The map of the week is about patriotism.
This is a purely-sentimental driven map, although things like economic opportunities could be interpreted in terms of the pride and identification that one has with the country, as with “I appreciate the opportunities I have in this country to realise my abilities”. In a similar way, ownership of property can also be interpreted in the same vein, as with, “I appreciate the fact that I can own my own home.” And then there are pride-destruction experiences, such as losing confidence with the political leaders, losing employment, seeing discrimination perpetuated by authorities of one form or another. This isn’t specific to any country, and I guess the weights of countries will be different. In others, the identification with a local or a national culture (if one exists at all) could be more important than pride for the local region or community.
Last weekend, I got back from climbing Mt Kinabalu, hence the absence. This time, I continue to view prominent issues via the same systems lens.
This time, I’m taking on this big-hairy topic called, “perception of national identity”. One way to read this is start with the “sense of national identity”. It is determined by family, perceptions of history, education, social life, national service, and relative attainment and quality of life. Perception of history is in turn, affected by education, which affects social life, economic activities, and relative attainment. Attainment is affected by economic activities and cost of living.
Yes, this map of relations is TOO simple. What about art, culture, ways-of-life? To subsume it under “quality of life” is a cop-out, I admit, but it’s what I’ve decided for now.
See – that wasn’t that hard! Just kidding.
This is a hypothetical concept/relations map. You can make your own maps too, and your map will probably be very different from mine.
Some caveats: this is obviously, one possible map amongst many. Please don’t take this to be definitive. Another point of personal discontent is that this is a concept map more so than a systems map. Mostly because the relations are nearly all one-way, which would make it amenable for regression analysis, if you were to do one. Feedbacks would be apparent over time, in thinking about the “stock of national identity” of the entire population. That might sound absurd, but that’s what systems thinking deals with – stocks and flows.
Obvious question: How do you quantify the stuff inside these maps? Great question – no obvious answer. Likert scales are an obvious choice; another way is to ask, “what difference would it make if something was taken out, or insufficient?” My other response would just be that placing all these things all at once gives you a starting point in thinking about what you want to change.
Watch out for more maps to come!
Thinking about testing assumption
The post today goes into some details into thinking about how can we think in clear terms about the assumptions and indicators that we want to implement. This post is not going to be as conceptual as the previous terms, although the concepts here remain very related to the ones that have been previously explored.
The main question this post addresses is this: it’s great that we are following through with all of these things. It’s great that we are exploring assumptions and cognizant of how indicators can petuntse the assumptions that we have laid down. We might even be doing this without consciously realizing the implications. So now the question is: how do we begin exploring our assumptions and indicators in robust ways? How do we know if the systems we want to establish with turn out in the way we truly desire?
There are entire fields of studies for this. In the post reviewing the literature of the field, I introduced a whole bunch of disciplines, the founding figures, and the seminal works in there. I’m interested in system dynamics and complexity science here, and the kinds of tools that they use to study the systems they are interested in. I’m only beginning to learn about the tools themselves and exploring how they can be used to address questions we are interested in.
There are tools for systems dynamics and there are tools to begin exploring complexity sciences. I’m still learning about the to tools themselves especially for those who are trying to learn about the disciplines themselves. After all, the only way to truly learn about these philosophies is to practice them, and the tools allow one to get into the language of thinking about these things in a real way.
For system dynamics, there’s STELLA and Vensim, and I have only used Vensim. For approaches to complexity, there’s Netlogo for agent based modeling – which allows for the easy creation of agent based models without a lot of knowledge of coding. I know that R does a few things as well, even social network analysis, but I have not used that myself, but it’s something I will be exploring. Santa Fe Institute has launched an online course, and there’s a module on learning Netlogo. Coursera has a course by Scott Page on Models Thinking; Michael Kearns’s Networked Life is worth a look too!
Having used Vensim to do system dynamics, one of the most immediate things that I realise is that it’s not easy to think in clear terms just what is going on in the entire system that’s being studied. Thinking about a predator-prey relationship already assumes knowledge about a bunch of things, such as the lifespans of the preys and predators, their death rates, the predation rates, and so on.
Knowing the numbers is often insufficient to construct a model. In fact the numbers that are needed might never be quantifiable – in constructing various models, one has to inject various assumptions especially in describing the relationships between one variable and another.
