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Learning, evolution and the future of work

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The Janus-headed rise of AI has prompted many discussions about the future of work.  Most, if not all, are about AI-driven automation and its consequences for various professions. We are warned to prepare for this change by developing skills that cannot be easily “learnt” by machines.  This sounds reasonable at first, but less so on reflection: if skills that were thought to be uniquely human less than a decade ago can now be done, at least partially, by machines, there is no guarantee that any specific skill one chooses to develop will remain automation-proof in the medium-term future.

This begs the question as to what we can do, as individuals, to prepare for a machine-centric workplace. In this post I offer a perspective on this question based on Gregory Bateson’s writings as well as  my consulting and teaching experiences.

Levels of learning

Given that humans are notoriously poor at predicting the future, it should be clear hitching one’s professional wagon to a specific set of skills is not a good strategy. Learning a set of skills may pay off in the short term, but it is unlikely to work in the long run.

So what can one do to prepare for an ambiguous and essentially unpredictable future?

To answer this question, we need to delve into an important, yet oft-overlooked aspect of learning.

A key characteristic of learning is that it is driven by trial and error.  To be sure, intelligence may help winnow out poor choices at some stages of the process, but one cannot eliminate error entirely. Indeed, it is not desirable to do so because error is essential for that “aha” instant that precedes insight.  Learning therefore has a stochastic element: the specific sequence of trial and error followed by an individual is unpredictable and likely to be unique. This is why everyone learns differently: the mental model I build of a concept is likely to be different from yours.

In a paper entitled, The Logical Categories of Learning and Communication, Bateson noted that the stochastic nature of learning has an interesting consequence. As he notes:

If we accept the overall notion that all learning is in some degree stochastic (i.e., contains components of “trial and error”), it follows that an ordering of the processes of learning can be built upon a hierarchic classification of the types of error which are to be corrected in the various learning processes.

Let’s unpack this claim by looking at his proposed classification:

Zero order learning –    Zero order learning refers to situations in which a given stimulus (or question) results in the same response (or answer) every time. Any instinctive behaviour – such as a reflex response on touching a hot kettle – is an example of zero order learning.  Such learning is hard-wired in the learner, who responds with the “correct” option to a fixed stimulus every single time. Since the response does not change with time, the process is not subject to trial and error.

First order learning (Learning I) –  Learning I is where an individual learns to select a correct option from a set of similar elements. It involves a specific kind of trial and error that is best explained through a couple of examples. The  canonical example of Learning I is memorization: Johnny recognises the letter “A” because he has learnt to distinguish it from the 25 other similar possibilities. Another example is Pavlovian conditioning wherein the subject’s response is altered by training: a dog that initially salivates only when it smells food is trained, by repetition, to salivate when it hears the bell.

A key characteristic of Learning I is that the individual learns to select the correct response from a set of comparable possibilities – comparable because the possibilities are of the same type (e.g. pick a letter from the set of alphabets). Consequently, first order learning  cannot lead to a qualitative change in the learner’s response. Much of traditional school and university teaching is geared toward first order learning: students are taught to develop the “correct” understanding of concepts and techniques via a repetition-based process of trial and error.

As an aside, note that much of what goes under the banner of machine learning and AI can be also classed as first order learning.

Second order learning (Learning II) –  Second order learning involves a qualitative change in the learner’s response to a given situation. Typically, this occurs when a learner sees a familiar problem situation in a completely new light, thus opening up new possibilities for solutions.  Learning II therefore necessitates a higher order of trial and error, one that is beyond the ken of machines, at least at this point in time.

Complex organisational problems, such as determining a business strategy, require a second order approach because they cannot be precisely defined and therefore lack an objectively correct solution. Echoing Horst Rittel, solutions to such problems are not true or false, but better or worse.

Much of the teaching that goes on in schools and universities hinders second order learning because it implicitly conditions learners to frame problems in ways that make them amenable to familiar techniques. However, as Russell Ackoff noted, “outside of school, problems are seldom given; they have to be taken, extracted from complex situations…”   Two  aspects of this perceptive statement bear further consideration. Firstly, to extract a problem from a situation one has to appreciate or make sense of  the situation.  Secondly,  once the problem is framed, one may find that solving it requires skills that one does not possess. I expand on the implications of these points in the following two sections.

Sensemaking and second order learning

In an earlier piece, I described sensemaking as the art of collaborative problem formulation. There are a huge variety of sensemaking approaches, the gamestorming site describes many of them in detail.   Most of these are aimed at exploring a problem space by harnessing the collective knowledge of a group of people who have diverse, even conflicting, perspectives on the issue at hand.  The greater the diversity, the more complete the exploration of the problem space.

