Futurists take what we know now and project into the future ideas about things will be like years from today using the models that have worked consistently up to now. Those models applied to human systems are changing quickly making marketing the future based on them senseless and potentially dangerous.
Earlier this past week a post on FastCoExist caught my attention and brought to mind why I have such an uneasy relationship with futurists and futures as a field. The post, 8 Ways the World Will Change in 2052, is look at the next 40 years written by Jorgen Randers, a professor of climate strategy at the BI Norwegian Business School and written with all the confident swagger that typifies futurists making statements about what is to come. After all, it’s hard to draw an audience (and the benefits that comes with that) when you don’t have a confident answer on your subject matter — even if that answer is wrong. In this latest post in the series on marketing complexity I look at futurists and their predictions and what it could mean for making sense of the threats and opportunities we will face in the years to come.
The Mathematical Problem of Futures and Complexity
The FastCoExist article paints a picture of a world that looks a lot like the one we have today, just with some shifts in economic and social structures. It suggests that much will remain the same even though a few key things will change, but our general relations will remain constant. It is that consistency that raises my concerns about futurist thinking (not all, to be sure) and its use of the data today to make predictions tomorrow. There is an assumption of linearity that weaves its way through the narratives spun by futurists that do not fit with how complex systems behave, nor does it account for the network effects created by interconnected systems.
Where I live now (Toronto), we have seen an almost uninterrupted heat wave for more than three weeks and that is forecast to continue for the week to come. This is the hottest year in recorded history (video), and as this short news clip shows the implications are many. At our current level of focus the implications may seem slight: changing growing conditions for gardens, better cottage swimming weather, brown lawns etc.. But at another scale and perspective, the interconnections between these things will start to reveal themselves if the pattern continues.
It is here where I see futurists getting it wrong as their predicts rest on largely linear trajectories of change and scientific knowledge that uses linear models to create predictions. The mistake is taking linear phenomenon and grafting that knowledge on to complex cases, while another mistake is taking science that works for static things and applying it to dynamic objects.
Complexity often produces change curves that follow a Pareto distribution, which is a way of accounting for things like ‘tipping points’, and is rarely linear in its effects for long periods of time. As the news report mentions, Toronto has an average temperature of 3.5 degrees higher than normal in a single year. It could be an aberration, but when we see record-breaking temperatures for years on end that looks like a pattern forming.
Climate change is not just about things getting warmer, cooler, wetter or dryer. From a human standpoint, how we adapt to these changes is what counts and in a networked world is that adaptations happen simultaneously and in a dynamic, interconnected manner. That means that many things change at the same time and that the relationship between dynamic objects means that the overall quantity and rate of change in the system is likely to be logarithmic (exponential) not additive.
Reframing change models: the language of complex systems.
If we are to create models that are more useful to us, we need to develop them with complexity in mind, think in systems and act as designers. To do this requires a change in the thinking models we use and the ways we communicate these models to the wider world. Yet, it isn’t as alien as it seems; we do it all the time with ourselves in explaining our social lives.
- A child goes from being peaceful and quiet to a tantrum in a matter of seconds.
- A calm, composed individual bursts into tears at a seemingly random event.
- A polite, warm conversation quickly turns cold at the slightest mention of a particular phenomenon
In many of these cases the ’cause’ might not be obvious. An example I use with my students is this:
Imagine a couple in their bedroom and one partner sees a wayward sock that has been left on floor and gets intensely angry at the other partner upon discovery of the sock. Why? Is is that the sock on the floor is so problematic that it reduces an otherwise peaceful environment into a space of conflict? Is the sock really that bad? Or is the sock a catalyst for something else? Does it represent something (or many things) that are embodied in the sock being left carelessly on the floor? Does the sock serve as a vessel for accumulated grievances and stressors only loosely related to its position on the floor?
This example of the sock illustrates how a Pareto distribution of social tensions in a relationship could be expressed. It points to how the most ‘obvious’ linear answer might not always be the case even if initial appearance suggest a relationship.
Explaining the reasons for problems opens a door to solving them. But we can do more.
The power of weak signals
The way to interject into a complex system is not to pay attention to everything all of the time, but to small things that show patterns. Eric Berlow has a remarkable 3 minute TED talk that illustrates how signals can be extracted from networks to reveal simplicity in complexity. A 2008 paper in the journal Physical Review shows the ways in which weak signals can be detected by reducing the overall volume of information or nodes in a network.
But what to pay attention to? This is where mindful evaluation and attention comes in. Mindfulness is not just a way to connect to one’s inner life, but also the outer world around us. A mindful approach to monitoring and evaluation means watching what happens around us and positioning tools, metrics and data gathering processes to give us the necessary feedback on our systems around us. To take the example of the couple’s conflict over the sock, paying attention within the relationship to minor conflicts, areas of tention, and moments of release earlier could have diffused energy enough to mean the sock was just a sock.
In social systems, this means paying attention to areas of intersection where natural tensions occur due to difference. These differences could be perspective, attitude, knowledge, beliefs or capabilities. These points of intersection are often where novelty emerges and innovation takes place, but they are also where deeper problems can begin. Constant, evolving and dynamic methods of data collection that recognizes change in non-linear and linear forms is more likely to enable the sorts of weak signal detection that can help us see the future more clearly.
