Tim Kastelle

Category: book riffs

  • The Problem With Solutions

    The problem with solutions is that answers stop thinking, as Chuck Frey says in a good post today.

    When trying to solve a problem, often the best thing to do is to leave the question open for a while. This is tough, because most people have a natural tendency to want to solve the problem as quickly as possible.

    I’ve noticed this tendency again working with our MBA teams on the Wharton Global Consulting Practicum (which I mentioned earlier here).

    We’ve moved into the problem-solving part of these projects now that we have our scopes defined. However, now that the problem is defined, it has been difficult to battle this tendency to jump straight to finding answers.

    I’ve been stressing with the teams the need to keep our options open for a while. Chuck has some good suggestions for doing this in his post. Another way to do this is to use the divergence/convergence strategy discussed in Gamestorming, which I outlined in more detail previously.

    That first figure outlines the general process. Of course, the actual path that you take in problem-solving ends up looking more like this:

    The problem with jumping straight to answers is that you reduce the amount of effort put into the first step, idea generation, and you put no time at all into the second step – experimenting, thinking and prototyping.

    When these two steps aren’t fully explored, you end up putting all of your effort into developing conclusions and planning actions for only one answer. You may do this extremely well, but the problem is that it might not be the best answer.

    People like jumping to answers because it reduces uncertainty. When you are expanding the range of options to consider, and then test out these ideas, you are increasing ambiguity. This makes many people uncomfortable.

    But if you’re disciplined enough to be able to live with that ambiguity for a while, you usually end up with a better answer to your problem.

    It’s natural to want to have an answer to a problem as quickly as possible – this is the way you make the problem go away. However, if you are able to hold off for a while, and make sure that you generate and test out a wide variety of possible answers, the odds of finding a good one improve.

    So the best way to solve a problem is to not solve it. At least for a little while.

  • Who Makes Education?

    Too often people think about things happening to them, rather than thinking about how they make things happen. Agency is important, and we must never forget that we have the capacity to act. You can see the results people can have in the current events in Tunisia and Egypt.

    Agency is a critical part of citizenship – a point that Lewis Hyde makes in his excellent new book Common as Air. After discussing how the concept of property includes both rights of action and exclusion, and that the former is often forgotten, he says:

    What might be called he active-verb part of property will be especially marked in those areas of social life where participation is essential. In a viable self-governing nation, for example, citizens can only know themselves by way of their civic agency. True citizens are not the audience of their government, nor its consumers; they are its makers. The same may be said of a viable culture.

    It struck me as I read this that the same can be said of education. Try this out:

    True students are not the audience of their education, nor its consumers, they are its makers.

    In order to learn, you have to participate, you have to take action, and you have to connect ideas. We’ve known this at least going back to the foundation of the Socratic method.

    What does education look like when you think of it in this way? I think it ends up looking a lot like the Connectivism and Connected Knowledge course put together by George Siemens and Stephen Downes. Here is part of how they describe it:

    CCK11 is an unusual course. It does not consist of a body of content you are supposed to remember. Rather, the learning in the course results from the activities you undertake, and will be different for each person.

    In addition, this course is not conducted in a single place or environment. It is distributed across the web. We will provide some facilities. But we expect your activities to take place all over the internet. We will ask you to visit other people’s web pages, and even to create some of your own.

    It’s easy for educators to just tell people a bunch of stuff and expect them to remember it. It’s more challenging to create a situation that facilitates agency and learning – but that’s what innovative educators do.

    In the same way, it’s easy to be a student when all you have to do is memorize and regurgitate. It’s a lot harder to create your own course.

    But creating your own course, and your own assessment is what true students do. They exercise their right to action in their education.

  • How to Respond to a Bad Idea

    The best response to a bad idea is to make it better.

    When I work with people from government agencies, and also those from many large corporations, they often talk about their risk-averse culture. One of the problems with risk aversion is that if someone tries out a new idea and it doesn’t work, they are punished. This leads to fewer and fewer people introducing new ideas, because the risk seems too high.

    The other affect is that the people that do have good ideas will leave, and go work for organisations that are more open to new ideas.

    These are big problems.

