A few weeks ago, while writing about Dan Pink’s great talk, we introduced a puzzle…and, well, we’re still thinking about it. Of course we’re not alone; the link between competition and innovation keeps a lot of economists (and organizational psychologists) busy—especially since innovation and competition don’t seem to go hand-in-hand in the way that many would expect.
In Pink’s talk, for instance, he discusses the surprising finding that creative thinking seems to be allergic to pay-for-performance type incentives (and by extension, we argued, competition).
From playing with Legos to investment policies: intuitions on competition
This struck us as intuitively right. Who wants to be distracted by the prospect of failure or ranking when solving a challenging problem? Think about the way kids are absorbed in play. Think about yourself playing Legos as a kid. Now imagine your performance at Legos if your mom offered to pay for the best construction after 45 minutes of “play.” The pleasure of total absorption in building something would have been lost.
It turns out that the dominant investment strategies for firms display a similiar aversion to competition. There is an observed negative impact on innovation in highly competitive fields, since investments seem more risky in these contexts (this effect is mitigated slightly in the case where two firms are running “neck and neck”). This means fewer firms enter a crowded field, and overall investments (viz., innovation) tend to be lower.
Last fall, Google launched Project 10^100, a call for ideas that would change the world by helping as many people as possible. Google will choose 5 winning ideas (to be announced shortly) and has pledged $10 million US towards their implementation. While the design of the contest isn’t very innovative—Google is essentially passing around a suggestion box, and the rewards are traditional grant-style funding—the company seems to have learned from previous crowdsourcing disasters. Here’s what they’ve done right (and what every company tempted to let the people decide should keep in mind) and what they haven’t:
1. Appeal to principles.
The project asks how the world could be changed by helping people. The 10^100 website is well-designed, with an appealing video. It seems like this project is important, not just something a marketing department was too lazy to do and left up to the public.
Normally, when asked its opinion, the crowd likes to see the mighty fall. If we’re not members of the community affected by a vote, for example the caring users of a product that we vote about, we have very little incentive to make the right choice. When Time magazine put its Most Influential Person of 2009 up to an Internet vote, for example, the crowd chose the founder of 4chan, the forum where memes are born and productivity goes to die. (The crowd in question was made up mostly of forum posters, who were later accused of hacking the vote, not magazine readers.) Not only did Time have to honor what it called a “stunning result,” it had to acknowledge the influence of the forum that a 2008 Time article, quoting Star Wars, referred to as “a wretched hive of scum and villainy.”
Google isn’t using the crowd’s ideas for a new ad campaign or its own website, but for charity. Project 10^100 appeals to people’s altruism, not their desire to make themselves heard. If you submit silly ideas, that is, you’re not hurting Google, you’re hurting the starving children of the world.
Former Al Gore speechwriter turned business-world bestseller, Dan Pink recently gave an excellent 19 minutes talk at TED2009. (For those of you not familiar with TED, hasten to their website—it offers an extensive video archive of important contemporary thinkers on technology, design and innovation).
In this talk, Pink tells us that most of our ideas about what motivates performance are wrong—or at least not well-adapted to important tasks of our times. His main point: our beliefs about incentives come from attempts to motivate tasks that we no longer do.
The mechanical, rule-following tasks of the 20th century have been replaced by those requiring open-ended, creative thinking. For creative tasks, contingent incentives (if you do X, you will get Y), have a negative impact on performance.
Correlatively, studies show a negative effect with the addition of a competitive element to a “task environment”. A contingent-reward structure often involves setting a solver against his or her peers performing similiar tasks (only some of them will get Y for doing X).
Pink highlights two studies which clarify the interdependence of contingent reward and competition. In one experiment, participants are asked to complete a challenging cognitive task, i.e. to solve it, participants need to operate what can be called a change of a perspective, (“the candle problem”). They are divided into two groups. One group is told that their work will be used to set a “norm” or performance median. The other group is told that they will be timed and rewarded according to their performance (relative to their peers). Not only did the norm-setting group do better at their task than the pay-for-performance group, but the more that was on the line (in terms of reward), the worse participants performed the task. When the task was slightly changed, implying less creativity and more mechanical behavior (what Pink calls “the candle problem for dummies”) contingent incentives do work. Study after study appear to corroborate this evidence.
