response has remained remarkably the same. Almost every student and executive — whether they worked individually on the case or in small groups — built a single, simple model that selects one and only one of the candidates as best for Zenith Life. The models are virtually the embodiment of Analytic-Deductive reasoning whether the students and executives were aware of this or not; in most cases, they were not.
The models essentially work as follows. A set of attributes that are characteristic of leadership is determined or specified: for instance, how charismatic each of the candidates is; their capacity to inspire others; the ability to formulate a vision of what Zenith Life needs to be in the coming decade; to present one’s ideas in a direct and persuasive manner so that others will want to join on; a clear sense of ethics and the ability to make decisions that are ethical and moral; their past job performance — job history, personality, and so on. Other variables such as”family support” were also included. Each candidate is then scaled on each attribute to the degree that the individual either embodies or possesses it. Typically, a score of “1” represents the absence of a particular attribute or poor performance on it, whereas “10” indicates the complete possession of an attribute or high performance. On more sophisticated models, the attributes are weighted differently so that, for example, the category “ethics” might be rated three times more important than one’s score in the area of “past job performance.” The “best candidate” to run Zenith Life is then selected on the basis of who has the highest score on all the attributes and their weightings. [p. 42]
So, the scorecard would look something like this:
|Attribute||Weighting||Candidate #1||Candidate #2||Candidate #3||Candidate #4|
|Charisma||a %||? / 10||? / 10||? / 10||? / 10|
|Capacity to inspire others||b %||? / 10||? / 10||? / 10||? / 10|
|Ability to formulate vision for next decade||c %||? / 10||? / 10||? / 10||? / 10|
|Presenting persuasively for followers||d %||? / 10||? / 10||? / 10||? / 10|
|Ethics, moral decisions||e %||? / 10||? / 10||? / 10||? / 10|
|Past job performance||f %||? / 10||? / 10||? / 10||? / 10|
|Rank (of 4)||#? of 4||#? of 4||#? of 4||#? of 4|
By the analytic-deductive scorecard, the “objective” top-ranked candidate has the highest weighted score. However, this may not be the “best” way to select a candidate.
Almost never does an individual or a group build more than one model in order to demonstrate explicitly that, depending on the initial assumptions one makes, not only can one specify very different leadership attributes — and hence build very different models — but, as a result, one can select very different candidates as “best.” Even rarer is the individual or group — although this has occurred — who turns the whole case on its head by working backwards with the presumption that each of the candidates is “best,” but for a very different kind of company. That is, suppose one starts by assuming that each candidate is “best” and then asks the critical question, “What are the characteristics of the different kinds of companies for which each is ‘best’?” This approach thus creatively reverses the whole decision as one of specifying a new company to carry Zenith Life ahead in the coming decades. [pp. 42-43]
The criticism comes from Mitroff having been a coauthor of Strategic Assumption Surfacing and Testing for approaching ill-structured problems (much of which was developed alongside West Churchman’s doctoral supervision).
In essence, nearly everyone who reads the case and analyzes it assumes, almost without question, that it is a bounded, well-structured problem. Most people believe that the attributes or characteristics of leadership are obvious or self evident; much like a machine, that phenomenon of leadership can be decomposed or broken down into its constituent parts. In addition, not only do they assume that any individual’s leadership abilities can be scaled in terms of each of the separate components, but, further, that the weighted sum of scores on each component makes sense and is virtually the same as the whole phenomenon itself. To put it mildly, this is quite a body of assumptions.
While there is often much argument and heated debate between various individuals and groups over who has the single best or the right model, very few individuals or groups doubt that “out there, somewhere, the definitive book, expert, or mathematical model on leadership exists. In essence, the fundamental assumption is that critical human problems can be reduced to a formula, a cookbook mechanical procedure. The trick is just to find the right model and apply it correctly.
