I drew the above picture of two ants talking to each other. It is a simple doodle sketch of two ants.
I would like to comment and talk about the subject context behind this simple doodle sketch. Hope you all will appreciate it.
A single bee or an ant is not smart but colonies of them are. We can draw intriguing insights from their behavior. We must be thinking, a single ant should be very intelligent and confident. We have always seen it marching so confidently up the sugar bowl in the kitchen cabinet. It is probably executing a planned action. After all, we have seen ants making long winding lines, build elaborate ant hills and forage food like crazy.
‘It is not the case, a single ant is very incapable of accomplishing anything’ says Stanford University biologist, Deborah Gordon. She had written a good book titled ‘Ant encounters, interaction networks and colony behavior’. If you have the time, read it. It is available on Amazon. You can find the link here.
Then a question comes to our mind, how are they successful as species on earth for so long years. The answer lies in their group behavior. Colonies of ants also known as swarms are far more intelligent than a single ant on its own. Colonies of ants can accomplish tasks, which are practically impossible for single ants to even think of. For example, colonies of ants can identify the shortest possible route to the food source and they can even organize and allocate various tasks to other ants. They are able to do all this with something called ‘Swarm intelligence’.
It is not just with ants. There is a host of other insects and animals, which display the swarm intelligence. A school of Herring fish, for example, can coordinate their behavior collectively and turn their direction in a split second to avoid a threat. They do this action collectively. It turns out there is no single fish within the school that is aware of the big picture. Scientists term this behavior as the ‘Swarm theory’.
Swarm theory practicality
Many scientists are intrigued by this swarm theory for the past 20 years and research is underway for the past 10 years to gain insights from this intriguing behavior. Applications of swarm theory are enormous and can be used for wide applications in Artificial intelligence and Robotics to bring business efficiency.
Further, an interesting insight from the colony of ants is that none of the ants have a boss or a manager. The ants forage food, follow other ants, through a pheromone trail, and have countless interactions. They are self-organizing and collaborate among themselves. They have learned to adapt to this swarm behavior for millions of years. Their strengths lie in being together as a swarm and therein lies their success.
Similarly, birds do not have a leader. Have you ever seen how migratory birds fly (There is Swarm theory in action!). There are countless pictures of them. Birds change the leadership often, as and when the lead bird gets tired. No bird is telling the other birds what to do. Birds just follow their neighbor as they fly across the sky. For once it is not about individual decision making. It is just blind following and trust on the neighbor birds, to coordinate their movement.
Crowd effect happens all the time. Sometimes they are so mundane that we hardly take notice. Yes, we hardly take notice that there is a lot to learn from a humble fish. The fish under the spotlight is the Golden Shiner. For starters, making an effort to be selfless is one.
Crowd effect is a special state of fascination, where the ‘hypnotized individual falls into the hands of the hypnotizer’.
I borrowed the above line from the book titled “The crowd: the study of the popular mind” written by the French author Gustave le Bon, published in 1895. Gustave le Bon was a French polymath and a gifted doctor. He worked in diverse fields such as Sociology, Psychology, and Physics.
Computer technology never existed then nor was there any social media during his time. But then, his study on the Psychology and Sociology of crowds led to the book. He is considered one of the pioneers in that area. You can find the book here on Project Gutenberg.
His works on understanding crowds and their behavior ring so true in today’s social media usage. Understanding human nature is important. It has a considerable influence on individual and the crowds, social institutions, religion, education, work execution, office rumors, industry unions, trade associations, fanaticism, celebrations, social mobilizations and much more.
The fact is Technology is secondary and it is an enabler. Understanding human nature is primary within the context of social media and the crowd effect.
Perfectly sane individuals behave so differently in the midst of a crowd. They are swayed by the opinions and feelings of the crowd. Isn’t it.
We have seen it happening to ourselves. How many times have we stopped and looked at a construction site or a random event that happens on a busy road? We have stopped because we have seen other onlookers standing by on the roadside. They are puzzled, just as you are and they are eager to know what is the hustle all about. Being curious, huh?
If you are looking at the above construction site picture and forming an opinion, then there are chances that hundreds of other people are also watching the same page, this very moment and forming opinions just as you.
It is pretty much in the same way as how we behave and interact on the internet. It is one massive crowd (3.7 billion active users, at any given time, to be precise). All of them swayed and influenced by each other’s opinions, thoughts, and feelings.
Crowd effect and the group mind theory
There are many theories surrounding this crowd behavior and the crowd effect. One such popular theory is the group mind theory. The theory states that individuals are motivated by each other. Even though they (individuals) exist apart, they act as one group.
Individual thoughts and feelings are stimulated by each other’s thoughts and feelings. The understanding is that when there is a common cause in a group, individual minds in that group co-operate towards that cause.
The group mind is not the sum of all the individual minds. In fact, the ‘crowd has a mind of its own’ distinct from the individual minds that constitute it.
Crowd effect: Answers from the animal world
There are numerous examples of such behavior in the animal world. Fish, bees, and ants are all good examples.