Pink does not spend too much time speculating on why contingent rewards would have this effect. Instead he uses his time to discuss the lessons we ought to learn from this research: pay incentives don’t work when tasks are cognitively challenging; people do better in less competitive, more autonomous environments. So whereas payment is the best incentive for mechanical tasks like those performed on mturk, payment alone would not seem the best motivation for more complex tasks like those performed on hypios. There has to be some intrinsic motivation: Solvers have to feel like solving problems “for their own sake” is rewarding.
I think Pink is largely right. In fact, the work environment he describes, ROWE (results-only work environment)—which allows for relative work autonomy—is what we’ve adopted here at hypios. What I’d like to do here is to speculate a little about why solvers might have reacted in the way that they did in the experiments and try to show that we shouldn’t be too quick to draw conclusions about the efficacy of either financial incentives or competition in general.
The Creativity Jam
Pink focuses on what he calls contingent incentives. Studies that Pink cites show that an incentive tied directly to the outcome of a single (timed) task cause worse performance for cognitive tasks.
Studies have also found that setting specific and difficult goals significantly increases solver performance even when the goals are out of reach. These same studies have found that, in the context of these specific and difficult goals, adding competition hurts performance when competition comes in the form of measurement against peers in view of reward. Correlatively, studies have shown that cooperative environments were superior to competive ones for enhancing task performance.
So it appears that competition and contingent reward work together to jam creative thought, at least under conditions where the reward was directly tied to the performance of a specific task. You might say that together they seem to create a virtual axe over your head, which is counter productive to creative results. Working in an autonomous environment where you set your own specific goals is most conducive to them. What none of these studies have shown (and they acknowledge this) is what happens when competition and incentives are on the horizon of an activity rather than directly tied to it.
Challenging Pink: Not all competition is zero-sum
If Pink is right, why do prize competitions—competitions that involve pay for winning performance—like the Netflix prize seem to work so well as creative-solution generators? Does offering payment for solution (as hypios does) discourage or “de-motivate” solvers? Can’t a little friendly competition (against a rival institution, say) be an added driver (even if it may not be as important as collaboration with peers)?
Pink is surely right when he says that intrinsic motivation (say, finding a task interesting) is more important than most anything else when it comes to the performance of high-level cognitive tasks. But it is hard to imagine an intrinsically motivating task withdrawn from a horizon of competition and reward. What makes it worth doing is exactly what makes it valuable to others. There will always be rewards (in the form of glory and gold) and competition for any genuinely worthwhile task. And people often (though by no means exclusively) get clues about what tasks are worthwhile from the array of incentives that surround a given task-environment. How can we put these sorts of intuitions together with the finding that Pink highlights in his talk?
Just as in economic theory, the price of something is thought of as a signal sending a message to a consumer or producer: financial incentives send messages that something has value for a seeker and even for a society as a whole. As such they focus attention on certain types of problems, issues, tasks, marking certian ones as valuable. Indeed, the X Prize foundation, which runs prize competitions with purses exceeding 1o million USD, uses these large sums to counteract what they see as a market failure, to signal attention to a series of goals that they see as undervalorized (by the market) but that they have identified as key for social advanacement. If there is only one winner, this does not seem to discourage pariticpants nor limit personal investment in pursuit of the prize.
The Ansari X prize, promoting civilian space travel, had dozens of participants investing millions of their own funds in pursuit of the goal. This is because the competition is not viewed as a zero-sum game. Competitors gain from the competition in terms of early entry into a promising market, experience and technology exposure. Here the award money structures the horizon, but is not linked to strategy development nor to the completion of individual tasks. How a solver gets to the winning solution is set by that solver or team, and not prescribed by a carrot-wielding outsider with a stopwatch.
