Feral Jundi

It has been awhile since I last did one of these posts. I think this one is a good one and it definitely got my mental juices flowing. In the past, I have talked about Offense Industry and how bounties are a great way to fire up an industry that profits from the destruction of a specific enemy. Well for this deal, the folks at DARPA and MIT came up with a type of bounty system that takes the whole concept to the next level.

Basically what DARPA did was to set up a contest that revolved around time critical social mobilization. The idea here is that they would have these ten red weather balloons located all over the country, and the team that was able to find all ten (or the most) balloons the fastest won. DARPA wanted to see how fast something could be found.

Now why would DARPA be interested in this?  Well if you are a student of warfare and a reader of this blog, you would remember the New Rules of War post. The second rule listed by John Arquilla was ‘finding matters more than flanking’.

Rule 2: Finding Matters More Than Flanking.
Ever since Theban general Epaminondas overloaded his army’s left wing to strike at the Spartan right almost 2,400 years ago at Leuctra, hitting the enemy in the flank has been the most reliable maneuver in warfare. Flank attacks can be seen in Frederick the Great’s famous “oblique order” in his 18th-century battles, in Erwin Rommel’s repeated “right hooks” around the British in North Africa in 1941, and in Norman Schwarzkopf’s famous “left hook” around the Iraqis in 1991. Flanking has quite a pedigree.
Flanking also formed a basis for the march up Mesopotamia by U.S. forces in 2003. But something odd happened this time. In the words of military historian John Keegan, the large Iraqi army of more than 400,000 troops just “melted away.” There were no great battles of encirclement and only a handful of firefights along the way to Baghdad. Instead, Iraqis largely waited until their country was overrun and then mounted an insurgency based on tip-and-run attacks and bombings.
Thus did war cease to be driven by mass-on-mass confrontation, but rather by a hider-finder dynamic. In a world of networked war, armies will have to redesign how they fight, keeping in mind that the enemy of the future will have to be found before it can be fought. To some extent this occurred in the Vietnam War, but that was a conflict during which the enemy obligingly (and quite regularly) massed its forces in major offensives: held off in 1965, defeated in 1968 and 1972, and finally winning in 1975.
In Iraq, there weren’t mass assaults, but a new type of irregular warfare in which a series of small attacks no longer signaled buildup toward a major battle. This is the path being taken by the Taliban in Afghanistan and is clearly the concept of global operations used by al Qaeda.
At the same time, the U.S. military has shown it can adapt to such a fight. Indeed, when it finally improved its position in Iraq, the change was driven by a vastly enhanced ability to find the enemy. The physical network of small outposts was linked to and enlivened by a social network of tribal fighters willing to work with U.S. forces. These elements, taken together, shone a light on al Qaeda in Iraq, and in the glare of this illumination the militants were easy prey for the small percentage of coalition forces actually waging the campaign against them.
Think of this as a new role for the military. Traditionally, they’ve seen themselves largely as a “shooting organization”; in this era, they will also have to become a “sensory organization.”
This approach can surely work in Afghanistan as well as it has in Iraq — and in counterinsurgency campaigns elsewhere — so long as the key emphasis is placed on creating the system needed for “finding.” In some places, friendly tribal elements might be less important than technological means, most notably in cyberspace, al Qaeda’s “virtual safe haven.”
As war shifts from flanking to finding, the hope is that instead of exhausting one’s military in massive expeditions against elusive foes, success can be achieved with a small, networked corps of “finders.” So a conflict like the war on terror is not “led” by some great power; rather, many participate in it, with each adding a piece to the mosaic that forms an accurate picture of enemy strength and dispositions.
This second shift — to finding — has the potential to greatly empower those “many and small” units made necessary by Rule 1. All that is left is to think through the operational concept that will guide them.

So as you can see, finding an enemy that hides amongst the population is crucial if you want to kill or capture him. It is also difficult for just one person to find an enemy, or enemy network. But if you can create a network of people to find one person or an enemy network, then that is gold.  It also lends itself to the concept of ‘it takes a network‘, to defeat a network.