Iain couzin from Princeton University has spent a considerable part of his life studying animal behavior in swarms and flocks. His particular interest fell on a very tiny bland fish called the Golden Shiner. The Golden Shiners swim in shoals and they prefer to swim in the shadier parts of the ocean which are darker than usual.
Both, the lab experiments as well as studying their (Golden Shiners) behavior in their natural environment and habitats, have shown remarkable patterns of intelligent crowd behavior and crowd effect.
We listen to social conversations all the time. Listening to these conversations helps us to understand other people and also understand the world. Active listening also helps us to learn and build relationship with others. That way, listening is a very important skill. A new kind of listening has surfaced in the recent years – listening to social media and it is called as “social listening” by some media experts.
Social listening happens when you listen to conversations that happen around your brand or company on social media. A simple act of listening to understand others has gained prominence in the commercial world. Now social listening (Commercially at least!) is all about gaining insight about your brand and company by paying attention to the conversations that happen on social media.
There is an emphasis on meaning management. Managers glean data, not just on consumption patterns of top brands and general perception of the products but also the culture, the geographical and political landscape of the place and the people. When you want to find meaning in a conversation, you need to understand the context.
Data managers and data scientists need to move away from looking at data as merely points on a graph, when they glean from social media sources for information processing. Even though Big Data analytics is essential, understanding social media conversations requires, delving deep into the culture and social perceptions of the people involved to gain insight.
Social listening requires cultural sensitivity and understanding context
Interestingly, the authors in the article point out that, modern day Data scientists lack the skill and effort required to understand and glean the meaning out of such conversations. Truly to their job and function, as data managers, they have the reductionist attitude. They reduce complex data into lower level data as Ones and Zeros. It is good for other data processing (for example: – Efficiency and profitability calculation), but may not add any value to the process of meaning management for online social media conversations.
Social listening efforts for gaining insight and understanding customers requires marketing professionals and company personnel alike to straddle between information and meaning. As mentioned earlier, finding meaning requires sound understanding of the context.
It is time for cultural sensitive data analysts and info-culture builders within organizations to read the meaning out of such conversations. Such culturally sensitive data analysts can take complex data and form higher order and meaningful information out of social media conversations.
Finding meaning in a conversation involves context. Context is naturally out of the question for information processing professionals and data scientists. Context involves, for example such information deriving questions such as: “Who said it?”, “Why they said it?” and “What are the challenges ?”. Answering such questions gives meaning and valuable context to social media conversations.
Insight and intelligence can be derived from the context.
It is touted that gaining insight through social media conversations should be a regular feature for company personnel. This should not be relegated to the marketing department alone. Infact, the ‘C’ positions of the organization should also get into this art of social listening as an everyday affair. Understanding “Customer thought and intent” is after all the Holy Grail in business.
Social listening has the potential to drive innovation and corporate strategy. A recent example was the social media conversation, about a major food chain brand which went viral on Whatsapp, a popular social media tool. The conversation and spread on the social media was about the poor quality of uncooked chicken which was served to customers. Even live photographs of the food condition went viral. The food outlet was shut down eventually after the event. This event alerted the company officials to rectify their grave mistakes.
There are many such examples around the world. Data scientists need to be sensitive to such information on social media. There are all kinds of signals sent about a brand. Some are true, some may not be and still some are amplified by culture as well. Thorough research may be required for the company to make a response but then the representative samples may not include the actual consumers. Any information coming out of social media is relevant as long it talks about the situation or the mistake at hand.
Finally, what makes it worthwhile is that, it pays every effort to interpret online social media conversations and embrace the context involved in the conversations to gain insight and to understand customers thought and intention.
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What is common among these three seemingly different but connected things – Ants, Birds and the Hollywood Movie, Lucy. Yes it is, Collective intelligence. Before we get into the subject of “Do ants have brains” we will understand briefly about collective intelligence.
Collective intelligence can be defined as
A shared or a group intelligence that is a result of collaborative, collective and coordinated efforts of individual members in a group.
We have known that general intelligence exists within individuals and can be correlated from various cognitive activities performed by individuals. The question is “Does Collective intelligence” also exist in the same levels for individuals in a social group or a social network?
Social networks have lives of their own depending on how we create them. They follow a cyclical pattern of creation, growth, rapid spread, global influence and long sustenance. Perhaps we can say that social networks can never die. We might be surprised to know that a social network thinks on its own and does its own independent actions.
The more we contribute the more it grows and each one of our contributions has a significant impact on the network as a whole and the network can collectively deliver where no one individual can do it alone.
Measuring collective intelligence – Learning from researchers.
There is overwhelming evidence and research that collective intelligence is so very important for a social group or a social network’s productivity and success.
MIT center for collective intelligence had published a report on “measuring collective intelligence” in a social group. It states that such intelligence is not about the individual intelligence in a group but it is about the sensitivity of the group as a whole, taking turns in the conversation for commenting, sharing opinions, ideas and answering to replies and the percentage of women in such groups.
