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Swarm Creativity Blog

All about the creativity of swarms.

Updated: 2017-12-10T19:10:59.409-08:00


What Is the Influence of Your Tweets?


What is the real-world impact of Twitter? It seems that one well-placed tweet can #makeAmericaGreatAgain – at least it got Donald Trump elected as US President, or did it?  One year after his election, even the Russian press says Trump did not “Make America Great Again”.In this post I would like to explore if Twitter really can help an aspiring leader to change the world, promote a cause, or further his or her own career. I will start by looking at the Twitter behavior of the World’s most famous people, and then identify some characteristics of the most active or prominent twitterers.In a project with GDI Switzerland we have used prominence in Wikipedia to identify the most influential people in the (English speaking) world. Wikipedia’s notability criteria are a robust mechanism to filter out the most notable 800,000 people. We have then constructed the network among these 800,000 people by linking two people, if their Wikipedia pages are mutually linked. (We call this a bi-directional link). The more central somebody in this network is by number of links, and the more popular her/his friends – in this case the people pages she/he is linked to – the more important that person is. The list below shows the most influential people on the world by this metric, which we call “reach-2”, i.e. how many people can one reach in two degrees of separation. Name reach_2 followers Num tweets barack obama 117,384 96,468,561 15,474 george bush 96,034 292,618 77 donald j. trump 82,783 41,625,028 36,273 bill clinton 75,089 9,008,717 645 Queen Elizabeth II 72,315 51,230 949 hillary clinton 69,449 19,461,949 9,949 Bob dylan 53,199 342,708 608 vladimir putin 52,902 772,890 1,721 elton john 51,989 649,167 2,528 Steven Spielberg 51,428 The top nine most influential people are all current or former heads of state, plus two world-famous musicians, the tenth does not tweet. Those nine influential leaders however have widely differing numbers of followers. While Queen Elizabeth only has 50,000 followers, Barack Obama not only leads the field with 96 million followers, he also has double the number of followers than his Twitter-trigger-happy successor as US President.The list of the most followed Twitterers is quite different from the Wikipedia list of World influencers. The top 20 most followed Twitterers are listed below: Name Num Followers katy perry 105,704,538 justin bieber 102,737,326 barack obama 96,468,561 taylor swift 85,604,763 rihanna 81,333,384 ellen degeneres 74,979,901 lady gaga 72,837,019 justin timberlake 62,988,786 cristiano ronaldo 62,726,679 kim kardashian 56,591,054 britney spears 55,887,840 ariana grande 54,645,815 selena gomez 53,737,379 demi lovato 50,930,613 jimmy fallon 49,528,047 shakira 48,878,622 jennifer lopez 43,816,866 donald j. trump 41,625,028 bill gates 40,432,413 oprah winfrey 39,542,724 The empress and emperor of the Twitter universe are singers and actors Katy Perry and Justin Bieber, followed by former politician Barack Obama. With the exception of Barack Obama and Donald Trump, all others of the most followed Twitterers are musicians and actors. There is just one entrepreneur among the top 20 most followed Twitterers, Bill Gates, who rounds up the top-20 list together with Oprah Winfrey.Counting the professions on the list of the top 30,000 twitterers by reach-2 confirms the leading position of actors and musicians.  Profession Number Profiles actor 9040 musician 5843 football player 4424 singer 3424 politician 2629 writer 2309 journalist 2015 model 1138 It seems that the world of Twitter is a world of appearance, and not of substance. Glory beats power, at least on Twitter, actors, musicians, athletes and singers come before politicians. People with real impact, and diligent workers behind the scene, like entrepreneurs, teacher[...]

E-Mail based Social Network Analysis is “Google Maps for Social Landscapes”


Our team at MIT and galaxyadvisors has been creating e-mail and other social media based social networking maps for the last 15 years. The question we have to constantly answer is why it is a good thing to have these social networking maps, which I call “social landscapes”. The way to think about them is like a "Google Map for social landscapes". Google Maps has become immensely useful to me for driving the congested roads in Boston and Zurich. While I have been living in both cities for multiple decades, and usually know my way around, Google Maps has become indispensable for seeing where precisely I am, and where to go next. Through location tracking it shows me where precisely I am. And by aggregating the changes in anonymous location tracking information of thousands of drivers like myself, it shows me where the traffic jams and road construction is, taking me on the fastest way to my destination, and accurately predicting when I will reach my goal.Our social media tools Condor and Condorview provide similar naviation tools for the social landscape of individuals and organizations. The aggregated social network map tells me how the overall social geography of my collaboration network looks like, where potential roadblocks and shortcuts might be to more efficiently reach my goal.  The picture below shows three screen shots of our Condorview tool, which gives an individual a personalized anonymized overview of their social environment.The left picture shows an aggregated overview, where, in a radar chart, the individual sees how well she communicates using the seven honest signals of creative communication our team developed. In the radar chart the individual signals are compared against the aggregated anonymous mean.  The middle chart show an exemplary bar chart where the individuals see their communication balance compared to team members, while the right chart shows their social network, with the individual in the center. Giving this information, and telling individuals how to interpret it will give them guidance how to improve communication for better collaboration similarly to how Google Maps allows the driver to drive more efficiently to their destination.This information gives each person a virtual mirror of their communication behavior, similarly to how Google Maps gives them a virtual mirror of their driving navigation. In prior work we have shown that virtual mirroring can significantly increase hard metrics like customer satisfaction.Recently a customer and research collaborator introduced Virtual Mirroring Based Learning at his company, using Condor to create e-mail based social networking maps for virtual mirroring for managers and employees.  Showing a pattern of the past allows each person to see a complete view of the present social landscape in a way they have never seen it before, which permits them much better navigation in the future.The Best Way to Predict the Future is to Create It Yourself.[...]

Why Hillary Clinton was NOT elected President


Yesterday the NYT wrote about Barack Obamanavigating the slippery slope of giving well-paid speeches (he gets up to $400,000 per speech) while avoiding the impression of selling out his status as a former president of the United States of America. This made me think again about the motivation pyramid I had described in “Swarm Leadership”, from money, power, glory, and love, to enlightenment. Derived from Maslow’s pyramid, it occurred to me that there is an additional "status" layer. Barack Obama can translate his high status as former President of the US into money, by charging enormous speaking fees, however he has to be extremely careful not to reduce his status by giving the impression of cashing in.  We can observe this effect on all layers of the motivation pyramid, which in its revised format looks now as follows. The boundary between financial and social capital is highly permeable. Wealthy people can buy higher status as so-called philanthropists by donating some of their wealth to charitable causes. On the other hand people high on social capital can convert their power and glory into financial capital. Politicians can use their positions of power to enrich themselves by selling jobs to their cronies and taking bribes. Actors, musicians, and athletes can use their glory to promote brands and products, charging hefty fees for their endorsements. And highly respected political and religious leaders can use their high-status positions to solicit donations to their organizations and themselves. However if they are too aggressive in converting their social capital to financial capital, they lose their social capital. The most admired high-status leaders lead comparatively modest lives that ordinary people can relate to. Pope Francis is one of the most globally respected leaders because he moved out of the Papal palaces into an unpretentious apartment and drives around in a Fiat instead of a stretch Mercedes car. And Barack Obama is very careful to only give paid speeches that are associated with the societal goals that he has been championing during his presidency, such as the healthcare reform. And that leads me to the failure of Hillary Clinton to capture the US Presidency during the 2016 elections. She was highly successful in converting her position of power as the wife of a US President, Senator of New York, and US Secretary of State into high status, and then using this high status to give highly paid speeches, amassing a large fortune in the process. Unfortunately this association with financial institutions widely seen as the culprits of the 2008 financial crisis destroyed her social capital, thus making her an undesirable president for large parts of the US population.The picture above also illustrates that there are two ways to obtain high status: either through power and glory like Roger Federer or Barack Obama, or through enlightenment, by creating a sense of belonging, and helping others. The Dalai Lama, and open source leaders like Linus Torvalds, creator of Linux, Jimmy Wales, creator of Wikipedia, and Tim Berners-Lee, creator of the Web, have all been working on a cause greater than themselves, giving up the direct way to status through power and glory, only to reach even higher status as the creators of these invaluable new technologies that have truly changed the way how we live, think, and act.[...]

