Archive for category Interdisciplinary teams
Those who conceptualize products – particularly software – often have the unpleasant task of explaining their conceptual gems to unimaginative, sanctimonious engineers entrenched in the analytic mire of in-the-box thinking. This communication directs the engineers to do some plumbing and flip a few switches that get the concept to its intended audience or market… Or, at least, this is how many engineers think they are viewed by designers.
Truth is, engineers and creative designers really don’t speak the same language. This is more than just a joke. Many posts here involve philosopher of science, Thomas Kuhn. Kuhn’s idea of incommensurability between scientific paradigms also fits the design-engineering gap well. Those who claim the label, designers, believe design to be a highly creative, open-ended process with no right answer. Many engineers, conversely, understand design – at least within their discipline – to mean a systematic selection of components progressively integrated into an overall system, guided by business constraints and the laws of nature and reason. Disagreement on the meaning of design is just the start of the conflict.
Kuhn concluded that the lexicon of a discipline constrains the problem space and conceptual universe of that discipline. I.e., there is no fundamental theory of meaning that applies across paradigms. The meaning of expressions inside a paradigm comply only with the rules of that paradigm. Says Kuhn, “Conceptually, the world is our representation of our niche, the residence of the particular human community with whose members we are currently interacting” (The Road Since Structure, 1993, p. 103). Kuhn was criticized for exaggerating the extent to which a community’s vocabulary and word usage constrains the thoughts they are able to think. Kuhn saw this condition as self-perpetuating, since the discipline’s constrained thoughts then eliminate any need for expansion of its lexicon. Kuhn may have overplayed his hand on incommensurability, but you wouldn’t know it from some software-project kickoff meetings I’ve attended.
This short sketch, The Expert, written and directed by Lauris Beinerts, portrays design-engineering incommensurability from the perspective of the sole engineer in a preliminary design meeting.
See also: Debbie Downer Doesn’t Do Design
This weekend I was pleased to take part in the Third Annual Berkeley Ancient Italy Roundtable (BAIR) conference. The event, chaired by Ted Peña, UC Berkeley Department of Classics, promotes interaction between an amazingly diverse group of scholars to develop a more cohesive professional community. This was a wonderful example of raw interdisciplinary innovation, essentially without commercial potential, done purely for the love of knowledge. Every time I’m in such a group, I can’t help but think that if I ran the corporate zoo, I’d institute a “20% time” (or similar) that required study outside an employee’s field rather than allocating 20% to in-field projects that are outside the employee’s normal job role (like Google and 3M used to do). The innovations that come out of “20% time” programs are rare to start with. I don’t mean they’re not worth the cost, just that they tend to produce a small number of high-value outcomes. Given that, why not push for even more creative thinking. After all, it’s easier to learn to think outside the box if you start by actually getting out of the box. Learning to work with lawyers, electron microscopists, aviation psychologists and the FAA makes aerospace engineers better at engineering. I think art and music classes make for better mathematicians, and I suspect digital archaeology teaches innovation better than many schools focusing directly on that goal. Radical innovation almost always involves stepping across knowledge and social boundaries. We’d probably be better at taking such steps if we got more practice.
The keynote talk for this year’s BAIR conference was by Professor John Dobbins of the University of Virginia. He combined art history, archaeology, architecture and advanced 3D modeling to study the Alexander Mosaic at Pompeii. Working with Ethan Gruber of UVA, Dobbins took us on a visual and intellectual journey to the House of the Faun in Pompeii, which held the most famous mosaic from antiquity – one showing the defeat of Darius by Alexander the Great. Dobbins examined the ancient viewing conditions that once existed in the House of the Faun and raised questions about how well the mosaic could be seen, tucked away in its exedra. He used a 3D model of the house including a lighting package set for summer and winter conditions in 100 BC, (rough date for the mosaic), and showed that modifications to the house, previously unexplained by archaeologists, were probably made to eliminate shadows of pillars that would otherwise ruin the viewing of the mosaic. With the 3D model and its lighting package, Dobbins could move the sun across the sky exactly as it would have happened in ancient times, seeing the lighting problems that emerged at different times of day and year. Dobbins made a case for the utility of this type of 3D modeling, not merely as a viewing aid but as as a research tool. He finishes by proposing a Roman date – i.e., after the Social Wars – for the Alexander Mosaic and suggesting that the mosaic and the expansion of the House of the Faun made sense with respect to the social and political context of Sulla’s conquest of Pompeii in 89 B.C and its subsequent absorption by Rome.