    The best response to an idea that doesn’t work isn’t to punish whoever came up with it. This stifles change and growth. Here is how Kevin Kelly puts it in his new book What Technology Wants:

    However, the proper response to a lousy idea is not to stop thinking. It is to come up with a better idea. Indeed, we should prefer a bad idea to no ideas at all, because a bad idea can at least be reformed, while not thinking offers no hope.

    Here’s the question to ask: do our systems encourage people to build on (reform) bad ideas, or do they encourage people to stifle new ideas?

    You’ll only have innovation if you are able to use bad ideas as building blocks for new, better ideas.

    Note: As John noted at the end of his last post, there was substantial flooding in Brisbane last week. We’ve both been very fortunate in the floods, but many people have lost their homes or businesses. If you’re interested in contributing to flood relief in Queensland, the Premier’s Flood Appeal is probably the best bet. And the floods in Brazil are even worse – if you want to support people hit by those, Plan is a good organisation.

  • Creating Value Through Innovation

    Who really gets excited about maximizing shareholder value? Or even profits? Is that enough to get you out of bed and in to work every single day? One of the reasons that I got interested in innovation is that it is about making things better – which to me is far more interesting. That’s why when I talk about innovation I define it as “executing new ideas to create value.”

    All three parts of that definition are important – you need a genuinely new idea, you have to actually execute it, and it has to create value. The last part can be tricky because value can also be defined in a number of ways. In his upcoming book The New Capitalist Manifesto: Building a Disruptively Better Business, Umair Haque talks about value as something that makes the world a better place. The book is a handbook for building organisations that create precisely that type of value.

    Personally, I like that as a goal much better than shareholder value or profits. After all, those are really just scorekeeping methods more than anything.

    Haque is trying to provide ideas that don’t just innovate at the margins – he is aiming to trigger behavioural innovation, which is very disruptive indeed.

    There are many appealing ideas in the book, which I recommend reading. One that jumped out at me is a story he tells about Google forming a unit called the Data Liberation Front. The objective of this group is to work with all of the Google products so that customers can transfer their data to other applications as easily as possible. In other words, if you do all of your work in Google Docs, and then decide that you want to switch to Microsoft Office, the DLF is trying to make that switch work as smoothly as possible.

    How does this make any sense? It works because it requires innovation to create products and services that pull people in – that keep them coming back. Here is a quote from Brian Fitzpatrick, the guy that founded the group:

    If we’re locking users in, chances are there’s no sense of urgency to innovate and make products better. What keeps people coming back to search? Is it because they signed a two-year contract? No way! The reason people keep coming is it meets their needs best…

    You can think of it as a better, new type of lock-in: lock-in through innovation. Yesterday’s was based on formats or barriers, like frequent flyer programs: the goal was to create a hostage situation.

    …

    We’re not liberating data out of altruism. We’re doing it because it makes good business sense, because it drives long-term sustainable growth.

    Haque then goes on to say:

    Listen, though, to Fitz’s final lesson, because he’s saved the best for last: “Disrupt yourself before someone else comes along and does it. Everyone says someone will come along and replace Google. We think it should be Google.” Now that’s the beating heart of a resilient organization.

    It’s counterintuitive, but initiatives like the Data Liberation Front are the real lifeblood of Google’s evolutionary edge. Much has been written about Google’s experimental approach: rapid, frequent, always-on “bucket” tests in which a baseline product is compared to versions with minor differences, so the “best” product or service can be discovered. But initiatives like DataLib go deeper: they provide the evolutionary pressure that makes Google keep experimenting in the first place.

    Continuous experimentation is a key to innovation success. But this is talking about innovating at a meta-level – creating an environment that both requires and supports experimentation, and consequently, which requires and supports innovation.

    The New Capitalist Manifesto is most useful in getting you to think about these types of issues. Haque focuses on how to change our behaviours in ways that create new structures, which is both innovative in itself, but which also help to support innovation.

    Many of his points resonate with ideas from The Power of Pull by John Hagel, John Seely Brown and Lang Davison. Haque argues that with the Data Liberation Front, Google is building a strategy based on creating value rather than one based on building barriers to competition. This is definitely a pull strategy – and you can hear that idea in the quotes from Fitzpatrick.

