This is part of the Expertise Acceleration topic cluster.

This is part of the Operations topic cluster, which belongs to the Business Expertise Triad.

When Action Beats Prediction

Feature image for When Action Beats Prediction

Table of Contents

Sign up for the Newsletter

    Once a week. Three links. No spam. Unsubscribe anytime.

    One of the big ideas that we’ve covered in the past couple of weeks is the notion that ‘management is prediction’ — the W. Edwards Deming observation that you cannot run your business effectively if you are unable to predict the outcomes of your actions. We’ve mostly talked about this in the context of becoming data-driven: the raison d'etre for using data is to gain knowledge, and the purpose of knowledge is so that you may make better, more accurate predictions when running your business.

    But I think it’s worth talking about business situations where prediction is overrated. It turns out that we’ve covered various variations of this idea over the course of Commoncog’s history:

    • At the end of the Forecasting series I admitted that forecasting wasn’t as useful to the average operator as I thought it would. I then homed in on ‘fast adaptation to uncertainty’ as an alternative to ‘become very good at forecasting’.
    • In the weeks after that admission I dug into and then wrote about John Boyd’s ideas around the OODA loop, pointing out that organisations that are built to orient quickly under uncertainty all seem remarkably similar.
    • In Action Produces Information we talked about how traditional decision making frameworks don’t regard acting to generate new information as a valid move, which limits their usefulness.
    • Years ago, I observed in the Chinese Businessman Series, that often the correctness of one’s beliefs do not map to real world success. Fast adaptation to uncertainty (more commonly viewed as a bias to action) explains a large chunk of successful business outcomes.
    • And in What I Learnt From Complexity I mentioned in passing that Dr Saras Sarasvathy’s work on effectuation better captures the sorts of thinking that entrepreneurs do — and that kind of thinking is diametrically opposed to prediction.

    Effectuation, as it turns out, is the best articulation of when action trumps prediction. I’ve mostly shied away from talking about effectuation directly, as I’ve assumed that it was well known (Saravasthy has been working on and popularising effectuation for the good part of two decades — a particularly hilarious annotated version of her paper is hosted on the Khosla Ventures website, for instance). But a number of conversations in Commoncog’s member forums reminded me that perhaps the ideas aren’t as widespread as I thought.

    If nothing else, the implications of effectuation are useful — they tell us that the types of thinking involved in early-stage businesses are different from the types of thinking we are taught to use in most other contexts.

    What is Effectuation?

    Effectuation is a fancy term used to describe a specific kind of thinking. The word ‘effectual’ is the inverse of ‘causal’, and it’s probably easiest to think of effectuation as the opposite of causal thinking — that is, the kinds of thinking taught in most MBA courses and business books.

    What does this mean? Sarasvathy has a nice cooking analogy to illustrate the differences:

    • If you use causal thinking, you’ll say something like “ok, we’re making carbonara tonight” and then you will work backwards from the end goal (carbonara for, say, five people) to checking for ingredients in your kitchen, to purchasing the ingredients you don’t have, to prepping and cooking carbonara for your dinner party.
    • If you use effectual thinking, you’ll say something like “ok, what ingredients and tools do I have right now, and what can I make tonight?” You work forwards from existing resources; the end product is unknown.

    In a business context, causal thinking is “we need to increase sales by 12% by the end of the quarter, what levers do I have available to do that?”; effectual thinking is “we have some spare capacity next quarter: one designer and three software engineers, what crazy new thing could we build that might have value for the company?”

    Saravasthy points out that most entrepreneurial stories employ the second type of thinking. It’s worth pausing here to say that there are many kinds of entrepreneurial stories, too many to count. Some founders start a company obsessed with a problem or idea, others do not. Some start with only a vague notion that they want to run a business — any kind of business — nothing more. Others don’t even want to start a business, they stumble into one. There are also ‘lucky’ stories: company histories where the founder starts a side project that turns out to be wonderfully, wildly commercially viable. Some of my favourite entrepreneurial stories, however, consist of some founder purchasing a tiny company or inheriting one, discovering that they aren’t too bad at this business thing, and then three decades later find themselves sitting atop a small empire.