That can help us make sense of future possibilities, rather than make empty predictions that guide what we do now at the expense of paying attention to what might come (and what is really happening).
Social innovation is often about engaging complicated systems like technology (dry) with complex systems like humans (wet). The implementation and evaluation approaches we take must match wet with dry and knowing when we are dealing with each.
If you’ve ever fixed any kind of machinery, you know that a device that’s exposed to the elements is incredibly difficult to maintain. A washing machine or the underside of a car gets grungy, fast.
On the other hand, the dryest, cleanest environment of all is the digital one. Code stays code. If it works today, it’s probably going to work tomorrow.
The wettest, weirdest environment is human interaction. Whatever we build gets misunderstood, corroded and chronic, and it happens quickly and in unpredictable ways. That’s one reason why the web is so fascinating–it’s a collision between the analytic world of code and wet world of people.
Much of social innovation is becoming like this: a collision between the wet world of people and the dry world of technology. It is hard not to be impressed at the technological capabilities we have at our disposal and how they can be put to use to serve humankind. Mobile handsets, low-cost portable computing tablets, social network platforms like Facebook or LinkedIn, or digital common spaces created by tools like Reddit and Twitter all provide incredible means to connect people and ideas together. Stop and think about what we have at our disposal and it is truly mindblowing, particularly when you think how much that’s changed in just 5 years, 10 years or 20 years.
Yet, the enormity of the scale of these tools and their ubiquity can mask their significance and not always for good. Take Facebook, which just launched its IPO and is the current champion of social networks with over 900 million users. It’s easy to forget that Facebook didn’t even exist 8 years ago and now almost one in 7 citizens on this earth have an account with its service.
This could be a tremendous opportunity for social innovation. Yet, it also speaks to the issue of Seth Godin’s wet and dry analogies for design.
Tom Chatfield, a tech writer from the UK, recently blogged about rethinking our social networks. He points to Dunbar’s number, a well-researched figure that estimates the limits to meaningful human relationships to be between 100 and 230. The drive to scale technologies (the dry) to ever-expanding and increasing numbers is problematic if the limits to my ability to meaningfully connect with the networks they create (the wet) are relatively fixed or difficult to change.
It’s dangerously easy simply to gawp and grimace at the sheer scale of the networks connecting us. The numbers are staggering, and offer a powerful index of how much and how fast our world is changing. But we mustn’t overlook the great lesson to be drawn from work like Dunbar’s: the weight of a special few will always outweigh the many, no matter how great the “many” becomes.
Some have argued that Dunbar’s number is a fallacy in the social media world, choosing to rely more heavily on sociologist Mark Granovetter’s work often summed up as the argument for The Strength of Weak Ties . His early research (see link [pdf] for original paper) focused not on the strong ties between people who were close, but the ‘friends of friends’ effects on transmission of information, which is the space where many innovations and novelty comes from in a network.
This confuses the potential innovation and the human capability to connect across large, diverse networks (a technical, ‘dry’ issue) with the quality of the interaction (a relational, ‘wet’ one). Both exist and both will exist, but there is a difference between learning something new and taking it to scale.
Novelty of information and new ideas comes from the intersection created by cognitive diversity in the design process. This is why designers seek to bring people with different perspectives together to explore concepts and generate ‘wild ideas’ as part of an ideation phase. Lots of information can be very useful in this situation and allow designers (social and otherwise) to see things they might miss if they stuck with a narrow band of perspectives. Yet, bringing these ideas to focus, refining them and transforming them into a social innovation that matters to people is far more relational than we give credit for.
Facebook might be great at linking us to ‘friends’ we’ve lost track of, but in applying a model where all of these friends are treated more or less equally, along with all of the information streamed at us through the main feed, our ‘wet’ interactions are made to feel ‘dry’. Drawing the motivation to scale ideas and engage in the efforts needed to make real change happen from such an approach is unlikely.
A recent post from FastCoExist, part of the Fast Company network of sites, by Ashoka changemakers Alexa Kay and Jon Camfield pointed to the barriers and facilitators for making change happen. Among their principal barriers is the need to connect deeper, rather than broader with each other:
How do we learn to be change makers? Much of the art of change making involves soft skills that we absorb from others that model or demonstrate change making behaviors. This means that learning opportunities are limited by one-to-one interactions and by exposure to other change makers. Compared to traditional fields like entrepreneurship, where there are plentiful resources for training, the practice of change making is still far from being widespread.
One of their principles for change reflects the complexity of social change by encouraging and supporting self-organized networks:
Often leaders or institutions promote dependency with a community. But successful change making communities depend on reducing dependence on one anointed leader. Flat networks and peer-based accountability structures are necessary if a community is to sustain change beyond one individual. The need for change communities and networks to be self-regulating is vital for their sustainability.