Another fabulous example of research across disciplines came from Patrick Beauchesne of UC Berkeley. Beauchesne works with bioarcheological studies of ancient Rome. He described findings regarding the daily lives of average citizens (as opposed to aristocratic circles), knowledge of whom has otherwise been lacking. He convincingly drew conclusions about the society from life course perspectives – ways to interpret the complex biocultural production of human bodies situated in specific historical contexts. Beauchesne noted that children in antiquity have been studied before, but mainly through biological lenses that focus on health and pathology derived from skeletal remains- of which Beauchesne described in some detail. He then used examples from the Roman antiquity to highlight how existing methods can be used in new ways to make inferences about the process of childhood in ancient Rome.
Sarah Witcher Kansa (Alexandria Archive Institute) shared new realizations about ancient life and architecture drawn from aspects of fauna remains including ratios of livestock to wild animal bone counts in Etruscan garbage pits. We then learned some secrets of Roman seawater concrete and how modern adoption of Roman concrete formulas could reduce the CO2 emissions from concrete, which constitute a surprisingly high percentage of global emissions (Marie Jackson, Civil & Environmental Engineering, Berkeley). Patrick Hunt (Medieval Studies at Stanford) led us through paleoclimatology and lichenometrics to determine the route Hannibal took across the Alps with African and Asian elephants. Patrick regularly crosses the Alps in the snow on foot, by the way. There were many more fine talks – all boldly crossing disciplinary boundaries. During a break I had a great discussion with Chris Johanson of UCLA on digital visualization techniques and aerial photography using helicopter drones.
My talk was on craniofacial anthropometry of Roman portraiture. It was connected to the work I did on the Getty Augustus (described here), and included an anthropometric comparison of the young general in the Ludovisi Sarcophagus battle scene against the Capitoline Hostilian, along with a case for the Vatican Pertinax being a modern forgery on the basis of measurements and statistics.
We can bridge knowledge boundaries if we put our interdisciplinary minds to it. Doing so may even be easier than crossing the Pyrenees, the Alps and flooded rivers with elephants in tow.
“Fail early and often.” This war cry du jour of speakers on entrepreneurial innovation addresses several aspects of what big companies need to learn from little ones about market dynamics at the speed of the internet. The shelf life of a product idea is pretty short these days. If you don’t cannibalize your own line, a nimble competitor will eat your lunch. Failure is a necessary step on the path to innovative solutions. Short-cycle failure is much cheaper than the long-cycle variety. Innovation entails new ideas, and the idea generation phase is not the time for Negative Nelly, the devil’s advocate, to demoralize your design team. A lot of bad ideas beget new insights that spawn good ideas.
My favorite story about letting crazy ideas fly deals with Pacific Power and Light, who supplies electricity to some remote spots in the Cascades. As the story goes, storms left thick ice on their power transmission lines. Linemen were sent out into the field, who climbed the icy towers and used long hooks to knock down the ice. The process was slow, expensive and dangerous.
PP&L’s brainstorming sessions initially yielded no clever solutions. They again attacked the issue, this time ensuring cognitive diversity by including linemen, accountants, secretaries, and the mail guy.
As a joke, a lineman suggested training bears to climb the poles and shake them. Someone else added that by putting honey pots on top of the poles, the bears would go for the honey without training, and perhaps shake the poles sufficiently to knock the ice off the lines. Continuing the silliness, someone suggested using helicopters to periodically fill the honey pots.
Bingo. A secretary, formerly a nurse’s aide in Vietnam, recalled the fury of the down-wash from the helicopter blades and asked if flying a helicopter near the power lines would be sufficient to shake the lines and knock the ice off. In fact, it is! By valuing cognitive diversity and by encouraging crazy thinking, the team found a solution. As the story goes, PP&L now uses helicopters to fly over the power transmission lines after ice storms and it works fabulously.
As is probably apparent to any student of mythology, literary form criticism or biblical criticism, the story is pure fiction. It appears in many tellings on the web, some dating back several decades. Veracity strike one: manuscript (version) differences indicate multiple independent secondary sources. Strike two: earlier versions have less textural detail than later versions (e.g., the lineman is named Bill in later tellings). Strike three: the setups for the convergence of a diverse group are strained and get more detailed over time (compare the aphorism setups in Gospel Mark vs. Matthew).