With modelling and simulations, one has to think about how assumptions translates into quantitative terms. This isn’t as obvious as it sounds. I often find that our assumptions are sometimes entire systems packaged in a few words. Think about a statement such as, “excessive gambling destroys families.” At face value, this statement seems true, and few people would find difficulty rejecting that. But what does “excessive gambling” mean? Are there studies about the prevalent rate of gambling? And what does “destroys families” mean? We are usually talking about a diverse of adverse consequences, ranging from loss of incomes, divorces, incurring debts and bankruptcies. We are also often referring to the psychological costs that come with the conflicts that often lead to the results mentioned. If even for simple cases the causal mechanisms are difficult to establish, imagine the work undertaken to understand larger systems. Often, there is little choice but to introduce factors on some assumptions, and to refine them as the model is developed. “All models are wrong, but some are useful.” – statistician George Box
These models should be thought of as sandboxes – a container in which to let our assumptions play out. Without the ability to run controlled experiments in social affairs, models and simulations offer a decent way to play with our own mental models of reality.
The explosion of breadth in the previous week’s post is not typical. This post is a reversion to the mean in thinking about smaller concepts, what they mean, and how they fit into a larger intellectual synthesis. Previous posts have covered the role of assumptions and how indicators are the expressions of assumptions. I’ve also covered very deep ideas about rationality, certainty and zero-sumness that seemed to be the uber-assumptions that shape the world very deeply.
Whatever I can build comes as the result of introducing concepts that are adjacent to these central ideas. I assume by default, a very direct relationship between indicators and assumptions, even as I have also alluded to the fact that interpretations matter. I only stated that interpretations matter, that decisions-making is key, and that assumptions can change. This juncture becomes an appropriate place to talk about systems dynamics, and the relationship between indicators and rationality.
The first thing to realise that assumptions and indicators reinforce each other. You cannot change an assumption without also changing the indicators. In reality, both hardly change at once; one usually changes first, followed by the next. Change is therefore not instantaneous. Change(s) take time for the assumptions and the indicators to take up new positions in the system to be moved into.
To develop this idea further, decision-making is therefore not a choice between two or more discrete options, but also the choice between different kinds of systems. Decision-making is about making (non)-changes in a system.
This is also why systems dynamics is about thinking in terms of relationships far more than the other concepts thus explored. Systems thinking explores the relationships between various indicators. Indicators don’t stand on their own; they affect each other, redirecting flows.
The interlocks between assumptions and indicators is the chief reason why changes in any organisation or institution can be so difficult. A lot of change takes place in the things that are measured – the indicators. Or, the stated goals can be different, even if the indicators remain the same. In either of these situations, the logic of the system can remain the same, and nothing gets changed at all. Lasting change at both the assumptions-level and the indicators-level requires simultaneous change at both fronts – and this is why change is so difficult.
The description thus can seem mechanistic, full of notions of rationality, and of gears grinding on. The reality is that the change in systems are far from rationally executed. They are fraught with failures. Sometimes they require open conflict with existing assumptions and indicators, and in the process resistance crops up. The key thing is often to connect with the people who are in control of the indicators and the response to those indicators. This connection has to be an emotional one – the building of new links through empathy and in sharing visions of new futures – and new systems.
This I guess, is one reason why futures exercises are often done with decision-makers. In one shot, the conduct of a futures exercise can change the assumptions of the decision-makers, and those decision-makers are often in the position to change the indicators.
There really are two systems in interactions when we think about it, and they meet in the process of thinking about futures within the organisation. The first system is the one in the contextual sphere – about the external components that are interacting with one another, and the one that we try to have a grasp on via systems dynamics or complexity theory. There is the second system that does not get as much mention – and that is the internal system within the organisation. For change to occur anywhere, one also has to look at the internal structures and the interactions within. Again, going back, the discussion on indicators and assumptions set the foundation for systems thinking within the organisation.
When thinking about change, we are really talking about different kinds of change all at once, and part of the purpose of this post is to breakdown in clearer terms what we mean when we talk about change.
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.
In the previous post, I mentioned three modes of knowledge that undergird our way of thinking. They are:
1. Certainty is possible;
2. Rewards are zero-sum;
3. Rational thought is the only acceptable mode of thinking.
I also posited that “indicators” are central to the modes of knowledge above. This post elaborates this claim, and points to other things in this map of change-making and leadership in society.
I want to first point out to examples in societies. We wonder why why is it that passion doesn’t take flight here; or that excellence doesn’t take flight here – often don’t see past the immediate reason of a lack of creativity and the indicators. A better question to ask is, if the current incentive structure in our system is really suitable for student-based initiative, or is it meant for the purposes of supplying people for an advanced economy. That to me, is a far better way of asking the question of education than to do direct rebuttals on the state of education.