This is where walled gardens like Facebook are likely to fall down, just as many custom Ning-based communities have fallen into disuse. Create systems that are too bounded (dry) and we risk sucking the moisture from the human elements (the wet) that make real social innovation happen. Our challenge is finding the right balance between the controlled, stable environments that these new technologies afford and the self-organized, emergent and innovative environments needed to implement and scale our initiatives more effectively.
Wet Leaf By Faustas L, via Wikimedia Commons used under Creative Commons License
Great leaders are often ascribed traits that include ample common sense. But what passes for common sense is often a grab bag of miscellaneous, inconsistent ideas that are context dependent and less useful in the complex environments where leadership is called for most.
common sense |ˌkɑmən ˈsɛns|
good sense and sound judgment in practical matters: use your common sense | [ as modifier ] : a common-sense approach.
Today Research in Motion announced that its founder Mike Lazaridis and his co-CEO Jim Balsillie would be relinquishing their roles with the company. In their place, a ‘pragmatic, operational-type guy ‘was installed. Presumably, Thorsten Heins has the common sense to lead RIM after the founders lost theirs. Yet, the pragmatic, common sense that RIM is looking for might not be what they need given the complexity of the environment they are leading in.
Common sense is a false lure in complex systems. In his recent book, Everything is Obvious *Once You Know the Answer, social network researcher and Yahoo! Research scientist Duncan Watts eloquently critiques the concept of common sense, illustrating dozens of times over how “common sense” doesn’t fare so well in decisions that go beyond the routine and into the complex. Indeed. the very definition of the term implies that the problems that common sense works towards addressing are relatively simple and pragmatic.
Certainly, navigating daily social conventions might lend itself well to what we might call common sense. Watts refers to sociologist Harry Collins’ term ‘collective tacit knowledge‘ that is encoded in social norms, customs and practices of a particular world to describe common sense. However, what becomes common is a byproduct of many small decisions, dynamic and flexible changes to perspective, an accumulation of knowledge gained from small experiments over time, and the application of all of this knowledge to particular, context-dependent, situations. This constellation of factors and its interdependent, contextual overlap is why artificial intelligence systems have such a difficult time mimicking human thought and action. It is this attention to context that is most worth noting for it is this context that keeps common sense from being anything but common:
Common sense…is not so much a worldview as a grab bag of logically inconsistent, often contradictory beliefs, each of which seems right at the time but carries no guarantee of being right any other time.
Watts goes on to argue:
Commonsense reasoning, therefore, does not suffer from a single overriding limitation but rather from a combination of limitations, all of which reinforce and even disguise one another. The net result is that common sense is wonderful at making sense of the world, but not necessarily at understanding it.
Thus, we often concoct a narrative about the way something happens that sounds plausible, rational and be completely wrong. Throughout the book, Watts shows how often mistakes are made based on this common sense approach to solving problems.
When it comes to RIM, some have pointed to the late Steve Jobs’ assertion that they would have difficulty catching up to firms like Apple given that the consumer market is not their strength, the enterprise market is. Yet, Steve Jobs didn’t let the fact that Apple was a computer company stop him from making music players (the iPod), mobile phones (the iPhone) or becoming book, music and movie vendors (iTunes). A read of Steve Jobs’ biography by Walter Isaacson reveals a man who was able to lead and be successful through what appeared to be common sense, yet was decidedly uncommon among media and technology leaders. That is why Apple is where it is and why so many other technology companies lag behind them or simply disappeared.
The reason is that common sense in leadership looks as simple in hindsight only, not in foresight or even in the present moment. This is one of the big points that Watts makes. He uses the example of Sony’s MiniDisc system that, when introduced, had all of the hallmark features of the innovations that Apple introduced (novel, high quality, portable, smaller, visible advantages over the alternatives), yet it was a spectacular failure. Canadian management consultant Michael Raynor has called this the strategy paradox. When qualities such as vision, bold leadership, and focused execution — all the commonsensical aspects of great leaders — are applied to organizations it can lead to great success (Steve Jobs and Apple) or resounding failures (RIM?).
Strategic flexibility, making small adjustments consistently, and imaging scenarios for the future in an ongoing manner are some of the potential ways to limit the damage from common sense (or use its advantages more fully). This requires feedback mechanisms and close monitoring of program activities, developmental evaluation, and a willingness to tweak programs and design on the go (what I call: developmental design) . It’s not a surprise that this incremental approach to development is consistent with the way change is best produced in a complex adaptive system.
By recognizing that common sense is less than common and is certainly not consistent, program designers, developers, evaluators and other professionals will be better positioned to provide true leadership that addresses challenges and complexity rather than adds to the complexity and creates more problems.
The terms innovation, networks and design are becoming “hot”, although nothing compares to what could come from bringing these three ideas together. But what might that look like and what ought to be considered in moving these three ideas closer?
Innovation is on the brain for business, health, and social services. Productivity, creativity and strategy execution are all tied to firms’ abilities to be innovative. Not surprisingly, there has been a flood of investigations into innovation and theories about why some organizations adapt and survive and why some do not.
