Archive for the ‘Organizations’ Category
On the shortcomings of cause-effect based models in management
Introduction
Business schools perpetuate the myth that the outcomes of changes in organizations can be managed using models that are rooted in the scientific-rational mode of enquiry. In essence, such models assume that all important variables that affect an outcome (i.e. causes) are known and that the relationship between these variables and the outcomes (i.e. effects) can be represented accurately by simple models. This is the nature of explanation in the hard sciences such as physics and is pretty much the official line adopted by mainstream management research and teaching – a point I have explored at length in an earlier post.
Now it is far from obvious that a mode of explanation that works for physics will also work for management. In fact, there is enough empirical evidence that most cause-effect based management models do not work in the real world. Many front-line employees and middle managers need no proof because they have likely lived through failures of such models in their organisations- for example, when the unintended consequences of organisational change swamp its intended (or predicted) effects.
In this post I look at the missing element in management models – human intentions – drawing on this paper by Sumantra Ghoshal which explores three different modes of explanation that were elaborated by Jon Elster in this book. My aim in doing this is to highlight the key reason why so many management initiatives fail.
Types of explanations
According to Elster, the nature of what we can reasonably expect from an explanation differs in the natural and social sciences. Furthermore, within the natural sciences, what constitutes an explanation differs in the physical and biological sciences.
Let’s begin with the difference between physics and biology first.
The dominant mode of explanation in physics (and other sciences that deal with inanimate matter) is causal – i.e. it deals with causes and effects as I have described in the introduction. For example, the phenomenon of gravity is explained as being caused by the presence of matter, the precise relationship being expressed via Newton’s Law of Gravitation (or even more accurately, via Einstein’s General Theory of Relativity). Gravity is “explained” by these models because they tell us that it is caused by the presence of matter. More important, if we know the specific configuration of matter in a particular problem, we can accurately predict the effects of gravity – our success in sending unmanned spacecraft to Saturn or Mars depends rather crucially on this.
In biology, the nature of explanation is somewhat different. When studying living creatures we don’t look for causes and effects. Instead we look for explanations based on function. For example, zoologists do not need to ask how amphibians came to have webbed feet; it is enough for them to know that webbed feet are an adaptation that affords amphibians a survival advantage. They need look no further than this explanation because it is consistent with the Theory of Evolution – that changes in organisms occur by chance, and those that survive do so because they offer the organism a survival advantage. There is no need to look for a deeper explanation in terms of cause and effect.
In social sciences the situation is very different indeed. The basic unit of explanation in the social sciences is the individual. But an individual is different from an inanimate object or even a non-human organism that reacts to specific stimuli in predictable ways. The key difference is that human actions are guided by intentions, and any explanation of social phenomena ought to start from these intentions.
For completeness I should mention that functional and causal explanations are sometimes possible within the social sciences and management. Typically functional explanations are possible in tightly controlled environments. For example, the behaviour and actions of people working within large bureaucracies or assembly lines can be understood on the basis of function. Causal explanations are even rarer, because they are possible only when focusing on the collective behaviour of large, diverse populations in which the effects of individual intentions are swamped by group diversity. In such special cases, people can indeed be treated as molecules or atoms.
Implications for management
There a couple of interesting implications of restoring intentionality to its rightful place in management studies.
Firstly, as Ghoshal states in his paper:
Management theories at present are overwhelmingly causal or functional in their modes of explanation. Ethics or morality, however, are mental phenomena. As a result they have had to be excluded from our theories and from the practices that such theories have shaped. In other words, a precondition for making business studies a science as well as a consequence of the resulting belief in determinism has been the explicit denial of any role of moral or ethical considerations in the practice of management
Present day management studies exclude considerations of morals and ethics, except, possibly, as a separate course that has little relation to the other subjects that form a part of the typical business school curriculum. Recognising the role of intentionality restores ethical and moral considerations where they belong – on the centre-stage of management theory and practice.
