I thought that this post would be a good opportunity to talk about the fields of academic inquiry that I’m covering. Another good reason for this post is that I’ve been spending time away from reading, and I’ll need more time before I get back to the substantive topics at hand.
So, what books am I reading?
By sheer dumb luck, I chanced upon the field of organizational sociology – the study of human organizations and what happens inside them. As a result of that, I’ve also had the chance to go through the literature on institution theory – the norms and social practices that form and last, of which organizations are a subset of. This is institution theory as its most abstract. For example – marriage, handshake, the limited-liability company, the public service – would all constitute institutions, but some are also organizations. By this definition, all organizations are institutions, and some institutions are also social practises. The intellectual landscape for this has been covered to great detail ever since the end of the WWII. The same authors who describe the phenomenon of organizations also tend to cover what happens inside them. There has been considerable amount of literature on decisions-making, and a strand of this eventually became what we know today as artificial intelligence, in an attempt to model and improve cognition processes, both human and otherwise. Some of the major names in this field include, Chester Barnard, Herbert Simon, Paul DiMaggio, Walter Powell, Lynne Zucker, W. Richard Scott, and others.
The other streams that I’ve been pursuing comes from futures studies. Futures Studies examines the premises and possibilities of alternative futures. As people and organizations, we are constantly looking ahead and making plans to prepare for the future. We develop resources and capabilities to anticipate future demands. The timescale varies largely, obviously, but it’s a large part of what we do everyday, whether we realise it or not. Futures Studies looks also at the assumptions of how we think about the future, and examines critically the way we look at them. Scenario Planning has been one major tool used by practitioners of futures, and there are others. In this series of posts, I think about futures studies and how they are applied to make better decisions within organizations. I might stray off to think about alternative futures for Singapore and the world, but I won’t say much here, because that’s also my day job. There’s Jim Dator at Hawaii University at Manoa, and Sohail Inayatullah who’ve been developing the intellectual foundation for futures. On the practice side of thing of things, there’s the Shell-GBN group consisting of Pierre Wack, Kees Van Der Heijden, Peter Schwartz, and Adam Kahane who’ve been active in developing and communicating insights from their practises at Shell and outside. Singapore has been a major user of scenario planning for a while and developing as a node for futures in the Asia-Pacific region.
There is one other major field that I’ve been looking at, and has been the one other discipline that I’ve been trying to develop my knowledge of, and that’s the entire field of complexity theory. There are no real definitions for it, but I use it loosely to include studies of classical chaos (small changes in initial conditions have big effects later on), networks and graphs, cellular automata, and system dynamics. The whole field describes interactions – how simple global rules can yield tremendous variation and structure in the final outcomes. The definitive examples for complexity includes the Game of Life, Schelling’s Segregation Models, the artificial societies of Joshua Epstein. And then there’s System Dynamics, a field that was born out of attempts to describe interactions within organizations and project management and which then later gave rise to studies about the global system of the environment and human systems. For the first part of complexity that I’ve describe, Thomas Schelling, and Joshua Epstein were the authors of the models I’ve mentioned. For an introduction to chaos and complexity theory there’s James Gleick’s Chaos, and numerous books on complexity including Melanie Mitchell’s Complexity a Guided Tour. The intellectual foundations were established at Santa Fe Institute by W. Brian Arthur, John Holland and others, and leading thinkers today include Geoffrey West, Albert-Lazlo Barabasi, Luis Bettencourt, Cesar Hidalgo, Ricardo Hausmann. University of Michigan, and Northeastern University are leading centres today, although many graduate programmes also use complexity methods in their analysis.
Systems Dynamics deserves its own portion, and its lack of attention is only because it’s a mainstream topic in engineering. Of the many contributions of Systems Dynamics, the one that’s brought the most attention is arguably the World Model for the Club of Rome, which focused attention on the degradation of the global environment and the possible overshoot and collapse in the global economy and material conditions later on. Donella Meadows was one the most important advocates for systems thinking. Jay Forrester developed the programing environment for Systems Dynamics and the creator for the first models before Donella Meadows and is one of the most important pioneering figures for Systems Dynamics.
This has been a whirlwind tour of the thinkers that I’ve gone through. I’m trying to think through in small steps their relationships to one another. The central thread that runs through all of them is in trying to get a firmer grasp on the difficult terrains that we as individuals and organizations find ourselves in. Organizations, Futures, Complexity and Systems are all pieces in the puzzle, and there are other pieces as well. I haven’t talked about participatory methods, social/power structures, information systems, cognitive biases, and behavioural economics – just to name a few.
I don’t know what the end-result is, and this I guess is an example of generative complexity, where the building blocks can lead up to strong and beautiful structures.
