Posts tagged ‘economics’

Personalized coupons as a vehicle for perfect price discrimination

Given the pervasive tracking and profiling of our shopping and browsing habits, one would expect that retailers would be very good at individualized price discrimination —  figuring out what you or I would be willing to pay for an item using data mining, and tailoring prices accordingly. But this doesn’t seem to be happening. Why not?

This mystery isn’t new. Mathematician Andrew Odlyzko predicted a decade ago that data-driven price discrimination would become much more common and effective (paper, interview). Back then, he was far ahead of his time. But today, behavioral advertising at least has gotten good enough that it’s often creepy. The technology works; the impediment to price discrimination lies elsewhere. [1]

It looks like consumers’ perception of unfairness of price discrimination is surprisingly strong, which is why firms balk at overt price discrimination, even though covert price discrimination is all too common. But the covert form of price discrimination is not only less efficient, it also (ironically) has significant social costs — see #3 below for an example. Is there a form of pricing that allows for perfect discrimination (i.e., complete tailoring to individuals), in a way that consumers find acceptable? That would be the holy grail.

In this post, I will argue that the humble coupon, reborn in a high-tech form, could be the solution. Here’s why.

1. Coupons tap into shopper psychology. Customers love them.

Coupons, like sales, introduce unpredictability and rewards into shopping, which provides a tiny dopamine spike that gets us hooked. JC Penney’s recent misadventure in trying to eliminate sales and coupons provides an object lesson:

“It may be a decent deal to buy that item for $5. But for someone like me, who’s always looking for a sale or a coupon — seeing that something is marked down 20 percent off, then being able to hand over the coupon to save, it just entices me. It’s a rush.”

Some startups have exploited this to the hilt, introducing “gamification” into commerce. Shopkick is a prime example. I see this as a very important trend.

2. Coupons aren’t perceived as unfair.

Given the above, shoppers have at best a dim perception of coupons as a price discrimination mechanism. Even when they do, however, coupons aren’t perceived as unfair to nearly the same degree as listing different prices for different consumers, even if the result in either case is identical. [2]

3. Traditional coupons are not personalized.

While customers may have different reasons for liking coupons, from firms’ perspective the way in which traditional coupons aid price discrimination is pretty simple: by forcing customers to waste their time. Econ texts tend to lay it out bluntly. For example, R. Preston McAfee:

Individuals generally value their time at approximately their wages, so that people with low wages, who tend to be the most price-sensitive, also have the lowest value of time. … A thrifty shopper may be able to spend an hour sorting through the coupons in the newspaper and save $20 on a $200 shopping expedition … This is a good deal for a consumer who values time at less than $20 per hour, and a bad deal for the consumer that values time in excess of $20 per hour. Thus, relatively poor consumers choose to use coupons, which permits the seller to have a price cut that is approximately targeted at the more price-sensitive group.

Clearly, for this to be effective, coupon redemption must be deliberately made time-consuming.

To the extent that there is coupon personalization, it seems to be for changing shopper behavior (e.g., getting them to try out a new product) rather than a pricing mechanism. The NYT story from last year about Target targeting pregnant women falls into this category. That said, these different forms of personalization aren’t entirely distinct, which is a point I will return to in a later article.

4. The traditional model doesn’t work well any more.

Paper coupons have a limited future. As for digital coupons, there is a natural progression toward interfaces that make it easier to acquire and redeem them. In particular, as more shoppers start to pay using their phones in stores, I anticipate coupon redemption being integrated into payment apps, thus becoming almost frictionless.

An interesting side-effect of smartphone-based coupon redemption is that it gives the shopper more privacy, avoiding the awkwardness of pulling out coupons from a purse or wallet. This will further open up coupons to a wealthier demographic, making them even less effective at discriminating between wealthier shoppers and less affluent ones.