Sensemaking techniques help in elucidating the context in which a problem lives. This refers to the the problem’s environment, and in particular the constraints that the environment imposes on potential solutions to the problem.  As Bateson puts it, context is “a collective term for all those events which tell an organism among what set of alternatives [it] must make [its] next choice.”  But this begs the question as to how these alternatives are to be determined.  The question cannot be answered directly because it depends on the specifics of the environment in which the problem lives.  Surfacing these by asking the right questions is the task of sensemaking.

As a simple example, if I request you to help me formulate a business strategy, you are likely to begin by asking me a number of questions such as:

  • What kind of business are you in?
  • Who are your customers?
  • What’s the competitive landscape?
  • …and so on

Answers to these questions fill out the context in which the business operates, thus making it possible to formulate a meaningful strategy.

It is important to note that context rarely remains static, it evolves in time. Indeed, many companies faded away because they failed to appreciate changes in their business context:  Kodak is a well-known example, there are many more.  So organisations must evolve too. However, it is a mistake to think of an organisation and its environment as evolving independently, the two always evolve together.  Such co-evolution is as true of natural systems as it is of social ones. As Bateson tells us:

…the evolution of the horse from Eohippus was not a one-sided adjustment to life on grassy plains. Surely the grassy plains themselves evolved [on the same footing] with the evolution of the teeth and hooves of the horses and other ungulates. Turf was the evolving response of the vegetation to the evolution of the horse. It is the context which evolves.

Indeed, one can think of evolution by natural selection as a process by which nature learns (in a second-order sense).

The foregoing discussion points to another problem with traditional approaches to education: we are implicitly taught that problems once solved, stay solved. It is seldom so in real life because, as we have noted, the environment evolves even if the organisation remains static. In the worst case scenario (which happens often enough) the organisation will die if it does not adapt appropriately to changes in its environment. If this is true, then it seems that second-order learning is important not just for individuals but for organisations as a whole. This harks back to notion of the notion of the learning organisation, developed and evangelized by Peter Senge in the early 90s. A learning organisation is one that continually adapts itself to a changing environment. As one might imagine, it is an ideal that is difficult to achieve in practice. Indeed, attempts to create learning organisations have often ended up with paradoxical outcomes.  In view of this it seems more practical for organisations to focus on developing what one might call  learning individuals – people who are capable of adapting to changes in their environment by continual learning

Learning to learn

Cliches aside, the modern workplace is characterised by rapid, technology-driven change. It is difficult for an  individual to keep up because one has to:

    • Figure out which changes are significant and therefore worth responding to.
    • Be capable of responding to them meaningfully.

The media hype about the sexiest job of the 21st century and the like further fuel the fear of obsolescence.  One feels an overwhelming pressure to do something. The old adage about combating fear with action holds true: one has to do something, but the question then is: what meaningful action can one take?

The fact that this question arises points to the failure of traditional university education. With its undue focus on teaching specific techniques, the more important second-order skill of learning to learn has fallen by the wayside.  In reality, though,  it is now easier than ever to learn new skills on ones own. When I was hired as a database architect in 2004, there were few quality resources available for free. Ten years later, I was able to start teaching myself machine learning using topnotch software, backed by countless quality tutorials in blog and video formats. However, I wasted a lot of time in getting started because it took me a while to get over my reluctance to explore without a guide. Cultivating the habit of learning on my own earlier would have made it a lot easier.

Back to the future of work

When industry complains about new graduates being ill-prepared for the workplace, educational institutions respond by updating curricula with more (New!! Advanced!!!) techniques. However, the complaints continue and  Bateson’s notion of second order learning tells us why:

  • Firstly, problem solving is distinct from problem formulation; it is akin to the distinction between human and machine intelligence.
  • Secondly, one does not know what skills one may need in the future, so instead of learning specific skills one has to learn how to learn

In my experience,  it is possible to teach these higher order skills to students in a classroom environment. However, it has to be done in a way that starts from where students are in terms of skills and dispositions and moves them gradually to less familiar situations. The approach is based on David Cavallo’s work on emergent design which I have often used in my  consulting work.  Two examples may help illustrate how this works in  the classroom:

  • Many analytically-inclined people think sensemaking is a waste of time because they see it as “just talk”. So, when teaching sensemaking, I begin with quantitative techniques to deal with uncertainty, such as Monte Carlo simulation, and then gradually introduce examples of uncertainties that are hard if not impossible to quantify. This progression naturally leads on to problem situations in which they see the value of sensemaking.
  • When teaching data science, it is difficult to comprehensively cover basic machine learning algorithms in a single semester. However, students are often reluctant to explore on their own because they tend to be daunted by the mathematical terminology and notation. To encourage exploration (i.e. learning to learn) we use a two-step approach: a) classes focus on intuitive explanations of algorithms and the commonalities between concepts used in different algorithms.  The classes are not lectures but interactive sessions involving lots of exercises and Q&A, b) the assignments go beyond what is covered in the classroom (but still well within reach of most students), this forces them to learn on their own. The approach works: just the other day, my wonderful co-teacher, Alex, commented on the amazing learning journey of some of the students – so tentative and hesitant at first, but well on their way to becoming confident data professionals.

In the end, though, whether or not an individual learner learns depends on the individual. As Bateson once noted:

Perhaps the best documented generalization in the field of psychology is that, at any given moment, the behavioral characteristics of a mammal, and especially of [a human], depend upon the previous experience and behavior of that individual.

The choices we make when faced with change depend on our individual natures and experiences. Educators can’t do much about the former but they can facilitate more meaningful instances of the latter, even within the confines of the classroom.

Written by K

July 5, 2018 at 6:05 pm

The two tributaries of time

with 2 comments

How time flies. Ten years ago this month, I wrote my first post on Eight to Late.  The anniversary gives me an excuse to post something a little different. When rummaging around in my drafts folder for something suitable, I came across this piece that I wrote some years ago (2013) but didn’t publish.   It’s about our strange relationship with time, which I thought makes it a perfect piece to mark the occasion.

Introduction

The metaphor of time as a river resonates well with our subjective experiences of time.  Everyday phrases that evoke this metaphor include the flow of time and time going by, or the somewhat more poetic currents of time.  As Heraclitus said, no [person] can step into the same river twice – and so it is that a particular instant in time …like right now…is ephemeral, receding into the past as we become aware of it.

On the other hand, organisations have to capture and quantify time because things have to get done within fixed periods, the financial year being a common example. Hence, key organisational activities such as projects, strategies and budgets are invariably time-bound affairs. This can be problematic because there is a mismatch between the ways in which organisations view time and individuals experience it.

Organisational time

The idea that time is an objective entity is most clearly embodied in the notion of a timeline: a graphical representation of a time period, punctuated by events. The best known of these is perhaps the ubiquitous Gantt Chart, loved (and perhaps equally, reviled) by managers the world over.

Timelines are interesting because, as Elaine Yakura states in this paper, “they seem to render time, the ultimate abstraction, visible and concrete.”   As a result, they can serve as boundary objects that make it possible to negotiate and communicate what is to be accomplished in the specified time period. They make this possible because they tell a story with a clear beginning, middle and end, a narrative of what is to come and when.

For the reasons mentioned in the previous paragraph, timelines are often used to manage time-bound organisational initiatives. Through their use in scheduling and allocation, timelines serve to objectify time in such a way that it becomes a resource that can be measured and rationed, much like other resources such as money, labour etc.

At our workplaces we are governed by many overlapping timelines – workdays, budgeting cycles and project schedules being examples. From an individual perspective, each of these timelines are different representations of how one’s time is to be utilised, when an activity should be started and when it must be finished. Moreover, since we are generally committed to multiple timelines, we often find ourselves switching between them. They serve to remind us what we should be doing and when.

But there’s more: one of the key aims of developing a timeline is to enable all stakeholders to have a shared understanding of time as it pertains to the initiative. In this view, a timeline is a consensus representation of how a particular aspect of the future will unfold.  Timelines thus serve as coordinating mechanisms.

In terms of the metaphor, a timeline is akin to a map of the river of time. Along the map we can measure out and apportion it; we can even agree about way-stops at various points in time. However, we should always be aware that it remains a representation of time, for although we might treat a timeline as real, the fact is no one actually experiences time as it is depicted in a timeline. Mistaking one for the other is akin to confusing the map with the territory.

This may sound a little strange so I’ll try to clarify.  I’ll start with the observation that we experience time through events and processes – for example the successive chimes of a clock, the movement of the second hand of a watch (or the oscillations of a crystal), the passing of seasons or even the greying of one’s hair. Moreover, since these events and processes can be objectively agreed on by different observers, they can also be marked out on a timeline.  Yet the actual experience of living these events is unique to each individual.