It’s all that simple — or is it really? Of course not. Indeed, it is often far easier to convince people that there are no simple models than to persuade them that there are. To see this, suppose we change the questions of this and the last chapter. [p. 43]
It’s possible that the preference for an “objective” answer is to reduce conflict. For greater creativity, perhaps more conflict is desired.
Instead of asking the seemingly neutral question that in turn seems to call for a factual response, “What are the expected tonnages in steel for the U.S. versus Japan in the year 2000?,” suppose we had asked instead a much more volatile question such as, “Suppose someone very dear and close to you and in their early teens had been raped brutally; whom would you appoint as a panel of experts to make the critical decision whether to grant an abortion or not?” Further, instead of asking for a model on something so prosaic as leadership as we did in this chapter, suppose that we had asked instead, “Build a model to make the decision whether to grant an abortion or not?” It comes as neither a great shock nor a surprise that these questions are treated very differently and evince very different responses. Now different assumptions become extremely vital. The discussion becomes even more heated between individuals and groups. The consensus over experts or models that may have flowed freely and easily before has all but evaporated. Everything has suddenly become contentious, as well it should. The problems or questions are no longer well structured. The very phrasings of the initial questions, which were not in dispute before and perhaps were even irrelevant, now become exceedingly critical. The ways in which the questions are posed and the assumptions made bear heavily on what counts as answers. The feelings aroused are so strong that they spill over to the supposedly more neutral and well structured issues, so that if we ask the question of forecasting steel production in the year 2000 and the selection of a president for Zenith Life after we have asked the more inflammatory questions, then these have become ill-structured issues as well. [pp. 43-44]
Way later, in the book, in Chapter 6 “Unbounded Systems Thinking: The Fifth Way of Knowing”, I noticed an exceptionally concise compression of the linkages from Edgar A. Singer (the doctoral supervisor of C. West Churchman) down to Ian Mitroff’s thinking.
In 1896, the great American philosopher William James of Harvard University wrote a letter to Provost Harrison of the University of Pennsylvania recommending Edgar Arthur Singer for a position in philosophy at his institution. James wrote that in his thirty years of teaching philosophy, Singer was the “best all around student” that he had had “in the philosophic business.” There was no aspect of philosophy that Singer could not do well.
Singer went on to a long and distinguished career in American philosophy. Among his many outstanding students was C. West Churchman from whom Mitroff studied philosophy of science at the University of California, Berkeley, in the 1960s.
The point of this all too brief bit of history is not just that the first author can trace his intellectual lineage back to one of the world’s most distinguished philosophers, and one whom both authors admire greatly, but that Singer was one of the most important participants in the founding of the modern systems approach. Churchman in turn extended Singer’s ideas significantly and their ideas form the philosophical basis for the modern systems approach. [p. 92]
With that, the Systems Approach is briefly described.
The upshot of Singer’s analysis was that there were no elementary or simple acts in any science or profession to which supposedly more complex situations could be reduced. Every act or action performed by humans was complex and therefore had within it a complex series of other actions. Furthermore, unlike the scientists and the philosophers of his day who believed that some sciences such as mathematics or physics were the most basic or fundamental, Singer believed that there were no fundamental sciences to which all others could be reduced. Since it was necessary at some point to involve every science in the actions of every other science, all the sciences and professions were equally fundamental. No single science stood at the top of the totem pole or hierarchy of science and in essence, every science depended on every other.
This fundamental notion of interconnectedness, or nonseparability, forms the basis of what has come to be known as the Systems Approach. In essence, the Systems Approach postulates that since every problem humans face is complicated, they must be perceived as such, that is, their complexity must be recognized, if they are to be managed properly. Notice the emphasis on the critical words “managed properly.” As a critical human activity, science, or the creation of a very special kind of knowledge, must be conceived of and managed as a whole system. [pp. 94-95]
The Unbounded Mind has been an easy-reading entry into the Systems Approach for many. It’s worth reading.