The study shows a interesting finding that they have found a general collective intelligence factor called “c” . The factor is not related to the average or maximum intelligence of the individual team members but to the collective intelligence of the entire group.
The sensitivity of the group as a whole towards commenting, turn taking in responding, answering to group members as well as number of women in the group or social network.
There is lot of research that is going on with respect to the factors that affect collective intelligence in a social network. The number of women and the degree of collaboration that happens within that group has an impact on the collective intelligence of the group.
Do ants have brains ? What we learn from Ants, Birds, Fungi and the Hollywood movie – Lucy.
If you had seen the Hollywood movie “Lucy” you would be thrilled to the end. Good story line and movie direction of a slightly complex subject. Good direction by Director and Writer, Luc Besson. The main protagonist Lucy, played by Scarlett Johansson gets transformed to a single invisible cell and disappears at the end. In the movie, Lucy gets injected with a special super chemical which gives her brain the ability to use 100 % of its capacity, gradually. With this capacity she has the ability to transform herself and in this case into a single cell. The individual cells in turn can act collectively together even though they are separated by space and time. Mind boggling isn’t it. Yes possible!
The movie is a worldwide hit but then it has received varied response from critics who say it is a misrepresentation of science. But that’s something which we have not known and not experienced so far. We feel it is a distant possibility.
Let’s take an example of a colony of ants. The properties and super characteristics of a colony of ants is far more greater than an individual ant on its own. The amount of super co-coordinated effort a colony of ants can put together for reaching a bottle of jam is tremendous and they end up achieving it anyway.
This coordination is possible only when the ants are collectively acting together and not the feat of an individual ant. Such feats are the result of coordination and collective intelligence of many individuals. It is no ordinary feat that they build huge ant hill all working together in a coordinated effort.
We human beings are multi cellular organisms. All the cells in the human body have their own individual properties, but then they all work together. By working together, they form a higher form of life called the human being. This form is far more different and evolved than a single cell with its individual properties.
The bottom line is that we are essentially a huge bundle of cells (A few trillion cells approx.). According to the Smithsonian magazine there are about 37.3 trillion cells in the human body.
Another manifestation of the same phenomenon is about our thoughts. Our thoughts are not the product of a single neuron in the brain but a collective making of billions of neurons working together to create a pattern.
Such cooperative action and collective intelligence in social networks and in our society makes our civilization progress in leaps, allowing us to evolve with that wisdom.
Birds for example form a social network. When a flock of birds can collectively coordinate and determine the direction by combining the desires of each and every bird in the flock, that’s real intelligence and you can say that’s wisdom.
Another living thing, the fungi also behaves intelligently and collaboratively work together to find the best patch of ground to grow. It can even find the best path as well to reach it.
The great science magazines of the world are over flowing with articles on the human social brain and how socially intelligent we are. Socially intelligent beings like us have complex social collective behaviors as well. Isn’t it. We can easily dismiss it, saying social collective behavior is like any group behavior. But then, understanding it within the context of a “how following a crowd benefits us” makes good sense, from the perspective of social networks and social media.
Interesting enough, the recent article in The Economist titled “Connective action” dated March 26th, 2016 talks about Olsen’s book “Logic of Collective action” on how large groups of people would organize, collaborate and group themselves based on certain incentives and whereas others, the vast majority of them would gladly do a “Free ride” on the efforts of others.
Olsen’s theories on group behavior questioned the then dominant wisdom, (this was in 1965) that if everyone in a group has common interests, then they will surely collaborate and work together for the same common goals or interests. Fair enough, but are we seeing this kind of behavior in our online social media networks?
The late economist Mancur Olsen’s theories on groups and political science holds good even today as we see how the social, economic and technology worlds are connected to organize networks and online behavior.
It is no different as we see through our own examples in Facebook and LinkedIn, that people do not have to be part of a group to post comments or likes but still can have their say on a common cause or movement. To make this impactful and effective, we will have to bundle it in a framework and present it in a way that is made resourceful and benefits the masses much like how Google and Facebook operate.
Now, let us look at some of the fundamentals from this interdisciplinary science of what makes social collective behavior tick and sustain. Let us also understand the concept of information cascades and how they can throw some light on what makes people abandon their previously built rationale and bind together.
What is social collective behavior?
Inspite of a growing interest on collective behavior and social intelligence, world over, there is concern of what “social collective behavior” would actually mean.
From a sociological perspective, social collective behavior would mean the study of crowds, fads, fashion, disasters and social movements. A specific collective behavior depends on the context. If it is in the case of crowd, then a question arises what actually is a crowd? We can say that a crowd is a group.
Certain structure and pattern arises when a social collective behavior happens in a crowd. Nevertheless, the behavior is shaped by the characteristics and the cultural background of the persons who form the crowd.
If the crowd, is from a typical office building or a religious movement we would always see shades of established behavior among them. Many research practitioners say that, from a functional understanding and perspective, and within a context of a crowd, social collective behavior need to defined, studied and understood in situations where there is no cultural guidelines, no cultural definition or structure attached to it.