I also think bounty systems, if done correctly, can involve a large portion of the population in the fight or whatever task. It stands to reason that a nation that can fully tap into the people power it has, as opposed to only depending upon select agencies or it’s limited law enforcement resources, will have way more capability when it comes to the task of ‘finding’ someone or something.

There are also examples of private industry and government using bounties or similar incentive mechanisms to find solutions to problems.  The X Prize Foundation is just one example of this kind of incentivizing process. They have held contests for all sorts of amazing deals, and definitely read through their wiki I posted to check those out.

But back to the title of this post and what I wanted to get too. This DARPA competition drew in 50 teams from all over the nation, but it was the MIT team that won the contest. My intent with this post is to highlight their winning strategy and explore other possible uses for their strategy. To basically chalk this one up as a new bounty type system that companies and government could use to great advantage.

What the MIT team did was to create a bounty system that not only paid those that found the balloon, but also paid those that helped in the finding process. And that payment system was flexible, based on how many folks were involved, and how much money was available for the process or was desired to spend. MIT used what is called ‘Recursive Incentive Mechanism’ or RIM, and they blew away the competition with this method.

Here is a description of what they did:

Only MIT’s team found all 10 balloons. To get the recruiting ball rolling, the researchers sent a link for the team’s website to a few friends and several bloggers about 36 hours before the contest began.
Portions of the $40,000 winner’s prize were promised to everyone who contributed to the search. A maximum of $4,000 was allocated to finders of each balloon — $2,000 to the first person to send in the correct balloon location, $1,000 to the person who invited the balloon finder onto the team, $500 to whomever recruited the inviter, and so on.
Participants received about $33,000 for their efforts.
The number of Twitter messages mentioning the MIT team rose substantially the day before the contest and remained elevated until the competition ended, a sign that the reward strategy worked, Pentland says.

It kind of looks like a pyramid scheme of sorts? lol But the power that comes with this, is the involvement of social networks in the finding of something or someone, all with the lure of making some money and spreading the wealth amongst your network. And what is cool, is that this bounty system benefits different types of folks.

You might be really good at recruiting ‘finders’, so of course this system will benefit you. You might be extremely active on Facebook, Twitter, a blog, and whatever, and have a massive network in place to tap into for spreading the word (hence why I post the bounty stuff on the blog from time to time). On Facebook, I could totally see stuff like this spreading like wildfire. So you could be the guy that made money from just spreading the news. Or you could be the one that actually found the balloon, and score that way. All of these actions helped to form an efficient ‘finding system’ that won the contest.

It is the speed at which all of this happened, which is amazing to me and something to ponder.

For war, the way I could see this being used is to create a more efficient bounty hunting system. Either to find enemy combatants, or to find recruits for the war effort. And as the rest of the world continues to be inundated with cell phones, and now smart phones, the ability to really reach out to them, and have them communicate back is there. An effective RIM could be the key to getting people sending in tips, or involving their personal networks for ‘finding’ the enemy, or getting new recruits for a military in need of man power.

For business, and especially our industry, I could see this being used for head hunting. Meaning if a company is looking for a specific type of unique individual, with a certain amount of qualifications and experience, then a recruiter using RIM might be able to find that individual and in a very fast and efficient way. They could also use the formula that Alex Pentland and his team created so that they only spend the amount of money they are willing to use for the finding operation.

A company could also use RIM for finding innovations or even new business.  Especially in today’s economy, and especially with how dangerous the world has become. In other words, a company can incentivize social networks to accomplish their goals.

Finally, for crime fighting, I think it would be interesting to see the Rewards For Justice program utilize RIM. The current bounty system is old and only focuses on the individual tipster. Perhaps RIM could help to fire up that program, and get more of the population involved in finding criminals.  Crime Stoppers could turn to such a system as well, because they too use a very simple bounty system that only caters to individuals.