5. The coupon is being reborn in a data-driven, personalized form.

With behavioral profiling, companies can determine how much a consumer will pay for a product, and deliver coupons selectively so that each customer’s discount reflects what they are willing to pay. They key difference is what while in the past, customers decided whether or not to look for, collect, and use a coupon, in the new model companies will determine who gets which coupons.

In the extreme, coupons will be available for all purchases, and smart shopping software on our phones or browsers will automatically search, aggregate, manage, and redeem these coupons, showing coupon-adjusted prices when browsing for products. More realistically, the process won’t be completely frictionless, since that would lose the psychological benefit. Coupons will probably also merge with “rewards,” “points,” discounts, and various other incentives.

There have been rumblings of this shift here and there for a few years now, and it seems to be happening gradually. Google’s acquisition of Incentive Targeting a few months ago seems significant, and at the very least demonstrates that tech companies are eyeing this space as well, and not just retailers. As digital feudalism takes root, it could accelerate the trend of individualized shopping experiences.

In summary, personalized coupons offer a vehicle for realizing the full potential of data mining for commerce by tailoring prices in a way that consumers seem to find acceptable. Neither coupons nor price discrimination should be viewed in isolation — together with rewards and various other incentive schemes, they are part of the trend of individualized, data mining-driven commerce that’s here to stay.

Footnotes

[1] Since I’m eschewing some academic terminology in this post, here are a few references and points of clarification. My interest is in first-degree price discrimination. Any price discrimination requires market power; my assumption is that is the case in practice because competition is always imperfect, and we should expect quite a bit of first-degree price discrimination. The observed level is puzzlingly low.

The impact of technology on the ability to personalize prices is complex, and behavioral profiling is only one aspect. Technology also makes competition less perfect by allowing firms to customize products to a greater degree, so that there are no exact substitutes. Finally, technology hinders first-degree price discrimination to an extent by allowing consumers to compare prices between different retailers more easily. The interaction between these effects is analyzed in this paper.

Technology also increases the incentive to price discriminate. As production becomes more and more automated, marginal costs drop relative to fixed costs. In the extreme, digital goods have essentially zero marginal cost. When marginal production costs are low, firms will try to tailor prices since any sale above marginal cost increases profits.

My use of the terms overt and covert is rooted in the theory of price fairness in psychology and behavioral economics, and relates to the presentation of the transaction. While it is somewhat related to first- vs. second/third-degree price discrimination, it is better understood as a separate axis, one that is not captured by theories of rational firms and consumers.

[2] An exception is when non-coupon customers are made aware that others are getting a better deal. This happens, for example, when there is a prominent coupon-code form field in an online shopping checkout flow. See here for a study.

Thanks to Sebastian Gold for reviewing a draft, and to Justin Brickell for interesting conversations that led me to this line of thinking.

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June 25, 2013 at 7:09 am 8 comments

What Happened to the Crypto Dream? Now in a new and improved paper form!

Last October I gave a talk titled “What Happened to the Crypto Dream?” where I looked at why crypto seems to have done little for personal privacy. The reaction from the audience (physical and online) was quite encouraging — not that everyone agreed, but they seemed to find it thought provoking — and several people asked me if I’d turn it into a paper. So when Prof. Alessandro Acquisti invited me to contribute an essay to the “On the Horizon” column in IEEE S&P magazine, I jumped at the chance, and suggested this topic.

Thanks to some fantastic feedback from colleagues and many improvements to the prose by the editors, I’m happy with how the essay has turned out. Here it is in two parts: Part 1, Part 2.

While I’m not saying anything earth shaking, I do make a somewhat nuanced argument — I distinguish between “crypto for security” and “crypto for privacy,” and further subdivide the latter into a spectrum between what I call “Cypherpunk Crypto” and “Pragmatic Crypto.” I identify different practical impediments that apply to those two flavors (in the latter case, a complex of related factors), and lay out a few avenues for action that can help privacy-enhancing crypto move in a direction more relevant to practice.

I’m aware that this is a contentious topic, especially since some people feel that the time is ripe for a resurgence of the cypherpunk vision. I’m happy to hear your reactions.