For example, there are strong cultural and established guidelines of crowd behavior with respect to disasters, contests and celebrations. These can be related to more general group behavior. Whereas a short lived crowd watching a construction building or a flash crowd which has just formed at a random accident site on the road, calls for studying patterns in social collective behavior.
For example, in a large religious group or crowd, people behave with lot of fervor and devotion. The experience of the people in that crowd is based entirely based on tradition and culture and people in those situations behave in a manner which makes best sense for them and for their perceptions.
Taking into account the above arguments and research findings, we can define social collective behavior as
When people are connected and collectively act, they influence each other’s behavior and any aggregate individual behavior in turn influences the masses. Together as a collective network, they produce path breaking outcomes.
The current discussion of ‘how following the crowd benefits us’ as a social collective behavior falls entirely into context where there is no established or set cultural guidelines for people to behave in a certain manner. Now, having said that, it becomes important for us to understand and study the social collective behavior of crowds. For example, understanding the behavior of the crowd in social network embedded within a social media allows us make the incentives structure better. We will talk about this in a little while from now.
Social collective behavior is always unpredictable
Much broader scope would be to understand where large number of people or crowds congregate or assemble. History has always pointed to disasters which could have been averted.
For example, the crowd which went berserk at the English Football stadium during the 1988 FA Cup in Hillsborough, England. Many people have died during this disaster. Investigations revealed that it was due to overcrowding. But no one new, how the crowd, the frenzied fans would behave during a semifinals match.
Another example, would be the stampede that happened during the Hajj at Mecca. Hajj is the annual Islamic pilgrimage that Muslims all over the world undertake the journey, at least once in their lifetime.
Both the incidents project unpredictable crowd behavior. There were no set cultural underpinnings behind these crowd behaviors.
Even as an individual belonging to a democratic society, it would be highly relevant to study the characteristics and workings of social collective behavior. Even as we seek to understand and study social collective behavior, it would be impossible to know and predict crowd behaviors in advance. It is only after studying similar instances where large number of people or crowd involved, the characteristics and quality of people interaction in such previous scenarios, would we be able to predict to a certain extent.
Social Collective behavior in social networks
Social collective behavior, within the context of a social network lies at the intersection of Mathematics, Economics, Social science and we can even say Cognitive Psychology and Ethnography.
From computer science and mathematics we learn about how complexity arises, as we design and find solutions. From Economics, we understand how people’s behavior affects by providing them specific incentives. From Social sciences, Psychology and Ethnography we understand the characteristics, the structures and behavior of people across cultures and within the groups and their mutual differences.
Social collective behavior, is nevertheless a phenomena which affects aggregate behavior. It has links that connect us and has long standing effects on the consequences of the behavior of the population as a whole.
New patterns and practices emerge over time. These practices are nothing but new ideas, opinions and new technological innovations of a large population. We can call them as social practices and they become popular culture over time. Some evolve and become established and some become obscure. We have always known the examples of Facebook and MySpace.
These new social practices spread very rapidly through a population and affects each other behaviors.
People would invariably want to belong to a group and conform. They are influenced by what others do and eventually would like to do what others do. Understanding this becomes the core to understanding social media networks and behavior.
At least on the surface, people want to make decisions based on how others have faced the same or similar situation. We all want to keep it simple. But then, the million dollar question is why are people influenced by others behavior.
Why we follow a crowd – A Social collective behavior unfolds
There could be many reasons. “Private Information” is one. Valuable private information is cited as one of the reasons. If a group is making a decision or a choice, others infer that the group might have better or private information on which they have based their choice among various alternatives. It is natural for us assume that the group has made a better choice and compelling us to follow suite.
Other reasons for such behavior can be attributed to the fact that when a “direct benefit” can be gained.
People want to align with others and gravitate towards a group when there is a direct benefit involved regardless of their own decisions.
A good example is Google’s You tube. Whether “You tube” had good features or not, people just thronged the site and once “You tube” became the most popular site for video sharing, people saw an added value in using it.
Overtime, this sort of behavior raises subtle issues. Interestingly, people would start making decision based on a mix of private information and also conformance to what has already happened. It so happens that they just “Follow the crowd” without any rationale.
This phenomena is what we can term as “Information Cascades” where there is no rationale or limited rationale and people make decisions based on the crowd and in fact they follow the crowd and leave the private information behind.
In other words, such network effects magnify the brand value of existing product or an established social media network, drawing in more crowds and groups and thereby increasing their perception and value manifold.
It would be very difficult to displace such established social media networks unless the newcomer has got a better technology, features, being way different and starts in an area of the population where new technology is welcomed and then again they follow the same inherent network behavior.
A recent article in the MIT talks about how social media systems can generate sustainable value in the future. The current social media systems generate social crowd mobilizations which are short lived. Such short lived crowd do not contribute to sustained societal and business change. The authors point to better incentive structures embedded within the social network as a long term solution.
I think, the discussions we are having on this blog post with respect to the degree of private information and direct benefits can sway the crowd and the behavior towards long term sustainable change.