Churchman, C. West. 1971. The Design of Inquiring Systems: Basic Concepts of Systems and Organization. Basic Books. Alternate search at https://scholar.google.com/scholar?cluster=2520515633142315676 . Snippet view at https://books.google.com/books?id=ZGhQAAAAMAAJ
Mitroff, Ian I., and Harold A. Linstone. 1993. The Unbounded Mind: Breaking the Chains of Traditional Business Thinking. New York: Oxford University Press. Alternate search at https://scholar.google.com/scholar?cluster=12095226026166110830. Preview at https://books.google.com/books?id=NyV-BwAAQBAJ
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Welcome to the Systemic Leadership & Change Network!
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Watch the invitation video from Systemic Leadership & Change Network host Jennifer Campbell
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Main source: Panarchy
What is Panarchy?
Panarchy is a conceptual framework to account for the dual, and seemingly contradictory, characteristics of all complex systems – stability and change. It is the study of how economic growth and human development depend on ecosystems and institutions, and how they interact. It is an integrative framework, bringing together ecological, economic and social models of change and stability, to account for the complex interactions among both these different areas, and different scale levels (see Scale Levels).
Panarchy’s focus is on management of regional ecosystems, defined in terms of catchments, but it deals with the impact of lower, smaller, faster changing scale levels, as well as the larger, slower supra-regional and global levels. Its goal is to develop the simplest conceptual framework necessary to describe the twin dynamics of change and stability across both disciplines and scale levels.
The development of the panarchy framework evolved out of experiences where “expert” attempts to manage regional ecosystems often resulted in considerable degradation of those ecosystems (Gunderson and Holling, 2002). Regional management efforts are generally linear in nature, targeting the maintenance of certain variables – forest growth rates, river clarity, fish harvest rates, etc.
It was noted that focusing on managing a single variable, usually one of economic interest, generally resulted in other variables in the system changing, sometimes abruptly, and eventually degrading the entire ecosystem. It was also noted that the changes triggered by attempting to sustain a particular variable were changes that occurred so slowly (over decades or more), that they often went unnoticed until they in turn triggered an abrupt change (e.g. the forest became infested, the river became polluted, or the fish stock collapsed).
Basic Concepts in Panarchy
Empirical evidence of natural, disturbed and managed ecosystems identifies four key characteristics:
- Change is neither continuout and gradual, nor continuously chaotic. It is epicodic, regulated by interactions between fast and slow variables
- Different scale levels concentrate resources and potential in different ways, and non-linear processes reorganize resources across levels
- Ecosystems do not have a single equilibrium; multiple equilibria are common. Ecosystems have processes that maintain stability in terms of productivity and biogeochemical cycles; as well as processes that are destabilizing, which provide diversity, resilience and opportunity
- Management systems must take into account these dynamic features of ecosystems and be flexible, adaptive and experiment at scale levels compatible with the levels of critical ecosystem functions.
Stages of the Adaptive Cycle: Basic Ecosystem Dynamics
Panarchy identifies four basic stages of ecosystems, represented in the Figure below: exploitation, conservation, release and reorganization. All ecosystems, from the cellular to the global level, are said to go through these four stages of a dynamic adaptive cycle (see below).
- The exploitation stage is one of rapid expansion, as when a population finds a fertile niche in which to grow.
- The conservation stage is one in which slow accumulation and storage of energy and material is emphasized as when a population reaches carrying capacity and stabilizes for a time.
- The release occurs rapidly, as when a population declines due to a competitor, or changed conditions
- Reorganization can also occur rapidly, as when certain members of the population are selected for their ability to survive despite the competitor or changed conditions that triggered the release.
The four stages of the adaptive cycle described above (analogous to birth, growth and maturation, death and renewal), have three properties that determine the dynamic characteristics of each cycle:
- Potential sets the limits to what is possible – the number and kinds of future options available (e.g. high levels of biodiversity provide more future options than low levels)
- Connectedness determines the degree to which a system can control its own destiny through internal controls, as distinct from being influenced by external variables (e.g. temperature regulation in warm blooded animals, which involves five different physiological mechanisms, is an example of high connectedness)
- Resilience determines how vulnerable a system is to unexpected disturbances and surprises that can exceed or break that control (see below for more details).
