In conversations with friends, I’ve had the chance to reflect about how I look through readings. This is an attempt to articulate what happens when I’m browsing for articles and books, both physically and digitally.
What usually happens is that I start off with a bit of grand theorizing – find the people who try to construct universal frameworks. These are only the beginnings and they are often discarded and/or refined as I encounter new facts and frameworks. After a while, I realise that I’m looking a lot at academics and specialised journalists who have spent a long time looking at a specific area. This is also that I try to avoid op-eds and authors of books who tend to only aggregate newspaper material.
Tapping into academics and specialised journalists helps me to construct detailed concepts about how a specific issue develops and its sub-issues. For example, if I was doing work on poverty, I would be looking at grand theories about how poverty happens – cultural framings, economic framings, cognitive framings and so on. Within each of these framings I would go into detail, all the time asking if the framings are appropriate. For example, with culture, I would ask, how do people talk about culture in useful ways? With economic, perhaps its an issue of skills and economic structure. With cognition, it could be the way people decide spending and investment decisions. And then go into greater detail into the linkages between say, economics and culture.
After exploring the silos, I’ve found it helpful to read works on how the different silos are related. I like the works by Vaclav Smil as he explores the interactions between energy, food production, consumption and natural processes. Sometimes they horizontal linkages become silos in themselves – such as system dynamics and complexity, both of which are vast disciplines in themselves. So with the poverty example, I would be interested in how cultural framings interfere with economics and/or with cognition, and how various countries have addressed poverty in various ways.
After a while, it’s possible to develop a meta-sense when looking at articles into: (1) things directly relevant to interests; (2) things that add to current interests; and (3) things that I never knew about. (1) and (2) overlap, and its a function of what am I interested in at the current moment, and also about rebalancing areas that I am more familiar with and what I’m not as familiar with.
I try to look for fact-heavy books with subtle arguments. They tend to be historical and supplemented by primary research – which as a result, becomes the domain of academic researchers, or very senior journalists who have spent a lot of time in an area.
I guess what drives me is that I’m trying to understand the world and constructing frames to guide my understanding.
So far, what I’ve described is pretty generic – I’m thinking this is the general process of what most people go through in many things, ranging from workplace implicit knowledge to how fan-fiction is generated.
To further categorize the knowledge acquired, another labels can be helpful. I’ve found Aristotle’s 4 causes to be useful labels: efficient, material, formal and final causes of things. In short, they describe the process, the materials/technology, the medium in which the happen and the purpose for why they occur, respectively.
I’ve found the Snowden’s Cynefin useful – in describing the epistemology of events/processes – whether the process are simple, complicated, complex or chaotic – terms to describe the relationship between cause and effects and the degrees to which they are known. Kahane’s notions of complexity are also useful – whether things are socially (involving diverse beliefs), generative (awkwardly, the expectedness of outcomes), and dynamic (again, relationship between cause and effects) – as I understand the terms. I hope to explore their notions and other notions of complexity in greater detail in a future blog post.
There are some limitations in my current understanding. I don’t have clear notions about aesthetics, spaces, tactility and perceptions. My design/aesthetic senses are not as developed, and its something I ought to get more experiences at.
Thanks for reading, and hope you find this helpful. 🙂
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.
I have been able to finish Irving Janis’s Groupthink – the classic study of poor decision-making as evidenced in the foreign policy catastrophes.
Any major decision is made in a small-group context, where senior people from varying backgrounds come together to contribute their perspectives with the aim of ultimately making an informed decision about a particular issue. However, the process described above is more of an ideal than an objective routinely achieved in reality. In the boardrooms across the world, decisions might be made in circumstances far less ideal.
The value of Groupthink lies in informing how poor and good decisions have been made in the US related to foreign policy. Janis includes the Pearl Habour attack, the Bay of Pigs, the decision to unify Korea during the Korean War, and the escalation of the Vietnam War as the starting points. Counter-examples, demonstrating sound decision-making process included the Cuban Missile Crisis and the Marshall Plan to rebuild Europe following the Second World War.
Janis describes how the team dynamics in the decision-making teams encouraged consensus-creation, and the active discouragement of dissenting views by ‘mind-guards’ – people who were close to the leader, and who were trusted. Having a sense of invulnerability greatly contributed to poor decision-making as well. Leadership matters, as when the leader already has an established view on things, and expects that the recommendation be inclined towards that view.
This is not to say that the people involved in fiascoes are always incompetent. If there is another finding from Janis, it would be that brilliant people caught in the wrong social/group-context can make catastrophic mistakes. In other words, few groups would be truly immune from the effects of groupthink symptoms. The counterpoints in the Marshall Plan and the Cuban Missile Crisis also illustrates how a similar group of people involved in fiascoes can also be wisely informed in a different context to make robust decisions.
The scary thing for anyone concerned about the phenomena of groupthink is that no institutional measure can truly curtail it when it happens. The closeted nature of these decision-making groups is one reason for it – these are typically decisions done in contexts that are shielded from public scrutiny, and even scrutiny from other quarters. After all, how could anyone interrupt the proceedings of leading to the Bay of Pigs fiasco, or tell MacArthur about the consequences of forcibly unifying Korea? Social dynamics can overpower rational objections to ill-conceived plans.