One of the latest books to explore this innovation challenge is Steven Johnson’s Where Good Ideas Come From. His book follows on others that have looked at the history of innovation and illustrated the benefits that come from social interaction. Social networks are one of the principal means of leveraging the benefits of interaction by creating more space for such interactions to occur. Johnson refers to the best of these as ‘liquid networks’, drawing on the analogy of a fluid and dynamic set of conditions that link people and ideas together.
While there is much written about the structure of networks and their benefits for innovation, relatively little is discussed on their design. While networks do happen somewhat unconsciously (we don’t always consider how we fill gaps, strengthen connection, or create weak-tie bonds in our relationships) they are also designed. Being more conscious of what kind of networks we create and what conditions are likely to produce favourable ones is something that is worthy of deeper thought and research.
A good example is looking through London (UK) where I’ve spent the last few days. If you’ve never been to London, it is easy to get lost in the diversity of languages, dress and styles of people everywhere you go. Some of these are undoubtedly tourists, but many are not. London’s diversity makes it a prime location for innovation now, just as it has been in the past. But while there is much diversity here, it is the way in which the space for these interactions have been laid out that causes some question to whether or not this space is leveraging its innovative potential effectively through the networks created.
Those areas of the city that are most attractive to the most number of people — and thus, creating the most diverse spaces — are also the most crowded, maximizing the number of contacts you’ll make with others. Yet this high number of contacts is not the same as quality contacts and it is those quality contacts that make the big difference in getting ideas moving from one state to another, while the size of the networks that makes something go bigger. Too often the discussion shifts to making bigger networks, without the cultural curation that goes into making deeper ones.
To that end, I was reflecting on the number of research networks that I am a part of and how very little time or energy is spent on nurturing quality interactions, just creating more of them. In an age where there is so much available to us — information and otherwise — the idea of creating more seems somewhat antithetical to what we are trying to do. It is like we are all trying to be London, yet without creating the Hyde Parks and spaces where the diversity that comes to these network members can really benefit from interacting with one another.
When we type ‘www’ as part of a URL, we refer to the World Wide Web, this vast expansive network of data and information that provides a universe of information possibilities and the ability to learn about almost anything from nearly any point of view.
But we don’t.
Indeed, we might just focus on some very narrow things and actually make our world, psycho-socially at least, a little smaller. Although I don’t think Marshall McLuhan had this in mind when he referred to this web as a global village.
Take today’s announcement by Apple on the state of the iPhone 4 in addressing ‘antennaegate‘. At issue is an under-performing cellphone transmission antennae in the iPhone that has caused a huge stir in the tech media world – which, if you read enough of it, assumes is important to the world at large.
One might think that this antennae problem is significant enough to derail a company like Apple and that people, outraged at what the tech pundits and media types have exposed, would abandon the newest iPhone in favour of something else. As mentioned elsewhere, the numbers, as reported in Fast Company, tell a different story:
During the presentation Apple wasn’t afraid to air some dirty laundry: Including the return rate for its premier device, the iPhone. The 3GS had a return rate of 6%, and so far the iPhone 4′s is running at 1.7%. Jobs thinks this illustrates that the end user is pretty satisfied with the phone, and that there’s no real problem with the antenna in day to day use. Ignoring the spin on this point, the fact Apple was prepared to share this internal business data at all is very unusual–and those figures will become used and referenced as new industry standards. Also unusual: Normally super-calm Steve Jobs swore on stage when answering a question about the now famous, and discredited, Bloomberg report that alleged an Apple engineer gave Jobs an early warning about the antenna. Apple is serious about defending its iPhone 4, folks.
Apple also shared one more statistic: Three million iPhone 4s have been sold in three weeks. That’s an amazing, sustained, million units a week folks. And if you think that’s just the early blush of success and excitement, then you need to remember that at the end of July 17 additional nations will start selling the iPhone 4. Which means that sales rate is going to soar past two million per week, and then stay there for a long time yet. No matter that Apple loves its customers … this is proof its customers love it right back, and aren’t worried about the antenna
Judging by the media firestorm, which included tech blogs to mainstream publications like The Economist, this is a significant issue. But to consumers, it isn’t…at least not enough to stop making the iPhone 4 the fastest selling device ever. Here, we have a very vocal, connected and articulate group of passionate media advocates in the tech world making a small issue and gigantic one. While such mis-steps are rare for Apple, it is probably fair to say that the iPhone issue was minor in real terms to the average person. The problem here, is that this highly connected group of writers seems connected to itself, and not the wider public who, by their actions, are far less concerned about this issue of the antennae.
This insularity is not just a media issue, it might be an Internet issue as a whole. In a recently published TED talk from Oxford, journalist and internet commentator Ethan Zuckerman, pointed to data that showed that people are pretty much keeping to themselves online, within some varied social boundaries. More importantly perhaps, this insularity is distorting our perception of the world we inhabit, making us think the world thinks and acts a lot like us. But as Zuckerman states:
The world is much wider than we generally perceive it to be
In the text and slideshow of his talk, Zuckerman points to the fact that Brazil has one of the highest rates of Twitter usage in the world, something of a surprise to most of us non-Brazilians. He adds:
About 170 million people visit Twitter each month, and 19m (11.2%) are Brazilian. More than one in ten Brazilian internet users visits Twitter each month, which is a higher proportion than in most nations – of the big internet using nations, the only one with a higher percent of people using the tool is Japan.There are millions of Japanese and Brazilian people on Twitter. If that seems surprising to you, it’s because most of your friends online aren’t Japanese or Brazilian. Twitter conducted a phone survey that revealed a quarter of their US users are African American… which was pretty surprising to most American users, who assumed that Twitter was just used by nerdy white guys.