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April 29, 2013 at 12:06 pm Leave a comment

Unlikely Outcomes? A Distributed Discussion on Decentralized Personal Data Architectures

In recent years there has been a mushrooming of decentralized social networks, personal data stores and other such alternatives to the current paradigm of centralized services. In the academic paper A Critical Look at Decentralized Personal Data Architectures last year, my coauthors and I challenged the feasibility and desirability of these alternatives (I also gave a talk about this work). Based on the feedback, we realized it would be useful to explicate some of our assumptions and implicit viewpoints, add context to our work, clarify some points that were unclear, and engage with our critics on some of the more contentious claims.

We found the perfect opportunity to do this via an invitation from Unlike Us Reader, produced by the Institute of Network Cultures — it’s a magazine run by a humanities-oriented group of people, with a long-standing interest in digital culture, but they also attract some politically oriented developers. The Unlike Us conference, from which this edited volume stems, is also very interesting. [1]

Three of the five original authors — Solon, Vincent and I — teamed up with the inimitable Seda Gürses for an interview-style conversation (PDF). Seda is unique among privacy researchers — one of her interests is to understand and reconcile the often maddeningly divergent viewpoints of the different communities that study privacy, so she was the ideal person to play the role of interlocutor. Seda solicited feedback from about two dozen people in the hobbyist, activist and academic communities, and synthesized the responses into major themes. Then the three of us took turns responding to the prompts, which Solon, with Seda’s help, organized into a coherent whole. A majority of the commenters consented to making their feedback public, and Seda has collected the discussion into an online appendix.

This was an unusual opportunity, and I’m grateful to everyone who made it happen, particularly Seda and Solon who put in an immense amount of work. My participation was very enjoyable. Research proceeds at such a pace that we rarely have the opportunity to look back and cogitate about the process; when we do, we’re often surprised by what we find. For example, here’s something I noted with amusement in one of my responses:

My interest in decentralized social networking apparently dates to 2009, as I just discovered by digging through my archives. I’d signed up to give a talk on pitfalls of social networking privacy at a Stanford workshop, and while preparing for it I discovered the rich academic literature and the various hobbyist efforts in the decentralized model. My slides from that talk seem to anticipate several of the points we made about decentralized social networking in the paper (albeit in bullet-point form), along with the conclusion that they were “unlikely to disrupt walled gardens.” Funnily enough, I’d completely forgotten about having given this talk when we were writing the paper.

I would recommend reading this text as a companion to our original paper. Read it for extra context and clarifications, a discussion of controversial points, and as a way of stimulating thinking about the future prospects of alternative architectures. It may also be an interesting read as an example of how people writing an article together can have different views, and as a bit of a behind-the-scenes look at the research process.

[1] In particular, the latest edition of the conference that just concluded had a panel titled “Are you distributed? The Federated Web Show” moderated by Seda, with Vincent as one of the participants. It touched upon many of the same themes as our work.

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March 27, 2013 at 7:44 am 1 comment

Price Discrimination and the Illusion of Fairness

In my previous article I pointed out that online price discrimination is suspiciously absent in directly observable form, even though covert price discrimination is everywhere. Now let’s talk about why that might be.

By “covert” I don’t mean that the firm is trying to keep price discrimination a secret. Rather, I mean that the differential treatment isn’t made explicit — e.g., by not basing it directly on a customer attribute — and thereby avoiding triggering the perception of unfairness or discrimination. A common example is selective distribution of coupons instead of listing different prices. Such discounting may be publicized, but it is still covert.

The perception of fairness

The perception of fairness or unfairness, then, is at the heart of what’s going on. Going back to the WSJ piece, I found it interesting to see the reaction of the customer to whom Staples quoted $1.50 more for a stapler based on her ZIP code: “How can they get away with that?” she asks. To which my initial reaction was, “Get away with what, exactly? Supply and demand? Econ 101?”