    What is rarer is the linear path of “founder seeks an idea; founder lists a number of possibilities and does some market research; founder picks the best idea from that list and pursues it; the idea works and said founder builds an amazing business.” This type of story fits right into a causal business framework: the entrepreneur decides to make carbonara and then works backwards to do all the things necessary for carbonara and succeeds. It is ridiculous when it does happen: the story falls together so logically, so pleasantly. Most entrepreneurial stories are not like this: what you usually get is a series of events where the founders bounce from random good event to random bad event before falling into something that works. And in fact the more common pattern in successful entrepreneurship is something like “Wow, that seemed to work! Let’s do more of it and see where this leads us!” and “Well, nobody thought that would work, but it did.” (Often accompanied with a post-facto explanation of why it worked; usually this is some factor outside the entrepreneur’s control). In fact when you look at good founding biographies, you’ll find many entrepreneurs who look back on their random journeys and go “What an insane ride. We got really lucky along the way, didn’t we?”

    Why is entrepreneurship like this?

    The trite answer is that entrepreneurship is fundamentally about navigating uncertainty, and uncertainty is by definition impossible to predict. Often a company succeeds due to some underlying change in the world — some new technology or consumer shift or demographic change that just so happens to benefit the founders and companies who were positioned correctly at just the right moment. These changes are unpredictable, which means that they are extremely difficult to position for; often the businesses that benefit the most from these changes don’t even realise what’s going on until a year or two into the wave.

    But all of this is besides the point. Sarasvathy’s research is not interested in why entrepreneurship is random; it is interested in the more useful question of how successful entrepreneurs deal with this irreducible uncertainty. When your success depends on chance opportunities, when your breakthroughs are contingent on environmental changes beyond your control, when luck plays a such a huge role on your success, how do you think about business?

    The answer to this is, broadly, effectuation: good entrepreneurs give up on prediction and actively distrust information that is not generated by action. They deal with uncertainty through execution. They embrace the fact that they can know nothing with certainty and that they cannot even calculate the probabilities of anything working (and anyway that this is mostly useless — entrepreneurship is often the act of the improbable). They learn to improvise their way to a working company. Sometimes this means they end up with a remarkable business. Other times it doesn’t. But in all situations, they learn to roll with it.

    The Principles of Effectuation

    Sarasvathy describes the process of effectual reasoning as follows:

    All entrepreneurs begin with three categories of means: (1) Who they are — their traits, tastes and abilities; (2) What they know — their education, training, expertise, and experience; and, (3) Whom they know — their social and professional networks. Using these means, the entrepreneurs begin to imagine and implement possible effects that can be created with them. Most often, they start very small with the means that are closest at hand, and move almost directly into action without elaborate planning. Unlike causal reasoning that comes to life through careful planning and subsequent execution, effectual reasoning lives and breathes execution. Plans are made and unmade and revised and recast through action and interaction with others on a daily basis. Yet at any given moment, there is always a meaningful picture that keeps the team together, a compelling story that brings in more stakeholders and a continuing journey that maps out uncharted territories. Through their actions, the effectual entrepreneurs’ set of means and consequently the set of possible effects change and get reconfigured. Eventually, certain of the emerging effects coalesce into clearly achievable and desirable goals — landmarks that point to a discernible path beginning to emerge in the wilderness.

    Effectual reasoning consists of the following principles:

    • While causal reasoning focuses on expected return, effectual reasoning emphasizes affordable loss;
    • While causal reasoning depends upon competitive analyses, effectual reasoning is built upon strategic partnerships; and,
    • While causal reasoning urges the exploitation of pre-existing knowledge and prediction, effectual reasoning stresses the leveraging of contingencies.

    Let’s go through these quickly.

    Effectual reasoning emphasises affordable loss

    Sarasvathy argues that while managers tend to analyse markets and choose target segments with the highest potential returns, successful entrepreneurs tend to look for methods to reach markets with a minimum expenditure of resources (be it time, effort, or money). In other words, they work forwards from some set of resources, and select ideas based on the affordable loss of those resources.

    One entrepreneur she interviewed told her that they would just ignore market research and just attempt to sell a product — any product, before the product was even made. “(I’d) just try to take it out and sell it. Even before I have the machine. I’d just go try and sell it. Even before I started production. My market research would actually be hands on selling. Hard work, but I think much better than trying to do market research.”

