[C]ompetitive balance improved from the 1930s until leveling off in the late-1980s and early-1990s. Much of this improvement was likely a natural consequence of more high-quality talent becoming available to more clubs, the addition of the amateur draft in 1965 (the mechanism Branch Rickey felt was most important for leveling the financial playing field across teams), and other minor structural tweaks to the league. Why would the improving trend disappear just as revenue sharing came into existence? While I’m not certain that revenue sharing stopped the progress, I doubt it was instituted just in time to counteract a trend reversal.

In my view, if revenue sharing worked, there would be some evidence of it working over the past two decades that it’s been tried under various formats. How much longer are we supposed to give it, especially when what we observe is exactly what theory predicts we should observe? If we think it’s important to correct inherent differences in revenue potential across teams, I think revenue sharing is a poor tool for achieving that goal.

For MLB teams, Bradbury shows that revenue earned on wins is pretty stable between 60 and 80 wins (approx. $100M).  80 wins represents an inflection point at which revenue in relation to wins climbs rapidly–e.g. teams that win 100 games can earn 80% more than teams winning only 85 games.  The problem is that many teams can spend relatively little and turn a solid profit by fielding a team that finishes the season 20 games under .500.  Rather than poor the shared revenue into their teams, owners have found it easier and safer to field a sub-.500 team.

It strikes me that MLB needs to think harder about the choice architecture it puts in place with revenue sharing. If teams can make a profit without being competitive and the returns on winning do not kick in until you surpass the 80-win mark (and, therefore, incur greater risk and less guaranteed profits) why not break up the disbursement of revenue sharing funds?

For example, why not disburse the funds in tranches? You could make additional tranches dependent on hitting certain win milestones and with a higher number of wins comes greater fund sharing (e.g. as teams approach 80 wins they are provided with an additional kicker that can be put toward the following season). It may also make sense to make the revenue shared less than the profits obtained at >80 wins. This way, teams are provided with an incentive to take greater risks and build more competitive teams and not fall back on the shared revenue.

Once you can incentivize a team to make an investment in talent above and beyond what they currently provide the team may take on a life of its own, creating a positive feedback loop that could catapult it into the 80+ wins area and thereby generate greater profits which allows for greater investment which provides greater talent, etc, etc.

Just a thought.

It is a lucrative business that has drawn financiers from the Somali diaspora and other nations—and now the gangs in Haradheere have set up an exchange to manage their investments.

[...] “Four months ago, during the monsoon rains, we decided to set up this stock exchange. We started with 15 ‘maritime companies’ and now we are hosting 72. Ten of them have so far been successful at hijacking,” Mohammed said. “The shares are open to all and everybody can take part, whether personally at sea or on land by providing cash, weapons or useful materials … we’ve made piracy a community activity.”
[...] “The district gets a percentage of every ransom from ships that have been released, and that goes on public infrastructure, including our hospital and our public schools.”

One commenter noted that the enterprise could not last without formal legal structures to provide proper oversight.  Chirol responded that “those structures essentially exist in Somalia, they are just “informal” according to us since they are illegal by our standards and not government run. But such mechanisms do exist.”

My question is, do they really exist? Theoretically, you have repeated interactions which discourage cheating in the short run as well as a community that could possibly impose reputational costs on particular actors, but these are not efficient mechanisms for compliance and trust. Whose is responsible for monitoring? Who pays those costs? If it is individual actors the transaction costs are sure to be quite high and, moreover, inefficient and ineffective. You need a (presumably) neutral body that can write, monitor, and enforce rules and regulations that act as the foundation for the entire investment process. The pirates themselves are not exactly a neutral party, and as they are effectively running the exchange it should create all kinds of perverse incentives and issues in the long run. In either case, it is a fascinating development.

To test this idea, Wired asked writer Evan Ratliff to disappear for one month.  He could tell no one of his plans (not even his family or girlfriend).  They announced the contest online on August 14th, set a bounty for his discovery ($5,000–$3,000 of which was Ratliff’s own money), and invited the general public to find Ratliff through whatever means they liked (preferably, legal).  The contest would be over when someone came face to face with Ratliff, snapped a picture of him, and said the word “fluke”.  All told, it took 17 days to catch Ratliff.  The article is a must read, providing the fascinating details of Ratliff’s moves as well as those of his pursuers.

What occurred to me when reading the article was that, without saying so, Wired essentially crowdsourced the manhunt for Ratliff.  Rather than hire a single investigator or firm to find him, Wired outsourced the task to a large, undefined, diverse group of people (basically, Jeff Howe’s definition of crowdsourcing).  For crowdsourcing to work (or for crowds to be “wise”) the crowd generally has to have three attributes, all of which the hunter group seemed to possess:

• Diversity: The more diverse a crowd is, the more likely it is to be intelligent. Why? Because a diverse crowd will bring many different views to bear on a problem, thereby increasing the likelihood that a solution will be found. From what I can tell, the crowd chasing Ratliff was quite diverse and included people of all ages, professions, and skill sets. And it was an interesting mix of people who eventually nabbed him.
• Independence: The crowd must also be relatively independent, meaning individuals are not reliant on the same source of information. This is important for two reasons. First, when information is independent it’s more likely the ‘errors’ of each individual will cancel each other out. Second, independent individuals are more likely to bring unique information to the table. The article provides evidence for the independence of individuals and clusters of individuals working on the search. While many people were sampling the same information via the Twitter #vanish hashtag, there were tons of individuals bringing their own information to the party.
• Decentralization: The more decentralized, or localized, members of a crowd the smarter it will be, the idea being that the more localized and spread out a crowd is the greater the sources of information that the crowd can pull from. The hunters in this case were highly decentralized, located all over the country and providing local intelligence that the group as a whole could benefit from.