GalaxyScope – Finding Influencers and Their Tribes


GalaxyScope is a new powerful and easy-to-use tool to find the most influential people about any topic on social media. You can try out GalaxyScope here. (You will need a Twitter account and have to login with it.)In an earlier post I described how to use Condor to run queries for GalaxyScope. While Condor is powerful in finding hidden trends and influencers on social media such as Twitter, Blogs, Wikipedia, and Web sites and online forums, it has quite a steep learning curve. If one is not a data scientist or computer savvy otherwise, Condor can be quite challenging to extract the results one is looking for. We therefore have developed a Web version of GalaxyScope described in this post as a much simpler to use tool.  GalaxyScope has been created by my colleague Joao Marcos de Oliveira as an easier-to-use alternative to Condor. Other than Condor it is embedded into the Web browser, which means that by just entering some key words in a browser window, and clicking a search button one will be able to get a result. GalaxyScope consists of three components, a People Map, a Tribe Map, and a Topic Map. The people map will create a social network for any person who is either in Wikipedia or in Twitter.  In the example below I am calculating the social network for Roger Federer, entering his name into the search box. I then select the Wikipedia pages and Twitter accounts, which are about Roger Federer, and click on the “Get Network” button., resulting in the social network of Roger Federer, leading to a network which reads like a “who is who” among the top tennis players of the world. The Topic map will create a tribe of people about any topic. It uses both Twitter and Wikipedia as its main sources for finding the influencers about the topics, which means that it will find concepts that have either been spoken about on Twitter, or that have one or more Wikipedia pages. Similarly, influencers will be found from among Twitter users or if they have their own people page on Wikipedia. In other words, hidden influencers who do not tweet and do not have a page on Wikipedia will remain hidden, as the GalaxyScope Topic mapping system will not be able to find them. The screen shot below shows the start screen of the topic mapI will illustrate creating a topic map by finding trends and trendsetters about OLED  and LG, which is a major manufacturer of OLED displays. First I define a new tribe called “OLED” in tribefinder and add the Wikipedia pages about “OLED” “LG”, “Samsung” and “Samsung Electronics” as input for the search for tribe members. This will add a new topic called “OLED” to the Topic maps. Clicking on the tab “Members” will show the tribe members of the “OLED” tribe. Initially it will be empty. Clicking on the tab “”Profile Description” in the search menu at left allows me to search for Twitter users who have the keywords “OLED” and “LG” in their Twitter profile description.Similarly, Twitter users can be searched by keywords they are tweeting about. These users are then shown in the main part of the window, and can selectively be added to the tribe. In addition, Twitter users can also be searched among their followers and friends of the people already in the tribe (friends are bi-directional links in Twitter, where people are following each other).Once the tribe has been created, it can be shown as a network. For this network, the tribe members are connected by their most popular Twitter friends. The picture below shows the tribe created by selecting the most important Twitter profiles from the search in tweets and in Twitter profile descriptions for the keywords “OLED” and “LG”. At the same time we can also look at the tag cloud by clicking on the tab “Hashtags” which is extracted from the tweets collected about the search terms, in this case the words [...]

From the Age of Emperors to the Age of Empathy


Chinese Emperor Qin Shi Huang unified the warring states of ancient China with iron fist and utmost brutality, for instance tying one of his enemies to five horses to tear him into pieces, and killing his entire family to the third degree. Today, things are decidedly different. The age of imperial CEOs of the GE Jack Welch type residing in the corner office of the top office floor is over, Mark Zuckerberg shares the same open office space with the rest of his Facebook employees. Today’s Millennials do not want to be lead by emperors high on testosterone and authority, but leaders high on empathy and compassion. Google ngrams and Google trend confirm this global trend, with compassion and empathy winning over tyranny. Interesting side note: As the picture below of the last 13 years of Google trends shows, tyranny had a short comeback November 8, 2017.November 8 was the night where Donald Trump was elected US President. As the zoomed-in picture below shows, “tyranny” is now almost down to its pre-election level. Empathy is back to being the leading trend, running in lockstep with compassion. Over the last few years universal awareness that all living creatures show empathy has been increasing. In my most recent book “SwarmLeadership”  I introduce a framework based on “social quantum physics”, which explains how all living beings are connected through empathy in entanglement, and learning.“Empathy” is the main driver for “entanglement”, which connects all living beings. “Entanglement” is the strong tie that for example every child has with his mother, or a husband to his wife, or that connects two best friends.  Entanglement happens by reading the mind of the other, trying to guess what the other might be thinking right now. The more empathy one has, the better she is at this game. At the same time, living beings never stop to learn in a ceaseless feedback loop of “reflect and reboot”, re-assessing the status quo and adapting to it if necessary.Empathy is by no means restricted to humans. There are spectacular examples of empathy among elephants, this video from the Seoul Zoo shows how an elephant family collaborated in saving a drowning elephant baby. There are also many stories of dolphins helping not just their own brethren, but also humans, whales, and other animals in distress. But empathy goes much wider, for instance rats will forgo a chocolate treat to free their trapped companions. There might even be empathy among plants. While trees operate at a much slower pace than animals, they nevertheless take care of their weak. In his book “the hidden life of trees”,  Peter Wohlleben. describes how old trees without any leaves are supported with nutrients from neighboring trees of the same species to keep a member of their own community alive. Empathy and entanglement thus are the drivers of  any living networks with members of any species. For instance networks of trees communicate in a wood wide web through fungi in symbiosis with the trees’ roots.  In our research we have developed “seven honest signals of collaboration” which can be used to measure empathy and entanglement on any level, from the global level on social media down to measuring face-to-face entanglement using the body sensors of smartwatches. If you would like to know more, you will find an in-depth description in my other new book “Sociometrics” . [...]

