From the coalface: an essay on the early history of sociotechnical systems
The story of sociotechnical systems began a little over half a century ago, in a somewhat unlikely setting: the coalfields of Yorkshire.
The British coal industry had just been nationalised and new mechanised mining methods were being introduced in the mines. It was thought that nationalisation would sort out the chronic labour-management issues and mechanisation would address the issue of falling productivity.
But things weren’t going as planned. In the words of Eric Trist, one of the founders of the Tavistock Institute:
…the newly nationalized industry was not doing well. Productivity failed to increase in step with increases in mechanization. Men were leaving the mines in large numbers for more attractive opportunities in the factory world. Among those who remained, absenteeism averaged 20%. Labour disputes were frequent despite improved conditions of employment. – excerpted from, The evolution of Socio-technical systems – a conceptual framework and an action research program, E. Trist (1980)
Trist and his colleagues were asked by the National Coal Board to come in and help. To this end, they did a comparative study of two mines that were similar except that one had high productivity and morale whereas the other suffered from low performance and had major labour issues.
Their job was far from easy: they were not welcome at the coalface because workers associated them with management and the Board.
Trist recounts that around the time the study started, there were a number of postgraduate fellows at the Tavistock Institute. One of them, Ken Bamforth, knew the coal industry well as he had been a miner himself. Postgraduate fellows who had worked in the mines were encouraged to visit their old workplaces after a year and write up their impressions, focusing on things that had changed since they had worked there. After one such visit, Bamforth reported back with news of a workplace innovation that had occurred at a newly opened seam at Haighmoor. Among other things, morale and productivity at this particular seam was high compared to other similar ones. The team’s way of working was entirely novel, a world away from the hierarchically organised set up that was standard in most mechanised mines at the time. In Trist’s words:
The work organization of the new seam was, to us, a novel phenomenon consisting of a set of relatively autonomous groups interchanging roles and shifts and regulating their affairs with a minimum of supervision. Cooperation between task groups was everywhere in evidence; personal commitment was obvious, absenteeism low, accidents infrequent, productivity high. The contrast was large between the atmosphere and arrangements on these faces and those in the conventional areas of the pit, where the negative features characteristic of the industry were glaringly apparent. Excerpted from the paper referenced above.
To appreciate the radical nature of practices at this seam, one needs to understand the backdrop against which they occurred. To this end, it is helpful to compare the mechanised work practices introduced in the post-war years with the older ones from the pre-mechanised era of mining.
In the days before mines were mechanised, miners would typically organise themselves into workgroups of six miners, who would cover three work shifts in teams of two. Each miner was able to do pretty much any job at the seam and so could pick up where his work-mates from the previous shift had left off. This was necessary in order to ensure continuity of work between shifts. The group negotiated the price of their mined coal directly with management and the amount received was shared equally amongst all members of the group.
This mode of working required strong cooperation and trust within the group, of course. However, as workgroups were reorganised from time to time due to attrition or other reasons, individual miners understood the importance of maintaining their individual reputations as reliable and trustworthy workmates. It was important to get into a good workgroup because such groups were more likely to get more productive seams to work on. Seams were assigned by bargaining, which was typically the job of the senior miner on the group. There was considerable competition for the best seams, but this was generally kept within bounds of civility via informal rules and rituals.
This traditional way of working could not survive mechanisation. For one, mechanised mines encouraged specialisation because they were organised like assembly lines, with clearly defined job roles each with different responsibilities and pay scales. Moreover, workers in a shift would perform only part of the extraction process leaving those from subsequent shifts to continue where work was left off.
As miners were paid by the job they did rather than the amount of coal they produced, no single group had end-to-end responsibility for the product. Delays due to unexpected events tended to get compounded as no one felt the need to make up time. As a result, it would often happen that work that was planned for a shift would not be completed. This meant that the next shift (which could well be composed of a group with completely different skills) could not or would not start their work because they did not see it as their job to finish the work of the earlier shift. Unsurprisingly, blame shifting and scapegoating was rife.
From a supervisor’s point of view, it was difficult to maintain the same level of oversight and control in underground mining work as was possible in an assembly line. The environment underground is simply not conducive to close supervision and is also more uncertain in that it is prone to unexpected events. Bureaucratic organisational structures are completely unsuited to dealing with these because decision-makers are too far removed from the coalface (literally!). This is perhaps the most important insight to come out of the Tavistock coal mining studies.