Even though some of us might not feel the same outrage, I think all of us share at least a vague sense of unease about overt price discrimination. So I decided to dig deeper into the literature in psychology, marketing, and behavioral economics on the topic of price fairness and understand where this perception comes from. What I found surprised me.

First, the fairness heuristic is quite elaborate and complex. In a vast literature spanning several decades, early work such as the “principle of dual entitlement” by Kahneman and coauthors established some basics. Quoting Anderson and Simester: “This theory argues that customers’ have perceived fairness levels for both firm profits and retail prices. Although firms are entitled to earn a fair profit, customers are also entitled to a fair price. Deviations from a fair price can be justified only by the firm’s need to maintain a fair profit. According to this argument, it is fair for retailers to raise the price of snow shovels if the wholesale price increases, but it is not fair to do so if a snowstorm leads to excess demand.”

Much later work has added to and refined that model. A particularly impressive and highly cited 2004 paper reviews the literature and proposes an elaborate framework with four different classes inputs to explain how people decide if pricing is fair or unfair in various situations. Some of the findings are quite surprising. For example: in case of differential pricing to the buyer’s disadvantage, “trust in the seller has a U-shaped effect on price fairness perceptions.”

The illusion of fairness

Sounds like we have a well-honed and sophisticated decision procedure, then? Quite the opposite, actually. The fairness heuristic seems to be rather fragile, even if complex.

Let’s start with an example. Andrew Odlyzko, in his brilliant essay on price discrimination — all the more for the fact that it was published back in 2003 [1] — has this to say about Coca Cola’s ill-fated plans for price-adjusting vending machines: “In retrospect, Coca Cola’s main problem was that news coverage always referred to its work as leading to vending machines that would raise prices in warm weather. Had it managed to control publicity and present its work as leading to machines that would lower prices in cold weather, it might have avoided the entire controversy.”

We know how to explain the public’s reaction to the Coca Cola announcement using behavioral economics — the way it was presented (or framed), customers take the lower price as the “reference price,” and the price increase seems unfair, whereas the Odlyzko’s suggested framing would anchor the higher price as the reference price. Of course, just because we can explain how the fairness heuristic works doesn’t make it logical or consistent, let alone properly grounded in social justice.

More generally, every aspect of our mental price fairness assessment heuristic seems similarly vulnerable to hijacking by tweaking the presentation of the transaction without changing the essence of price discrimination. Companies have of course gotten wise to this; there’s even academic literature on it. One of the techniques proposed in this paper is “reference group signaling” — getting a customer to change the set of other customers to whom they mentally compare themselves. [2]

The perception of fairness, then, can be more properly called the illusion of fairness.

The fragility of the fairness heuristic becomes less surprising considering that we apparently share it with other primates. This hilarious clip from a TED talk shows a capuchin monkey reacting poorly, to put it mildly, to differential treatment in a monkey-commerce setting (although the jury may still be out on the significance of this experiment). If our reaction to pricing schemes is partly or largely due to brain circuitry that evolved millions of years ago, we shouldn’t expect it to fare well when faced with the complexities of modern business.

Lose-lose

Given that the prime impediment to pervasive online price discrimination is a moral principle that is fickle and easily circumventable, one can expect that companies to do exactly that, since they can reap most of the benefits of price discrimination without the negative PR. Indeed, it is my belief that more covert price discrimination is going on than is generally recognized, and that it is accelerating due to some technological developments.

This is a problem because price discrimination does raise ethical concerns, and these concerns are every bit as significant when it is covert. [3] However, since it is much less transparent, there’s less of an opportunity for public debate.

There are two directions in which I want to take this series of articles from this point: first a look at how new technology is enabling powerful forms of tailoring and covert price discrimination, and second, a discussion of what can be done to make price discrimination more transparent and how to have an informed policy discussion about its benefits and dangers.

[1] I had the pleasure of sitting next to Professor Odlyzko at a conference dinner once, and I  expressed my admiration of the prescience of his article. He replied that he’d worked it all out in his head circa 1996 but took a few years to put it down on paper. I could only stare at him wordlessly.