Introducing incentives – Creates information cascade
Let’s see how people behave with structural effects in the network structure and how they influence each other’s behavior. The way a network is structured provides useful information and insights in how they influence people.
We need to understand here that the network or group behavior is based on
“Private information” and “Direct benefits” and they exist both at the individual and population level.
We have seen that as individuals, we sometimes get influenced by what our neighbors do rather than the whole population network. So it is implicitly understood that the underlying network structure reflects this aspect.
Here comes the idea of introducing incentives. When individuals are provided incentives to adopt to the behavior in the network, there can be information cascade effects where a small group within the network adopts and slowly spreads the adoption to a wide population throughout the reaches of the network.
The underlying network structure, which can also be the software application and the technology architecture of such structures, play a pivotal role in cascading this effect.
The Contagion mechanism – Similar to how epidemic spreads
How epidemic spreads. An example underlying structure
The Contagion mechanism – Similar to how epidemic spreads
This cascading behavior is also known as the “contagion”. In business terms, when the underlying network structure of a competitor is superior in terms of the application of technology then there is every chance to displace the leading social media network.
This so called “Contagion” mechanism is very much similar to how an epidemic spreads throughout the world. Studying the spread of epidemics throws light on the underlying processes which helps the proliferation of networks.
This act of spreading is dynamic and happens right inside social media networks. A good example of an underlying network structure is the “Search” mechanism in search engines and portals. It provides links in such a way that it extends referrals to others and accomplishes tasks this way.
Coming back to the definition of social collective, it so happens that when people are connected and collectively act together, they influence each other’s behavior and any aggregate individual behavior in turn influences the masses.
A wonderful example of this social collective behavior, can be seen in Whatsapp groups. People in the group remain calm till others in the group open up and start the conversation or post pictures.
Together as a collective network, they produce path breaking outcomes and this is in way how “collective intelligence” operates as well.
Individuals though these networks influence others in their opinion, the products they buy and the political parties they support and so on.
There is a lot of information cascade that happens where people abandon their private information and follow the crowd. People imitate others. Sometimes there is no rationale. Sometimes there is.
A random crowd: An experiment on Social collective behavior
Let us look at this experiment as an example which was conducted in the 1960’s. The experimenters created a group ranging from 1 person to 15 people. When one person in the group was asked to stand in the street and look up, very few people stopped by. If 5 people from the group were asked to stand in the street and look up, then more passersby were looking up. Finally with 15 people looking up, they found 45% of passersby stopped by and also stared up in the sky. So the threshold level of 15 can be considered as a tipping point to pull and influence a good crowd.
The experimenters concluded that, the more the number people looked up, the conformity grew stronger and the activity becomes larger.
There is also the role of information cascade that is at play here. Initially, when few people were looking up, the crowd saw no reason or rationale in following them. But when more people started to look up, the crowd decided to join as they perceived that there was some rationale or some reason behind it, they thought that the group of people had some private information that they didn’t know.
There are so many live examples as we see in the market, like the success of books being in the bestsellers category for a long time and the choice of consumers for a specific product or technology.
In conclusion, the wealth is in the network
The benefits of following the crowd are obvious. There are so many collaborative experiments that happen all the time on the internet. From the open source technology projects that happened in the last two decades the “Wiki” and the “Linux” are good examples. A group of volunteer programmers from all over the world, displayed social collective behavior and just followed the crowd to produce it.
The online posts and comments that people make, need not just be some unconnected stray ones nor the people who make them be part of any group or network. Their mere presence is enough.
As mentioned earlier, to make them effective we will have to bundle all of them together to make an impact. Such bundling is what is already done by social media networks in the likes of Facebook and others.
A very influential book called the “Wealth of Networks” written by a Harvard professor Yochai Benkler talks about how networks can be put to good use. Yochai writes about “Peer production” but in the sphere of political activity. Such collaborative “peer production” on the internet was useful in stopping two controversial bills (SOPA and PIPA) for strengthening IP rights online.
Such “Peer production” was serving its way in the recent rejection of Facebook’s free internet in India. The people of India organized the online ‘Save the internet campaign” to tell Mark Zuckerberg that his efforts are not welcome. We will talk more about “Peer production” on this blog soon.
So much for Mancur Olsen’s book “The logic of Collective action” written in the 1960’s that it is so influential even today and hits the mark in understanding the online world. None of the claims and arguments presented so far rejects Olsen’s theories.
People will not collaborate as a social collective behavior just because they have a common cause. They will collaborate only when incentives are provided and the masses will take a free ride for sure. There is so much to learn for emerging and new social media networks from this very thought provoking statement and just to mention, it is not a theory any more.
For further resources on this subject, please find below:-
Networks, Crowds and Markets-reasoning about a highly connected world, by David Easley and Jon Kleinberg. The book is available at Amazon.
Collective behavior and social movements – MIT . The webpage can be found here.
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Collective intelligence examples are everywhere -We need to be discerning to know them.