As Claudio Ciborra puts it in his classic book on teams:
Since the production process at any seam was much more prone to disorganisation than due to uncertainty and complexity of underground conditions, any ‘bureaucratic’ allocation of jobs could be easily disrupted. Coping with emergencies and coping with coping became part of worker’s and supervisors’ everyday activities. These activities would lead to stress, conflict and low productivity because they continually clashed with the technological arrangements and the way they were planned and subdivided around them.
Thus we see that the new assembly-line bureaucracy inspired work organisation was totally unsuited to the work environment because there was no end-to-end responsibility, and decision making was far removed from the action. In contrast, the traditional workgroup of six was able to deal with uncertainties and complexities of underground work because team members had a strong sense of responsibility for the performance of the team as a whole. Moreover, teams were uniquely placed to deal with unexpected events because they were actually living them as they occurred and could therefore decide on the best way to deal with them.
What Bamforth found at the Haighmoor seam was that it was possible to recapture the spirit of the old ways of working by adapting these to the larger specialised groups that were necessary in the mechanised mines. As Ciborra describes it in his book:
The new form of work organisation features forty one men who allocate themselves to tasks and shifts. Although tasks and shifts those of the conventional mechanised system, management and supervisors do not monitor, enforce and reward single task executions. The composite group takes over some of the managerial tasks, as it had in the pre-mechanised marrow group, such as the selection of group members and the informal monitoring of work…Cycle completion, not task execution becomes a common goal that allows for mutual learning and support…There is basic wage and a bonus linked to the overall productivity of the group throughout the whole cycle rather than a shift. The competition between shifts that plagued the conventional mechanised method is effectively eliminated…
Bamforth and Trist’s studies on Haighmoor convinced them that there were viable (and better!) alternatives to those that were typical of mid to late 20th century work places. Their work led them to the insight that the best work arrangements come out of seeking a match between technical and social elements of the modern day workplace, and thus was born the notion of sociotechnical systems.
Ever since the assembly-line management philosophies of Taylor and Ford, there has been an increasing trend towards division of labour, bureaucratisation and mechanisation / automation of work processes. Despite the early work of the Tavistock school and others who followed, this trend continues to dominate management practice, arguably even more so in recent years. The Haighmoor innovation described above was one of the earliest demonstrations that there is a better way. This message has since been echoed by many academics and thinkers, but remains largely under-appreciated or ignored by professional managers who have little idea – or have completely forgotten – what it is like to work at the coalface.
TOGAF or not TOGAF… but is that the question?
“The ‘Holy Grail’ of effective collaboration is creating shared understanding, which is a precursor to shared commitment.” – Jeff Conklin.
“Without context, words and actions have no meaning at all.” – Gregory Bateson.
I spent much of last week attending a class on the TOGAF Enterprise Architecture (EA) framework. Prior experience with IT frameworks such as PMBOK and ITIL had taught me that much depends on the instructor – a good one can make the material come alive whereas a not-so-good one can make it an experience akin to watching grass grow. I needn’t have worried: the instructor was superb, and my classmates, all of whom are experienced IT professionals / architects, livened up the proceedings through comments and discussions both in class and outside it. All in all, it was a thoroughly enjoyable and educative experience, something I cannot say for many of the professional courses I have attended.
One of the things about that struck me about TOGAF is the way in which the components of the framework hang together to make a coherent whole (see the introductory chapter of the framework for an overview). To be sure, there is a lot of detail within those components, but there is a certain abstract elegance – dare I say, beauty – to the framework.
That said TOGAF is (almost) entirely silent on the following question which I addressed in a post late last year:
Why is Enterprise Architecture so hard to get right?
Many answers have been offered. Here are some, extracted from articles published by IT vendors and consultancies:
- Lack of sponsorship
- Not engaging the business
- Inadequate communication
- Insensitivity to culture / policing mentality
- Clinging to a particular tool or framework
- Building an ivory tower
- Wrong choice of architect
(Note: the above points are taken from this article and this one)
It is interesting that the first four issues listed are related to the fact that different stakeholders in an organization have vastly different perspectives on what an enterprise architecture initiative should achieve. This lack of shared understanding is what makes enterprise architecture a socially complex problem rather than a technically difficult one. As Jeff Conklin points out in this article, problems that are technically complex will usually have a solution that will be acceptable to all stakeholders, whereas socially complex problems will not. Sending a spacecraft to Mars is an example of the former whereas an organization-wide ERP (or EA!) project or (on a global scale) climate change are instances of the latter.
Interestingly, even the fifth and sixth points in the list above – framework dogma and retreating to an ivory tower – are usually consequences of the inability to manage social complexity. Indeed, that is precisely the point made in the final item in the list: enterprise architects are usually selected for their technical skills rather than their ability to deal with ambiguities that are characteristic of social complexity.