    There are significant benefits to using this kind of pull strategy:

    • As Haque argues, it forces you to innovate continuously to stay ahead.
    • More importantly, it forces you to innovate so that you create genuine value – you have to create products, services and ideas that attract people, and that materially make their lives better to succeed. This is a risky strategy, because it is hard to do this. But the payoffs are substantial.
    • Finally, by forcing you to innovate to create genuine value, pull strategies are inherently more sustainable.

    Innovate to create genuine value. Making the world a better place is a good reason to innovate. And it is a lot more interesting and rewarding than all those scorekeeping reasons.

  • The Problem of Filters and Silos

    Here is a quote from Why The West Rules – For Now by Ian Morris – explaining some of the issues with the inter-disciplinary approach he has taken in writing the book:

    This courts all kinds of dangers (superficiality, disciplinary bias, and just general error). I will never have the same subtle grasp of Chinese culture as someone who has spent a lifetime reading medieval manuscripts, or be as up-to-date on human evolution as a geneticist (I am told that the journal Science updates its website on average every thirteen seconds; while typing this sentence I have probably fallen behind again). But on the otehr hand, those who stay within the boundaries of their own disciplines will never see the big picture.

    And therein lies the problem. Science updates every thirteen seconds – it’s impossible to keep up with that much new knowledge. Our only hope is to filter the flow somehow.

    One way that we do this is by working in silos – our silo becomes the filter. Everything from outside our area of specialty gets ignored.
    silo

    This helps with the information overload problem, but it creates a new one. Big ideas come at the edge of specialisations, and, particularly, at the intersections. To come up with big ideas you need to be outside of the core (see this post for some ideas on how to do this).

    This is another tension in innovation – the need to be both in the core and at the edge. As usual, the best answer is to change this from an either/or into a both/and.

    Both/and solutions are hard to execute. You have to accommodate yourself to conflicting intellectual demands, and you have to be comfortable with a relatively high level of uncertainty. That’s what makes innovation both challenging and rewarding.

    (photo from flickr/contemplative imaging under a Creative Commons License)

  • The Social Construction of Business Models

    One of the tricky parts of doing social science is that a lot of the things that we try to study are not actually real. Or, as Steve Horwitz puts it in an interesting post trying to define Austrian Economics:

    The “facts” of the social sciences are what people believe and think.

    This has some important implications. One of them is that we always need to question the assumptions that underlie what we do. Many of the elements of the markets and environments that we work in are actually socially constructed. This means that they exist and have meaning because we all agree that they exist and have meaning.

    One dollar bill

    One of the best examples of this is money. A dollar bill doesn’t have any inherent value. We are able to use it to buy things because we all agree that it has value, and moreover, we all agree (roughly) what that value is (here is a paper explaining how this works in a fair bit of detail).

    One consequence of this agreement is that we take money for granted – it’s just there and it works. But the form of money that we use has biases built into it – that is one of the points that Douglas Rushkoff makes in this talk summarising some of the points from his book Life, Inc.:

    The thing about social constructions though, is that since we’ve built them ourselves, we can also change them ourselves. If we start to question some of these deeply embedded concepts, it actually provides opportunities to create new things. We tend to think of money as the something that we get from banks. But if you start to question the basic assumptions around money, you start to realise that there are range of creative ways that we can redesign the money business model.

    If you search for “the future of money”, you’ll get a Wired magazine article, a book, and a web project – just for starters. The web project includes this video talking about some of the ways that money could change once we start thinking about it:

    The Future of Money from KS12 on Vimeo.

    There are a lot of different ideas about how to best create and exchange value flying around. As people experiment with these, we’ll start to see which ones might work, and which ones aren’t as good. I’d be surprised if we don’t see some significant changes to the way we conceptualise money over the next 10 years or so.

    There is an important innovation story in all of this as well: to find opportunities to build new business models, look for socially constructed platforms in the economy, and look for opportunities to redesign them.