It also occurred to me that the story reveals and illustrates a few other important aspects of crowdsourcing:

• Rewards: Motivating a crowd is a key component when implementing a crowdsourcing strategy to problem solving. If the crowd isn’t properly motivated they are unlikely to put the time and effort into the task at hand. Wired did provide a monetary incentive (the $5K), the crowd seemed just as motivated (if not more so) by the reputational incentive of being known across the Wired-world as the one who found Ratliff. And not to ruin the ending of the article, but readers will see that on balance the reputational motivation won the day in this case. The lesson is that you should take into account multiple types of incentives, not just monetary, when trying to motivate a crowd.
• Self-organization & Collaboration: Even though there are individual incentives to solve the problem, it did not deter hunters from pooling their knowledge and working together in teams. The story mentions that almost immediately folks shared their information and thoughts via the hashtag #vanish, formed groups on Facebook, and even relocated to secure chat rooms to prevent moles from providing Ratliff with counterintelligence. Most of these people had never met each other. Almost instantly hunters self-organized, without the benefit of hierarchical direction. And much like the recent Netflix contest, individual hunters banded together in ad hoc and informal teams to work collaboratively towards finding Ratliff faster than if they simply worked alone.

Be sure to check it out.

The essence of behavioral economics is that humans fail to meet the assumptions made by economists in important ways.  As a result, economic models of human behavior often fall short in their predictions of what people “should” do.  By understanding the limitations that actual people have in terms of their knowledge, computational ability, and emotions organizations can design products, services, and processes that lead or ‘nudge’ actors towards better choices. The article mentions a few examples, such as using peer pressure (people do not like being outliers, or being left behind by trends) and short circuiting the inertia of past choices (once we make a choice it tends to stick, regardless of how suboptimal it may be), that have been shown to significantly alter behavior.

One theme that I have heard again and again from some critics is that businesses are using strategies that are underhanded or manipulative in order to get customers to spend money in ways that run against their interests.  No doubt, some may use nudge strategies to take advantage of others, but using a behavioral approach does not necessarily imply that a business is simply trying to manipulate customers into making choices that run counter to their interests–in fact, I would argue the opposite is true.

Because none of us are perfectly rational actors (i.e. homo economicus), we all need help from time to time understanding what action or choice is truly in our self-interest–what choice maximizes value for us. What behavioral economics tells us is that this isn’t as straightforward as previously thought. How a choice and/or information is presented to us makes as much of a difference as what is presented. To me, the lesson to be drawn from behavioral economics is not that businesses need to manipulate their customers, but that they need to be more strategic and more precise in how they make their case for their product or service.  Businesses have a tremendous amount of information about how their service is used by a wide range of actors.  This means they are in a unique position to analyze 1) the most efficient and effective ways to use their services 2) the relative use of their service by different types of clients.  Most clients don’t have access to this kind of information and are therefore at the mercy of their supplier for these insights, or frankly they just guess.

Successful businesses take into account what service will be of the greatest value to clients and customers. By providing great value they ensure that clients will stick around, building a long lasting and reciprocal relationship. If businesses manipulate clients into less valuable purchases in exchange for short term sales, both parties will be worse off in the long run.  The key is to align interests; businesses have an interest in recurring revenue and clients have an interest in finding high quality solutions to their problems–when these interests are aligned both parties will be better off.  (The “Shadow of the Future” plays a significant role here.)

However, as behavioral studies have shown, people don’t always make the best choices for themselves.  Why should customers be any different?  How businesses present choices and service options to clients is just as important as the content of those services.   Clients are people, and therefore have the same barriers to overcome in terms of choices that serve their interests.  In order to nudge clients towards optimal services and service utilization, businesses should present solutions to clients in a relational way–e.g. people like you use this version of the service, or use the service at this rate–presenting solutions in this way can help to counteract the inherent inertia of human behavior.

The key to a relational strategy is data and the visualization of that data.  You have to present the right data in a way that tells a compelling story to the client or else they will not be jolted off of their current path.  Looking back on situations where I was nudged it was typically the result of a profound story about my peers told through compelling data.  It is important to remember that data isn’t just important for bean counters–its critical to story tellers.  The better story you tell with your data about how a client’s peers leverage your service the more likely they are to choose the same behavior, see greater value as a result and appreciate your helping them get there, and, most importantly, remain clients in the future.