What Zuckerman points out is that we’re not getting out as far as the Internet can take us because we’re choosing to socialize in places we find comfortable (my words, not his). We’re not venturing further from where we sit — physically, psycho-socially, politically or anywhere, really.
In another TED talk, psychological Jonathan Haidt spoke about the moral differences between conservatives and liberals, pointing to the same idea in politics about how much distance there is between those who might be Democrat vs. those who identify as Republican in the United States.
The social networking technologies we have right now offer the opportunities to see the world and communicate with its residents. But it is the everyday social and psychological tools that require deployment if we are to do anything more with these networks than we could have done without them. Is the Internet really creating a World Wide Web or is it more of a louder, more convenient clique at a high school party?
Perhaps Jaron Lanier is more right than we first thought.
One of the more taken for granted aspects of human life is knowing where you are. You probably have a clear sense of what you’re reading this on (e.g., laptop, mobile device), where you and that device are situated (e.g., home, office, sidewalk cafe), and where that situation is located in the world (e.g., city, state, country). In some of these settings, this could be viewed as a simple issue where there are clear markers, conventions and shared realities that dictate where you are. If I say “I’m at my desk at home” that conveys a very clear sense to those who know me about the physical space I’m in and where that space is located.
Online, things are a little more tricky because the locations are not tethered to something physical, only addresses that point to some server that presumably brings to Yahoo!, Google, Wikipedia, BBC News, the Journal of Medical Internet Research, create your own Star Wars movie, or wherever you’re intending to go. As diverse as these sites are, you still have some idea of where you are and where you’re going. The space in all these settings is defined by markers such as addresses, navigation bars, and the limits of the screen you’re using whether it be a 24 inch monitor, an iPad or a handheld phone. This is more complicated. So while I might be visiting the JMIR site, I could be on many of the hundreds of articles published, the author area, the editorial pages or somewhere linking between them. If I have multiple browser windows open, I could actually be at two places at the same time. But in each case, we can deduce through some effort about where I am.
Social systems, particularly those with multiple overlapping layers of organization (individuals within teams within organizations within communities) are complex. Understanding where someone is within that system provides only a partial sense of where a person really is. Consider social networks. Below is an example of a social network taken from a paper I published in 2006 with Tim Huerta that looked at the Web Assisted Tobacco Intervention community of practice.
Social networking maps are very useful for illustrating to people where they might fall within a social cluster, but more importantly, it also shows where others fall in relation to themselves. So while we’re obviously familiar with who we know, we might be less familiar with who knows us, and almost completely ignorant of who those we know are familiar with. These secondary connections are commonly referred to as weak ties, popularized by the work of Mark Granovetter. Often the most powerful changes come from mobilizing these weak ties — in large part because the further away from the starting point you go, the more diverse the elements are that you engage. Engaging diversity, creates conditions for new patterns of behaviour to emerge and thus, innovation, learning and change.
It sounds pretty simple: map the network, find the connections, exploit those connections and (shazam!) you have change (e.g., knowledge translation! healthy behaviours!). Unfortunately, as a phrase often attributed to H.L. Mencken suggests:
For every complex problem there is a solution that is neat, simple…and wrong.
Social networks are gaining in popularity. Recent mass-market books by Nicholas Christakis and James Fowler and the newly released book by social network scientist Albert-Laslo Barabasi have (or will) accelerate this. But what is missing from the discussion of social networks is place and theories of taking these complicated visuals and translating them into strategies for navigating complex environments. Maps of social networks are great as a start, but they actually offer little practical advice on where to go or even where you are in terms of knowledge space. Yes, you see where you are relative to others in a graphical representation, but social networks for collaboration are layered with organizational bureaucracy, likes and dislikes, technological and time constraints that are easily forgotten about when one maps out connections on chart or PowerPoint presentation.
In other words, social networks are treated as complicated, when really they are complex in the manner in which they are negotiated. Wayfinding is considerably more complex when one considers the reality of trying to navigate through a social network to get something done. Just as design thinking might be viewed as a stance, its value goes beyond seeing things different towards actually producing new things that have value. Likewise, social network maps provide us with a stance for viewing the social landscape differently, but offer little in understanding how to traverse that landscape.
For knowledge translation and public health, better wayfinding in complex, rather than complicated systems is the next step in the journey towards navigating a path to health.
Day two of the Science of Team Science Conference wrapped up yesterday with a lot of energy and enthusiasm (plus some anticipation at today’s 1/2 day workshop on social network analysis). The tell-tale sign that the conference was a hit was the observation that nearly 4/5 of the room was full to hear the convener provide general closing remarks on a Friday afternoon (this after 20 hours of sitting in a hotel ballroom for two days). That speaks volumes about the conference and how much interest there is in the topic.