“Birds of a feather flock together” - Building a Tribe Detection System


As voiced by his press secretary Sean Spicer, US President Donald Trump frequently seems to live in an alternative reality. Reality is different for every living being, and that of course includes us humans.  Artists illustrate this very nicely, for them what is truth might change from one day to the next. For example, famous French painter and sculptor Matisse changed his perception of truth quite drastically over two years, repeatedly sculpting his model Jeannette from 1910 to 1913, shown in the progression of heads just below from highly realistic to highly abstract.There are however groups of people, whose realities and definitions of what is “truth” are quite similar within their group. I call those groups “tribes”. Tribes, as defined in anthropology, share a name, a sacred symbol, rituals and beliefs, and they trust their leaders. For example, a well-known (although highly negative) example of how tribes are defined was Nazi Germany. It had its own name, the swastika as the sacred symbol, Hitler’s “mein Kampf” defining the shared value and belief system, the “Nazi salute” as its ritual, and the “Führer” as its trusted top authority. Looking at today’s alternative realities, I proposed an earlier framework consisting of three main tribes, the “fatherlanders” or ultra-nationalists, the “nerds” or progressives, and the “treehuggers” or environmentalists. However, reality is more complex than that, in particular if we include alternative realities. I therefore consulted the collective mind as articulated on Wikipedia and Twitter, and tried to group the tribes based on their expressions on these online social media. Starting with Wikipedia categories, I ended up with three additional dimensions that define what tribe one belongs to, leading to the following four categories:Alternate Realities, which according to Wikipedia are defined as “a hypothetical self-contained reality co-existing with one's own”Ideology, which is a “comprehensive set of normative beliefs, conscious and unconscious ideas, that an individual, group or society has.” Lifestyle, which “denotes the interests, opinions, behaviors, and behavioral orientations of an individual, group, or culture”Culture, which “is the social behavior and norms found in human societies”.As we all are multidimensional creatures, we will belong to multiple tribes. The picture below shows the four tribal categorizations, with the key tribes in each tribal category:How did I create this framework?By consulting Wikipedia and its categories.While “alternate reality” does not have a Wikipedia category, the other three tribal categories are rather detailed, see Using Condor’s Wikipedia Category Fetcher, I created the category network for each of the tribe categories. Below is the ideology category network:I then looked on twitter for each of the major tribes how many people self-identified with a particular tribe. This led to the major ideology tribes: Feminism, Transhumanism, Socialism, and a collection of rightwing ideologies, which, based on our Twitter analysis, we label “Libertarians”. Repeating the same analysis for the lifestyle categories gave the full tribal map shown below:Again checking on Twitter, for instance nobody self-identifies with the “sedentary lifestyle”, although probably a large fraction of the Western population would belong into that tribe. A similar problem arises with the “off-the-grid” lifestyle, as these people are not identifiable in social media. In the end, we are left with the “Fitness”, “Veganism”, and “YOLO” lifestyles. The Condor analysis for the Wikipedia category “culture” lea[...]

My 2 new books out: Swarm Leadership and Sociometrics


This week my 2 new books Swarm Leadership and Sociometrics were published by Emerald:Swarm Leadership and the Collective MindUsing Collaborative Innovation Networks to Build a Better Business(from the book cover) The future of business is swarm business. Whether it’s at Uber, Airbnb, Tesla, or Apple, it’s not about being a fearlessleader, but about creating a swarm that works together in collective consciousness to build great things and generate success. In this pioneering guide, MIT researcher and entrepreneur Peter Gloor shows how you can take your business on a journey from homo competitivus to homo collaborensis, channeling the competitive energies of all of your stakeholders toward collaboration.The journey starts with recruiting and assembling an intrinsically motivated group of early enthusiasts, the Collaborative Innovation Network. These teams combine the principles of social quantum physics to create collective consciousness: empathy which builds entanglement, and reflection which leads to refocus. Gloor then demonstrates how collaboration can be tracked and boosted using the six honest signals of collaboration, which will further increase the performance of the swarm. These fundamental concepts are illustrated with a wealth of examples from leading ventures–from household names like Uber to Fortune 500 high tech firms and healthcare from AmazonSociometrics and Human RelationshipsAnalyzing Social Networks to Manage Brands, Predict Trends, and Improve Organizational Performance Today we can use social media to not only find the next great restaurant but to address complex problems impacting society. Using a variety of tools and software, Sociometrics and Human Relationships provides an in-depth tutorial to analyzing social networks for practitioners and students with backgrounds in marketing, design, sociology, psychology, and the humanities. Employing these straightforward but powerful software tools, MIT researcher and entrepreneur Peter Gloor demonstrates how to gauge all types of online social networks such as Twitter, Wikipedia, Blogs, Facebook, as well as e-mail or Skype logs to predict election outcomes, perception and strength of brands, customer and employee satisfaction, and even fraudulent behavior. A targeted guide with step-by-step instructions, turning social buzz into business strategies by translating the latest academic research into practical techniques. Gloor provides a wealth of examples of how to apply social network analysis for prediction of trends and also illustrates how even email can improve organizational performance by optimizing communication and from AmazonI'd love to hear back from you?[...]

Why Should We Measure Happiness?


Our team is currently working on measuring happiness using the body sensors of a smartwatch. In particular we have developed "happimeter" software to measure individual happiness, and show the influence of individuals on team happiness. Our premise is that measuring and giving feedback about happiness will increase individual and team happiness.

When I describe our work to potential sponsors and other interested people, at some point in the discussion invariably the question comes up: WHY measuring happiness?

When I then answer that our goal is to better understand what the determinants of happiness are to increase individual and team happiness, I get one of two reactions:

Particularly in the US, most of the time, people will say "ah, I understand,  if you have happier people working with you, their productivity will go up, and the company will make more money." In other words, the capitalist's reaction is: "make people happier, so my company makes more money".

The second reaction, which I experience more seldom is that people will say "ah, that's cool, let's understand how happiness works so we can make our people happier". In other words, being happy is the main goal in itself.

I know which reaction I like better. What do you think?

Finding Fatherlanders, Nerds, and Treehuggers on Social Media


In my previous blog post I introduced the three big virtual tribes of our Western world, which I called the Jingoists, Progressives, and Treehuggers. In more popular terms, the Jingoists can also be called the “Fatherlanders”, as they believe in God and the Fatherland, while “Nerds” is another term to describe the Progressives, whose religion is science and technology.  Each of the three tribes has their distinctive leaders and role models. The Fatherlanders look up to Donald Trump, who promises to makeAmericaGreat again, cutting back on almost everything inside the fatherland except on a strong army and building a wall around the fatherland. The Nerds identify with science geeks like Elon Musk whose goal in life it is to bring humanity to the Mars. The treehuggers admire Pope Francis who is organizing conferences about global warming and climate change in the Vatican.In this post I will illustrate how our Coolhunting approach makes it possible to find the key topics and news of the days for each of the three tribes.  The goal will be to identify the language that each of the three tribes speak, defining a vocabulary and networking behavior for each of the tribes indicative of its value system. My core hypothesis is that each of the three tribes will have its own sphere of collective consciousness.Towards that goal I collected 4000 tweets each about Donald Trump, Elon Musk, and Pope Francis on February 4, 2017. I used Condor’s Twitter EgoFetcher, which also factors in the popularity of the people retweeting about these three people. Technically speaking, Condor's EgoFetcher takes the first 4000 results for the search string, for example “Donald Trump”, as well as the first 100 retweets for each of these tweets. It constructs a link between two tweeters if one is retweeting or mentioning the other in her tweet. It also adds all the tweets of the 480 most influential of these tweeters, measured as their popularity (degree centrality).I collected the following tweets in total:“Donald Trump”–      71,521 tweets from Mar 15, 2007 to Feb 4, 2017–      71,520 actors, 349,539 ties“Pope Francis”–      61,617 tweets from Apr 24, 2009 to Feb 4, 2017–      61,616 actors, 344,188 ties“Elon Musk”–      24,813 tweets from Mar 7, 2009 to Feb 4, 2017–      24,812 actors, 312,728 ties The resulting network for each of the three tribal leaders looks very similar, below is the picture for Elon Musk as an example.We get a big connected component in the center of the network, this is the active tweeters retweeting and mentioning each other. In the periphery, we have the “asteroid” belt”, made up of the “nobodies”, tweeting their lungs out about “Elon Musk”, while nobody is listening. The coloring tells us that the largest groups of tweeters are from the US Pacific, Eastern and Central Time zones. Zooming in on the connected component leads to the following picture. Coloring of the actors (the people tweeting) is by using Condor’s cluster detection algorithm to find the largest subgroups.The most surprising finding in this picture is that although I have been collecting tweets about “Elon Musk”, the towering presence of realDonaldTrump is overbearing: he is more central (size of the nodes is by betweenness centrality) than even elonMusk. Showing the same picture for Pope Frances leads to the same conclusion:The realDonaldTrump is more central in the Twitter network about the Pontifex, than then Pontifex himself. And the community about the realDonaldTrump is the largest one in the different subcommu[...]