Secondly, recognizing the role of intentions in determining peoples’ actions helps us see that organizational changes that “start from where people are” have a much better chance of succeeding than those that are initiated top-down with little or no consultation with rank and file employees. Unfortunately the large majority of organizational change initiatives still start from the wrong place – the top.
Summing up
Most management practices that are taught in business schools and practiced by the countless graduates of these programs are rooted in the belief that certain actions (causes) will lead to specific, desired outcomes (effects). In this article I have discussed how explanations based on cause-effect models, though good for understanding the behaviour of molecules and possibly even mice, are misleading in the world of humans. To achieve sustainable and enduring outcomes in organisation one has to start from where people are, and to do that one has to begin by taking their opinions and aspirations seriously.
A data warehousing tragedy in five limericks
It started with a presentation,
a proforma regurgitation:
a tired old story,
of a repository
for all data in an organization.
The business was duly seduced
by promises of costs reduced.
But the data warehouse,
so glibly espoused,
was not so simply produced.
For the team was soon in distress,
‘cos the data landscape was a mess:
data duplication,
dodgy information
in databases and files countless.
And politics had them bogged down;
in circles they went round and round.
Logic paralysed,
totally traumatised,
in a sea of data they drowned.
In the light of the following morn,
the truth upon them did dawn.
An enterprise data store
is IT lore
as elusive as the unicorn.
All models are wrong, some models are harmful
Introduction
One of the ways in which we attempt to understand and explain natural and social phenomena is by building models of them. A model is a representation of a real-world phenomenon, and since the real world is messy, models are generally based on a number of simplifying assumptions. It is worth noting that models may be mathematical but they do not have to be – I present examples of both types of models in this article.
In this post I make two points:
- That all models are incomplete and are therefore wrong.
- That certain models are not only wrong, but can have harmful consequences if used thoughtlessly. In particular I will discuss a model of human behaviour that is widely taught and used in management practice, much to the detriment of organisations.
Before going any further I should clarify that I don’t “prove” that all models are wrong; that is likely an impossible task. Instead, I use an example to illustrate some general features of models which strongly suggest that no model can possibly account for all aspects of a phenomenon. Following that I discuss how models of human behaviour must be used with caution because they can have harmful consequences.
All models are wrong
Since models are based on simplifying assumptions, they can at best be only incomplete representations of reality. It seems reasonable to expect that all models will breakdown at some point because they are not reality. In this section, I illustrate this looking at a real-world example drawn from the king of natural sciences, physics.
Theoretical physicists build mathematical models that describe natural phenomena. Sir Isaac Newton was a theoretical physicist par excellence. Among other things, he hypothesized that the force that keeps the earth in orbit around the sun is the same as the one that keeps our feet firmly planted on the ground. Based on observational inferences made by Johannes Kepler, Newton also figured out that the force is inversely proportional to the square of the distance between them. That is: if the distance between two bodies is doubled, the gravitational force between them decreases four-fold. For those who are interested, there is a nice explanation of Newton’s law of gravitation here.
Newton’s law tells us the precise nature of the force of attraction between two bodies. It is universal in that it applies to all things that have a mass, regardless of the specific material they are made of. It’s utility is well established: among other things, it enables astronomers and engineers to predict the trajectories of planets, satellites and spacecraft to extraordinary accuracy; on the flip side it also enables war mongers to compute the trajectories of missiles. Newton’s law of gravitation has been tested innumerable times since it was proposed in the late 1700s, and it has passed with flying colours every time.
Yet, strictly speaking, it is wrong.
To understand why, we need to understand what it means to explain something. I’ll discuss this somewhat philosophical issue by sticking with gravity. Newton’s law enables us to predict the effects of gravity, but it does not tell us what gravity actually is. Yes, it’s a force, but what exactly is this force? How does it manifest itself? What is it that passes between two bodies to make them “aware” of each other’s existence?
Newton is silent on all these questions.