There is a huge compendium of collective intelligence examples at this MIT site. It lists down all the social media and knowledge exchange technology platforms available on the market.
Here, in this article, I have written about two collective intelligence examples. Examples where participants self-organise and regulate themselves without needing an external medium like a technology platform to make things happen.
Mass collaboration has allowed us to create huge low-cost collaborative infrastructure projects. These projects allow millions of people to collaborate and co-create products. Productive capability of people will now be better through such collaboration as the value creation is quick and easy flowing to reach the masses. Research profoundly says that the new collaborative way of working and capability, will be the future of business models in companies and how individuals perform work.
Research profoundly says that the new collaborative way of working and capability, will be the future of business models in companies and how individuals perform work.
This collective action of bright minds joining together as one social activity to create value and there by leading to a coordinated decision making is what “Collective intelligence” is all about.
Infact, collective intelligence is a sub-set of such mass collaboration. In other words, we need to have mass collaborations for collective intelligence to take place.
We are going to talk about two diverse Collaborative collective intelligence examples as initiatives that has happened in the past and look at how these initiatives sustained and were a great success. These stories were told over and over again by many. But then, what is important is to capture the essence of what is to come in the future.
Goldcorp Inc. – The Goldcorp Challenge as collective intelligence examples
It would be interesting to talk about Rob McEven, the then CEO of Goldcorp Inc. who had a spark of an idea in the year 1999 and went on to launch one of the World’s most successful collaborative collective intelligence efforts which made history.
Goldcorp Inc. is a gold mining and gold producing company headquartered in Canada and was going through a rough patch facing bankruptcy and closure in 1998. Goldcorp also owned the underperforming fifty-year-old mine at Red lake in Ontario, Canada. With its mine dying, the company was also doomed to die eventually. There was unrest within the employees with strikes and debts looming.
In-house geologists within Goldcorp couldn’t find and locate gold deposits within the mining site and nor they came up with favorable solutions. They looked forward for McEven for a turnaround and for his leadership.
With his company still in lot of uncertainty by the year 1999, McEven attended a conference sponsored by the MIT. He was listening through the lectures and then a striking story came about on how “Linus Torvalds” created the Linux, the world class operating system by assembling a group of software developers.
When Torvalds revealed his code to the world, it allowed some thousands of willing programmers to voluntarily contribute to the development of the operating system. The contributors would vet it, make amends and further released newer versions of the software.
McEven contemplated and thought about it. He had a moment of insight. If his employees were not able to find gold deposits at the Red Lake then may by someone else could. He set of with his spark of insight and discussed many follow through ideas with his team.
The Goldcorp challenge,
In the year 2000, the Goldcorp challenge was born. It is one among many collective intelligence examples. He (McEven) decided to throw open the exploration of gold deposits to the world. Sharing all the knowledge and expertise they had with them from the last 50 years from 1948.
Initially, there was lot reluctance from within his team members. As his ideas were unconventional. The concept of Collaborative ‘Collective intelligence” and Wisdom were not known to the mining industry and the world audience.
Largely, the mining industry was a secretive industry. Nobody ever revealed information about a site and its geology. It was considered precious and proprietary data. Nevertheless, they decided to go ahead with the Goldcorp challenge in March, 2000 with a prize money of $ 575,000 available for participants and contributors who had the best ideas, techniques and estimates for finding and locating gold deposits at the dying site at Red lake.
All the geological data that Goldcorp had from the last fifty years was shared with the public though software and sharing infrastructure. People from all walks of life – Geologists, ex-military, students, scientists and consultants participated and contributed. It was a tremendous collective response and intelligence working together. McEven had his team were surprised at, the amount of information and expert talent and intelligence that was available externally.
Even as the concept of Collective intelligence existed many years before, McEven chance stumbled into “Collective intelligence” through a spark of an insight for the mining industry. So great was the response, that within weeks of sharing the information, the contributors had identified 110 targets within the Red Lake mining site. Out of which 50 % of the targets were totally new and not identified before and about 80% of them yielded vasts amounts of Gold deposits.
Fractal Graphics, an Australian Geoscience consulting firm and Taylor Wall and Associates together shared the first place winner’s prize of $ 105,000 in the contest. The scientific team at Fractal graphics based in Australia and had not visited Canada and the mining site before but they still managed to make collective efforts, pooled their resources and minds together and worked on from Australia to present winning locations for gold deposits.
Goldcorp today, is enjoying the seeds of ideas and effort that was sown 16 years back. It has now become the 4th largest producer of gold in the world. The company has successfully harnessed collective intelligence and turned itself around.
Cooperative computing at the SETI @ home project – A collective intelligence example
SETI@home (Search for extraterrestrial intelligence) is an experimental project for using distributed public resources across the world. Public resources in the likes of idle computing power in our PC’s and Laptops at our homes and offices. This idle computing power can be used to analyze radio telescope signals. A distributed cooperative computing happening in time where the collective intelligence of all machines across the world, can be used to speed up the enormous processing and computing power and resources required for the analysis.