Living in Alternate Universes: Are you a Jingoist, Progressive, or Treehugger?


Quantum physics suggests that there are many different universes, with our current world being embedded into just one of infinitely many other universes. Currently it seems humans on earth are breaking up locally in many different multiverses. Based on most recent history, I would group these different multiverses into three main universes. Each of these universes has its own reality, defining fact or fiction for the inhabitants of the universe. Each universe is inhabited by its own tribe, with its own belief system. The three tribes are the jingoists, the progressives, and the treehuggers. While members of these three tribes are spread out around the world, living side by side, they are living in different worlds. Members of these three tribes live in all countries, although usually members of one tribe rule a particular country. The jingoisttribe, currently on a roll, wants to recreate the national states of the early twentieth century with strong borders protected by fences and walls. The members of the progressive tribe, believers in a global world ruled by capital and technology, just got back from their annual January trek to Davos. Together with the members of the third tribe, the treehuggers, who want to conserve nature and the environment, they are currently on the defense against the jingoists.  With the election of Donald Trump, the jingoistic tribe has gotten its clear leader. Some European representatives of this tribe are Germany’s Frauke Petry or France’s Marine Le Pen.  The jingoists believe in the “good old times”, they believe in “God and Fatherland”, they want to “#makeAmericaGreatAgain”.  They think that their country is the best, and that potential troublemakers are best kept out, if need be by force. They want a strong army and police, to keep crime at bay.  They cherish the family as a bulwark against evil from the outside world. They are very suspicious about new ideas. They will help their neighbors, but don’t think the state should generally support the weak, and refuges should be kept out. Their motto is “help yourself, then God helps you”.The progressive tribe believes into continuous progress through science and technology.  Politicians like Germany’s Angela Merkel or Canada’s Justin Trudeau are role models for this tribe.  Among industrialists, Elon Musk, or the late Steve Jobs stand out. Their goal is to reach the Mars and live forever through clever exploitation of the advancements of science. They think that global networking is key to overcoming all obstacles, they preferably do that at gatherings such as the World Economic Forum in Davos every January. The third tribe are the treehuggers. They believe that the world’s resources are limited, and are afraid of unabashed exploitation of nature. The also don’t trust unquestioning scientific progress such as genetically modified crops, or fracking to get access to more oil. They congregate in in Global Warming summits, and self-organize in groups like Greenpeace or the WWF. Pope Francis, inviting scientists and world leaders to Rome to discuss Global Warming, has become a champion of their cause. They don’t believe in growth at any cost.It seems to me each of these tribes has their own strengths and weaknesses. Based on their belief that local is better than global, jingoists will compartmentalize in their nation states, so they will mix less with others different from themselves and become more homogeneous within their local echo chambers. Scientific and technical progress will come – obviously – through the progressives, but there are many examples where the progressives have gone wrong. For example, applying scientific results about increased fuel efficiency, lead [...]

Fact-checking Fake News - "It's easy to lie with statistics; it is easier to lie without them."


What is fact? And what is fiction? What might be seen as a fact by one person is seen as fake news by somebody else. Depending on political orientation and cultural background people quickly categorize news as fake or fact.When beginning of November 2016 right-wing fanatics constructed “pizzagate”, they were claiming that owners and customers of a popular pizza restaurant in Washington were running a covert pedophile operation, directed by a group of people around Hillary Clinton. The mainstream press agreed that this was a fake news smear campaign constructed to damage Hillary Clinton’s reputation and the liberal agenda. Nonetheless, a significant group of the US population took the rumor at face value, see my previous blogpost.Even “facts” published in highly respected newspapers such as the New York Times can be seen as fiction by other news media. For instance, in a recent article in the New York Times, whistleblower Ed Snowdon was depicted as a puppet of Russian spy agencies in a report produced by US government agencies.  The report listed various claims by US intelligence agencies as “facts”, which, according to other journalists, were not true.In God we trust. All others must bring data (W. Edwards Deming)To make sense out of emerging news and to decide whether to categorize them as fact or fiction, it would be useful to track their origin and identify the main promoters of a particular news item. Harvard statistician Gary King and his colleagues have done as much tracking the flow of fake news in China. According to Chinese urban myths, there are up to 2 million microbloggers in China who are paid “50cent” per post by the Chinese government to drown out critical voices on social media and spread news favorable of the government. In a research paper, King and his team have been identifying the “50cent” microbloggers spreading news supporting the Chinese communist party on Sina Weibo and other Chinese blogs. King and his team grouped the posts into five categories: (1) taunting of foreign countries, (2) argumentative praise, (3) non-argumentative praise, (4) factual reporting, (5) cheerleading. Using sophisticated statistical and machine learning methods mining an e-mail archive leaked from the Internet Propaganda office from Zhanggong district, they showed that these “50cent” bloggers primarily engage in a massive amount of positive cheerleading with little to no central oversight, to some extent debunking the urban myth of a vast shadow army of bloggers at the beck and call of the Chinese government. However, the key problem with the analysis of Gary King and his team is that the analysis tools they used are so complex that only somebody with a graduate degree in statistics has a chance to understand it, and nobody except the team doing the analysis has the full insight into the results. As Winston Churchill reputedly said “Do not trust any statistics you did not fake yourself.”  The average reader thus has close to zero chance to actually understand why the statisticians came to their conclusion. It therefore boils down to trust: does the reader trust the conclusions of the analyst/statistician/journalist? Faith-based and Science-based Belief Systems As has been repeatedly shown, humans are much more likely to trust and accept as true news close to their own beliefs and values. What this means is that it depends very much on the belief system of an individual whether a particular news item is accepted as fact or as fiction.  Each individual has to decide for her or himself what is fact and what is fiction. At least in the Western world I therefore group the major belief systems into two opposite stereotypes:Faith-focused: Believing in Go[...]

Can Social Media Analysis Debunk Fake News? – Analyzing “Pizzagate”


While fake news is nothing new – according to rumors Elvis Presley is still alive, and Bigfoot has been sighted numerous times – social media allows susceptible people to spread unfounded and wrong rumors at the speed of light. Spreading false and damaging news is a proven and tested campaign strategy of fanatics during elections. The “swift boat veterans for truth” campaign by conservaties falsely claiming that 2004 democratic presidential candidate John Kerry showed dishonest behavior in the Vietnam war was seen as a key factor in swing states contributing to John Kerry’s defeat. At the end of the 2016 US  Presidential elections, in early November an even more absurd claim was made, accusing Hillary Clinton to run a pedophile ring out of a pizza restaurant in Washington. Called “pizzagate”, it became a favorite call to arms among right-wing extremists and Donald Trump supporters, leading one incensed fanatic to drive a few hundred miles from Salisbury, North Carolina to Washington DC, and firing his automatic gun in the pizza restaurant. As most of this rumor spreading was (and still is) happening on social media, I was curious to see if I could identify some discernible characteristics of fake news on Twitter and the Web, using our social mediaanalysis tool Condor and Coolhunting.I started by creating the Wikipedia link map around the pizzagate article on Wikipedia. The network below shows the links of Pizzagate to Donald Trump, as well as to Michael Flynn, the son of a Trump campaign team member, who was dismissed after a tweet supporting the conspiracy theory.I then proceeded to collect 18,000 tweets about “pizzagate” on December 10, 2016 at about 2pm. As there was feverish tweeting, the last 18,000 tweets only covered about 8 hours. There were between 40 and 120 tweets per minute about pizzagate in this time. The sentiment of the tweets, not surprisingly considering the grisly topic, was rather negative, hovering around 0.4 (sentiment of 0.5 would be neutral, from 0.5 to 1 would be positive.)The next picture shows the word cloud generated from the 18,000 tweets, most of the words are dark red, indicating that they are used in negative context.  The word “Clinton” is in dark red, as the tweeters are mostly accusing Hillary Clinton to molest little children. The word “Trump” stands out in green, as they see him as the savior.The picture below shows the twitter network, each node is a person tweeting, a link between two people means either that one person is retweeting a tweet sent by the other person, or is mentioning the other person in a tweet.There is a large cluster in the center of the network, made up of believers in the fake news.  They are reinforcing each other, and increasing the traffic in their echo chamber.  The few supporters of Hillary, trying to debunk the fake news, are pushed aside, their tweets are ignored by the large echo chamber of conspiracy theory believers. The people in the periphery (the “asteroid belt”) are tweeting into the void, as their tweets are ignored from friends and foes alike.Using Condor’s influencer algorithm reinforces this picture. Condor’s influencer algorithm makes somebody an influencer, if the words she or he is using, are picked up by others and spread quickly through the network. As the picture below shows, there is just one voice of reason left, while the proponents of pizzagate reinforce each other even more, with a cluster of influential spreaders of wild ideas in the center, and other conspiratorialists in the periphery of the cluster, being retweeted by hundreds of others (shown as “parachutes” in the graph). Comparing Fake News with Real NewsNext I wanted to explore if the networ[...]