It is perhaps because of this interest that there is genuine hope that something will come from this beyond just another conference. The question I asked myself is: Why did this conference and this topic yield such interest and a positive response?
What is it about teams that makes this such a compelling issue?
I see three primary reasons:
1. Teams fit our basic need for human relatedness. As the barrier between work and the rest of life (ROL) dissolves further due to changing job structures, information technology, and human mobility the potential to become isolated is high. The gap between connection and community is enormous. We have ‘friends’ on Facebook, ‘followers’ on Twitter, and ‘connections’ on LinkedIn, yet of these many dozens or hundreds only a few really count. Of those, even fewer are ones that we can comfortably relate to. Yet, this appearance of hyperconnectedness provides a false sense of relationships and transmits into a remarkable leveling off of human experience (see Jaron Lanier‘s You Are Not a Gadget, discussed here).
David Whyte’s Crossing the Unknown Sea , Parker Palmer’s A Hidden Wholeness, or Meg Wheatley’s Turning to One Another are works that do a wonderful job of pointing to this problem of disconnection in work and argue for greater integration between one’s personal and spiritual life and their vocation. Seth Godin’s Linchpin (discussed in previous posts) is another book that illustrates the power of bringing one’s “art” to work with others. Science has traditionally been the domain of individual effort, working in small groups at best, but generally alone. This is isolating in itself, but add to the myriad other factors that foster isolation in modern scientific work it is not surprising that any avenue to build connections to others, while continuing to do the work that scientists love, has been embraced.
2. Teams confer genuine advantages in terms of productivity and outcomes. The conference offered a blend of theory, research and strategy, which is probably why it had such broad appeal to an audience that comprised people interested in all three of those things. When the focus was on evidence, it became clear that there is an emergent literature on team science impact. Team science is not a panacea, but it is effective for certain types of problems and provides an alternative option for those wishing to do research, stay social, and tackle complex, wicked problems. Some of the data presented in panels or posters points to teams being more successful at getting large grants, and that, for some, team science can boost productivity. Much more research is needed, but the early results are promising.
Conceptually, this makes sense. Diverse teams of individuals will see problems differently and, particularly with complex problems, complex responses are necessary and diversity provides this complexity. Teams are an ideal structure to addressing a problem that requires new ways of working, knowledge from many areas, and a method of coordinating that knowledge in order to mobilize it.
3. Team science is becoming “hot”. This is the more cynical perspective, but it nonetheless describes reasons why people pursue fields of inquiry. In recent years the creation of funding structures from the National Institutes of Health and National Science Foundation in the U.S. has led a lot of people to consider team science simply as a mechanism to raise research funding. This conference is a byproduct of those decisions. This is not to say that those who pursue team science funding are doing it just because of the money, but it is a powerful incentive. Research flourishes where there are resources to sustain it. It draws in researchers, attracts graduate students and post-docs, and shapes the way many create proposals.
Last night over dinner, a group of us discussed the role that financing plays and whether teams that come together because they want to work together and are looking for funding to support that function differently than those that come together to get funding and then do research based on the details of that grant. Like the conference as a whole, the responses were diverse and no agreement on what would work and why was made. Nor was one expected.
The conference organizers have proclaimed that this is the first annual event, which will mean that we have an opportunity to see where this goes and what a year will do to shaping this field. The conference website is going to be transformed into a community website, enabling researchers, practitioners and policy makers to interact and even create teams. Whether they form based on personal interest, whether we need a ‘coach’ or two, or whether there will be funding to draw people in remains to be seen.
For readers looking for another take on the conference and some insightful reflections on what was discussed, I’d encourage you to visit Stephanie Jo Kent’s Reflexivity blog and read the play-by-play comments on Twitter by searching the hashtag #teamsci10.
Networking is as big of a buzzword as you can find these days for good reasons: networks solve a lot of problems by connecting people together and leveraging the knowledge of many for social benefit (and sometimes not). Simply put: networks allow us to do more.
But more can also be a problem.
It is not just that there is a lot of information out there, which creates its own set of problems, its that there is also so much to DO with this information. With all of the data streaming at us from social networks like Twitter, Facebook, Google Buzz, LinkedIn, Ning, and the myriad other ways in which social media allows us to connect it is hard to stay on top of it all. Even with good filters, the wealth of information available on even very narrow topics can be remarkable. I find this creates a temptation to try and get to it all. How often have you heard people lament about not being able to catch up on all of their blogs, tweets, magazine articles and beyond — let alone your conversations with friends, colleagues and loved ones?
While sociologist Mark Granovetter’s concept of the ‘strength of weak ties’ has been promoted vigorously in the social sciences and business to justify the potential for social networking, the value of the concept has some clear limitations that often get dismissed in the hype. One of the risks is that the time and energy it takes to invest in social networks broadly can take you away from creating strong ties. Think of how we socialized a generation ago: we had a close set of friends and family and associates, a few pen and phone pals, and some who we saw at occasional events like family reunions and conferences. Now, we “see” them daily, maybe hourly. That creates a lot of encounters, but at a superficial level for the most part.