Sure, the story is fiction – but what of it? The tale itself is aphoristic – an adage. It does not rely on the credibility of its source or the accuracy of the details to be valid; it’s validity is self evident. Or as Jack Nicholson (R.P. McMurphy) is often quoted as saying in One Flew Over the Cuckoo’s Nest, “Just because it didn’t happen, doesn’t mean it isn’t true.”
But as any movie fan with access to web-based movie scripts can attest, that quote never happened either. But just because McMurphy never said that just because it didn’t happen, doesn’t mean it isn’t true doesn’t mean that that isn’t true. (That last sentence contains a level-two embedded phrase, by the way.)
Further, just because Nicholson didn’t say it doesn’t mean it wasn’t said. It turns out a few others are cited as sources for this saying as well. The earliest one I could find. oddly enough, is Marcus Borg, theologian and New Testament scholar who found himself in the odd position of trying to defend Christianity while denying that Jesus said the things attributed to him. Borg’s tools are the same ones I used on the helicopter scriptures above.
Quote attribution is a tricky matter, especially when a more famous guy repeats a line from a less famous guy. Everyone knows the one about Oscar Wilde saying to James Whistler, “I wish I had said that.” To which, Whistler replied, “You will Oscar, you will.” I love this one, because it’s a quote about a quote. And none the worse when we discover, as you might expect, that it never happened – which, of course, doesn’t mean it isn’t true.
The exchange between Whistler and Wilde is cited in the Oxford Dictionary of Quotations. They give the source as page 67 of Leonard Cresswell Ingleby’s 1907 book, Oscar Wilde. As you might expect from my mentioning it here, Inglesby’s book contains no such quote on page 67 or anywhere else in the book. However, the 1973 Monty Python skit, Oscar Wilde, does include this exchange between Whistler and Wilde. Inclusion by the Monty Python crew, who tend to research history better than most textbook authors, is reason enough to dig a bit further for a source. Oscar Wilde researcher Peter Raby would be the guy to check on this trivia. I did. Raby traces the quote back to rumors in the early 1900s. He finds that some time after Wilde’s death Herbert Vivien, Douglas Sladen and Frank Harris all recalled the quote but disagreed on whether Wilde or Whistler or neither were involved.
I will never be ashamed of citing a bad author if the line is good. – Seneca
Mix a little foolishness with your prudence: It’s good to be silly at the right moment. – Horace
In a world of crowdsourcing and open innovation, it barely matters – beyond frivolous patents of course – where an idea originates or if its pedigree is respectable. Fables about bears, helicopters and Jack Nicholson are fair game. Let a thousand crazy flowers bloom.
My previous post, Wisdom and Madness of the Yelp Crowd, was a bit on the long side so I didn’t want to clutter it up with references. Here’s two books and a list of relevant papers I’ve enjoyed reading, with a few comments.
Charles MacKay Extraordinary Popular Delusions and The Madness of Crowds, originally published in 1841
This antique is uneven – often dry and boring. Nevertheless, the spine of copy I bought 20 years ago is thoroughly creased. Despite being written in a stuffy archaic style that sometimes bring to mind the worst of Edward Bulwer-Lytton, quite a bit of it remains relevant.
James Surowiecki. The Wisdom of Crowds 2004
Surowiecki is constantly cited by crowdsourcing fans, peer-based sites, and social activists, most of whom do not appear to have read it, based on their seeming ignorance of his criteria of independence, decentralization and diversity. This book is well-thought and well-written, and is essential for anyone interested in crowdsourcing, social software, and collective decisions. Quibble: I disagree with his conclusions about the sociological implications of crowd-based estimates and the promise of crowd wisdom in the area of coordination.
Mostapha Benhenda A Model of Deliberation Based on Rawls’s Political Liberalism, Social Choice and Welfare, Vol. 36, 2011
Sushil Bikhchandani, David Hirshleifer, et.al. A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades, The Journal of Political Economy; Oct 1992
B. Fischhoff. Hindsight ≠ foresight: the effect of outcome knowledge on judgment under uncertainty Journal of Experimental Psychology, 1975.
Benjamin Golub, Matthew O. Jackson. Naive Learning in Social Networks and the Wisdom of Crowds, American Economic Journal Microeconomics. February 2010. A really interesting study showing, among other things, that accuracy of crowd judgment is unrelated to the time elapsed before consensus is reached.