    Here’s an example closely related to the ideas around money – personal lending. In What’s Mine is Yours, Rachel Botsman and Roo Rogers include an interesting discussion of the evolution of the personal lending market. They include this illustration of the changes (also available on the book’s website):

    Take a look at the initial state – personal lending was in the domain of banks. Then we started to see peer-to-peer lending systems, building on the micro-finance idea. The cutting edge in this area right now are collaborative systems that are built around alternative currencies. And the future (and a great source of opportunity) is building reputation-based exchange systems.

    None of these innovative business models would have happened if people hadn’t first questioned the basic assumptions upon which money and lending are built. But once these assumptions are questioned, then the opportunity arises to build novel business models that are based on a different socially constructed system. This is one very interesting method for finding innovative new business models.

    What’s Mine is Yours has a number of good examples of how new business models can be built out of collaborative systems. I’ll talk more about these later this week.

    But for now, just think about this idea of social construction. Most of our economic systems are built on concepts that exist simply because we all agree that they do, and which also have an agreed-upon meaning. If you start thinking about these concepts, you can find ways to build systems based on different concepts.

    This is a great opportunity for business model innovation.

    (photo from flickr/Sami Keinänen under a Creative Commons License)

  • Welcome to the Attention Economy

    Most of the economy now is based on information. Even physical things are embodied information. Consequently, the scarce resource that is being competed for now is our time. Here is how Richard Lanham talks about it in an interview discussing his book The Economics of Attention:

    The basic argument is simple enough. We’re told that we live in an information economy. We remember from Econ.1 that economics studies “the allocation of scarce commodities that have alternative uses.” But information is not a scarce commodity; we’re drowning in it. What is scarce is the human attention needed to make sense of it. We really live in an attention economy. What does such an economy look like? What are we to make of it?That attention is in short supply seems to be born in upon us from all sides. From frantic multi-tasking two-career parents to soldiers in computerized fox holes or pilots inundated by cockpit information, we’re all drowning in a sea of information.

    This is important for innovation. Connecting ideas is the fundamental creative act in innovation – so innovation is a knowledge-based exercise. But having ideas is only part of the process. You also have to able to select the best ideas to invest in, be able to execute ideas, and then get ideas to spread.

    Getting ideas to spread is often a challenge – especially for smaller organisations that aren’t very well connected within the network of the economy. This is where the value of the attention economy concept lies – it makes you explicitly think about whatever product or service you provide as information, as an idea – and it makes you think about how to get that idea to spread.

    Part of getting ideas to spread is based on the idea of influence. I ran across this interesting video about influence and the spread of ideas on Rasul Sha’ir’s blog (his original post has some useful comments on the video as well) – and it’s worth watching:

    INFLUENCERS FULL VERSION from R+I creative on Vimeo.

    The best way to think about influencers and the role that they play in spreading ideas is by thinking about the economy as a network. When you start thinking this way, the structure of the network can provide some insight into where influence might lie.

    This really brief interview with Valdis Krebs from Angela Dunn shows how this works:

    Here are the key points from all of this:

    • Innovations are ideas – in fact, they are the result of connecting ideas in a novel way.
    • In order to successfully innovate, you have to get these new ideas to spread. There are two concepts that can help you do this more effectively:
    • Think about the economy as competition for attention. We’re not really competing for resources anymore, we’re competing for peoples’ time and attention.
    • The attention economy plays out across economic networks. This means that network analysis is an important tool to support innovation efforts. In order to compete successfully within the attention economy, you need to understand the connections through which influence and attention flow.

  • Innovate Through Appreciation

    One of the critical parts of the innovation process is getting our great ideas to spread. Diffusion is often the stumbling block for innovative new ideas. There is a section towards the end of Making Ideas Happen by Scott Belsky that provides some interesting insights into how to attack this problem.

    Belsky describes a storytelling workshop that he took which was run by Jay O’Callahan. One aspect of the workshop that is striking is that all of the feedback in it was given in the form of appreciations. After each person told a story, the other participants were not allowed to criticise either the story or the delivery – instead they were all asked to comment on what they appreciated.

    Here is a terrific talk from O’Callahan in which he explains this and few of his other key ideas. Well, he doesn’t explain them, he tells us some stories that make the points:

    Jay O’Callahan: The Power of Storytelling from 99% on Vimeo.