While this is good for some things, particularly like getting simple messages out quickly, it is a problem for dealing with complex information or messages with multiple layers and potential meanings. Those require a little depth of contact that many networking tools or “networking events” don’t encourage.For those kinds of complex problems, we need tighter bonds and more meaning-making opportunities in our networks.
Mario Luis Small from the University of Chicago has explored the role of social networks and how they benefit those with little social capital. His recent book, Unanticipated Gains: Orgins of network inequality in everyday life, looked in depth at how social capital could be grown with a community of low-income, New York City mothers:
Social capital theorists have shown that some people do better than others in part because they enjoy larger, more supportive, or otherwise more useful networks. But why do some people have better networks than others? Unanticipated Gains argues that the answer lies less in people’s deliberate “networking” than in the institutional conditions of the churches, colleges, firms, gyms, childcare centers, schools, and other organizations in which they happen to participate routinely. The book illustrates and develops this argument by exploring the experiences of New York City mothers whose children were enrolled in childcare centers.
Unanticipated Gains examines why scores of these mothers, after enrolling their children in centers, dramatically expanded both the size and usefulness of their personal networks, often in ways they did not expect. Whether, how, and how much the mothers’ networks were altered—and how useful these networks were—depended on the apparently trivial but remarkably consequential practices and regulations of the centers, from the structure of their PTOs, to the regularity of their fieldtrips to amusement parks and zoos, to their ostensibly innocuous rules regarding pick-up and drop-off times.
Relying on scores of in-depth interviews with mothers, quantitative data on both mothers and centers, and detailed case studies of other routine organizations (from beauty salons and bath houses to colleges and churches), Unanticiapted Gains shows that how much people gain from their connections depends substantially on institutional conditions they often do not control, and through everyday process they may not even be aware of. (Original post here)
Although Small was not intending to write about what makes a network ‘sticky’, to use Gabriel Szulanski‘s term, he winds up with a set of recommendations that do just that. Indeed, Small’s suggestions – create intimate, cooperative, active, stable, yet flexible and adaptive networks — make networks sticky (and resilient) while mitigating the effects of creating widening gaps between the well-connected and capital-rich and the rest.
Small suggests that there are 7 ingredients that help dampen harmful, unintended consequences of networks:
1. Create frequent opportunities for interaction;
2. Ensure frequent and regular interactions between agents;
3. Interactions must be long lasting and exist beyond simple, quick exchanges;
4. Interactions are minimally competitive;
5. Interactions are maximally cooperative;
6. Intrinsic motivation consistent with that of the organizations or networks drive interactions and encourage engagement over time;
7. Extrinsic motivators must also be present to support the maintenance of ties over time.
Perhaps it is time to consider employing some design thinking towards creating stickier, rather than bigger or broader networks.
For more reading on the phenomenon of “stickiness”, consider the following:
Szulanski, G. (2000). The Process of Knowledge Transfer: A Diachronic Analysis of Stickiness. Organizational Behavior and Human Decision Processes.
Szulanski, G. (2003). Sticky knowledge: Barriers to knowing in the firm. London: Sage.
Szulanski, G., & Jensen, R. (2004). Overcoming stickiness: An empirical investigation of the role of the template in the replication of organizational routines. Managerial and Decision Economics, 25(67), 347-363.
A couple days ago I wrote about the idea of social sense-making and how fostering a climate of knowledge sharing that involves trusting people teach and giving them the opportunity to do so. One powerful argument is that teaching is a powerful method of learning in its own right and evidence suggests that we retain much more when we teach someone than when we simply take something in passively. The value here is predicated on a constellation of assumptions that the teacher is providing something of value, can communicate the message effectively, inspires a response in the learner that activates pathways in the brain that encourages reflection and retention, and that the learner and teacher are co-participating in this process.
What is sometimes forgotten and more problematic is whether or not the content being shared is true.
The Oxford English Dictionary defines true as:
When you parse through this definition in the context of social learning, much of its component terms such as ‘reality’, ‘genuine’ , ‘standard’, and ‘accurate’ become highly problematic. Much of the literature on sense-making supports the concept of knowledge being socially constructed within a context. The work of Dave Snowden at Cognitive Edge, John Seely Brown, or Gary Klein at Klein Associates are worth looking at in this regard. The critical realist perspective, which posits that reality is co-created by humans who function within a set of conditions that can be known, but only partly, is the most common expression of this viewpoint. It is a perspective that is congruent with much scholarship in the social sciences and philosophy (although purists will argue how true — as in the definition above — this is).
The sense-making scholarship looks at how relationships influence our decisions and the meaning that is constructed from it. When you engage in a relationship with someone, you’re able to send signals that convey meaning through gesture, tonality, and circumstance that go well beyond what we often bound as the “information” we are trying to share.