[2] I’m struck by the similarities between price fairness perceptions and privacy perceptions. The aforementioned 2004 price fairness framework can be seen as serving a roughly analogous function to contextual integrity, which is (in part) a theory of consumer privacy expectations. Both these theories are the result of “reverse engineering,” if you will, of the complex mental models in their respective domains using empirical behavioral evidence. Continuing the analogy, privacy expectations are also fragile, highly susceptible to framing, and liable to be exploited by companies. Acquisti and Grossklags, among others, have done some excellent empirical work on this.

[3] In fact, crude ways of making customers reveal their price sensitivity lead to a much higher social cost than overt price discrimination. I will take this up in more detail in a future post.

Thanks to Alejandro Molnar, Joseph Bonneau, Solon Barocas, and many others for insightful conversations on this topic.

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January 22, 2013 at 10:24 am 10 comments

Online price discrimination: Conspicuous by its absence

The mystery about online price discrimination is why so little of it seems to be happening.

Consumer advocates and journalists among others have been trying to find smoking gun evidence of price discrimination — the overt kind where different customers are charged different prices for identical products based on how much they are willing to pay. (By contrast, examples of covert or concealed price discrimination abound; see, for example, my 2011 article.) Back in 2000 Amazon tried a short-lived experiment where prices of DVDs for new and for regular users were different. But that remains essentially the only example.

This should be surprising. Tailoring prices to individuals is far more technically feasible online than offline, since shoppers are either identified or at least have loads of behavioral data associated with their pseudonymous cookies. The online advertising industry claims that this is highly effective for targeting ads; estimating consumers’ willingness to pay shouldn’t be much harder. Clearly, price discrimination has benefits to firms engaging in it by allowing them to capture more of the “consumer surplus.” (Whether or not it is beneficial to consumers is a more controversial question that I will defer to a future post.) In fact, based on technical feasibility and economic benefits, one might expect the practice to be pervasive.

The evidence (or lack thereof)

A study out of Spain last year took a comprehensive look at online merchants, by far the most thorough analysis of its kind. They created two “personas” with different browsing histories — one of which visited discount sites and the other visited sites for luxury products. Each persona then browsed 200 e-commerce sites as well as search engines to see if they were treated differently. Here’s what the authors found:

  • There is evidence for search discrimination or steering where the high- and low-income personas are shown ads for high-end and low-end products respectively. In my opinion, the line between this practice and plain old behavioral advertising is very, very slim. [1]
  • There is no evidence for price discrimination based on personas/browsing histories.
  • Three of the 200 retailers including Staples varied prices based on the user’s location, but necessarily not in a way that can’t be explained by costs of doing business.
  • Visitors coming from one particular deals site (nextag.com) saw lower prices at various retailers. (Discounting and “deals” are very common forms of concealed price discrimination.)

A new investigation by the Wall Street Journal analyzes Staples in more detail. While the Spain study found geographic variation in prices, the WSJ study goes further and shows a strong correlation between lower prices and consumers’ ability to drive to competitors’ stores, which is an indicator of willingness to pay. I’m not 100% convinced that they’ve ruled out alternative hypotheses, but it does seem plausible that Staples’ behavior constitutes actual price discrimination, even though geography is a far cry from utilizing behavioral data about individuals.

Other findings in the WSJ piece are websites that offer discounts for mobile users and location-dependent pricing on Lowe’s and Home Depot’s websites but with little evidence of being based on anything but costs of doing business.

So there we have it. Both studies are very thorough, and I commend the authors, but I consider their results to be mostly negative — very few companies are varying prices at all and none are utilizing anywhere near the full extent of data available about users. Other price discrimination controversies include steering by Orbitz and a hastily-retracted announcement by Coca Cola for vending machines that would tailor prices to demand. Neither company charged or planned to charge different prices for the same product based on who the consumer was.