TOGAF offers enterprise architects a wealth of tools to manage technical complexity. These need to be complemented by a suite of techniques to reconcile worldviews of different stakeholder groups. Some examples of such techniques are Soft Systems Methodology, Polarity Management, and Dialogue Mapping. I won’t go into details of these here, but if you’re interested, please have a look at my posts entitled, The Approach – a dialogue mapping story and The dilemmas of enterprise IT for brief introductions to the latter two techniques via IT-based examples.
<Advertisement > Better yet, you could check out Chapter 9 of my book for a crash course on Soft Systems Methodology and Polarity Management and Dialogue Mapping, and the chapters thereafter for a deep dive into Dialogue Mapping </Advertisement>.
Apart from social complexity, there is the problem of context – the circumstances that shape the unique culture and features of an organization. As I mentioned in my introductory remarks, the framework is abstract – it applies to an ideal organization in which things can be done by the book. But such an organization does not exist! Aside from unique people-related and political issues, all organisations have their own quirks and unique features that distinguish them from other organisations, even within the same domain. Despite superficial resemblances, no two pharmaceutical companies are alike. Indeed, the differences are the whole point because they are what make a particular organization what it is. To paraphrase the words of the anthropologist, Gregory Bateson, the differences are what make a difference.
Some may argue that the framework acknowledges this and encourages, even exhorts, people to tailor the framework to their needs. Sure, the word “tailor” and its variants appear almost 700 times in the version 9.1 of the standard but, once again, there is no advice offered on how this tailoring should be done. And one can well understand why: it is impossible to offer any sensible advice if one doesn’t know the specifics of the organization, which includes its context.
On a related note, the TOGAF framework acknowledges that there is a hierarchy of architectures ranging from the general (foundation) to the specific (organization). However despite the acknowledgement of diversity, in practice TOGAF tends to focus on similarities between organisations. Most of the prescribed building blocks and processes are based on assumed commonalities between the structures and processes in different organisations. My point is that, although similarities are important, architects need to focus on differences. These could be differences between the organization they are working in and the TOGAF ideal, or even between their current organization and others that they have worked with in the past (and this is where experience comes in really handy). Cataloguing and understanding these unique features – the differences that make a difference – draws attention to precisely those issues that can cause heartburn and sleepless nights later.
I have often heard arguments along the lines of “80% of what we do follows a standard process, so it should be easy for us to standardize on a framework.” These are famous last words, because some of the 20% that is different is what makes your organization unique, and is therefore worthy of attention. You might as well accept this upfront so that you get a realistic picture of the challenges early in the game.
To sum up, frameworks like TOGAF are abstractions based on an ideal organization; they gloss over social complexity and the unique context of individual organisations. So, questions such as the one posed in the title of this post are akin to the pseudo-choice between Coke and Pepsi, for the real issue is something else altogether. As Tom Graves tells us in his wonderful blog and book, the enterprise is a story rather than a structure, and its architecture an ongoing sociotechnical drama.
Three types of uncertainty you (probably) overlook
Introduction – uncertainty and decision-making
Managing uncertainty – deciding what to do in the absence of reliable information – is a significant part of project management and many other managerial roles. When put this way, it is clear that managing uncertainty is primarily a decision-making problem. Indeed, as I will discuss shortly, the main difficulties associated with decision-making are related to specific types of uncertainties that we tend to overlook.
Let’s begin by looking at the standard approach to decision-making, which goes as follows:
- Define the decision problem.
- Identify options.
- Develop criteria for rating options.
- Evaluate options against criteria.
- Select the top rated option.
As I have pointed out in this post, the above process is too simplistic for some of the complex, multifaceted decisions that we face in life and at work (switching jobs, buying a house or starting a business venture, for example). In such cases:
- It may be difficult to identify all options.
- It is often impossible to rate options meaningfully because of information asymmetry – we know more about some options than others. For example, when choosing whether or not to switch jobs, we know more about our current situation than the new one.
- Even when ratings are possible, different people will rate options differently – i.e. different people invariably have different preferences for a given outcome. This makes it difficult to reach a consensus.
Regular readers of this blog will know that the points listed above are characteristics of wicked problems. It is fair to say that in recent years, a general awareness of the ubiquity of wicked problems has led to an appreciation of the limits of classical decision theory. (That said, it should be noted that academics have been aware of this for a long time: Horst Rittel’s classic paper on the dilemmas of planning, written in 1973, is a good example. And there are many others that predate it.)