Cars Hommes , Joep Sonnemans, et. al. Coordination of Expectations in Asset Pricing Experiments, Tinbergen Institute Discussion Papers, Mar 2004.
Vassilis Kostakos, Is the crowd’s wisdom biased? A quantitative assessment of three online communities, Proceedings of the 2009 International Conference on Computational Science and Engineering – Volume 04. Kostakos studied three voting/rating websites, Imdb, Amazon and BookCrossings, finding the aggregate voting behavior in them – particularly Amazon – to be very similar to what I saw in Yelp data.
Jaron Lanier. Digital Maoism: The Hazards of the New Online Collectivism, Edge Essay, May 30, 2006
Albert E. Mannes. “Are We Wise About the Wisdom of Crowds? The Use of Group Judgments in Belief Revision” Management Science, Aug 2009.
Mannes shows substantial evidence that in cases where members of organizations agree that group wisdom has yielded a better conclusion, most people still overvalued their original beliefs and conclusions, while underweighting the more correct judgment reached by group wisdom. Is this merely managerial arrogance, or perhaps something more complex, related to pluralistic ignorance?
Jan Lorenz, Heiko Rauhut, et. al. How social influence can undermine the wisdom of crowd effect, Proceedings of the National Acadamy of Science, May 31 2011.
Excerpt from Abstract: “Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) Nature 75:7] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks.”
Ville Miettinen. Crowdsourced Design: It’s a Crowd, Not a Committee, Crowdsourcing.org blog post, Mar 26, 2012.
Matthew J. Salganik, Peter Sheridan Dodds et. al. “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market,” Science Feb 10 2006.
This is a fabulous piece of web-based experimental psychology of peer influence in cultural markets. They explore the phenomenon of “hits” in popular music, showing that increasing the strength of social influence increased both inequality and unpredictability of success. They make sense of the fact that while music superstars are orders of magnitude more popular than average selling bands, music experts invariably fail to predict superstars’ success.
Joseph P. Simmons, Lief D. Nelson, et. al. Intuitive Biases in Choice vs. Estimation: Implications for the Wisdom of Crowds, Carnegie Mellon UniversityResearch Showcase, July 22, 2010.
The study looks at how different types of judgments show varying degrees of crowd wisdom. In an NFL football betting context, incorrect intuition led a crowd to predict favorites more than underdogs where betting on underdogs would have won the bets. Moreover, they showed that crowd bias toward this error increased over the season.
Amos Tversky, Daniel Kahneman , Judgment under Uncertainty: Heuristics and Biases, Science, Sep. 27, 1974
Do you think in words – or in pictures?
Various communities use the term visual thinking in different but related ways. Thinking about pictures, communicating with pictures, advertisement and propaganda, visualization of data, and storyboarding share this concept. Football coaches, branding experts, math gurus and choreographers think and communicate in pictures. Visual Thinking champions, like XPlane founder Dave Gray, make an excellent case that everyone else should learn to do the same.
Do a web search on visual thinking and you’ll find that most of the results apply to visual learning, a closely related concept. Visual learning involves concept maps, diagramming, storyboarding and analysis of art for meaning. Proponents of visual learning (e.g., Visual Thinking Strategies) hold that connecting concepts to images leads to better comprehension, retention, and critical thinking (see also Dual Coding Theory, Cognitive Load Theory). While the same has been claimed for every alternative education concept of the past four decades, the claims for visual learning seem to be on solid ground.
The term visual thinking appears often in the study of art – particularly ancient art. The ancient Romans were highly visually literate; the message of a panel (below photo) from the Cancelleria reliefs would have been immediately apparent to its viewers. Few modern viewers except those trained in ancient iconography can see the persuasive devices at work here. Irresistible forces have called the emperor of Rome to invade Sarmatia, and the senate and the citizens are behind the decision – or so we’re led to believe. The advantage the ancients had over us moderns is not merely their ability to recognize the deities in the scene. The Romans would have correctly interpreted the scantily clad, muscular gal as representing Rome’s people and its soul; and would have noticed that she was pushing the emperor toward his conclusion to invade. They would also appreciate the goddess of war’s flirtation and eye-lock with the emperor.
Art historians observe that what we see – or how we interpret what we see – is controlled by our culture. Historian Betty Ann Brown, in Art & Mass Media, puts it this way:
Most of us consider seeing an essentially biological and “natural” process. We assume that we open our eyes and automatically see whatever is in front of them. In fact, looking is learned. We “see” as our culture teaches us to see.