But as work popularized by Jeff Howe in Crowdsourcing or James Surowiecki in The Wisdom of Crowds points out, having little relationship with others and partial knowledge is more than sufficient if one’s ability to make sense of the whole is leveraged with collective decision-making capacity of many others. In these models, one only need to see part of the problem to make effective decisions when combined with the equally limited perspective of many other people who, when working together, see the whole. This form of collective decision making has become exceptionally popular in business and even health. One of the other ways to view this model is that it operates something like the SETI@home Project, which was one of the first initiatives to use the power of grid computing to solving problems that required massive computing power to make calculations based on large, complicated datasets. Grid computing uses excess processing power from dormant computers to feed into a large, networked ‘grid’ to create a virtual supercomputer. Howe and Surowiecki describe social decision-making models that look a lot like grid computing. In these models we can afford to use less than our full capacity to understanding a problem because the collective capacity is so much more powerful and will fill in the blanks. This kind of decision-making works well with complicated tasks, those with many different parts, but configured in a manner where we can understand their relationship to each other. Complex problems are quite different. Here, knowledge of the parts and their relationship to each other is only partially useful in understanding the impact on the whole. The crowd-sourcing model might be good for the former, but the latter is where many of the challenges in our health and social system lie and I’m not convinced that this is always a good thing.
Combine cognitive off-loading with a massive amount of information and the tools to enable this information to be distributed and re-distributed quickly and you create new problems, ones that are exacerbated by the shift in our social network ties. Media scholar Clay Shirky recently spoke to this issue in a recent ‘rush’ on the BBC’s Virtual Revolution show by pointing to the example of the Obama administration’s implementation of change.gov and how, in spite of the economic challenges facing the US, two wars, and the threat of climate change, participants on the site chose legalizing marijuana as the #1 issue to solve problems on. The Change.gov site was not making decisions for the country, but the model it employs is consistent with social decision making. It’s probably why we haven’t heard much about this initiative that was the much promoted way to take the engaged citizenry that supported Obama’s election and transform it into a guide for government.
On Facebook, people posted their bra colour on their status to show support for breast cancer (even when there appears to be doubt as to its origins, motives or even rationale for how this was to work). Have you joined a group to show support for something that has no method of converting that support into anything except through collecting names? On email, have you received or been sent a note promising you a free laptop if you forward something on or help save someone by doing the same because each forwarded message will raise money for a good cause? These things abound and the social web allows it to flourish. Yet, by indulging in such things we are creating patterns of decision-making that continue the off-loading of cognition (and maybe action) to the group and go from social sense-making to nonsense making. Someone else will take care of it.
Taking it slow, reducing media consumption to allow processing of information mindfully, and building up your strong social ties (relationships) are three ways to address this problem. But the latter is what I think is critical. In his interview with the BBC, Clay Shirky discusses the challenge a world where weak ties are growing at the expense of strong ties and wonders aloud what impact this will have on democracy and decision making. In a world that is currently fascinated with social networks, the ‘strength of weak ties’ argument posed by Mark Granovetter and many other social networking researchers has become cocktail party talk. While I am glad that social networking research is getting its time in the sun, the concern is that – perhaps for the very reasons I’ve discussed here — people are off-loading the deep thought about it and going into the realm of weak ties over-enthusiastically. Because it is a lot harder to off-load when your close relations will hold you to account and know you well enough to tell when you’re not making sense (and will be comfortable telling you as much). It is in these interactions that the concept of ‘true’ can really be known.
After two weeks of travel I’ve found myself back at the office and the usual “stuff” of life. That is, the things that one becomes accustomed to like your office chair, your home, your neighbourhood and even your sock drawer. You know where everything is and there is a certain level of comfort in that. When you’re on the road, you wake up in new rooms to new smells and new places for your socks. But what is remarkable is how attentive you are to things in this new environment. You notice the smells, the texture of the sheets, the sounds from outside the window. But, with every night that goes by, the familiarity creeps in and you stop paying attention.
I tried a little experiment and shut out most of my Twitter feeds, Facebook posts and Google Reader feeds for the past two weeks. This was as much about not having a lot of time or reliable Internet connections, but also to try some of the low-calorie information dieting that I wrote about earlier. So what happened when I got back? I found myself getting a lot better at skimming through content, sifting through the noise that inevitably comes with any information channel. My Twitter feed, which has many nuggets of gold, nonetheless subscribes to the Pareto principle , which is basically the 80/20 rule. This means that, at best, 20 per cent of what I get is useful, while the rest is useless or not useful.
What taking a break did was enable me to recalibrate my message system and rest, which has shown itself to be a good predictor of cognitive performance. Meditation is another means of recalibrating one’s system to small extent, which can break the patterns of habit. The reasons are largely attributed to disrupting cognitive patterns enough to enable the brain to rewire itself, or at least provide new connections that could compete with the pre-existing pattern. These new connections are important, because it is through these that we learn and grow.
What makes this so unnerving is how little we take time to break the habits, rest, reflect until it is too late, or at time of someone else’s choosing and not our own. Think of Haiti. That country has been suffering for decades, yet it took an earthquake for people to pay attention.
So let’s consider some ways to re-calibrate our organizations and ourselves by taking a break now and then from the relentless chatter of social media and the steady stream of information every so often. By doing that, maybe we’ll actually take in (and learn) more. I sure did.