In this post I look into some hard-to-tackle aspects of uncertainty by focusing on the aforementioned shortcomings of classical decision theory. My discussion draws on a paper by Richard Bradley and Mareile Drechsler.
This article is organised as follows: I first present an overview of the standard approach to dealing with uncertainty and discuss its limitations. Following this, I elaborate on three types of uncertainty that are discussed in the paper.
Background – the standard view of uncertainty
The standard approach to tackling uncertainty was articulated by Leonard Savage in his classic text, Foundations of Statistics. Savage’s approach can be summarized as follows:
- Figure out all possible states (outcomes)
- Enumerate actions that are possible
- Figure out the consequences of actions for all possible states.
- Attach a value (aka preference) to each consequence
- Select the course of action that maximizes value (based on an appropriately defined measure, making sure to factor in the likelihood of achieving the desired consequence)
(Note the close parallels between this process and the standard approach to decision-making outlined earlier.)
To keep things concrete it is useful to see how this process would work in a simple real-life example. Bradley and Drechsler quote the following example from Savage’s book that does just that:
…[consider] someone who is cooking an omelet and has already broken five good eggs into a bowl, but is uncertain whether the sixth egg is good or rotten. In deciding whether to break the sixth egg into the bowl containing the first five eggs, to break it into a separate saucer, or to throw it away, the only question this agent has to grapple with is whether the last egg is good or rotten, for she knows both what the consequence of breaking the egg is in each eventuality and how desirable each consequence is. And in general it would seem that for Savage once the agent has settled the question of how probable each state of the world is, she can determine what to do simply by averaging the utilities (Note: utility is basically a mathematical expression of preference or value) of each action’s consequences by the probabilities of the states of the world in which they are realised…
In this example there are two states (egg is good, egg is rotten), three actions (break egg into bowl, break egg into separate saucer to check if it rotten, throw egg away without checking) and three consequences (spoil all eggs, save eggs in bowl and save all eggs if last egg is not rotten, save eggs in bowl and potentially waste last egg). The problem then boils down to figuring out our preferences for the options (in some quantitative way) and the probability of the two states. At first sight, Savage’s approach seems like a reasonable way to deal with uncertainty. However, a closer look reveals major problems.
Problems with the standard approach
Unlike the omelet example, in real life situations it is often difficult to enumerate all possible states or foresee all consequences of an action. Further, even if states and consequences are known, we may not what value to attach to them – that is, we may not be able to determine our preferences for those consequences unambiguously. Even in those situations where we can, our preferences for may be subject to change – witness the not uncommon situation where lottery winners end up wishing they’d never won. The standard prescription works therefore works only in situations where all states, actions and consequences are known – i.e. tame situations, as opposed to wicked ones.
Before going any further, I should mention that Savage was cognisant of the limitations of his approach. He pointed out that it works only in what he called small world situations– i.e. situations in which it is possible to enumerate and evaluate all options. As Bradley and Drechsler put it,
Savage was well aware that not all decision problems could be represented in a small world decision matrix. In Savage’s words, you are in a small world if you can “look before you leap”; that is, it is feasible to enumerate all contingencies and you know what the consequences of actions are. You are in a grand world when you must “cross the bridge when you come to it”, either because you are not sure what the possible states of the world, actions and/or consequences are…
In the following three sections I elaborate on the complications mentioned above emphasizing, once again, that many real life situations are prone to such complications.
State space uncertainty
The standard view of uncertainty assumes that all possible states are given as a part of the problem definition – as in the omelet example discussed earlier. In real life, however, this is often not the case.
Bradley and Drechsler identify two distinct cases of state space uncertainty. The first one is when we are unaware that we’re missing states and/or consequences. For example, organisations that embark on a restructuring program are so focused on the cost-related consequences that they may overlook factors such as loss of morale and/or loss of talent (and the consequent loss of productivity). The second, somewhat rarer, case is when we are aware that we might be missing something but we don’t quite know what it is. All one can do here, is make appropriate contingency plans based on guesses regarding possible consequences.
Figuring out possible states and consequences is largely a matter of scenario envisioning based on knowledge and practical experience. It stands to reason that this is best done by leveraging the collective experience and wisdom of people from diverse backgrounds. This is pretty much the rationale behind collective decision-making techniques such as Dialogue Mapping.
Option uncertainty
The standard approach to tackling uncertainty assumes that the connection between actions and consequences is well defined. This is often not the case, particularly for wicked problems. For example, as I have discussed in this post, enterprise transformation programs with well-defined and articulated objectives often end up having a host of unintended consequences. At an even more basic level, in some situations it can be difficult to identify sensible options.