More education in visual thought and analysis of imagery could help us lift this cultural veil from our eyes and eliminate the distrust we have for what we see. And it could help the bottom line.
Rudolph Arnheim, in his much-cited 1969 book, Visual Thinking, made a passionate case that thinking and perceiving are inseparable. That seems a bit extreme – as do his assertions that all reasoning is intuition and all observation is invention. But I think Arnheim nailed it when he said the arts are neglected because they’re based on perception and that perception is downplayed in education because it is assumed not to involve thought.
Many engineers and designers think in pictures, whether alone or working in teams, where schematics do most of the talking. Visual thinking in the world of systems engineering can involve a remarkable rich, yet accessible, symbolic language. In the 1960s, Larry Lamm, an engineer at Douglas Aircraft in Long Beach, pioneered the concept of an integrated, functional “systems” approach to schematics. Lamm published aircraft system schematics, initially as part of Delta Airlines maintenance manuals, that addressed the need for communication within interdisciplinary teams. His schematics mixed electrical signals and components, hydraulics, mechanics, and logical operations in the same diagram, each bounded by its interfaces and connections to related systems. They magically explained the way complicated systems interconnected and worked together, in a manner accessible to executives, marketing reps, maintenance crews, and non-English speakers.
Ancient fragment of my schematic of a hypothetical redundant autobrake control system for a jet aircraft in the style of Larry Lamm. From a brainstorming session on common mode failures and unwanted activation during takeoff.
The diversity of teams addressing today’s need for not only product innovation but also the expanded scope of joint product, business-model, and branding innovation cries out for visual thinking. This isn’t simply because the problems at hand are complex, unbounded, and fluid. As with the diverse-community gaps bridged by Larry Lamm’s schematics, great corporate gulfs are dug by discipline-specific language and isolated organizational perspectives. Visual thinking bridges the gaps.
I’ve been in idea-generation (Ideation) sessions with business model experts Alex Osterwalder and Julian Loren where conference room walls were covered floor to ceiling with posters filled with sketches and sticky notes. This wasn’t art class, but a healthcare innovation workshop. Osterwalder’s process, described in his book, Business Model Generation, uses visual thinking, along with sketches, diagrams, sticky notes and concept maps not only to document and communicate but to construct meaning and critique it.
Alex Osterwalder conducting a business model innovation session. Background image by Rachel Smith
A business model is a system. As with the components of an aircraft, each element in a business model influences and imposes constraints on all the others. And like an aircraft system, a business model only makes sense as a whole – at least to most of its many stakeholders. Without visualizing it first, creating the big picture is next to impossible; and so is refining it. As Osterwalder sees it, visual thinking exposes logic flaws and allows for clearer discussions and model changes while moving discourse from the abstract to the concrete.
Imagination should have no boundaries. Business creativity is about turning imagination into something useful. Visual thinking puts us on the fast track to shaping idea into novel products, brands, and business models. Let’s get visual.
Foursquare is in the news again today. Naveen Selvadurai’s is leaving. I’m not interested in predicting the outcome of Foursquare; plenty of people already do that. I’m interested in looking at the tug-of-war over Foursquare’s key resource – its location data – between the two distinct customer segments in the multi-sided platform. It’s right at the intersection of business model, technology, and game psychology.
With Highlight and Glancee taking the SXSW LBS spotlight and Naveen’s departure, Foursquare likely has more its mind than data quality. However, most of Foursquare’s potential revenue streams might ultimately be sensitive to their data quality. First a quick review of where Foursquare is and how they got there.
Foursquare co-founder Dennis Crowley is concerned that users and the media have misconceptions about what Foursquare does. Its critics say Foursquare itself isn’t quite sure of this either. Let’s look at a couple of different angles of the square.
I’d guess the perspective of most Foursquare users is that it’s a location-based game plus a location data service somewhat like to Gowalla, Grindr and bits of Facebook and Yelp. Users get points and badges for logging their presence in locations. At the same time they can get tips from other users, though I’ve never known anyone to use that feature. That was the feature set when the iPhone app was first released. It’s more fun than it sounds, and people love to compete. Now it’s also an affiliate marketing/deal platform, e.g., free dessert with dinner when you check in at Roy’s. It claims 15 million users, and Google Trends reports about 400,000 unique visitors a day.
