Emergence and crowdsourcing seem like good bedfellows. Here’s why I think they belong together.

Emergence

This term was originally coined in 1875 by the pioneer psychologist G. H. Lewes, and his conception is based on the idea that many things that occur in nature are the result of the sum of two or more things, or the difference between them. Basically addition or subtraction. Based on this, t it should be possible to trace back the calculations, and thus define a basis for predict future outcomes. With emergence, however, Lewes states that the above theory does not apply. Here, outcomes are not simply the result of the sum, or difference, of inputs.

This reminds me a of the 1+1=3 rule that I talk about in this post. I think of emergence as the manifestation of a system’s properties that are not anticipated from the properties of its components or parts. To put it another way,  emergence happends when forces interact in such a way as to produce unexpected patterns, or structures, in nature. Ants are commonly used as an example of insects exhibiting emergent behavior because they seem to “think as a colony” rather than as individuals (i.e. the group is smarter than the individual). Each ant appears to act out simple and predictable behaviors, but together they build massive structures that embody a pattern, or plan, that appears to be greater than the sum of it’s parts.

Crowdsourcing

Crowdsourcing is simply taking as task that would have been done by one person, or a small group of people, and outsourcing it to a community of people. Depending on the kind of problem, this approach can offer greater efficiency and improved quality of the work product. The difference between crowdsourcing and outsourcing is simply that there is no defined party or community that can take on the work. It should also be noted that crowdsourcing is not the same as open-source, which is more about making the plans for something available to a community to alter. With crowdsourcing, a data set being worked on can remain proprietary and individuals may, or may not, be able to share information.

One benefit of crowdsourcing is that it may increase the diversity of approaches that are used to resolve the problem. There can also be greater efficiency in the allocation of resources through the use of competitions. In this case, the amount and quality of work produced internally for X dollars may be less than the amount and quality of work produced in sum by many people/groups working for a prize of X dollars.

Putting It Together

My favorite story that ties these two ideas together follows an experiment conducted by Francis Galton back in the late 1800’s. I first learned about this story from James Surowiecki’s Wison of Crowds, which uses the story in it’s opening. Galton basically went to a market and offered an open challenge to guess the weight of an ox. Hundreds of people guessed, including some experts. Despite the large number of estimates, nobody guessed the weight within a pound. What Galton discovered, however, was that by taking all the guesses and averaging them he could estimate the weight within the required margin. In other words, the crowd is an excellent estimating mechanism for weighing ox, even though no of the individuals participant had any idea that they were working together.

I find these ideas inspiring and am always looking for ways to bring them together to solve problems from customer service to product development. I actually view writing this blog as a way of participating in a project to overhaul marketing that is much bigger than any individual. Together with my fellow marketing bloggers, we are slowly transforming what it means to be a marketer.  I hope you’ll join me in this effort and that together we can define a new discipline.

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3 Comments to Emergence + Crowdsourcing = Insight

  1. […] the fact that this winery has found a way to crowdsource recruiting. I’ve been watching how crowdsourcing has been affecting marketing, and how it fits well with a community engagement approach. Today, big companies like Proctor and […]

  2. […] BzzAgents takes a somewhat more democratic approach to recruiting such that anyone can become a member. P&G’s services are a bit more picky about who they accept to participate. BzzAgents also has a different rewards structure, such that members earn points for engagement. For example, you can earn points for filling out the non-product/service surveys, for participating in a buzz campaign, and for reporting about your experiences buzzing a product. What’s interesting is that BzzAgent’s CEO Dave Balter has stated that most members do not redeem their points for discounts or products. In fact, members are motivated mostly by getting products and services before anyone else, having the opportunity to share discounts with friends and family, and  having the opportunity to participate in the product development process. Yet another interpretation of crowdsourcing. […]

  3. Tim Todd says:

    I think Nanocrowd (I am part of the core founding team) is a good example where the analysis of crowd sourced data leads to emergent insight that can then be used to solve a problem. In our case, we help users find movies.

    We take in millions of movie reviews and analyze them algorithmically to find out what a movie is about and how it relates to other movies…Without editors. The results (what you see at our site) at Nanocrowd are completely algorithmic. No one names nanogenres, no one edits groupings.

    The sum of the parts: millions of reviews (the crowd source part) + our algorithms = emergent information and insight greater than the whole. This in turn enables users to find movies they will like and find them fast! 🙂

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