    Here is how Belsky describes the benefits of this approach:

    The exchange of appreciations is meant to help you build upon your strengths, with the underlying assumption that a creative craft is made extraordinary through developing your strengths rather than obsessing over your weaknesses. And I noticed that a natural recalibration happens when you commend someone’s strengths: their weaknesses are lessened as their strengths are emphasized. As my storytelling compatriots recounted their stories a second and third time, the points of weakness withered away naturally as the most beautiful parts became stronger.

    Or, as O’Callahan says in the talk, quoting cellist Pablo Casals – “we have to leave it to the ignorant and the stupid to just point out flaws, we have to be glad about any bit of beauty.”

    There’s an important innovation idea in this. Take a look at this cartoon from Tom Fishburne:

    That’s what happens with criticism – we chip away at anything that makes our idea unique or interesting, until there’s nothing left. That’s where I think appreciation could help.

    Take a new idea, and instead of looking for weakness, thing about what makes it great. How could you emphasize that even more? Well, do that. Don’t patch up weakness, build on your strengths. Be great at one thing, not average at everything.

  • Why New Ideas Can be Bad

    Innovation is about more than just having great ideas – a point we’ve made here repeatedly. To innovate, you also have to execute ideas relentlessly. For many people, this is actually the hard part. I’m currently reading Making Ideas Happen by Scott Belsky, and it has some of the most sensible advice on this topic that I’ve run across.

    Here is a talk in which Belsky outlines some of the key points from the first part of the book:

    Scott Belsky: How to Avoid the Idea Generation Trap from 99% on Vimeo.

    The book supports a couple of points that we’ve made here before. One is that idea execution is essential. People are idea-generation machines. Belsky started the 99% Conference based on the old Edison quote – that invention is 1% inspiration and 99% perspiration. The issue is that if you look at the books, tips and consultants that address this topic, it would sure look like the equation is reversed. Given that, it’s great to see someone trying to address the 99%.

    The second issue that he addresses nicely is the idea of constraints – he correctly points out that we’re more creative when we have to deal with constraints. One of the key reasons is that constraints make us focus, which is a critical step in executing ideas. Here’s how Belsky puts it:

    Constraints serve as kindling for execution. When you’re not given constraints, you must seek them. You can start with the resources that are scarce – often time, money and energy (manpower). Also, by further defining the problem you are solving, you will come across certain limitations that are helpful constraints. As you find them, try to better understand them.

    Brilliant creative minds become more focused and actionable when the realm of possibilities is defined and, to some extent, restricted. …

    Despite your natural tendency to thrive on untethered creativity, you must recognize and harness constraints. And it is ultimately your responsibility to seek constraints when they are not given to you.

    These ideas are pulled together with the graphic that shows the project plateau (which he discusses in this post from Smashing Magazine):

    This shows the levels of excitement and energy that we have for ideas over time. When they are new, we have lots of both. However, once we settle into trying to make the idea real, the levels of both excitement and energy go down – it starts to feel more like work. How do we respond to this?

    According to Belsky, the natural response is to look for the excitement of a new idea again – and succumbing to this temptation is deadly. If you do, you’ll end up with a lot of partially-executed ideas, which is functionally equivalent to having, well, no ideas at all.

    The book (and the supporting website) has a lot of ideas for how to work through this. The main idea is to break down ideas (and the projects that result from them) down into action steps, and then focus on getting these done. It is easier to get big projects done when you are able to build momentum by achieving small steps on them on a near-constant basis.

    In some ways this is similar to Dave Allen’s Get Things Done approach, but Belsky’s is more oriented to people doing creative work. Consequently, for me at least, this approach seems more useful. And since innovation is definitely creative, Making Ideas Happen will probably be useful for most people trying to improve innovation.

  • Fear and Scorn versus Idea Diffusion

    Lots of new ideas fail. Many of them are great ideas, and they’ve been proven to solve an important problem, yet they still fail. Why? Because in addition to having a great idea, and making it work, if we are innovating we also have to get the idea to spread.

    Part of the problem is that to get people to take up our idea, we often have to get them to abandon a competing idea first. This if often challenging.