From the perspective of its investors – $70 million in funding since June 2010 – Foursquare can’t lose. Andreessen Horowitz named three reasons it sees Foursquare as unstoppable: a great CEO – Dennis Crowley, a killer product, and a gigantic market. They think Foursquare can make money in a way where everyone wins – users, merchants, brand advertisers. I suspect Andreessen Horowitz’s reasons are actually much more sophisticated and their analysis much deeper.
Critics of Foursquare point to its identity crisis. Its founders have variously called it a friend-finder, a social city-guide, a game, a deal locator and so on. Then there are concerns about adoption. Foursquare recently claimed 750,000 venues, but it appears only about half of check-ins are for food or shops. One reports cited an 80/20 male female split (security concerns), though more recently, Foursquare’s VB of BusDev, Tristan Walker, said they were approaching parity. In 2010, when asked about a revenue stream, Crowley offered, “I think it’s going to take time to figure it out.” Subsequent partnerships with Groupon and Amex don’t yet seem to have nailed it.
A Dec., 2012 report from Forrester showed low usage – around 5% – of location-based apps by U.S. adults and a low growth rate. Deeper-pocket adults are growing leery of being served even more advertisements in even more intrusive ways. Addressing a tiny user base compare to Facebook and LinkedIn, Walker said it’s not just about reach; it’s about engagement. But without more users (or more active users) few brands are likely to have the ability to make this work – except maybe those with a very high venue density. Coffee, anyone?
Having browsed Foursquare from those angles, let’s get back to the data and how it’s affected by all the above. As Crowley said in December, “the data is more important than the check-in.”
Foursquare is – right now at least – a two-sided platform that, like a free newspaper, relies exclusively on one side for revenue. It attracts readers in sufficient number for venues to justify an affiliate program. The value proposition for users has been primarily the game (competition and intrinsic rewards); and the venues get targeted ads and similar. A key resource is the immense amount of location data added by users to Foursquare’s original seed data. In simpler days, the identifying data about users would be the real asset, but in this model, the venue data they supply is also essential. User information is easy to verify. Venue data is a bit trickier.
Competition was a great way to build a user base fast, but the competitive crowd that sources this data has little incentive to ensure its quality. Users aren’t all trustworthy and they make errors. But Foursquare needs them; they’re an essential source for new venue data – highly volatile in the world of food service. But Foursquare motivates its users against to provide the data through competition over who visits the most venues.
Two problems emerged immediately after Foursquare’s app release. One was duplicate entries resulting from user errors in naming venues. You search and don’t find “Brick House,” unaware that the correct name is “Brickhouse Cafe,” so you create a duplicate entry. One venue is now in the system with two names, reducing apparent visitation, check-ins now split between two logical venues. Competition-minded users are incented to subdivide venues, since first check-in at a location scores more points than subsequent ones. The San Francisco International Airport location soon split into many terminal locations, then specific gates, then specific flight numbers. Competition simultaneously drives participation and pollutes the data.
More important yet, with little down-side, competition invites cheating. One day last year I walk around San Francisco regularly checking Foursquare for nearby venues, finding all sorts of bus stops, nonexistent museums, and someone’s house where Grand Theft Auto was played. The bogus museums weren’t mistakes; they were Foursquare entries with no corresponding reality, apparently created by a kid craving that treasured 9-point first check-in.
In the world of big data, techniques exist for finding and removing likely-duplicate entries. In crowd sourcing, there are techniques for crowd-policing and establishing the reliability or trustworthiness of contributors prior to trusting their submissions. The sparsity of submissions by any particular player makes that approach useless to Foursquare. So Foursquare has to rely on sophisticated heuristics (e.g., statistical analysis of mobility and check-in patterns) and super-users who’ve established trust and can delete bogus entries and merge duplicates. Managing this is a constant battle. It consumes resources and reduces vendor confidence. It could also impact consumers of Foursquare’s data – whether venue info source or customer behavior/intent.
The data integrity problem is obviously not insurmountable, and I’m guessing Foursquare has higher priorities right now. As I said, my intent wasn’t to critique or forecast, but to point out an interesting intersection of business model, technology, game psychology and risk analysis consistent with the theme of this blog. But it’s a problem that Yelp doesn’t have, nor does Angry Birds or Fruit Ninja; and it could have been avoided through the right design process.
You might be a Foursquare addict if you’ve checked into venues without coming to a full stop in your car.