Why I did NOT predict Donald Trump’s Electoral Win


Have you ever heard of Karl Ernst Krafft?He was a Swiss astrologer in the early 20th century. Krafft correctly predicted that Hitler would be in danger between November 7 and November 10, 1939, and wrote to a friend working for Himmler, warning him of an attempt on Hitler’s life. After a bomb exploded in the Munich beer hall November 8, barely missing Hitler, Krafft became a favorite with the Nazis and was made their court astrologer. However, when towards the end of the war, he (correctly) predicted that bombs would soon destroy the Propaganda ministry in Berlin, he was viewed as a traitor. He was put into jail, and died of typhus on the journey to the Buchenwald concentration camp.Reading the final blog post of famed election forecaster Nate Silver on his fivethirtyeightblog, he says 10.41AM on election day morning of Nov 8, 2016 “…..Clinton is a 71 percent favorite to win the election according to our polls-only model and a 72 percent favorite according to our polls-plus model…..”Next morning Hillary Clinton conceded defeat to right-wing populist Donald Trump.Donald Trump is only the last in a long list of populist leaders swaying public opinion, where “ordinary” people are afraid to admit their preferences, leading conventional pollsters astray. I still remember a visit together with my colleague Manfred Vogel to the offices of highly respected Swiss pollster Claude Longchamp. At that time Longchamp was reeling from a similarly spectacular mis-prediction, where he had forecast a clear rejection of a Swiss National referendum to forbid construction of minaret towers for mosques in Switzerland (the “Minaret Initiative”), only to see the Swiss voters clearly approving this restrictive clause. In my own social media analysis on Twitter, Facebook, and blogs, using an early version of the six honest signals of communication I had identified a surprisingly strong showing for the Minaret Initiative well before the election.The explanation for the surprising success of the “Minaret Initiative”, Brexit, and Donald Trump, is that “ordinary” people are afraid to express their true beliefs when asked in phone polls. In a silent revolution of the disenfranchised, they lie to the pollsters, to only voice what they really believe on election day.  The social network created using our galaxyscope tool through analyzing Wikipedia links, blog links, and re-tweet follower networks illustrates this point. Donald Trump - for all his billionaire bluster - is a clear outsider, and underdog of the establishment. Many people therefore will be reluctant to tell their preference for Trump to the pollsters.The "ordinary" people will however tell what they really think on social media to their friends, so interpreting their “honest signals” would be a better way than asking on the phone. This analysis is extremely hard and time consuming, because social media usage and popularity of particular tools change year by year. It used to be that Facebook posts could be easily read by anybody – not anymore. It used to be that Chileans used to express themselves on Twitter – not anymore. In the age of Snapchat, Whatsapp, WeChat, Instagram, and legions of other point-to-point communication tools it gets near to impossible to collect this datastream (well, maybe there might be a hidden backdoor if you are NSA, but not for ordinary researchers). Therefore, the art and science of prediction is now to interpret publicly available sources such as Google and Wikipedia search logs, blog posts, Tweets, Wikipedia pages, and online news articles, and disentangle their honest signals from the straightforward network picture shown above.However, as Karl Ernst Krafft illustrates[...]

Why Money, Power and Glory Are Bad Motivators


If all we want is money, power, and glory, the world becomes a sad place. Academic research provides solid evidence that the pursuit of these three things makes collaboration among humans miserable.For instance, it appears that students of management and economics, who make the pursuit of money and power their life's goal, are more greedy even before they start their studies, and that they become even more so over the course of their education.  In behavioral research, first year economics students have been shown to be more likely to free-ride in public goods games: In one experiment, students could deposit money into a public account where it was multiplied and distributed to all participants, or keep their money in a private account, and still participate in the distribution of the public pool.  First year graduate students in economics kept eighty percent of the money for themselves, and only put twenty percent into the public pool, compared to all other participants in the game who put fifty percent of their money into the public pool. In a follow-up survey the researchers asked the students about their understanding of fairness. While for all other students the concept of fairness was an important one, a large part of the economics students either refused to answer this question or were unable to give an understandable answer.When the students got the opportunity to play the prisoner’s dilemma game, which rewards participants for cheating, economics majors were almost twice as like to cheat on their teammates as students with other majors. The researchers also explored if economics students became even more selfish over the course of their studies. This seems indeed to be the case, documented by having the students play the prisoner’s dilemma game over extended periods of time. Normally participants become more collaborative over time, cheating less on their teammates. This effect of increasing collaboration over time was conspicuously absent for the economics students. In an experimentcomparing economics students and students from other majors in the first and second years, economists were significantly less fair, and more selfish than their peers, and this effect became stronger in the second year. It seems that economists start out more selfish than others, and that their selfish behavior gets reinforced over time in daily interaction with other economists.The researchers also found that economics professors are much more stingy as charitable givers than professors in other disciplines.  In a survey answered by 576 academics, there were almost ten times as many non-givers among the economics professors than in all other disciplines. In another natural experiment, Bruno S. Frey, a professor of economics at the university of Zurich, investigatedthe charitable behavior of 28,586 students at the university of Zurich. Each semester students could decide if they wanted to contribute a small amount of money towards a fund for needy students. Frey and his colleague found that in particular students of business economics were significantly less generous than students from other majors, and this effect stayed over the entire duration of their studies.  Frey even found that the effect goes back to high school, as students from high schools with emphasis on business economics were stingier than their peers. The late Stanford professor Hal Leavitt put it succinctly, stating that business education transforms students into “critters with lopsided brains, icy hearts, and shrunken souls.”Power corrupts – behavioral economists have demonstratedthis folk wisdom in a series of ingenious experiments. In a research project[...]