    In I Live in the Future & Here’s How it Works, Nick Bilton illustrates two of the other enemies of idea diffusion: fear and scorn. First up – fear. Here is one of the first responses to the invention of the telephone:

    No one who can sit in his study with his telephone by his side and thus listen to the performance of an opera at the Academy will care to go to Fourteenth Street and to spend the evening in a hot and crowded building… It is an unpleasant task to point out a possibly sinister purpose on the part of an inventor of conceded genius and ostensibly benevolent intentions. Nevertheless, a patriotic regard for the success of our approaching Centennial celebration renders it necessary to warn the managers of the Philadelphia exhibitions that the telephone may really be a device of the enemies of the Republic.

    So, telephones will mean that no one will ever leave their house again (why would you?), and are actually designed to bring about the downfall of America!

    Then there’s this a year later concerning an even great threat – the phonograph!

    There is good reason to believe that if the phonograph proves to be what its inventor claims that it is, both book-making and reading will fall into disuse…. Blessed will be the lot of the small boy of the future. He will never have to learn his letters or to wrestle with the spelling book…

    Fear is often used to try to prevent the spread of new ideas. Another weapon is scorn. Consider this from Clifford Stoll from 1995:

    But today, I’m uneasy about this most trendy and oversold community. Visionaries see a future of telecommuting workers, interactive libraries and multimedia classrooms. They speak of electronic town meetings and virtual communities. Commerce and business will shift from offices and malls to networks and modems. And the freedom of digital networks will make government more democratic.

    Baloney. Do our computer pundits lack all common sense? The truth in no online database will replace your daily newspaper, no CD-ROM can take the place of a competent teacher and no computer network will change the way government works.

    Why do we see this? In part, it’s because supporters of new ideas often wildly oversell their benefits, thus inviting a backlash. We’re seeing this play out again with the is social media good or bad for social change argument (great summary here, which also shows that of course the truth is somewhere in the middle).

    A big part of the problem is that when a new idea is introduced, we have absolutely no idea what it’s impact will actually be. Everyone is speculating – both those who are trying to support it, and those who are fighting against it. In some respects, these ideas are Rorschach tests – the reactions that we read aren’t really about the impact of the idea, but instead are projections of the obsessions of the authors.

    In any case, if you are an innovator, fear and scorn are problems. They will be used to argue against your new idea, no matter how great it is. That is one of the reasons why getting your idea to spread is a critical part of the innovation process. Diffusion problems can kill even the best ideas.

  • I Was Wrong

    When is the last time that you wrong? Hugely, spectacularly wrong?

    I’m wrong a lot. I’ve learned to live with it. Here’s an example of one of my biggest mistakes – the fundamental premise in my PhD research was completely wrong!

    I had an idea when I read a paper by M. Angeles Serrano and Marian Boguna called Topology of the World Trade Web. In it, they showed that if you mapped international trade as a network, with countries as the nodes and trade relations as the links, it was a complex network (see Greg Satell’s excellent discussion of networks for more information on the basics of network analysis). I saw this, and I thought that if you could map international trade as a network over time, then that would be a great way to try to measure the impact of globalisation. After all, we all knew that globalisation was changing the fundamental structure of the international economy, right?

    So that’s what I did for my PhD. I found international trade data from the International Monetary Fund that went back to 1938, and I mapped the networks as they changed over time. One of the key measures in all of this is In-Degree. For any particular country, this measures the number of other countries that send a significant percentage of their exports to that country. If you are an important trading partner for many other countries, your in-degree will be high. If few countries export goods and services to you, your in-degree will be low.

    One of the important measures of the overall structure of the network is the distribution of degree. This is what the distribution of in-degree looks like from one of my sample years:

    This shows that most countries have a very low in-degree. The majority of countries are clustered in the 0-5 range. In other words, the majority of countries in the international trade network are important trading partners for very few other countries. At the other end of the spectrum, you can see that a handful of countries have really big in-degree values on the right side of the graph. These are the hubs in the international trade network – countries like the US, UK, Germany, and Japan.