Aside & historical note: I love Foursquare and I use it daily when I chose to tell either the world or my friends (the app provides for either) where I am at the moment. The serendipitous-meeting aspect is big fun. Once, in New Zealand, I ran into a friend from New York. I first heard of Foursquare from Robert Scoble in 2009, who wrote that he had checked into Sequoia Hospital while waiting for his wife to give birth. Starting in 2001 (yes, 11 years ago), I worked with Boston based LiveSky, Inc., and later independently, on location-aware, mobile phone (PDA) apps. We designed location-based games – as I’m sure many others did – and built fully functional prototypes of location aware deal push-notification systems. But we never considered anything like a game/deal hybrid. I recall several large obstacles stemming from being way too early in this space. 802.11g had not yet been invented, so wandering between routers with wireless was impossible. The telcos delayed release of LBS for years on end despite promises of imminent release. I remember reaching a Sprint exec in 2003 who told me they weren’t convinced there would be a market for LBS, especially since not all phones had e911 provisions yet. We plugged standalone GPS units into our Compaq iPaqs (interesting name, eh?) and powered them through the car cigarette lighter to get mobile location info.
If the idea of expert generalist doesn’t sit well with you, please bear with me through some terminology. The terms multidisciplinary, interdisciplinary and transdisciplinary are tossed around in a variety of circles these days. Innovation management, academic research, primary education, organizational management, and clinical practices use these terms in related but distinct ways. The perils of pedantry in mind, some soft definitions seem warranted. Multidisciplinary, usually applied to a team or a study topic, means it involves several distinct traditional disciplines. Interdisciplinary refers to knowledge that is between existing disciplines. Transdisciplinary indicates that we’re outside the realm of existing disciplines. Epistemologists will please excuse me for merging this with interdisciplinary for this discussion.
A person can be multidisciplinary – by having multiple discrete, related skills. Think of a double major or a major in Finance and a minor in Mathematics. Now think of these disciplines as real tools on a belt, not merely badges on a diploma. Then imagine someone with a rare collection of such tools, skillful with each, having no preference for any tool, but using them together for the real objective, which is not tool usage but building something cool. You might call such a person a multidisciplinarian – or an expert generalist.
A team can be multidisciplinary too. A clinical team might include a consultant clinician, a dietitian, a pharmacist, a nurse and a psychiatrist. Those disciplines combine to make the team multidisciplinary. But the clinician and the nurse can accurately be seen as multidisciplinary people too.
A team can also be interdisciplinary – if its members are trained in multiple disciplines. Better still, through their interaction team members might transfer to each other enough knowledge and perspective so that they achieve an otherwise unavailable synthesis. They might solve a problem unsolvable by one discipline alone, or by all their disciplines in sequence. A few studies have concluded that interdisciplinary teams can succeed where multidisciplinary teams cannot.
Ages ago, before the web gave me research at my fingertips, I was discussing these topics with Jim Bouey, then Director of Engineering at McDonnell Douglas in Long Beach. I was struggling to communicate my observations on the difference between multidisciplinary and interdisciplinary teams, finally arriving at something like the unwieldy definitions you can now find on Wikipedia. Jim looked up and eloquently offered, “I see. You don’t want people together in a room; you want ideas together in a mind.” Yeah, that’s it… Wish I’d said that.
Effective interdisciplinary teams excel at dealing with ambiguity and conflicting requirements. They experience enhanced creativity and the ability to synthesize and can develop rare skills like debunking experts and finding long-accepted, invalid constraints. Best of all, members of interdisciplinary teams tend to individually acquire skills that were initially held only by the team collectively. That is, an interdisciplinary team can be a valuable learning experience (think corporate knowledge transfer – with no overhead). Obviously, such harmony requires commitment and nurturing. And, yes, there are dangers.
Critics and silo builders will observe that this apparent panacea has grave risks. An individual juggling unrelated independent skills ends up mastering none of them. If team members’ knowledge becomes sufficiently diffused, each ends up with the same perspective and creativity is lost. Collective tunnel vision ensues. Excessive cohesiveness (dear comrades) leads to the preference for consensus over the objective comparison of alternatives. And freeloaders love a crowd where no one holds individual responsibility or accountability.
Success in interdisciplinary and multidisciplinary teams means integrating parts while staying aware of most of their boundaries. As John G Falcioni, editor of Mechanical Engineering magazine, put it in a piece praising expert generalists: “Interdisciplinarity celebrates the whole without jeopardizing its parts.”