An explanation had to wait for a century and a half. In 1914 Einstein proposed that every body that has mass creates a distortion of space (actually space and time) around it. He formalised this idea in his General Theory of Relativity which tells us that gravity is a consequence of the curvature of space-time.
This is difficult to visualise, so perhaps an analogy will help. Think of space-time as a flat rubber sheet. A marble on the sheet causes a depression (or curvature) in the vicinity of the marble. Another marble close enough would sense the curvature and would tend to roll towards the original marble. To an observer who wasn’t aware of the curvature (imagine the rubber sheet to be invisible) the marbles would appear to be attracted to each other. Yet at a deeper level, the attraction is simply a consequence of geometry. In this sense then, Einstein’s theory “explains” gravity at a more fundamental level than Newton’s law does.
Now, one of the predictions of Einstein’s theory is that the force of gravitation is ever so slightly different from that predicted by Newton’s law. This difference is so small that it is unnoticeable in the case of spacecraft or even planets, but it does make a difference in the case of dense, massive bodies such as black holes. Many experiments have confirmed that Einstein’s theory is more accurate than Newton’s.
So Newton was wrong.
However, the story doesn’t end there because Einstein was wrong too. It turns out, that Einstein’s theory of gravitation is not consistent with Quantum Mechanics, the theory that describes the microworld of atoms and elementary particles. One of the open problems in theoretical physics is the development of a quantum theory of gravity. To be honest, I don’t know much at all about quantum gravity, so if you want to know more about this other holy grail of physics, I’ll refer you to Lee Smolin’s excellent book, Three Roads to Quantum Gravity.
Anyway, the point I wish to make is not that these luminaries were wrong but that the limitations of their models were in a sense inevitable. Why? Well, because our knowledge of the real world is never complete, it is forever work in progress. We build models based on what we know at a given time, which in turn is based on our current state of knowledge and the empirical data that supports it. The world, however, is much more complex than our limited powers of reasoning and observation , even if these are enhanced by instruments. Consequently any models that we construct are necessarily incomplete – and therefore, wrong.
Some models are harmful
The foregoing brings me to the second point of this post.
There’s nothing wrong in being wrong, of course; especially if our understanding of the world is enhanced in the process. I would be quite happy to leave it there if that was all there was to it. The problem is that there is something more insidious and dangerous: some models are not only wrong, they are positively harmful.
And no, I’m not referring to nuclear weapons; nuclear fission by itself is neither benign nor dangerous, it is what we do with it that makes it so. I’m referring to something far more commonplace, a model that underpins much of modern day management: it is the notion that humans are largely rational beings who make decisions based solely on their narrow self-interest. According to this view of humans as economic beings, we are driven by material gain to the exclusion of all other considerations. This is a narrow, one-dimensional view of humans but is one that is legitimised by mainstream economics and has been adopted enthusiastically by many management schools and their alumni.
Among other things, those who subscribe to this model believe that:
- Employees are inherently untrustworthy because they will act in their own personal interests, with no consideration of the greater good. Consequently their performance needs to be carefully “incentivised” and monitored.
- Management’s goals should be to maximise profits. Consequently they should be “incentivised” by bonuses that are linked solely to profit earned.
These are harmful because
- Treating employees like potential shirkers who need to “motivated” by a carrot and stick policy will only demotivate them.
- Linking senior management bonuses to financial performance alone encourages managers to follow strategies that boost short term profits regardless of the long term consequences.
The fact of the matter is that humans are not atoms or planets; they can (and will) change their behaviour depending on how they are treated.
To sum up
All models are wrong, but some models – especially those relating to human behaviour – are harmful. The danger of taking models of human behaviour literally is that they tend become self fulfilling prophecies. As Eliyahu Goldratt once famously said, “Tell me how you measure me and I’ll tell you how I’ll behave.” Measure managers by the profits they generate and they’ll work to maximise profits to the detriment of longer-term sustainability, treat employees like soulless economic beings and they’ll end up behaving like the self-serving souls the organisation deserves.
Eight to Late 