We are all concerned about technology and innovation. We want to get a sense of how things will develop and to hope that we can anticipate the developments to come. The offerings by Kevin Kelly and W. Brian Arthur aim to help people get a sense of how technology develops. Kelly would know – he is the founding editor for Wired magazine. From him, the sense is that technology is fast becoming a branch of life by itself. There is first the observation that technology is developing in the trajectory of life; that as it is possible to think about the speciation of life into many diverse niches, so technology continues to specialise into smaller and smaller niches, becoming more ubiquitous. W. Brian Arthur describes the process of technological development – how new technology has to come from old technology; solutions to existing problems become the bases for new solutions. There isn’t a straightforward process as to how innovation comes about – old parts can become re-adapted for entirely new purposes unforeseen. The interesting details are in the individual processes where this has happened.
The two titles are related in how they talk about ideas. Gleick writes about the history of information and how information came to be understood. Gleick goes through the usual pantheon of heroes in information science – Shannon, Watson and Crick (the discovery of DNA and how it compresses the information needed for life), Vannevar Bush, von Neumann, up to the study of networks and the typology of information. Johnson writes about the history of innovation. Johnson notes how innovation comes from networked environments without any clear market incentive. There are numerous case studies of the discovery of the serendipitous discovery of the microwave background radiation and the conceptualisation of GPS.
Taking all 4 together, what do we get? There is unlikely to be a straightforward way to predict or foretell the future of technology/innovation. There is only the possibility of creating the conditions for innovation. Technological development is non-linear, and here the hope is that non-linear trajectories are precisely what one would hope for for the unexpected technological solutions to present issues.
A single idea by itself doesn’t stand for much, but one often finds a series of ideas, that when brought together, have powerful implications.
The 4 books above, when brought together, represent a compelling story about the trajectory of the world that we are on. Tainter’s Collapse of Complex Societies tells of the fundamental reasons why civilisations rise and fall. The main reason is simply that social organisations can become too complex that they collapse under their own weight when they can’t find new resources to solve new problems. Hence the western Roman Empire could not always tax the population while fighting the barbarians and improve food output in the context of changing climatic conditions. In this post, Collapse serves as the main meta-narrative – how the story of the world’s collapse might be told.
Systems Thinking, as represented by Donella Meadow’s Systems Thinking: A Primer, and the Limits to Growth: A 30-Year Update (LTG) represent another crucial element in understanding the processes through which a plausible environmental and socio-political collapse of the world might occur. While the words might sound abstract, these processes have real consequence. LTG belongs to the category of ideas that ought to be proven wrong. To cut the story short; LTG notes that the world is already in overshoot in the drawing of resources from the planet – renewable resources are being extracted without thought of their capabilities to regenerate; non-renewable resources are being extracted without thought of how they might be substituted with renewable sources; and the actual improvement of human welfare is being undermined by the increase in pollution and eventually by their actual health consequences. LTG’s example of CFC’s ban and the preservation of the ozone hole represents a positive example of how action is possible to avert a global catastrophe. Not all is lost, but the window for change before collapse is imminent is narrowing very quickly. With every year of inaction, we hurtle towards our own collapse in our interconnected world.
Annie Leonard’s The Story of Stuff is an illustrative example of systems and processes. Without explicitly using the language of systems thinking, Leonard nonetheless illustrates the flows and stocks of natural and human resources that come together to create the products that we take for granted. Plastics and the trace compounds used in their production present as-yet unknown health hazards, and preliminary findings of their role as hormone disruptors and as carcinogens are extremely worrying. The costs to human welfare in developing countries are tragic in all the sense of the word – from irresponsible toxic dumping to the horrid conditions of work – these represent a moral case against the excesses of the lifestyle of those in the developed countries. The entire system that creates the stuff in the first place is also clearly presented: the kind of economic system that believes in the unadulterated power of markets to bring about human welfare and the creation of demand via advertising and the grafting of status upon material goods at the expense of other expressions of human dignity.
What is the synthesis then? The only way to avoid collapse, as far as the books seem to indicate, is to embark on a lifestyle that reduces the emphasis on the material goods.To want less stuff, and to find contentment in the many other ways beauty and wonder are expressed. If the end goal is human happiness and dignity, these qualities can be attained through other creative means other than to demand more stuff in our lives. The slackening of this demand ultimately reduces the extraction of resources from the planet and the accompanying pollution; in the pursuit of being less centred on stuff, we can become more connected to the social milieu around us, and find the happiness that we so crave.