The radio telescopes erected around the world listen to the narrow bandwidth radio signals from outer space. These radio signals are not naturally occurring and so analyzing them would provide evidence for the existence of Alien intelligence lurking out there or maybe even a contact is possible.
For those of you, who have grown up seeing the television serial “COSMOS” in the year 1980 where Carl Sagan (The man behind the SETI project) narrates the mysteries of the universe, the project and such extraterrestrial experiments for finding life outside earth would be a thrilling experience. The Hollywood movie “Contact” is also based on such radio telescope signal analysis where in a riveting story unfolds based on it. So popular is the SETI project for people across the world that it has remained as a fantastic exploration and quest for life outside earth in the minds of people.
The SETI @HOME project was launched by the Space sciences laboratory at the University of California, Berkeley in May, 1999. The Challenge was that the computers at the SETI@home project had to analyze each and every radio signal frequency and decide whether it is an intelligent signal or a noise and it had to listen to a huge number of frequencies. It required massive computational resources to accomplish this.
Buying such massive computational resource is expensive. So they thought about a clever way of using idle computing power lying at our homes and offices. All we need to do is to download the software program which is now available as part of the BOINC (Berkeley open infrastructure for network computing) infrastructure.
Machine intelligence is also collective intelligence
The software program comes in the form of a screen saver for your PC or laptop. When your PC is idle, the screen saver actually downloads a packet of data from SETI@home, processes the packet of data and sends it back to SETI@home. Each packet of data contains a work Unit of radio signals. It is as simple as this but then the infrastructure provisions and the challenges inherent in such large distributed computing was very evident.
Even though there is machine intelligence involved, this is real computational collective intelligence at work. Processing the radio signals in a cooperative fashion and helping towards the signal decision making for a greater cause is real collective intelligence that the world can see and wait for the results.
So far, the project has not detected any extraterrestrial signal but has identified candidate sky positions where likely concentration of intelligent radio signals might be lurking. Expectations are that somewhere between in the years of 2020 to 2025 that enough evidence of extraterrestrial intelligence would be found.
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Further resources on this subject
MIT Centre for collective intelligence – Good resource for collective intelligence examples.
Stories and examples of mass collaboration – A brief summary
Whether you are looking to hear and read the story of online collaboration that happened at the Goldcorp Inc. in Canada or the Collaborative distributed computing power that was shared for the SETI@home project, the Search for extraterrestrial intelligence project launched by the University of California, Berkeley, collective intelligence and the “Power of crowds” in online collaboration is here to stay on earth.
The story at Goldcorp Inc. was pretty amazing and it went straight into the records of history on how online mass collaboration can happen and turnaround an ailing company. This story will be told many times over. This story happened in the year 1997, when much of the world was still grappling with the inadequacies of the internet in terms of its network speed, reach and penetration.
More to come on this, but before that we will explore a bit on this book called the “Wikinomics” written by Don Tapscott and Anthony D Williams. The book talks eloquently on the subject of “Mass Collaboration” and its effects on the way we work, our collaboration with others and the future of the industrial economy itself.
Such examples like Goldcorp is happening around the world and is no longer a blip anymore in the present day. Now we are seeing them all the more commonly happening in all the sectors of the Industry. In the coming few paragraphs we will explore them through the following collaborative approaches that happens in the online world. They are
Collaborative co-production or peer production
Collaborative free sharing.
Being a Collaborative citizen of the world.
It is well known fact that throughout history we have companies who operated around strict hierarchical lines of authority. It happened that everyone was reporting to someone else. Employees were answerable to their Managers, Managers in-turn to their Stakeholders and they in turn to their Customers. The argument is that this type of hierarchical model is not vanishing but then with the onset of new online global communities they are giving rise to new models business based on a community of online collaboration and self-organization.
There are millions who use wikis, chats and online forums for speaking out and voicing their opinion on the internet. This is the new “Blogosphere” so to speak. Even in major corporations, it is proved that employees perform better when they collaborate with each other. The supply chains are no different. They work better when the rewards and capabilities are spread across and outsourced to other networks and partners.
It is increasingly, seen that companies are willingly participating in huge online communities and are reaping the benefits of mass online collaboration. A good example is Dell’s “Ideastorm”. Ideastorm was an online collaborative platform used by Dell to source ideas and solutions for all kinds of in-house problems and challenges. Though idea storm was used within employee network many such similar online collaboration platforms opened up in recent years across many companies.
There is also word that such online collaboration and self-organizing will eventually replace the hierarchical models of corporations. We are already seeing it some big corporations like IBM where this a matrix like structure for employees to freely move laterally across functions for performing similar work. Then there is also the ‘Lattice structure” recently talked about in HR circles. There was a recent article where a project manager would be directly able to meet the CEO of the company to get instant inputs. This is possible with the lattice structure.
Then there are the open source projects like “Linux and “Wiki” which have seen phenomenal success. Others that followed this route are the likes of YouTube, Facebook, Flickr and Innocentive and so on. All these organizations have been breathing mass collaboration and harnessing collective intelligence ever since their inception in the online world and all of them have enjoyed great success.