Does Twitter Tell Who Will Be the Next President? Comparing Donald Trump’s and Hillary Clinton’s Twitter Influence


Over the last year Donald Trump has been doing a brilliant job kindling his initially highly unlikely candidacy as US Presidential applicant. Following the principle that there is no good or bad PR, that any news is good news as long as it is in the news, he has acted as a master provocateur. He has been a genius in hitting the soft spots of US society, constantly provoking increasingly broader parts of society with over 32,000 (and rapidly growing) racist, sexist, and religiously offensive tweets. I was curious to see how his Twitter behavior would compare with the articulations on Twitter by his Democrat competitor for the job, Hillary Clinton. Therefore I used Condor’s Twitter EgoFetcher (thanks, Joao, for coming up with the idea) to collect the most recent 10,000 tweets about each candidate on August 4, 2016 at 10.00AM.  The EgoFetcher works in four steps: in step 1 it takes the last N (for example 10,000) tweets about the search term or Twitter handle (e.g. “Donald Trump”). Note that normally – except for an individual’s own tweets -  the  search API of Twitter only returns last week’s tweets.  This is not a problem for this search, as people send thousands of tweets about each candidate per hour. In step 2 it constructs a network with a link from twitterer B to twitterer A if B retweets A, or B mentions A in a tweet. In step 3 it takes the timelines of the 480 most influential people in the search results, the influence of these people is measured through their degree in the retweet network from step 2. For twitter users, their timeline is all their tweets, sorted from newest to oldest. In step 4 it adds for each tweet collected in the previous steps the first 100 retweets. The picture below shows the combined Twitter network, with the tweets about Donald in yellow, and about Hillary in green. When combining the search results of the queries for “Donald Trump” and “Hillary Clinton”, and measuring their betweenness centrality, which in social network analysis is commonly taken as a metric of influence, Donald easily beats Hillary. The pie chart below shows the betweenness centralities of both the search queries for the candidates, as well as the betweenness of their Twitter personalities (realDonaldTrump and HillaryClinton). So will Donald become the next president? Not so fast. When looking at each of the network separately, a different picture emerges. The picture below shows the network for Hillary at left, and for Donald at right. The green twitterers are the ones returned in the original search about the search term (“Hillary Clinton” or “Donald Trump”). The yellow twitterers are the people tweeting about the most influential 480 twitterers among the green people.The first thing we note is that not only is the timeline crowd of Hillary more numerous than Donald’s (18,363 people retweeting about Hillary compared to 17,295 for Donald), but they are also much more retweeted (91,681 retweets instead of 85,890). When calculating the six honest signals of collaboration described for example in my new book manuscript, the picture becomes even more pronounced. The metrics shown above are calculated using the dynamic social network analysis features of Condor. Activity is the total number of tweets in the EgoFetcher network above originating from each candidate, where Hillary beats Donald. Emotionality, sentiment, and complexity of language are calculated using the machine learning natural language processing features of Condor, based on the language used in the Tweets. There is not much difference here, al[...]

Your Organization's Digital Communication Network: An Archive of Film Footage about Organizational Performance


Thanks a lot to Ken Riopelle for providing this guest postI believe we are all familiar with sports film. We see sports film highlights everywhere. On the TV, on our phones, tablets, and computers. We hear commentators referring to coaches and their teams reviewing and studying game film as a routine process for game preparation and also for self and team improvement.Why do sports teams, coaches and players look at game films?The answer is quite simple. People use game films to slow down and freeze frame the field of action and analyze the play at key moments in time. It allows everyone to pause, reflect, and discuss what happened. The benefit is simple, the more we understand the patterns of our behavior, the better prepared we are to make a change when needed.But business is played 24/7/365 all over planet earth. In global networked organizations, business interactions are largely unseen and hidden from view, except that we might notice that everyone is preoccupied with one or more digital screens of various sizes.One way to create a film of our business interactions among our teams, suppliers, and partners is to use our email  exchanges. Email's five standardized message elements of "To, From, Date, Subject and Message Content" makes it possible to create a "communication film." Computer software, such as Condor, can replay a single person's communication or a team's, an entire department's, or even an entire enterprise's message exchanges.Of course, email is just one type of communication channel. Business does rely on other communication channels, such as face-to-face, text, telephone, chat, etc.  Nevertheless, research has shown that email is a good surrogate to represent your business relations.Competitive sports teams and players review and study the patterns of their opponents and their own behavior using game films. We as spectators have come to expect the instant replay for close official calls and spectacular plays.Just as sports teams do, in business, we too can observe, study, and reflect on our communication behavior. This can help us to spot strong and weak areas of our performance and develop ways to become better at what we do.I invite you to create a film of your email communication. How does your communication network look and behave around your calendar of events, deadlines and milestones?  What patterns do you see? What happens if you remove yourself from the network? Who keeps it together?  Or, does it fall apart?Using a movie of your email network may seem very new as a way to examine the patterns of communication that you, your team, your department, or even your enterprise exhibits. However, networked communication has been and is being studied across many academic disciplines including anthropology, sociology, physics, mathematics, computer science, communication, and now in business under the phrase, "social network analysis" or the study of human relations.In summary, your email archive represents a hidden canister of film  ready to be played. You can slow down, freeze frame, zoom-in and zoom-out on the film to examine, measure and reflect to improve communication behavior and collaboration with scalability to the enterprise level and all levels in between.Interested?Download Condor, the desktop software to create and play your email communication film archiveSports and Network Language Of course, there is a wide variety of different kinds of sports.  There are team sports, such as soccer, basketball, and ice hockey as well as individual sports, such as golf, boxing, and swimming to mention just a few.  We know that each &nb[...]

Homo Collaborensis - Why Steve Jobs did NOT create Apple


Of course Steve Jobs started Apple – together with Steve Wozniak! But he did not create it. He could never have done it on his own. From the very first day on he was relying on untold legions of engineers, scientists, technicians, accountants, and janitors. Not to speak of four thousand years of accumulated wisdom, scientific, and technological expertise accumulated from Chinese, Indian, Mesoamerican, Greek, Roman, German, English, French, and American philosophers, scientists, engineers, and entrepreneurs.One human on its own is as useful as a single ant in creating the next Tesla, Apple, Google, or Facebook. However just like the ants or the bees, a swarm of humans can do amazing things. And just like a swarm of ants or bees, the human swarm needs a queen bee, which is where Steve Jobs, Larry Page, Mark Zuckerberg, or Elon Musk come in. The key however to their endeavor is communication! Only by communicating their goals, and channeling the accumulated energy and wisdom of their swarm can they set out to create the next big thing changing the world.The goal of this post (and a soon to be published book) is to describe how to communicate for innovation in large groups of people. Better communication leads to better collaboration, which leads to more innovation. The information stored in a single neuron in the brain only becomes meaningful through the massively parallel network of connecting axons and synapses. This is no different for thousands of human brains, which can only work together to innovate by communicating with each other in the best possible way.Humans have always been torn between competition and collaboration. This apparent contradiction of the benefits of collaboration puzzled Charles Darwin, as evolutionary survival of the fittest should favor the most competitive at the expense of the most collaborative. Research of the last fifty years indicates the opposite. Super social species like ants, bees, and humans have been spectacularly successful at the expense of more solitary and competitive species. The conclusion is that humans need to channel their competitive energies towards supporting collaboration – a process I call competitive collaboration. This is in contrast to collaborative competition, where humans collaborate to compete more effectively. Musicians in an orchestra are competitive collaborators, they collaborate to play the most beautiful music. Orchestra and audience are all elated and happy after the concert, with individual competition between the musicians channeled towards a superior collaborative experience. A soccer game demonstrates the opposite process of collaborative competition. The two soccer teams play against each other with each team internally collaborating to compete for victory, with one team ending up the winner, leaving the other, unhappy team in the dust, together with its disappointed fans. We can find similar examples in industry, where more collaborative companies leave the most competitive ones behind. Texas energy company Enron was hailed the most innovative company six years in row by Fortune magazine. CEO and former McKinsey consultant Jeffrey Skilling had introduced an up-or-out process where the least performing fifteen percent of the workforce were yanked out every year, leading to a culture of backstabbing and mutual denigration. In 2002 Enron went bankrupt, when its large-scale corporate fraud was exposed. Compare this with company W. L. Gore & Associates, inventors and manufactures of waterproof fabric Gore-Tex. Other than Enron’s short rise and demise, Gore & Associates has been consiste[...]