Individual perception of time

As we have seen, organisations treat time as an objective commodity that can be represented, allocated and used much like any tangible resource.  On the other hand our experience of time is intensely personal.  For example, I’m sitting in a cafe as I write these lines. My perception of the flow of time depends rather crucially on my level of engagement in writing: slow when I’m struggling for words but zipping by when I’m deeply involved. This is familiar to us all: when we are deeply engaged in an activity, we lose all sense of time but when our involvement is superficial we are acutely aware of the clock.

This is true at work as well. When I’m engaged in any kind of activity at work, be it a group activity such as a meeting, or even an individual one such as developing a business case, my perception of time has little to do with the actual passage of seconds, minutes and hours on a clock. Sure, there are things that I will do habitually at a particular time – going to lunch, for example – but my perception of how fast the day goes is governed not by the clock but by the degree of engagement with my work.

I can only speak for myself, but I suspect that this is the case with most people. Though our work lives are supposedly governed by “objective” timelines, the way we actually live out our workdays depends on a host of things that have more to do with our inner lives than visible outer ones.  Specifically, they depend on things such as feelings, emotions, moods and motivations.

Flow and engagement

OK, so you may be wondering where I’m going with this. Surely, my subjective perception of my workday should not matter as long as I do what I’m required to do and meet my deadlines, right?

As a matter of fact, I think the answer to the above question is a qualified, “No”. The quality of the work we do depends on our level of commitment and engagement. Moreover, since a person’s perception of the passage of time depends rather sensitively on the degree of their involvement in a task, their subjective sense of time is a good indicator of their engagement in work.

In his book, Finding Flow, Mihalyi Csikszentmihalyi describes such engagement as an optimal experience in which a person is completely focused on the task at hand.  Most people would have experienced flow when engaged in activities that they really enjoy. As Anthony Reading states in his book, Hope and Despair: How Perceptions of the Future Shape Human Behaviour, “…most of what troubles us resides in our concerns about the past and our apprehensions about the future.”  People in flow are entirely focused on the present and are thus (temporarily) free from troubling thoughts. As Csikszentmihalyi puts it, for such people, “the sense of time is distorted; hours seem to pass by in minutes.”

All this may seem far removed from organisational concerns, but it is easy to see that it isn’t: a Google search on the phrase “increase employee engagement” will throw up many articles along the lines of “N ways to increase employee engagement.”  The sense in which the term is used in these articles is essentially the same as the one Csikszentmihalyi talks about: deep involvement in work.

So, the advice of management gurus and business school professors notwithstanding, the issue is less about employee engagement or motivation than about creating conditions that are conducive to flow.   All that is needed for the latter is a deep understanding how the particular organisation functions, the task at hand and (most importantly) the people who will be doing it.  The best managers I’ve worked with have grokked this, and were able to create the right conditions in a seemingly effortless and unobtrusive way. It is a skill that cannot be taught, but can be learnt by observing how such managers do what they do.

Time regained

Organisations tend to treat their employees’ time as though it were a commodity or resource that can be apportioned and allocated for various tasks. This view of time is epitomised by the timeline as depicted in a Gantt Chart or a resource-loaded project schedule.

In contrast, at an individual level, the perception of time depends rather critically on the level of engagement that a person feels with the task he or she is performing. Ideally organisations would (or ought to!) want their employees to be in that optimal zone of engagement that Csikszentmihalyi calls flow, at least when they are involved in creative work. However, like spontaneity, flow is a state that cannot be achieved by corporate decree; the best an organisation can do is to create the conditions that encourage it.

The organisational focus on timelines ought to be balanced by actions that are aimed at creating the conditions that are conducive to employee engagement and flow.  It may then be possible for those who work in organisation-land to experience, if only fleetingly, that Blakean state in which eternity is held in an hour.

Written by K

September 20, 2017 at 9:17 pm

The improbability of success

with 2 comments

Anyone who has tidied up after a toddler intuitively understands that making a mess is far easier than creating order. The fundamental reason for this is that the number of messy states in the universe (or a toddler’s room) far outnumbers the ordered ones.  As this point might not be obvious, I’ll demonstrate it via a simple thought experiment involving marbles:

Throw three marbles onto a flat surface.  When the marbles come to rest, you are most likely to end up with a random configuration  as in Figure 1.

Figure 1: A random configuration of 3 marbles

Indeed, you’d be extremely surprised if the three ended up being collinear as in Figure 2.   Note that Figure 2 is just one example of many collinear possibilities, but the point I’m making is that if the marbles are thrown randomly, they are more likely to end up in a random state than a lined-up one.

Figure 2: an unlikely (ordered) configuration

This raises a couple of questions:

Question: On what basis can one claim that the collinear configuration is tidier or more ordered than the non-collinear one?