Wherever you look, indicators are everywhere. Indicators help to simplify the world. All the understanding of the student is distilled into a single factor; a country’s economic performance, for all its messiness and complexity, becomes drilled down to one figure; a company operations and worth, similarly. Indicators simplify the world with all its messiness into a few numbers. Indicators solve the problem of computation and the cost of information. In interaction with the three modes of knowledge from before, indicators contribute to decisions that are concrete, specific and actionable. And here’s the problem: not every problem out there is amenable to a numerical interpretation; and the reality is that a single figure, or even a few figures, will not be able to describe the nuances of the situation.
We often get tired of the existing set of indicators. The easy answer to that is to say that “the system has to change”, or “the indicators have to change”, and we often fail to think about why the indicators existed in the first place. Grades are after all, easier to evaluate than the concept of understanding. For indicators to change, the modes of thinking behind the indicators have to change too.
There’s still another aspect to indicators and information, which is about the organization of information – we are still not entirely sure what the best ways are to represent reality with all its messiness. This is one reason why we have distributed information systems – the economy with its system of prices of goods and services is actually one such way. Companies rise and fall and rise again when they get the price information right or wrong. Overall, everyone benefits – or so the hope goes.
As a result of this dependence on indicators, the indicator tail can wag the system dog. And besides, it’s not always about the raw form of the indicators that exist, and always about how those indicators get played out. Indicators are about accountability and trust – and it’s also when indicators become more important than the matter at hand that we should worry.
As a summary to this post, I want to point out that when viewed through the lens of indicators, one also have to look at organizations as cognition systems. Indicators are used in organizations as a way to manage information flows and where decisions are needed. Indicators are also the ways through which mindsets and assumptions get played out – it’s often the indicators that reveal how a person thinks – much like how our behaviours can demonstrate the things we value. In the same way, the models of thinking of an organisation’s leadership gets demonstrated in the indicators and the behaviours of the organisation.
So far, I’ve talked about knowledge modes and indicators. I’m still some distance away from the thoughts about change-making and leadership that served as the original motivations for this series of posts. I hope to get to that soon enough.
Many young people have passed by this station before – thinking about how they can make the world a better place. With that lens, identifying the opportunities for change becomes too easy. You see how there are poor people, and wonder why they are poor; you think about why there is congestion in the day and how to reduce that; you think about the stresses that people experience and think about if there are other modes of living that haven’t been thought of.
And then you wonder if other people have started to embark on making changes, and you will soon find many – there are many associations and organizations that are founded on the premise on making the lives of some better; on a more philosophical point, you start to see the point of effective governance, and the need for a strong public sector; along the way, you see how products that are meant to make people’s lives better become the way to make money.
Another step along the way to imagining better worlds often comes passes the stop of “mindset change” or “paradigm change” – thinking about things through a systems-lens – understanding how there are people and structures that interact with one another to create the phenomena we see. Systems thinking/dynamics is a pretty established discipline – we actually think like this everytime we think in a broader context. The same also applies to any project of creating change.
The structures we see in reality depend on indicators. More than that, indicators reflect the assumptions and the thinking behind the way systems operate. Over time, these indicators become important and become the points on which whole systems turn. Grades that were used to evaluate students’ level of understanding become the basis for promotion for teachers and principals; GDP runs the economic thinking of policy makers across the world even though all it measures is economic activity; on the other hand, there is less emphasis on the ecosystem services – just to point to one underrated variable.
The other thing that people ought to think about when creating change is this: think about the structures in organizations. Think about who could win and who could lose from these changes that are being proposed. Think about how the losers and winners might react to this. Will the losers lose too much that they object violently to new changes? Who wins more? How can these changes be equitable? How do we keep the losers on the side of change?
I find that the talk of change-making often does not go deeper than this. Prevailing discussion tends to have a naive view that change will come about and be introduced without resistance, and will be accepted wholeheartedly in any organization or institution in society. That is clearly not the case. Any change results in people winning and losing. In the rational sense, we often weigh this as costs and benefits but in reality, change is an emotional issue. To ignore the emotional aspect of change would be to ignore the reality of human psyche.
There’s a lot more to this entire issue, obviously, and there’s an entire industry – literally devoted to change management and all that. More to come in future posts.