Option uncertainty is a fairly common feature in real-life decisions. As Bradley and Drechsler put it:
Option uncertainty is an endemic feature of decision making, for it is rarely the case that we can predict consequences of our actions in every detail (alternatively, be sure what our options are). And although in many decision situations, it won’t matter too much what the precise consequence of each action is, in some the details will matter very much.
…and unfortunately, the cases in which the details matter are precisely those problems in which they are the hardest to figure out – i.e. in wicked problems.
Preference uncertainty
An implicit assumption in the standard approach is that once states and consequences are known, people will be able to figure out their relative preferences for these unambiguously. This assumption is incorrect, as there are at least two situations in which people will not be able to determine their preferences. Firstly, there may be a lack of factual information about one or more of the states. Secondly, even when one is able to get the required facts, it is hard to figure out how we would value the consequences.
A common example of the aforementioned situation is the job switch dilemma. In many (most?) cases in which one is debating whether or not to switch jobs, one lacks enough factual information about the new job – for example, the new boss’ temperament, the work environment etc. Further, even if one is able to get the required information, it is impossible to know how it would be to actually work there. Most people would have struggled with this kind of uncertainty at some point in their lives. Bradley and Drechsler term this ethical uncertainty. I prefer the term preference uncertainty, as it has more to do with preferences than ethics.
Some general remarks
The first point to note is that the three types of uncertainty noted above map exactly on to the three shortcomings of classical decision theory discussed in the introduction. This suggests a connection between the types of uncertainty and wicked problems. Indeed, most wicked problems are exemplars of one or more of the above uncertainty types. For example, the paradigm-defining super-wicked problem of climate change displays all three types of uncertainty.
The three types of uncertainty discussed above are overlooked by the standard approach to managing uncertainty. This happens in a number of ways. Here are two common ones:
- The standard approach assumes that all uncertainties can somehow be incorporated into a single probability function describing all possible states and/or consequences. This is clearly false for state space and option uncertainty: it is impossible to define a sensible probability function when one is uncertain about the possible states and/or outcomes.
- The standard approach assumes that preferences for different consequences are known. This is clearly not true in the case of preference uncertainty…and even for state space and option uncertainty for that matter.
In their paper, Bradley and Dreschsler arrive at these three types of uncertainty from considerations different from the ones I have used above. Their approach, while more general, is considerably more involved. Nevertheless, I would recommend that readers who are interested should take a look at it because they cover a lot of things that I have glossed over or ignored altogether.
Just as an example, they show how the aforementioned uncertainties can be reduced. There is a price to be paid, however: any reduction in uncertainty results in an increase in its severity. An example might help illustrate how this comes about. Consider a situation of state space uncertainty. One can reduce- or even, remove – this by defining a catch-all state (labelled, say, “all other outcomes”). It is easy to see that although one has formally reduced state space uncertainty to zero, one has increased the severity of the uncertainty because the catch-all state is but a reflection of our ignorance and our refusal to do anything about it!
There are many more implications of the above. However, I’ll point out just one more that serves to illustrate the very practical implications of these uncertainties. In a post on the shortcomings of enterprise risk management, I pointed out that the notion of an organisation-wide risk appetite is problematic because it is impossible to capture the diversity of viewpoints through such a construct. Moreover, rule or process based approaches to risk management tend to focus only on those uncertainties that can be quantified, or conversely they assume that all uncertainties can somehow be clumped into a single probability distribution as prescribed by the standard approach to managing uncertainty. The three types of uncertainty discussed above highlight the limitations of such an approach to enterprise risk.
Conclusion
The standard approach to managing uncertainty assumes that all possible states, actions and consequences are known or can be determined. In this post I have discussed why this is not always so. In particular, it often happens that we do not know all possible outcomes (state space uncertainty), consequences (option uncertainty) and/or our preferences for consequences (preference or ethical uncertainty).
As I was reading the paper, I felt the authors were articulating issues that I had often felt uneasy about but chose to overlook (suppress?). Generalising from one’s own experience is always a fraught affair, but I reckon we tend to deny these uncertainties because they are inconvenient – that is, they are difficult if not impossible to deal with within the procrustean framework of the standard approach. What is needed as a corrective is a recognition that the pseudo-quantitative approach that is commonly used to manage uncertainty may not the panacea it is claimed to be. The first step towards doing this is to acknowledge the existence of the uncertainties that we (probably) overlook.
Eight to Late 