    You and I might call this ‘the hustle’. Saravasthy takes this as an example of an extreme ‘zero resources to market’ principle (you can’t lose anything if you’ve not made anything). She notes that multiple expert entrepreneurs she’s interviewed all seemed to have the same bias towards sales in order to validate an idea; she follows this up with the following passage:

    In finding the first customer within their immediate vicinity, whether within their geographic vicinity, within their social network, or within their area of professional expertise, entrepreneurs do not tie themselves to any theorised or pre-conceived ‘market’ or strategic universe for their idea. Instead, they open themselves to surprises as to which market or markets they will eventually end up building their business in or even which new markets they will end up creating (emphasis mine).

    The strategic partnerships principle

    Managers are likely to reach for systematic competitive analysis in the startup phase. Experienced entrepreneurs tend to assume that they don’t know which market their initial idea will target — or even what their final idea will look like! — which means competitive analysis is largely useless at the early stages of a startup. Saravasthy notes that one entrepreneur told her, “At one time in our company, I ordered people not to think about competitors. Just do your job. Think only of your work.” Of course, this changed as the company grew. But early on in a business’s life, most entrepreneurs focus more on building ‘strategic partnerships’ with their customers.

    Saravasthy writes:

    ... the strategic partnerships principle dovetails very well with the affordable loss principle to bring the entrepreneurs’ idea to market at really low levels of capital outlay. Furthermore, obtaining pre-commitments from key stakeholders helps reduce uncertainty in the early stages of creating an enterprise. Finally, since the entrepreneur is not wedded to any particular market for their idea, the expanding network of strategic partnerships determines to a great extent which market or markets the company will eventually end up in.

    The leveraging contingencies principle

    I like to think of this as the ‘just roll with it rule’; Sarasvathy calls this “the heart of entrepreneurial expertise (...) the ability to turn the unexpected into the profitable.”

    And on this topic, I think, Sarasvathy has an absolutely beautiful passage. She writes:

    Great entrepreneurial firms are products of contingencies. Their structure, culture, core competence, and endurance are all residuals of particular human beings striving to forge and fulfil particular aspirations through interactions with the space, time and technologies they live in. (...) The realisation that not all surprises are bad and that surprises, whether good or bad, can be used as inputs into the new venture creation process differentiates effectual reasoning from causal reasoning which tends to focus on the avoidance of surprises as far as possible.

    In other words, successful startups are random, path dependent accidents. But instead of resisting this fact, expert entrepreneurs embrace it. Saravasthy continues: “causal reasoning is based on the logic, ‘To the extent that we can predict the future, we can control it.’ That is why both academics and practitioners in business today spend enormous amounts of brainpower and resources on developing predictive models. Effectual reasoning, however, is based on the logic, ‘To the extent that we can control the future, we do not need to predict it.’”

    Sarasvathy is careful to say that both causal and effectual thinking are prevalent in business; one is not better than the other. But in general, the earlier it is a company’s life, the more effectuation dominates. Effective founders are primarily effectual thinkers; good managers are primarily causal thinkers. At some point in a company’s life, founders have to switch from effectuation to causal reasoning as a primary mode of thinking. Sarasvathy doesn’t call this out explicitly, but I presume that entrepreneurs who aren’t able to switch modes tend to fail as the company grows. If true, this might be one reason founders leave companies (with the narrative typically being “running a large company isn’t fun to me” or the venture capitalist (VC)-fueled “the founder couldn’t adapt to the needs of the business so we ... replaced them.”

    The Worldview of Effectuation

    Why might an entrepreneur resort to effectual thinking? Sarasvathy argues that this is a worldview thing: entrepreneurs think effectually because they believe they can shape the future. But I am reminded of expertise researcher Lia DiBello’s assertion that perhaps skill in business is a triad because that’s all our brains can handle. Similarly, you could say that effectuation is a cognitive adaptation to uncertainty. Sarasvathy writes (all emphasis mine):