Now let’s think a little more about competition. Is collaboration really the only game in town? I would say (and for reasons that Pink is willing to call “ideological” and “lazy”) collaboration has traditionally gotten short shrift. But, there’s evidence from studies of organizational competition that show that organizations that are “neck to neck’” spur innovation. In the Netflix competition we saw two teams—agglomerations of single players and smaller teams—that were, in the end, competing neck-and-neck. Neither the competitive element nor the prize money ($1 million) seemed to create a dysfunctional task environment among the leaders.
Let me offer a reason why. Netflix created something called a leaderboard, which showed the performance of all pariticpants and thus allowed participants to gauge their performance against their peers in relative “real time.” In the cited studies (and in many real “task environments”) participants are in the dark about others’ performance, causing them to feel a loss of control and autonomy with respect to their task.
In the Netflix case, the reward was tied to a specific and challenging goal (10% increase in the quality of the algorithm) and contest rules made it so that teams were given time to match or catch up with the winning team. Basically, this mimics the information that rival firms would get about their competition in a more or less free market. Now it’s important to not generalize (as I’ve been doing) too quickly between the response of individuals and teams in a task environment. This deserves much more study.
Pink points us towards some very important insights into the nature of motivation. Financial incentives not only cannot replace intrinsic motivation, they can acutally inhibit it. Intrinsic motivation, which comes from internal values and goals matching external problems and tasks, is decreased when factors of reward and punishment change the task environment and make the participant feel like they are no longer in control of the situation. With the solver in control, goal, guts, glory and gold all factor into what challenges he or she will take up. Watch Pink’s video (if you feel motivated to do so) and then let us know your thoughts. We promise, we won’t pay you for it
Apple is famous for the secrecy of its product development. It claims not to believe in market research, relying instead on its designers (and executives) to come up with products that will stun and awe the community, beginning with a set of hip early adopters. It has, however, welcomed outside innovation when it comes to its iPhone.
The iPhone Apps store sells thousands of special applications that are developed externally. Not only does Apple pull in tons of revenue (according to Jobs, at least $1 million/day) through the sale of these apps, but some credit the apps and their buzz with iPhone’s competitive advantage. Brad Stone, from the New York Times’ bits blog writes, “The breadth and depth of Apple’s app store is a big reason why the iPhone continues to maintain its lead against up-and-comers like the Palm Pre and the phones running Google’s Android operating system.”
Even so, the process for approval is by all accounts strenuous, even censoring, and there are some who, choosing to bypass it completely, market their apps through third-party sites. Running these apps requires the iPhone to have been “jailbroken” (downloading a small piece of code). Apple is vehemently opposed to this, claiming that it strains and breaks the iphone operating system. Others counter that Apple is just being a control freak and ungrateful to the thousands of app producers that have worked to make the iPhone into a sort of Swiss Army knife of consumer electronic (CE) devices. In short, some outsiders applaud this sort of hacking, claiming that the process of application innovation and end-user specialization should be more open and symbiotic than the Apple-model allows. (more…)
We’ve been watching, over the years, a certain contest with great interest. Netflix is an American DVD rental company that revolutionized its industry with the simple idea of letting people select and order films online and then receive them in the mail. It was a huge hit. What they always wanted to do better, however, was predict the kinds of movies that their customers would like, based on other films they’ve seen and the rating they assign it (customers are sent a brief survey to evaluate recent rentals), in order to push films towards customers that they’re more likely to enjoy.
This search for a predictive algorithm is the first reason for our interest, and we are not alone. Predictive algorithms are on many companies’ wishlists. Now that we have access to all this data, like social network profiles that list a user’s preferences and interests (plus the site’s internal trackers that record users’ behavior patterns), the thought is that this massive amount of data should allow us to predict what someone will like or dislike, purchase or ignore. (more…)
The Creativity Jam
We’ve been watching, over the years, a certain contest with great interest. 
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