The expert-generalist concept addressed in Falcioni’s article is fairly natural for systems engineers and business managers, but can be a tough nut for those who see experts and specialists as synonymous. An expert generalist doesn’t juggle a bunch of independent, unrelated skills. She uses related but distinct skills to create a whole having qualities not present in any of its parts. She is a multidisciplinary individual capable of interdisciplinary problem solving and synthesis. Yet, she avoids the master builder syndrome (see Atul Gawande); this isn’t about control. Expert generalists may soon have their day in the sun.
Management and the press are obsessed with innovation – and rightly so. A novel product with high demand, protected by patents, gets you a shorter time to milk the cow than it used to. Competition moves fast. The need for product and business model innovation may soon result in a cry for our education system to produce more expert generalists and more graduates geared to function in interdisciplinary teams (I’ll have some thoughts on interdisciplinary learning in another post). The innovation models taught by progressive business schools imply a need for (or explicitly call for) interdisciplinary teams and expert generalists. This is a good thing. Perhaps now the press – who’s enjoyed the genius-specialist-geek motif long enough – will catch wind of this and help dispel the myth that all experts are intensely specialized.
Some professions are inherently multidisciplinary. Primary care doctors, engineers, patent attorneys, magicians, architects, toy designers, music theorists, and movie set designers involve various degrees of multidisciplinarity. Firms involved in bioinformatics, medical device manufacture, cognitive brain imaging, accident analysis, statistical linguistics, search engine design, robotics, market forecasting, behavioral ecology and nanotechnology rely heavily on interdisciplinary teams and multidisciplinary individuals. A critic might observe that these are all high-tech or esoteric fields. But the need for innovation in firms with mundane consumer products could (and should) land its key players in a product-design or business-model innovation workshop. In this setting, expert generalists and interdisciplinary-team-players will shine.
Overwhelming evidence shows that the current world of business requires more of these individuals than the talent pool can possibly supply. I’ll have more on developing skills for working in interdisciplinary teams later. Start by learning to juggle. Just kidding.
I spent a decade or so as an aerospace engineer. I designed control systems for aircraft and worked in the design office for a new jet. Control systems are incredibly complex. They’re comprised of mechanical components, electrical and hydraulic actuators, processors, software, logic, analog and digital feedback loops, and human interfaces. Their design involves all sorts of engineering, provision for support equipment, maintenance, integration with other systems, anthropometry and human factors. Add to this mix the economics of tooling costs, acquisition cost, manufacturability, maintenance costs and frequency, spares provisioning, operator, support equipment and maintenance crew training, and first-flight schedule delays.
Then there’s the complex matter of risk. Despite the phenomenal track record of commercial and even military aviation, flying has obvious inherent dangers – more of them than you can imagine, unless you’re a systems engineer. It makes no sense for any link in a chain to be stronger or weaker than any other. The analogy between aircraft and chain is fairly good for structural strength, but designing so that no system contributes a larger share of risk of catastrophic failure than any other is an immensely challenging task. Nevertheless, aerospace engineers pull it off with grace.
I also spent some time in the nuclear industry, another undertaking that pioneered the systems-engineering approach, emphasizing the need to view a system as more than the sum of its components and subsystems. Working with such systems is intrinsically and necessarily multidisciplinary and interdisciplinary, requiring coordination of large teams. Thus all systems engineers are engineering managers by definition. They’re not managers in the sense of supervising and directing the activities of department members, but in the sense that they coordinate and integrate the activities of a great number of designers, suppliers, stress analysts, reliability and maintainability engineers, regulatory proxies, contract administrators, estimators, and so on. Work in this environment for long, and you’re bound to notice that the interaction of your team, facilities, highest-level business requirements, profitability, customers and suppliers can rightly be considered a system too, and can be modeled, managed, and coordinated according to the same principles and techniques.
When I left the nuclear and aerospace worlds, I was grateful for the many skills beyond basic engineering that I’d acquired there – including life cycle cost analysis, profitability studies, design for manufacturing, and probabilistic risk analysis. But beyond these I appreciate that I rather automatically view new challenges at a higher level than that of their nominal disciplines. A decade or so of systems engineering – with a sprinkling of other roles – leaves one very comfortable with interdisciplinary and multidisciplinary perspectives. At least it did for me – a multidisciplinary state of mind.