In short, despite all the hubbub, I find overt price discrimination conspicuous by its absence. In a follow-up post I will propose an explanation for the mystery and see what we can learn from it.

[1] This is an automatic consequence of collaborative recommendation that suggests products to users based on what similar users have clicked on/purchased in the past. It does not require that any explicit inference of the consumer’s level of affluence be made by the system. In other words, steering, bubbling etc. are inherent features of collaborative filtering algorithms which drive personalization, recommendation and information retrieval on the Internet. This fact greatly complicates attempts to define, detect or regulate unfair discrimination online.

Thanks to Aleecia McDonald for reviewing a draft.

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January 8, 2013 at 4:57 am 4 comments

A Critical Look at Decentralized Personal Data Architectures

I have a new paper with the above title, currently under peer review, with Vincent Toubiana, Solon Barocas, Helen Nissenbaum and Dan Boneh (the Adnostic gang). We argue that distributed social networking, personal data stores, vendor relationship management, etc. — movements that we see as closely related in spirit, and which we collectively term “decentralized personal data architectures” — aren’t quite the panacea that they’ve been made out to be.

The paper is only a synopsis of our work so far — in our notes we have over 80 projects, papers and proposals that we’ve studied, so we intend to follow up with a more complete analysis. For now, our goal is to kick off a discussion and give the community something to think about. The paper was a lot of fun to write, and we hope you will enjoy reading it. We recognize that many of our views and conclusions may be controversial, and we welcome comments.

Abstract:

While the Internet was conceived as a decentralized network, the most widely used web applications today tend toward centralization. Control increasingly rests with centralized service providers who, as a consequence, have also amassed unprecedented amounts of data about the behaviors and personalities of individuals.

Developers, regulators, and consumer advocates have looked to alternative decentralized architectures as the natural response to threats posed by these centralized services.  The result has been a great variety of solutions that include personal data stores (PDS), infomediaries, Vendor Relationship Management (VRM) systems, and federated and distributed social networks.  And yet, for all these efforts, decentralized personal data architectures have seen little adoption.

This position paper attempts to account for these failures, challenging the accepted wisdom in the web community on the feasibility and desirability of these approaches. We start with a historical discussion of the development of various categories of decentralized personal data architectures. Then we survey the main ideas to illustrate the common themes among these efforts. We tease apart the design characteristics of these systems from the social values that they (are intended to) promote. We use this understanding to point out numerous drawbacks of the decentralization paradigm, some inherent and others incidental. We end with recommendations for designers of these systems for working towards goals that are achievable, but perhaps more limited in scope and ambition.


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February 21, 2012 at 8:27 am 3 comments

The Surprising Effectiveness of Prizes as Catalysts of Innovation

Although the strategic use of prizes to foster sci-tech innovation has a long history, it has exploded in the last two decades—35% annual growth on average, or doubling every 2.3 years.[1] Much has been said on the topic, but I have yet to see a clear answer to the core mystery:

Why do prizes work?

Specifically, why are they more effective than simply hiring people to do it? The question is more complex than it sounds, and a valid explanation must address the following:

  • Why shouldn’t government and industry research funding be switched over entirely to a prize-based model?
  • Why did the prize revolution happen in the last two decades, and not earlier?
  • How do prizes succeed in spite of the massive duplication of effort that you’d expect due to numerous contestants trying to solve the same problem?

Prizes exploit the productivity-reward imbalance

In many fields there is a huge disparity—order of magnitude or more—between the productivity of the top performers and the median performers. The structure of the corporation, having co-evolved with the industrial revolution for harnessing workers to build railroads or textiles, is fundamentally limited in its ability to reward employees in creative endeavors in proportion to their contribution, or even measure it. Academia is a little better due to the precedence of fame over monetary reward, but has its own problems.

Enter prizes. The winner-take-all structure gives individuals or small organizations of exceptional caliber a chance to earn prestige as well as cash that they don’t otherwise have a shot at.