The case of the Cuban Missile Crisis was where Janis presented how the lessons from the Bay of Pigs fiasco was applied. The participants in the Cuban Missile Crisis were asked to be critical thinkers and not only representing their respective agencies; to have leaderless sessions to provide a more permissive environment. During the proceedings, participants experienced discomfort as they switched positions before coming round to a consensus view. Vigilant appraisal marked the attitude of the discussions.
At this point, there are ways of organizing decision-making processes that avoid the pitfalls of groupthink. The scenario-process can be thought of as a way of organising information in a way that deliberately prevents premature closing off of options. There are drawbacks, for sure. Scenario-processes are often protracted, and can take up large portions of personnels’ time. As with groupthink, there have been clear examples of past successes – Shell was able to rehearse a possible collapse of the Soviet Union to take advantage of the opportunities then; South Africa political, social and economic elite used the scenario process to create a shared language to navigate the transition post-apartheid.
Obviously there have been shortcomings in the scenario process, in ways that mirror the characteristics of poor decision making. Peter Schwartz wrote about the instances where scenarios were not as effective as they could be, such as having the wrong clients, underestimating the effects of the global downturn, and not hearing from sufficiently-diverse viewpoints.
With this short exploration we can think about the hallmarks of a good decision, and what makes a good process. A good decision process is the result of having a diversity of relevant views, one that lays out in detail the implications of contingencies and consequences of the decisions. A good decision process should then be designed for diversity, open airing of dissenting views, where the leader should not dictate policy preferences early on in the process.
The decisions that Irving Janis studied took place largely in a limited group. What about decision-making in large bureaucratic organisations, with many hierarchies of command?
As an admin note, I’ll be away from 12 January to 18 January – the timing of the posts will be affected by this. I’ll probably have a post on Friday, and maybe another on 19 Jan.
Why do organisations exist? For all my discussion on organisations and change, I have not dealt with the important question of why do organisations exist in the first place. The other point I should address is why is it that I start a discussion on change-making from an organisational point of view.
With this post, I’ll answer the fundamental question first; of why do organisations exist. Organisations exist in contemporary society mainly as a way for groups of people to come together to achieve a common purpose over some period of time. This is certainly a laymen’s foundation, but it’s a decent starting point. The telos over time is what separates an organisation from a social movement.
Some responses can seem obvious – that organisations exist because there are things that need to be done by a group of people with different abilities. Is a community an organisation? I can’t say for sure. I think community deserves its own term. Communities might be one example of how people are organised, but I’m not sure that communities are telic (ends-driven, from telos) organisations, unless one were to accept a broad definition of telos. The state – the bureaucratic structure of governance – is an organisation, or a collection of organisations. Is a family an organisation? How about a tribe? They are certainly forms of human organisation – and in a loose sense, they are organisations in themselves too.
So organisations are a basic form of human activity.
A more rigorous view of human organisations advanced with Coase’s theory of the firm. Why do companies exist? Why can’t the disparate functions of the firm be parcelled out to smaller units? As I understood it, Coase was referring to transaction costs, and one way of reducing it was to have these functions incorporated under a single authority. This could presumably reduce the communication costs compared to having them outside the firm. So if dealing with internal departments was difficult, think about how much more difficult it would be if those departments were separate firms to be negotiated with.
This is also an appropriate place to talk about information and the ways the flow across the organisation. In more abstract terms, an organisation is a way for social information to be concentrated and managed in ways that result in decisions and then ultimately, in actions in the environment. The concentration, management and enactment of information are important functions of an organisation.
This also ties back in the prior discussions on indicators. The way information moves around the organisation can be represented in terms of stocks and flows – and lend themselves to systems analysis.
Information flows are the result of information asymmetry – that someone out there has more information than someone else. In the market system, prices were signals that aim to reduce information asymmetry. In the examples of the firm, having departments within the firm reduces the information asymmetry for the entire company to get something done. Achieving information flows is thus one reason for the existence of functionally-self-sufficient firms.
The other concept that comes out of this discussion on information flows is the principal-agent problem. The principal-agent problem is one representation of information asymmetry – what happens when the agent – the person acting on behalf of the principal, who is often a superior responsible for the agent – acts for himself. This happens when the agent has typically more information about actual circumstances, and can often exploit the situation for their own benefit, and not for the principal’s benefit. When people talk about aligning incentives, or reducing corruption, the abstract issue is often those of information asymmetry or the principal-agent problem.
And organisations do not stand on their own. They are interacting with other organisations in society, with other companies, community groups, and politicians. The internal dynamics within any organisation come into contact with other organisations, and with the physical environment. The issues of information asymmetry, principal-agent problems, information management and decisions-making all come together in reality. Without first identifying the nature of the monsters we are looking at, our actions are often impotent, or worse, cause issues to deteriorate.
With this post, I hope I’ve clarified why I’ve gone through a long and tedious process. The next post will directly address why I’m looking at change within organisations specifically.
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.