Interesting stuff, and I think it is a concept worth researching. Also, if you look further into Alex Pentland’s research on ‘reality mining‘, you will see why they were able to come up with the winning system. They were leagues ahead of their peers when it came to understanding the human dynamic, and they knew it. Here is the quote that I liked:

“It was trivial for us to slap together the balloon thing,” says the 58-year-old Pentland. That’s because other groups’ tactics were based on guesswork, he argues. His were based on lessons learned through data-mining research. “We won because we understood the science of incentivizing people to cooperate.”
Since 1998 Pentland has been engaged in an unusual blend of sociology and data mining that he calls “reality mining.” His researchers place sensors that he’s dubbed “sociometers” around hundreds of subjects’ necks and install tracking software into their cellphones, capturing the movements of every individual in a group, whom he or she interacts with, even body language and the tone of his or her voice. Then they mine the resulting reams of data to identify facts as elusive as which member of the group is most productive, who is the group’s real manager or who tends to dominate conversations.
“Data mining is about finding patterns in digital stuff. I’m more interested specifically in finding patterns in humans,” says Pentland, who has a Ph.D. in artificial intelligence and psychology from MIT. “I’m taking data mining out into the real world.”

Very cool and I look forward to your thoughts on RIM? Don’t ask me to interpret the math of the thing though. lol Just read through the paper and if you can understand the proofs, then good on you. All I know is that RIM won the contest, and that is what is most important to this discussion. I also think that Pentland could probably come up with a custom tailored system for war, business, or crime fighting, and perhaps some kind of modified RIM is what he would come up with. Either way, this is the go to guy for Offense Industry. –Matt

Alex Pentland, balloon hunter and MIT 'reality miner'.

Digital bounty hunters unleashed
Online pay strategy quickly coordinates cross-country balloon posse
By Bruce Bower
November 19th, 2011
These days, bounty hunters aren’t deputized, they’re digitized: Online crowd-sourcing strategies to induce masses of people to solve a task, such as locating far-flung items or alleviating world hunger, work best when financial incentives impel participants to enlist friends and acquaintances in the effort, a new study concludes.
In a competition to find 10 red weather balloons placed across the United States, a team of MIT researchers used online social media and a simple reward system to recruit balloon-searchers in the 36 hours preceding the contest. Their pay-based strategy garnered them 4,400 volunteers who located all the balloons in a contest-winning eight hours, 52 minutes.
“Our incentive system offers monetary rewards, but perhaps more importantly it builds social capital between you and the people you recruit, who get an opportunity to participate in something interesting,” says MIT computer scientist Alex Pentland. This strategy could boost the effectiveness of humanitarian and marketing campaigns, Pentland and colleagues conclude in the Oct. 28 Science.