Given that the best innovators are more likely to feel that an academic or corporate job under-rewards them, self-selected prize contestants are likely to skew toward high-performers.

Prizes channel existing research funding

The Netflix prize attracted 34,000 contestants. At an average of just 1 hour (valued at $100) per contestant, the monetary value of the time spent on the contest dwarfs the prize amount. And the majority of contestants—or at least the ones with a serious chance—were already employed as researchers. This effect is broadly true: for example, contestants spent a total of over $100 million in pursuit of the Ansari X Prize which carries a $10 million award.

The real funding for prize-winning efforts comes from Government grants and corporate research labs. The prize itself serves to mainly to legitimize the task as a research goal.

This is in no way meant to be a criticism of prizes—sure, prizes direct attention away from other problems, but one expects that on average, problems for which prizes are offered are more important than others.

Nor does the ability of prizes to spur effort far in excess of the monetary award necessarily mean that contestant behavior is irrational, since the prestige and media attention are typically worth far more than the cash, and because failure to win the prize doesn’t mean the effort is wasted.

That said, the well-known human tendency to systematically overestimate one’s own abilities certainly has a role in explaining the power of prizes to attract talent. According to the same McKinsey report linked above, “many of the participants that we interviewed were absolutely convinced they were going to win [the Ansari X Prize], if not this year, then surely the next.”

What about democratization?

The openness of prizes is often advanced as a key reason for their superiority over traditional research funding. There are two very different components to this assertion: the first is that prizes encourage hybridization of expertise from different fields, given that researchers often fall into the trap of collaborating only within their own communities. There is evidence for this from a study of Innocentive.

The second argument is that prizes allow even non-expert members of the general public, who might otherwise never be involved in research, to participate. I find this argument unconvincing and there is little evidence to support it, if you ignore anecdotes from the 19th century when science funding was meager by today’s standards. However, crowdsourcing to the public seems a good strategy for prizes that are more about problem solving than original research. Challenge.gov may be a good example, depending on how it pans out.

The Internet as an enabler

Now let’s look at the three auxiliary questions I posed above. My explanation for prize effectiveness—self-selection, redirection of funding, and interdisciplinary collaboration—can answer them comfortably. If all research funding were based on prizes, it would defeat the purpose since prizes only serve to redirect existing research funding.

The rapid growth of the sector since 1990 is an obvious indication that the Internet had something to do with it. But how exactly? I think there are several reasons. First, the Internet could be making it easier for experts from different physical locations and/or areas of expertise to team up and to collaborate.

Second, increased reach, shorter cycles and improved economies of scale in most markets in the Internet era have exacerbated the performance-reward imbalance, as well as making the imbalance more obvious to all involved. This is a factor fueling the startup revolution as well.

Finally, and perhaps crucially, I believe the Internet has largely nullified one of the key disadvantages of prizes, which is duplication of effort. The Netflix prize, for one, was marked by a remarkable degree of sharing, and sponsors of new contests are increasingly tweaking the process to ensure that teams build on each other’s ideas.

These factors are only going to accelerate in the future, which suggests that the torrid growth of prizes in number and amount is going to continue for some time to come. There are now many companies dedicated to running these contests—Innocentive is the leader, and Kaggle is a startup focused on the data-mining space. Exciting times.

[1] My numbers are based on this McKinsey report which seems by far the most comprehensive study of prizes and is well worth reading for anyone interested in the subject. The aggregate purse of prizes over $100,000 grew from $50MM to $302MM from 1991 to 2008, during which period the share of “inducement prizes,” the kind we’re concerned with here, showed remarkable growth from 3% of the total to 78%.

Thanks to Steve Lohr for pointers to research when he interviewed me for his NYTimes Bits piece, and to @dan_munz and other Twitter followers for useful suggestions.

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June 6, 2011 at 3:03 pm Leave a comment

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About 33bits.org

I'm an assistant professor of computer science at Princeton. I research (and teach) information privacy and security, and moonlight in technology policy.

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