    How does one control an unpredictable future? The answer to this question depends on our beliefs about where the future comes from. Is the future largely a continuation of the past? To what extent can human action actually change its course? While the future is always uncertain, not all uncertainties are the same. In fact, the simplest way we can model the different types of uncertainties is through the classic statistical model of the future as an urn containing different colored balls wherein the drawing of (say) a red ball, results in a reward (of say, $50). Assume the first urn contains 10 red balls and 10 green balls. In this case, the player can calculate the odds as an expected return of $25 on every draw since there is a 50-50 chance of winning $50. This is the model of a risky, but predictable, future. Entrepreneurs, as well as most human beings in the real world, however, usually have to operate without such predictability. The urn they have to deal with does not have a given number of balls of known colors. Instead it contains an unknown number of balls of unknown colors, but the game remains the same. In this case, the best strategy for the player is to draw balls randomly several times and to carefully note the result of each draw so that the distribution of balls in the urn can be discovered over time. This is a model of an uncertain, but learnable future that becomes predictable over time. Using the causal logic — to the extent we can predict the future, we can control it — makes sense in both these cases.

    But entrepreneurs choose to view the future through effectual logic. Consciously, or unconsciously, they act as if they believe that the future is not “out there” to be discovered, but that it gets created through the very strategies of the players. In other words, the entrepreneur using effectual logic says: “Whatever the initial distribution of balls in the urn, I will continue to acquire red balls and put them in the urn. I will look for other people who own red balls and induce them to become partners and add to the red balls in the urn. As time goes by, there will be so many red balls in the urn that almost every draw will obtain one. On the other hand, if I and my acquaintances have only green balls, we will put them in the urn, and when there are enough, will create a new game where green balls win. ” Of course, such a view may express hopes rather than realities, and many entrepreneurs in the real world do fail. But the fact remains that entrepreneurs use this logic to try and build new urns and devise new games all the time. In fact, several of the expert entrepreneurs I studied explicitly stated that being in a market that could be predicted was not such a good idea, since there would always be someone smarter and with deeper pockets who would predict it better than they could. But being in an unpredictable market meant that the market could be shaped through their own decisions and actions working in conjunction with pre-committed stakeholders and customer-partners. Together they could use contingencies along the way as part of the raw materials that constitute the very urn they are constructing.

    Expert entrepreneurs are not usually in the ball counting business or the gaming business. Instead they are actually in the business of creating the future, which entails having to work together with a wide variety of people over long periods of time. Study urns of the future are filled with enduring human relationships that outlive failures and create successes over time.

    I found Saravasthy’s observation about human relationships interesting; we’ll return to this in a minute.

    How Much Can We Trust This?

    Sarasvathy’s PhD supervisor was Herbert Simon, the Nobel Laureate, Turing Award winner and polymath who made significant contributions to artificial intelligence, political science, economics and cognitive psychology. I bring this up because the method Sarasvathy used to validate effectuation is what we now know as a ‘think-aloud protocol’ built around highlighting expert-novice differences. This is fancy name for a research approach where a small number of experts and novices are given a task to complete and are asked to ‘think aloud’ as they complete it. Simon co-wrote the ‘bible’ on think-aloud protocols in the early 1980s.

    Academic lineage matters a little here because it tells you about the approaches and biases that researchers have towards their work. The other co-author of that ‘bible’ on think-aloud protocols was a researcher by the name of K. Anders Ericsson — the same Ericsson who came up with ‘deliberate practice’ several years later, and established the field of expertise research in the decade after his collaboration with Simon. Needless to say, Sarasvathy’s research is grounded in this particular expertise research tradition — which means that whatever limitations exist in her work are also the limitations of this broader tradition.

    What does this mean?

    It means that, first of all, we need to pay special attention to who the study regards as ‘expert’. Sarasvathy and her team interviewed 27 expert entrepreneurs and 37 novices for her findings. The 37 novices were MBA students. The 27 expert entrepreneurs were persons who ‘either as individuals or as part of a team, had founded one or more companies, and remained with at least one company they founded for more than ten years and taken it public’. They applied this criteria to entrepreneurs from two sources: a list of 100 most successful entrepreneurs compiled by VC David Silver in 1985, and a list of Entrepreneur of the Year awards, compiled by Ernst & Young. Doing so, they argued, meant that the sample for the study was indirectly drawn from a complete population of founders of enduring companies in the US from 1960 to 1985.

    I accept their set as representative of successful entrepreneurship. The researchers’s bar for selection is remarkably high; the source lists are large enough that Sarasvathy et al could reasonably claim that they sampled from a representative population of repeat entrepreneurs. For reference, the average founder in their sample had founded seven companies, the minimum number of new ventures started was three.