Joel Garreau’s Radical Evolution is probably the best nuanced book on technological developments. Garreau examines four spheres of technology evolution:
1. G – genetics;
2. R – robotics and artificial intelligence;
3. I – information – the ongoing developments in “cyber” space;
4. N – nanotechnology – the increasing miniaturization of components down to the molecular development.
Garreau takes cue from Kurzweil’s Singularity but goes deeper than that, in acknowledging other patterns of human development. For Garreau, Kurzweil’s exponential curves are not deterministic – we should not take for granted that these curves can go on forever. Yet, there is a case for the argument that human evolution is becoming engineered, as opposed to being driven by biological or cultural evolution, as has happened through human history. I think the term evolution should not be interpreted in the deterministic sense – there is no single pathway that the human race is proceeding towards. The term to use here is muddling, and muddling through is what is likely to happen despite the exponential curves. This picture of humanity muddling through these GRIN development is best represented through Garreau’s Prevail scenario, a sort of middle-of-the-road picture in contrast to Kurzweil’s overly-optimistic Heaven, and Bill Joy’s overly-pessimistic Hell scenario, where a single mistake can become catastrophic for much of life, human or otherwise.
The point of discussing all of these technological development can be described in the context of the larger question of how technology is going to change human nature irrevocably. Garreau references Fukuyama’s Our Posthuman Nature and Jaron Lanier’s thoughts on technology, and together they paint an ambivalent picture of GRIN developments. The consequences of these technologies together challenge many of our existing notions of what it means to be human, especially when these technologies disrupt the very deep equality that people still share with each other. The adoption of many of these technologies will mean a qualitative leap in human capabilities – watching the movie Gattaca gives a picture of what biotech enhancements could bring about, if not dealt with equitably. These questions are undoubtedly ethical in nature, and Garreau is able to weave these threads in a coherent fashion.
The Prevail scenario looks likely to be the picture that we will be dealing with, and these will be real questions that we will be addressing a few decades down the road. The development of the exponential curve is another question altogether. I want to differentiate between capability and capacity. Japan has the capacity/potential to start a nuclear weapons program if it wanted to, although as yet, it does not have a capability for it. The end results of the exponential curve in performance are likely to provide us with the capacity and potential to do all sorts of things, but the capabilities we will actually have is likely to be context drive. Studies in Science-Technology-Studies have revealed all sorts of path-dependence in the development of many of the technologies we take for granted, and it is the rare occasion that technical premises were the main drivers of the outcome. Nuclear power was one example, as is the QWERTY keyboard, and there are others. The translation from capacity to capability is a separate question.
My guess? We are likely to see Singularity-type developments in the timeline that Kurzweil demonstrates, but the outcome will be so different from what he envisions, that we won’t recognise it as such. Super-capable computing abilities will be ubiquitous, as are our mobiles today, and we won’t notice it. All sorts of intelligence will be available to us, including super-human contextual intelligence, but won’t be recognised as such. Self-aware systems will become one feature, but not the main feature. As for brain-computer interfaces – I don’t know. The futures are too far out for this post, although this topic is something I suspect I will get back to.
Someone vague familiar with systems thinking might think this to be a warning. Systems thinking must be some very big academic subject with a lot of information to plough through. Not so. The primer is a cute little book, barely over 200 pages, in a comfortable font size with straight writing.
Donella Meadows writes clearly and simply to illustrate the principles and power of systems thinking. The concepts of stocks and flows, reinforcing and balancing loops are all illustrated with great clarity. Reinforcing loops – that’s the technical term for what we term as vicious or virtuous cycles – the spirals. Balancing loops refer to a system or sub-systems that tend to check their own growth – so predator-prey relationships are a class of balancing loops.
The most important nuggets for someone interested in introducing changes in systems would be in the last chapter, where she lists out 12 ways to change the behaviour of a system. I won’t go through all 12 here, although a list is available on Wikipedia.
I’ll just list a few that I think are crucial:
6. The structure of information flow – how information is received, , perceived, and acted on;
4. Self-organisation – allowing for systems to change.
3. Goals – what is a system for?
2. Paradigms – what are the assumptions that underly the premises of a system?
Actually, chapter 7 is also important, because of its prescriptive nature, and it offers the following:
– Get the beat, watch how a system behaves;
– Expose mental models, and test them;
– Honour, respect and distribute information;
– Language matters – so use systems-terms such as loops, feedback, self-organisation; (but don’t abuse them!)
– Pay attention to the important, not just quantifiable;
– Make feedback policies for feedback systems (create feedback loops for feedback loops);
– Aim to enhance the system as a whole;
– Use a current system’s capabilities to grow itself;
– Locate responsibility in the system – design for intrinsic responsibility.
I can imagine these things be applied to all sorts of systems, especially socio-political cultures. Systems thinking is undoubtedly an important mental toolkit to figure out how the world works and how to enact change in it.
When we think about the things we want to change, the usual linear logic is to identify problems and figure out the individual factors that contribute to a particular problem. Systems thinking re-frames the entire trajectory of change, and examines the whole ecosystem behind the problem. Anything, from the economy to poverty to transportation issues are usually amenable to systems-thinking analysis, although they present us with the issue of how to isolate the specific issue. Unfortunately, bounding the system is something that’s not discussed as much – I can guess that can only come about with actual experience and practice in doing systems-thinking.