Naive answer:  It looks more ordered. Yes, tidiness is in the eye of the beholder so it is necessarily subjective. However, I’ll wager that if one took a poll, an overwhelming number of people would say that the configuration in Figure 2 is more ordered than the one in Figure 1.

More sophisticated answer : The “state” of collinear marbles can be described using 2 parameters, the slope and intercept of the straight line that three marbles lie on (in any coordinate system) whereas the description of the nonlinear state requires 3 parameters. The first state is tidier because it requires fewer parameters.  Another way to think about is that the line can be described by two marbles; the third one is redundant as far as the description of the state is concerned.

Question: Why is a tidier configuration less likely than a messy one?

Answer:  May be you see this intuitively and need no proof, but here’s one just in case. Imagine rolling the three marbles one after the other. The first two, regardless of where they end up, will necessarily lie along a line (two points lie on the straight line joining them). Now, I think it is easy to see that if we throw the third marble randomly, it is highly unlikely end up on that line. Indeed, for the third marble to end up exactly on the same straight line requires a coincidence of near cosmic proportions.

I know, I know, this is not a proof, but I trust it makes the point.

Now, although it is near impossible to get to a collinear end state via random throws, it is possible to approximate it by changing the way we throw the marbles. Here’s how:

  1. Throw the marbles consecutively rather than in one go.
  2. When throwing the third marble, adjust its initial speed and direction in a way that takes into account the positions of the two marbles that are already on the surface. Remember these two already define a straight line.

The third throw is no longer random because it is designed to maximise the chance that the last marble will get as close as possible to the straight line defined by the first two. Done right, you’ll end up with something closer to the configuration in Figure 3 rather than the one in Figure 2.

Figure 3: an “approximately ordered” state

Now you’re probably wondering what this has to do with success. I’ll make the connection via an example that will be familiar to many readers of this blog: an organisation’s strategy. However, as I will reiterate later, the arguments I present are very general and can be applied to just about any initiative or situation.

Typically, a strategy sets out goals for an organisation and a plan to achieve them in a specified timeframe. The goals define a number of desirable outcomes, or states which, by design, are constrained to belong to a (very) small subset of all possible states the organisation can end up in.  In direct analogy with the simple model discussed above it is clear that, left to its own devices, the organisation is more likely to end up in one of the much overwhelmingly larger number of “failed states” than one of the successful ones.  Notwithstanding the popular quote about there being many roads to success, in reality there are a great many more roads to failure.

Of course, that’s precisely why organisations are never “left to their own devices.” Indeed, a strategic plan specifies actions that are intended to make a successful state more likely than an unsuccessful one. However, no plan can guarantee success; it can, at best, make it more likely. As in the marble game, success is ultimately a matter of chance, even when we take actions to make it more likely.

If we accept this, the key question becomes: how can one design a strategy that improves the odds of success?  The marble analogy suggests a way to do this is to:

  1. Define success in terms of an end state that is a natural extension of your current state.
  2. Devise a plan to (approximately) achieve that end state. Such a plan will necessarily build on the current state rather than change it wholesale. Successful change is an evolutionary process rather than a revolutionary one.

My contention is that these points are often ignored by management strategists. More often than not, they will define an end state based on a textbook idealisation, consulting model or (horror!) best practice. The marble analogy shows why copying others is unlikely to succeed.

Figure 4 shows a variant of the marble game in which we have two sets of marbles (or organisations!), one blue, as before, and the other red.


Figure 4: Two distinct configurations of marbles (or organisations)

Now, it is considerably harder to align an additional marble with both sets of marbles than the blue one alone. Here’s why…

To align with both sets, the new marble has to end up close to the point that lies at the intersection of the blue and red lines in Figure 5. In contrast, to align with the blue set alone, all that’s needed is for it to get close to any point on the blue line.

QED!

Figure 5: Why copying others is not a good idea (see text for explanation)

Finally, on a broader note, it should be clear that the arguments made above go beyond organisational strategies. They apply to pretty much any planned action, whether at work or in one’s personal life.

So, to sum up: when developing an organisational (or personal) strategy, the first step is to understand where you are and then identify the minimal actions you need to take in order to get to an “improved” state that is consistent with  your current one. Yes, this is akin to the incremental and evolutionary approach that Agilistas and Leaners have been banging on about for years. However, their prescriptions focus on specific areas: software development and process improvement.  My point is that the basic principles are way broader because they are a direct consequence of a fundamental fact regarding the relative likelihood of order and disorder in a toddler’s room, an organisation, or even the universe at large.

Written by K

April 4, 2017 at 9:16 pm

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