Bernie Sander’s Presidential Campaign – The Perfect COIN


We have no clue yet how far Bernie Sander’s campaign to become the next President of the US will go, but what is sure is that the process of how it is unfolding is a great story of COINs.For a start, the entire progress of the campaign is documented online, on reddit, from its humble beginnings, to the prominence of September 2015.  In December 2013 the reddit forum SandersForPresident  was started, and four month later, on April 30, 2014, also on the same Reddit forum, Sanders announced his candidacy: “Reddit -- I am running for President of the United States, and seeking the Democratic nomination. I need you to stand with me and organize an unprecedented grass-roots campaign. Are you in? –B”In true COIN fashion, it was three people forming the original reddit COIN, by the reddit sceen names Vermonty_Python, IrrationalTsunami, and scriggities who created the SandersForPresident forum.Making excellent use of social media, Sanders is a heavy user of Reddit, Twitter, and Facebook. The reason why he resonates so much on online social media is that he has been very consistent in his message for the last 30 years. As of September 2015 he is closing in on Hillary Clinton, until recently the undisputed front runner as democratic presidential candidate. As of September 2015 Sanders is leading in the critical early voting state New Hampshire and a close second in Iowa. The hundreds of thousands of people on reddit, Facebook, and Twitter form a perfect CLN (Collaborative Learning Network) learning about Sanders’ viewpoint. Some of them even self-organize their own COINs to further Sanders’ cause. For instance, Sanders succeeded in tapping into the Web savvy of young IT professionals, with whom his message of Northern European style social democracy resonates very well. Jumpstarted by a young IT professional in NYC, hundreds of software developers volunteered their time, energy, and creativity to create all sorts of social media apps, Websites, and idea tracking tools. Titled “A legion of tech volunteers are leading a charge for Bernie Sanders” the NYC describes how this group created a Website “”  to showcase Bernie Sander’s position on key issues. They coordinate their work using the communication tool “slack” , moonlighting and contributing their skills to create interactive maps, donation collection apps, and grassroots organizing tools.Coolhunting Bernie Sanders, Hillary Clinton, Jeb Bush, and Donald TrumpAfter all this amazing COINs-based grassroots organizing, I was curious to compare the social media footprint of the campaign of Bernie Sanders with his counterpart on the right spectrum of the political landscape, Donald Trump, and contrast it with their more established competitors Hillary Clinton and Jeb Bush. In a nutshell, the two outsiders Sanders and Trump, at least right now, share the spotlight, while the two candidates of the establishment, Hillary and Jeb Bush, are badly trailing not just in the polls, but also on social media. I started by comparing the global Twitter footprint of the four candidates. On September 6 I collected the most recent 4000 tweets about “Bernie Sanders”, “Hillary Clinton”, “Donald Trump”, and “Jeb Bush”. I also collected an additional 4000 tweets with their most popular hashtags #feelthebern, #Hillary2016, #makeAmericaGreatAgain, and #jeb2016.As the curve above tells, the messages are quite emotional, and just above the positivity line.  There are also quite a few people around the world tweeting ab[...]

Coolfarming Ideas through Knowledge Flow Optimization - Boosting Organizational Performance through E-Mail Social Network Analysis


Over the last fifteen years our research group at the MIT Center for Collective Intelligence, University of Cologne and University of Applied Sciences Northwestern Switzerland (FHNW) has studied hundreds of organizations through the lens of their social networks, extracted from the organization’s e-mail archive.Among many others we have studied R&D organizations at car manufacturers, marketing departments at banks, sales teams at high tech manufacturers, medical researchers and doctors at large hospitals, and service delivery teams at large consulting and service provider firms. In addition, we have also looked at collaboration in open source organizations like Eclipse software developers, Wikipedians, and online communities on Facebook and elsewhere. We have developed a 4-Step Process which we call “Knowledge Flow Optimization” to study and increase the performance of organizations, to “coolfarm ideas” (see figure below).It consists of the four steps “Analyze – Predict – Mirror – Optimize”. To illustrate our approach, I describe the analysis of a fortune 500 high-tech company, where we compared e-mail communication of the organization with sales success of their sales teams in the different geographical regions.Step 1: Determining Social Network Metrics and Communication PatternsIn the first step we analyze and quantify the communication patterns and social network structure embedded within organizational communication archives such as email, video conferencing and instant messaging. Quantified communication patterns include metrics such as average response time to messages, sentiment and contribution index. Contribution index is a measure of the balance of communication in terms of the messages sent and received by an individual. These are complemented by metrics computed using Social Network Analysis that are measures of social influence (or centrality) and their trends over time.Step 2: Honest Signals: Comparing structural attributes with business successIn the second step we compare communication behavior found in step 1 with communication patterns that we have identified over a period of 12 years in over a hundred ONA projects carried out by our team. These patterns, also called “honest signals”, are indicators of better connectivity, interactivity and sharing among the individuals in the network. There are 6 honest signals that we look for, namely: Central Leaders, Rotating Leadership, Balanced Contribution, Rapid Response, Honest Sentiment and Innovative Language. Having calculated these honest signals from the data in the communication archives, we then correlate them with quantified success and failure criteria. The success and failure criteria vary significantly depending on the type of organization, the industry and the individuals being measured. In this example we measured sales performance of the sales teams in different geographic regions and for different products.Step 3: Virtual MirroringIn the next step we mirror the communication behavior we have identified for the different parts of the organization back to the teams and individuals. By showing them how they differ from the best practices we found in past projects, we help them to improve their behavior for better performance. Just like with a real mirror, looking at how a team “really” communicates can be an eye-opening experience for the team members, leading to fundamental changes in their behavior for the better.Step 4: Devising a plan to optimize communication for greater successOnce we fig[...]

Swarms do “what is right”


In a public vote at the town hall meeting on May 4, 2015, the Swiss town of Duernten (population 7000) decided to return $250,000 to a worker, who had not filled out his tax declaration since 1995 due to dyslexia. In successively higher tax bills the tax authorities had overcharged Ernst Suter by $280,000, forcing him to sell land to pay taxes on money he had never made. As a worker in a butcher’s shop Suter made about 60,000 a year, which, by Swiss standards, is a fairly low salary. Based on information from the town administration, the tax office of the canton of Zurich had second guessed Suter’s income at about 300,000 per year, and sent him tax bills accordingly, which Suter always paid, nearly forcing him into bankruptcy more than once. Only when Suter ran totally dry, did the branch of the town government that has the task to collect late payments (called “Betreibungsamt”, office of payment enforcement in Switzerland) pass on the case to a custodian, who brought the whole tragedy to light.This is where things took a positive turn for Ernst Suter. After the custodian sent a detailed list of the overpayments to all citizens in Duernten, the town hall meeting decided last December to return the money. When the mayor of the town and his tax administration dragged their feet and decided to cut the return into half – because in the Swiss system, half of the collected town tax of individuals is passed on to the canton – citizens of Duernten took the matter into their own hands. They again put the issue on the agenda of their town hall meeting in June, voting overwhelmingly for Suter. Fairness and ethical behavior is more important than strict adherence to the letter of the law. In this case, the swarm – the town hall meeting - also censored its leaders, asking the elected town officials to not only return the 250,000 they had taken from Suter, but even to make sure that in case the canton would want to tax the return payment as income, to allocate an additional 75,000 to pay for the income tax. They also asked the mayor to formally apologize to Suter. This shows that the swarm (citizens of Duernten) knows better than the experts (the tax authorities) what is fair, independent of what the tax code says. This case lead to intensive echo in the Swiss press (in German)original exposure of articles in "Blick" [...]