Many established companies like Proctor and Gamble have grabbed the opportunities created by mass collaboration and organized themselves to leverage these levers for cost cutting and building efficiency within their business processes, partners and customers.
Don Tapscott and Anthony D Williams wrote in their book “Wikinomics” that various studies have been conducted to understand the benefits and how these online networked collaborative communities contribute to the Economy, Wealth creation and Innovation. The results have been astounding.
Masses of people can now collaborate and can now collectively advance the arts, culture and education and economy. The results indicate that it is not just about open source or social networking or Collective intelligence or Wisdom of Crowds rather it is about profound changes in the way companies operate, in terms of their organizational structure, business units and the mode of conducting business with their customers and how mass collaboration has created new digital economies. Each of the collaborative approach would outline how these changes would be brought forward.
We all have varied images of when we think about “Collaboration”. In our everyday lives, we collaborate with our colleagues to get the job done. We see images of people working together and setting objectives to-gether in a meeting room. We collaborate with our partners in the supply chain. We collaborate with our neighbors and work together for a community cause.
Let’s hear from Google CEO “Eric Schmidt”. Eric says, when you say Collaboration, the average “45 year old thinks they know what they are talking about- A team sitting down, having a nice collaboration and nice objectives and nice attitude. That’s what collaboration means to most people”.
With the advent of mass collaboration there is a new promise. The promise of harnessing peer production, utilizing the skills, ingenuity and intelligence of everyone involved much more effectively than before.
The new collaboration can then be defined as “Groups of people acting together, moving together, thinking together to accomplish a common unified goal”
This is akin to another concept called the “Collective intelligence” where groups move as one, act as one and compute as one. There is a whole lot of intuition that comes into play here.
The collective knowledge and capabilities are all embedded in horizontal networks spread across the world. Now the question is “What do we do to leverage this?” One small mention about the old web. The old web was all about websites, clicks and scrolls. The new web paradigm is all about communities, interaction and Peer production.
The collaborative approaches outlined increasingly determine how the next generation of companies would compete and they would be very different from the hierarchical models discussed earlier.
Co-innovation would be the way how new companies would operate in the future. It would be no longer the approach of think globally and act locally or any of the conventional wisdom of innovation through differentiation and protecting IP (Intellectual property).
Scenarios of collaborative approach
1. Collaborative openness
Many corporate functions like the Human resources and Innovation have made their first move towards openness and flexibility. Others are following suite. Companies are making themselves open to external ideas and they do not rely on internal resources alone. The technology has made possible for companies to open their doors for external talent.
Good examples of such collaborative approach and openness are the development of Open source technologies and platforms like Apache for web servers, Linux for Operating system, MySQL for databases and Firefox for web browsers have all been developed by collaborative community having an open culture of working together, pooling abilities and capabilities for a common cause.
An open platform for sharing engineering curriculum content at the undergraduate and graduate level from a World’s leading university.
2. Collaborative Co-production or Peer production:
Let’s look at what is meant by Peer-production. The wiki defines “Peer production” as “A way of producing goods and services that relies on self-organization of individuals and communities”. The entire effort and labor is organized towards a shared outcome.
This type of collaborative approach works very well on horizontal networks and critics say it works more effectively than the hierarchical models. So far, this collaborative approach had a great impact on the production of software, music, entertainment and popular culture. The examples that are already happening are the “Open source Hardware” like PCB boards and Layouts and “Open source software”.
There is general criticism as well as encouragement in the open market on this collaborative approach. The co-production or Peer production models resemble being Utopian in nature and as such are very informal. Though this collaborative approach is good for individuals, experts feel that there may be quality problems. Many experts vouch that there would be more in the coming years. Where automobiles, airplanes and other high value commercial goods will be produced through this mode.
3.Collaborative free sharing.
Sharing your resources freely and openly on the internet is widespread. People share from music files, office documents to video downloads.
It is only through sharing and giving can innovation happen. This helps in building knowledge communities for growth and development. This Collaborative approach of free sharing also happens in “Sharing computing power” and internet bandwidth
An excellent example is the SETI@home project. SETI (Search for Extra-terrestrial intelligence) project is a collaborative volunteering program of distributed computing. This project was launched by the University of California, Berkeley in 1999. Individuals can volunteer to take part in this program and download a special software. The software program (Berkeley Open Infrastructure for Network Computing (BOINC)) runs in the background and makes use of the idle computer power. Excellent in the way it was conceived. As of June, 2009 there were 180,000 active participants volunteering over 290,000 computers.
4.Being a collaborative citizen of the world.
Global citizenship for both individuals and companies operating in the online world, is the boon that is bestowed. Individuals and companies have to leverage this in the way they conduct business, innovate and organize themselves for growth is the new paradigm for this collaborative approach.
Companies can tap global talent pool, they have access to new markets and technologies. Being wise and being connected is the next big thing and the world is teeming with immense possibilities.
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