Many digital crowdsourcing strategies have recently appeared. Paying people works for some tasks, as the MIT team found, but other tasks hinge on self-motivation that may get undermined by promises of money, says computer scientist Matthew Lease of the University of Texas at Austin. Wikipedia, for instance, parlays individuals’ inherent interests and specialized knowledge into collective encyclopedia entries.
Other crowdsourcing incentives include creating opportunities for people to socialize with others, to gain public recognition and to contribute to a valued cause, Lease says.
The goal of the 2009 balloon-hunting contest was to explore digital strategies for rapidly mobilizing people into intelligence-gathering networks. More than 50 groups entered the competition, which was sponsored by DARPA, the Pentagon’s Defense Advanced Research Projects Agency.
Only MIT’s team found all 10 balloons. To get the recruiting ball rolling, the researchers sent a link for the team’s website to a few friends and several bloggers about 36 hours before the contest began.
Portions of the $40,000 winner’s prize were promised to everyone who contributed to the search. A maximum of $4,000 was allocated to finders of each balloon — $2,000 to the first person to send in the correct balloon location, $1,000 to the person who invited the balloon finder onto the team, $500 to whomever recruited the inviter, and so on.
Participants received about $33,000 for their efforts.
The number of Twitter messages mentioning the MIT team rose substantially the day before the contest and remained elevated until the competition ended, a sign that the reward strategy worked, Pentland says.
Second-place finishers from the Georgia Institute of Technology recruited 1,400 participants by offering to donate winnings to the American Red Cross. They located nine balloons in nine hours. Their strategy sparked a brief rise in team-related tweets that faded before the contest started, suggesting that do-good appeals didn’t mobilize volunteers as much as pay incentives did.
Story here.
—————————————————————
Mining Human Behavior At MIT
Andy Greenberg,
08.30.10
With enough data at your command, a large group of humans can be coaxed into accomplishing practically any task. Even, as Alexander Pentland recently proved, when that task involves driving around every town, city and suburb in the U.S. searching for enormous red balloons.
Late last year the Pentagon’s mad-scientist research wing, Darpa, announced the Network Challenge, a $40,000 prize for the first group to find and report the locations of ten red weather balloons that the agency would set aloft one day in secret locations around the country. Most of the thousands of groups that signed up quickly realized that crowdsourcing was the way to find the 8-foot spheres. So, naturally, they offered bounties to balloon hunters.
But Pentland’s crew at MIT’s Human Dynamics Lab–part of the MIT Media Lab–took their crowd control a step further. In their incentive structure, anyone who reported a balloon to MIT received $2,000 if the team won. The person who recruited the finder earned $1,000. The person who recruited that recruiter bagged $500, and so on. The maximum payout with such a formula would add up to $4,000 per balloon, just affordable out of the prize money.
MIT’s simple system meant that participants were just as eager to enlist their friends as they were to search solo. In the two days leading up to the competition the group recruited 100,000 volunteers.
Six hours after the balloon release, that horde of searchers reported the tenth balloon in a park in Katy, Tex. Three hours later MIT had sorted through its thousands of submissions and won the competition.
“It was trivial for us to slap together the balloon thing,” says the 58-year-old Pentland. That’s because other groups’ tactics were based on guesswork, he argues. His were based on lessons learned through data-mining research. “We won because we understood the science of incentivizing people to cooperate.”
Since 1998 Pentland has been engaged in an unusual blend of sociology and data mining that he calls “reality mining.” His researchers place sensors that he’s dubbed “sociometers” around hundreds of subjects’ necks and install tracking software into their cellphones, capturing the movements of every individual in a group, whom he or she interacts with, even body language and the tone of his or her voice. Then they mine the resulting reams of data to identify facts as elusive as which member of the group is most productive, who is the group’s real manager or who tends to dominate conversations.
“Data mining is about finding patterns in digital stuff. I’m more interested specifically in finding patterns in humans,” says Pentland, who has a Ph.D. in artificial intelligence and psychology from MIT. “I’m taking data mining out into the real world.”
That tag-and-trace method means Pentland doesn’t have to rely on imperfect measurements culled from social networking websites or, worse, self-reported surveys. “Computer scientists understand data but not social dynamics. Sociologists understand social dynamics but lack instrumentation and analytics,” says Sinan Aral, a professor at New York University’s Stern School of Business. “Sandy is a visionary because he has both.”

Last year, for instance, Pentland’s lab put sociometers on 80 employees at a Bank of America call center in Rhode Island. The inconspicuous badges used Bluetooth and infrared signals to measure which co-workers the test subjects talked to every minute for a month and, later, another period of six weeks. After the first month the MIT researchers could see that individuals who talked to more co-workers were getting through calls faster, felt less stressed and had the same approval ratings as their peers. Informally talking out problems and solutions, it seemed, produced better results than following the employee handbook or obeying managers’ e-mailed instructions.
So the call center tried its own experiment. Instead of staggering employees’ coffee breaks as it had previously, it aligned their breaks to allow more chatter. The result, Bank of America told MIT a few months later: productivity gains worth about $15 million a year.
The lesson of the call center is the same one that Pentland’s balloon hunters used to motivate thousands of followers to spend a Saturday assembling search posses: create incentives that lubricate information-sharing and teamwork. “The sociometric stuff told us what the facts really are, independent of the sociology and cultural clutter,” says Pentland. “And some of the facts are surprising, like the fact that gossip improves productivity.”
The red balloon victory and the call center experiment also demonstrate that Pentland’s sensor studies can do more than fulfill his data fetish. Reality mining can teach us how to change reality. Upcoming projects will aim to encourage better energy use and health habits. “How do you get people to stop smoking or find a lost child? You leverage social networks,” says Pentland. “We’ve studied human behavior, and now we’re learning how to shape it.”
Story here.