The adaptive cycle is the process that accounts for both the stability and change in complex systems. It periodically generates variability and novelty, either internally such as through genetic mutations or adaptation, or by accumulating resources that change the internal dynamics of an ecosystem. These changes are the triggers for experimentation. In the reorganization stage various experiments are tested and resources are reorganized in new configurations, some of which enter a new exploitation stage to repeat the cycle.
Interconnectedness of Levels
Panarchy places great emphasis on the interconnectedness of levels, between the smallest and the largest, and the fastest and slowest. The large, slow cycles set the conditions for the smaller, faster cycles to operate. But the small, fast cycles can also have an impact on the larger, slower cycles. There are many possible points of interconnectedness between adjacent levels; however, two specific points are of particular interest with respect to sustainability:
- “Revolt” – this occurs when fast, small events overwhelm large, slow ones, as when a small fire in a forest spreads to the crowns of trees, then to another patch, and eventually the entire forest
- “Remember” – this occurs when the potential accumulated and stored in the larger, slow levels influences the reorganization. For example, after a forest fire the processes and resources accumulated at a larger level slow the leakage of nutrients, and options for renewal draw from the seed bank, physical structures and surrounding species that form a biotic legacy.
The fast levels invent, experiment and test; the slower levels stabilize and conserve accumulated memory of past, successful experiments. Sustainability in this framework is the capacity to create, test and maintain adaptive capability. Development becomes the process of creating, testing and maintaining opportunity.
Resilience is the capacity of an ecosustem to tolerate disturbances without collapsing into a qualitatively different state. The greater the resilience is in a particular ecosystem the more it can resist large or prolonged disturbances. If resilience is low or weakened, then smaller or briefer disturbances can push the ecosystem into a different state, where its dynamics change.
According to this model, after a disturbance, ecosystems evolve through time as ecological niches fill in (increasing connectedness), biomass accumulates (increasing potential) and more successful species outcompete less successful species (decreasing resilience). This makes ecosystems vulnerable to exogamous shocks that they cause a release of resources and a period of rapid reorganization.
Once resilience is overwhelmed and an ecosystem enters a new state, restoration can be complex, expensive, and sometimes even impossible. Research suggests that to restore some systems to their previous state requires a return to environmental conditions well before the collapse.
Resilience can be degraded by a large variety of factors which largely depend on underlying, slowly changing variables such as climate, land use, nutrient stocks, human values and policies. Resilience is a characteristic of natural systems. When resilience is weakened it is sometimes possible to restore it. Diversity is believed to be a key issue in restoring resilience – both biological and social diversity are important to the extent they contribute functional redundancy (i.e. similar services can be provided by some element in the diversity). But as biological diversity is lost, or as human systems and institutions become homogenous and rigid, then the likelihood of restoring lost resilience declines.
The ability to anticipate and plan for the future is a unique characteristic of human systems, and has the potential to increase their resilience.
Strengths of Panarchy
Panarchy is a complex and controversial framework for describing ecosystem and human system dynamics and interactions, and it is beyond the scope of this overview to provide a thorough critique. Despite its broad sweep it does have the advantage of relative simplicity in terms of the basic concepts used to describe an array of complex phenomena. This framework developed over several years, is solidly based in empirical research across a broad range of ecosystems, and continues to develop conceptually and generate policy relevant research.
Panarchy is a sophisticated attempt to connect ecosystem functioning with economic activities and human institutions for managing the relation between the two. It is an evidence-based approach that forces us to think in non-linear terms about complex systems, while providing the conceptual tools to understand the complexities involved.
Panarchy remains a hypothesis, despite the many empirical studies it has generated. It’s broad sweep requires more empirical testing. While it proports to be an integrative model of ecological, economic and social dynamics, it’s focus is primarily ecological. There are competiting attempts at integration,1 which may also account for the observed phenomena. There are also different ways of thinking about resilience (e.g. Fraser et al, in press). Despite these limitations, the panarchy framework continues to stimulate constructive debate and guide empirical studies.