Galaxy-Scope: Finding your virtual tribe (for example near the PayPal Mafia)


Whether it’s sitting in the same restaurant as George Clooney, or being on a picture with Warren Buffet, we are defined by whom we know and derive great satisfaction by being close to celebrities. Thanks to the Web and social media the six degrees of separation that separate any two people are shrinking rapidly. In addition, we can use the same insights to define who we are by looking at whom we are close to.The following example illustrates how this idea can be applied to measure how close any aspiring Internet entrepreneur is to the “PayPal Mafia”.  In an article on April 1, 2015, the NYT describes the far reaching influence of PayPal alums in Silicon Valley. I was curious to measure the influence of the names listed in the article: Chad Hurley David Sacks Elon Musk Jawed Karim Jeremy Stoppelman Keith Rabois Max Levchin Peter Thiel Reid Hoffman Roelof Botha Russel Simmons Scott Banister Steve Chen I plugged them into our network data collectors for Wikipedia, the Web, and on Twitter. I did a Condor Coolhunting on the three infospheres using the names of the people as search terms.The picture below shows their Wikipedia network: Peter Thiel and Elon Musk lead the group, the rest is clearly recognizable, but none of them stands out. Thiel is close to Facebook, Musk to SpaceX. Chen and Hurley are close to YouTube, Levchin close to Yahoo, Stoppelmann and Simmons to Yelp.The next pictures shows their importance in the Web: The Degree-of-separation search uses the Google CSE API to collect the top 20 search results for each member of the PayPal Mafia, and then the top 20 links pointing back to each of the search results. Measuring the betweenness of the search results in the resulting bi-modal graph gives a proxy for the importance of each PayPal Mafia member in the Blogosphere as well as the most important Websites. Reid Hoffmann, Chard Hurley, Peter Thiel and Elon Musk are all similarly central, while and are the most central sites. The Web Co-occurrence network shown above is constructed from the 3134 pages collected with the degree-of-separation search described above. Using named entity recognition and natural language processing, all people names are extracted from the thousands of pages collected. A link between two names is drawn if the two people are on the same page  - literally speaking. In this network, people like Barack Obama, Hillary Clinton, Steve Jobs, and Jon Stewart are more central than the members of the PayPal Mafia who were used to construct the network.For the Twitter network, we combined the Tweets made of the members of the paypal mafia with all the tweets about them. An actor is a person tweeting, a link between two actors is drawn if a tweet is retweeted. Some people are very active tweeters, but are not so much tweeted about, others, like Peter Thiel, only tweeted once, but still has 90,000 followers, and is much tweeted about. And then there is Elon Musk, who does not tweet that much, but increased the value of his company Tesla by one billion with a single tweet.  By combining the two input sources, we get a Twitter network reflecting the real importance of the PayPal Mafia in the Twittersphere. It turns out that Peter Thiel and Elon Musk again rule the roost.In the final picture we combined all of these networks (Wikipedia, Weblinks, Web Co-occurrence, Twitter). Pete[...]

Creativities, Innovation, And Networks In Garage Punk Rock: A Case Study Of The Eruptörs


Today Gareth Dylan Smith and Alex Gillett published an excellent article, describing the evolution of their rock band the Eruptörs as a Collaborative Innovation Network (COIN).

Francogeddon – Uncapping the Swiss Franc – a Signal of Global Consciousness?


Does an organization – and thus ultimately humanity - show some sort of consciousness or self-awareness?  One might think so, at least in moments such as on the day when princess Diana died, or more recently, on the day when I was stuck at home in Cambridge while the Boston Marathon bomber was roaming at large in the neighborhood. In those intense moments we feel maybe not “collectively intelligent” but certainly “collectively aware” or “collectively conscious”. If we meet a stranger in those moments, we know what they are thinking, namely “it’s so sad Diana died,” or “where might the marathon bomber be hiding and hitting next”. Moments like these motivate an informal definition of “organizational consciousness”. It is analogous to the human body, where the brain is conscious of the toe, and will respond differently depending on whether a person hits her toe at the door, or somebody else steps on her toe. Extending this metaphor, a “collectively conscious” organization will respond differently if somebody hits a member purposefully, or if a member hurts her/himself. Similarly to the neurons in the brain that are communicating through their synapses to create consciousness, humans communicate by interacting with each other verbally, through text, or other signals, either face-to-face or over long distance by phone or Internet.To prove existence of consciousness on the individual level, Descartes famously stated “cogito ergo sum” - I think, therefore I exist. Extending this definition to an organization, “if the organization thinks and acts as one cohesive organism, it exists” and thus shows collective consciousness, defining organizational consciousness as common understanding on an organization’s global context, which allows the members of the organization to implicitly coordinate their activities and behaviors through communication.As an example of a global level event, in the case of the Boston Marathon bomber, everybody in the Boston area was trying to stay abreast of the most recent developments on Twitter, Facebook and the News, and looking out for traces of the terrorists. On the organizational level, a well-oiled team of software developers working together closely f2f, using chat, or using e-mail trying to debug a jointly developed application also shows a high level of organizational consciousness, as they are able to coordinate their work with minimal use of words.Our aim is trying to make this implicit understanding more measurable, similarly to brain researchers, who measure individual levels of consciousness by attaching probes to individual neurons, tracking the electrical flow of current flowing through synapses between the neurons. In our work, we measure interaction among people through online media such as e-mail, Twitter, Facebook, and blog posts, applying a framework of “six honest signals of communication” that was introduced previously.In this blog post I would like to illustrate global consciousness by the example of Francogeddon. On January 15, 2015 financial markets were in turmoil. In a surprise move – later termed Francogeddon - the Swiss National Bank removed the artificial exchange rate of Swiss Franc 1.20 to the Euro, which it had set and defended by buying massive amounts of Euro and Dollars since September 6, 2011. Within hours the exchange rate between Euro and Swiss Franc fluctuated from 1.20 Francs per Euro to 95 cent[...]

The fairest Country on Earth?


Today’s Swiss Newspaper Tagesanzeiger showed statistics published in a paper in the Swiss Economic Journal in 2011 which show that a person in Switzerland who has an income - well below the poverty level - of Swiss Franc 12,100 (=USD 13,000 at today’s exchange rate) gets supplemental income of CHF 54,700 by the Swiss state. In fact, with totally CHF 66,800 this person gets more money to spend than a middle class earner making 58,800 Swiss Francs, who pays CHF 7200 taxes with a net income of CHF 51,600.Even somebody who makes 100,000 Swiss Francs per year has less money left after taxes to spend than the person on welfare with the 12,100 income.While there is a lively discussion ongoing – the article quickly triggered hundreds of comments - at least for now, nobody seems to fundamentally question the basic mechanisms of redistribution.   It also seems to work to the benefit of the overall Swiss population. In most of the world happiness rankings, the Swiss rank near the top (#2 on the Legatum ranking, #3 on the UN World Happiness Report).  If we take life expectancy as the final metric of individual health and happiness, Switzerland is also doing very well, #7 on the most recent WHO ranking, and #1 in an OECD ranking.Why?I think one critical point is that there are other criteria besides money contributing to health and happiness. People with low socioeconomic status (the welfare recipients in Switzerland) get supplements by the state to compensate for the stress and frustration in their low paying part-time jobs; many of them will also collect disability benefits as a large part of their supplement. It is impossible to make that little money for a full job, as the minimum wage is about 36,000 Swiss Francs per year, which will then be complemented by the state to the 54,000 Francs listed above.The effect of these compensatory payments is that everybody has enough money to life in dignity.  And people making the least might need a little more to compensate for their stress and frustrations compared to middle income earners.[...]