    The physicists that started this line of research usually convert these histograms into a chart that shows degree probability distribution functions. This is what the PDF for the 1938 world trade network looks like:

    Here’s where I was wrong. I thought that the shape of this distribution should change over time. We hear two stories about globalisation. The first is that everyone is trading with everyone else now. If that is the case, the degree distribution of the international trade network should be changing to more closely resemble the shape of the curved line in this figure:

    However, other people say that globalisation leads to the rich getting richer. If this is true, then the shape of the degree distribution line should be changing to be more like a straight line – more closely resembling one of the lines in that figure.

    I was pretty certain that my study would prove that one of these assertions was correct.

    Here is what I found – this is the degree distribution of the international trade network as it evolved from 1938-2003:

    What that shows is the shape of the degree distribution hasn’t changed at all. The lines have shifted to the right a bit as the number of countries in the network increased from about 100 to around 200, and that’s the only real change.

    I was completely, totally wrong about the impact that globalisation would have on the structure of the overall network.

    I was able to get a PhD out of that because that’s actually an interesting finding in and of itself (and I did a fair bit of work investigating other aspects of the network that have actually changed). But the core hypothesis that I had at the start of the research was wrong.

    I thought of this when I was reading Where Good Ideas Come From by Steven Johnson. It’s a fantastic book. He includes one chapter discussing the importance of error in innovation, which includes this quote from William Stanley Jevons:

    It would be an error to suppose that the great discoverer seizes at once upon the truth, or has any unerring method of divining it. In all probability the errors of the great mind exceed in number those of the less vigorous one. Fertility of imagination and abundance of guesses at truth are among the first requisites of discovery; but the erroneous guesses must be many times as numerous as those that prove well founded. The weakest analogies, the most whimsical notions, the most apparently absurd theories, may pass through the teeming brain, and no record remain of more than the hundredth part.

    In other words, to be innovative, we have to be wrong a lot. Being wrong is the first step towards being right.

    Don’t hide your mistakes, learn from them. If every idea that you try works, it’s a sure sign that you’re not trying enough ideas.

    When was the last time that you were massively, gloriously wrong?

    Note: I’ve got a couple of papers close to publication on this topic, but if you’d like to see it all explained, you can take a look at this conference paper from a few years ago.

  • Shades of Grey

    Almost every single time you are offered a black or white choice, the real answer is grey.

    This is inconvenient, because we like things to either white or black, right or wrong, easy or hard, incremental or radical. But the simple fact is that all of these are false dichotomies. Nearly everything that is presented to us as an either/or choice usually represents a spectrum.

    I was reminded of this again by a passage from Philip Ball’s terrific chapter in Seeing Further: The Story of Science and the Royal Society edited by Bill Bryson. If you are at all interested in science, this book is a must read. Here is Ball’s passage on basic vs. applied research:

    A dividing line between pure and applied science makes no sense at all, running as it does in a convoluted path through disciplines, departments, even individual scientific papers and careers. Research aimed at applications fills the pages of the leading journals in physics, chemistry and the life and Earth sciences; curiosity-driven research with no real practical value is abundant in the ‘applied’ literature of the materials, biotechnological and engineering sciences. THe fact that ‘pure’ and ‘applied’ science are useful and meaningful terms seduces us sometimes into thinking that they are real, absolute and distinct categories.

    We often talk in absolutes because it makes things simpler. I certainly do that here on occasion, especially when I’m trying to make a point. But these dichotomies hide spectrums. Outside of computer programs, where everything is a 1 or a 0, there are very things that are either/or choices. Most things exist along a spectrum.

    Innovation isn’t either radical or incremental – it is usually somewhere in between. Thinking and doing are not two opposite activities – they are intricately interlinked, and usually if we’re doing one we’re doing the other as well. Science is not just pure or applied – there are plenty of shades of grey in between. Ball includes a quote from Lord Porter, who won a Nobel Prize in Chemistry, and was President of The Royal Society, who said: “There are two types of research, applied and not-yet-applied.” Research exists along a spectrum of applied-ness.

    Labels are useful – they help us identify things, and that is important. However, false dichotomies are less useful. Thinking that something must be in only one of two possible states when it is actually somewhere in between leads to mistakes.

    Thinking in black and white terms can be comforting, because it’s simple. But it’s a false comfort. You’ll be a better manager and a better innovator if you can learn to identify the various shades of grey.