Relation to Sustainable Scale
Many of the conclusions and observations made within the Panarchy framework are congruent with those regarding sustainable scale. There is recognition that:
- due to the inherent instability of ecosystems it is extremely difficult to detect or predict transitions to new ecosystem equilibria (e.g. when maximum scale might occur, see Considerable Uncertainty)
- sustainability is about retaining capabilities to continue contributing ecosystem services (i.e. natural income does not deplete natural capital, see Natural Capital and Income)
- resilience, the ability to resist disturbances, is a key characteristic of ecosystems (e.g. when throughput exceeds regeneration, resilience is reduced, see Sustainable Or Unsustainable)
- uncertainty is an inherent characteristic of the adaptive cycle and must be a key factor in any ecosystem management activity
- both uncertainty and risk increases with scale (i.e. problems at the global scale pose the greatest risks)
- precautionary policies are necessary to limit surprises (surprises increase as more natural income is used than is regenerated, see Wisdom in Precaustion in Visions For A Sustainable Future)
- the interaction of different time cycles is important, and that by the time efforts to keep fast variables within desired limits (e.g. GHG emissions) are recognized, it may be too late to avoid a major system change (e.g. climate stability) (see Climate Change)
- science uses uncertainty to drive inquiry, while vested interests use and foster uncertainty to maintain the status quo
- biodiversity is an important component of resilience, and is therefore important even if the types of biodiversity have no market value (see Biodiversity)
- new institutions are needed that gather better information on the slow variables, place greater emphasis on the future, maintain social flexibility for adaptive response, and which maintains and restores ecosystem resilience (see Institutions for a Sustainable Future)
- economic globalization contributes to simplification of ecosystems (as well as to their degradation), reducing resilience.
Panarchy is not a way of measuring sustainable scale. It does provide some interesting and challenging conceptual tools to assist in our understanding of how ecosystems and economic activities and institutions interact. It also identifies a variety of practical approaches to restore and conserve ecological sustainabilty.
Fraser, E., Figge, F., and W. Mabee. “A framework for assessing the vulnerability of food systems to future shocks,” Futures (2004).
Fraser, E. “Social vulnerability and ecological fragility: building bridges between social and natural sciences using the Irish potato famine as a case study,” Conservation Ecology, 7.2 (2003): 9.
1Fraser, E. “Social vulnerability and ecological fragility: building bridges between social and natural sciences using the Irish potato famine as a case study,” Conservation Ecology, 7.2 (2003): 9.
Gunderson, Lance and C. S. Holding. Panarchy: Understanding Transformations in Human and Natural Systems. Washington: Island Press, 2002.
“Resilience Alliance Home page.” Resilience Alliance. www.resalliance.org
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Systemic design agendas in education and design research
Since 2014, an international collaborative of design leaders has been exploring ways in which methods can be augmented, transitioning from the heritage legacy focus on products and services towards a broad range of complex sociotechnical systems and contemporary societal problems issues. At the RSD4 Symposium (2015), DesignX co-founder Don Norman presented a keynote talk on the frontiers of design practice and necessity for advanced design education for highly complex sociotechnical problems. He identified the qualities of these systems as relevant to DesignX problems, and called for systemics, transdisciplinarity and the need for high-quality observations (or evidence) in these design problems. Initial directions found were proposed in the first DesignX workshop in October 2015, which were published in the design journal Shè Jì. In October 2016, another DesignX workshop was held at Tongji University in Shanghai, overlapping with the timing of the RSD5 Symposium where this workshop was convened. The timing of these events presented an opportunity to explore design education and research concepts, ideas and directions of thought that emerged from the multiple discussions and reflections through this experimental workshop. The aim of this paper is to report on the workshop as a continuing project in the DesignX discourse, to share reflections and recommendations from this working group.