    Which leaves us with the findings. Here the claims differ slightly. Sarasvathy notes that expert entrepreneurs display the ‘affordable loss principle’ and the ‘strategic partnerships’ principle; all of them treated market research with suspicion. It is also clear from the interviews that they accepted the fundamental ‘unknowability’ of entrepreneurship — all the expert entrepreneurs treated contingency as part of the game. But I should note that there is limited evidence from the administered protocol that founders regarded their network as the basis for entrepreneurship — perhaps because the thinking tasks they were given were too far removed from their real-world entrepreneurial networks. Whatever claims Sarasvathy makes on entrepreneurship as being built on networks of people are probably from broad observation, not from this study alone.

    It’s of course worth asking why expert entrepreneurs think like this. And this is the tricky thing about expertise research — if you interview a whole bunch of experts from some domain, and realise that all of them think in the exact same way, you can’t really tell what properties of the domain lead them to think in this manner. Sarasvathy gives her reasons, and I’ve listed mine throughout this piece — but I should remind you that we are speculating; we cannot know for sure. All we can surmise is that some … set of domain properties serve as a forcing function, such that the only participants that succeed all seem to have adapted in the exact same way.

    But, to be clear, this is good enough for me.

    Expertise research isn’t an exact science — possibly some entrepreneurs think more causally than others, depending on the domain. However, as far as research goes, Sarasvathy’s work does seem to tell us something true about the world, and I find it valuable for that reason.

    How Can We Use This?

    I think there are several takeaways from this body of work. The first thing is a reminder that not everything in business is causal in nature. New venture creation is a crapshoot: successful companies are the result of random contingencies, reality has a surprising amount of detail that can’t be observed ex-ante, and markets have a way of surprising you. All of this is to say that you can’t predict what might work in advance: often, the only information you can trust when doing something new is what you learn when you poke at reality.

    On some level it makes sense that entrepreneurs think effectually. When you can’t know what happens next, or what shapes your idea will take, you will, quite naturally:

    • Take bets that are affordable to lose.
    • Build partnerships with customers and other stakeholders such that a loss is spread across your partners.
    • Accept that you must improvise when random events occur. (If the only information you can trust is generated when you’re taking action and poking at reality, then, well ... sometimes reality pokes back. What you do in response to that is what separates the entrepreneurs from the managers.)

    What falls out of this is a worldview that prizes action above analysis. This explains why, when push comes to shove, founders don’t think probabilistically. They can’t afford to. As Ben Horowitz puts it, in The Hard Thing About Hard Things:

    I learned one important lesson: Startup CEOs should not play the odds. When you are building a company, you must believe there is an answer and you cannot pay attention to your odds of finding it. You just have to find it. It matters not whether your chances are nine in ten or one in a thousand; your task is the same. … I don’t believe in statistics. I believe in calculus.

    Over the course of writing this piece, I’ve been thinking a fair amount about Sarasvathy’s assertion that entrepreneurs start with who they are, what skills they have, and who they know.

    (This set perhaps suggests a handful of things to work on if you want to start a company in the future.)

    And of course I’ve been reflecting on these three elements as applied to my life. I’ve spent some time — as one does — learning about the type of person I am in a variety of situations, and I’ve racked up a fair number of skills over the years. But what have I done in building networks of people? The honest answer is: not much. Sarasvathy writes beautifully that “sturdy urns of the future are filled with enduring human relationships that outlive failures and create successes over time” — and I will admit that many stories of entrepreneurship I know bear this out. I just haven’t paid much attention to it in my recent past.

    Of course, I may be missing the point. The bigger lesson here is to roll with it. Don’t think too much about what you have, or what you’ve done to get ‘ready’. There’s only so much you can do before you begin. Entrepreneurship is, after all, the art of turning random events into profit.

    Which implies a weighty question — at least for those who are entrepreneurially minded: now that you’ve learnt this, what are you going to do about it? The core trait in entrepreneurial expertise seems to be about embracing contingency. (Well that, and surviving). Which makes entrepreneurship a lot less mysterious — and a lot more accessible — than you might think.

    Originally published , last updated .

    This article is part of the Expertise Acceleration topic cluster. Read more from this topic here→

    This article is part of the Operations topic cluster, which belongs to the Business Expertise Triad. Read more from this topic here→

    Member Comments