Data Mining Reddit Posts Reveals How to Ask For a Favor–And Get it

Emerging Technology From the arXiv: “There’s a secret to asking strangers for something and getting it. Now data scientists say they’ve discovered it by studying successful requests on the web

One of the more extraordinary phenomena on the internet is the rise of altruism and of websites designed to enable it. The Random Acts of Pizza section of the Reddit website is a good example.

People leave messages asking for pizza which others fulfil if they find the story compelling. As the site says: “because… who doesn’t like helping out a stranger? The purpose is to have fun, eat pizza and help each other out. Together, we aim to restore faith in humanity, one slice at a time.”

A request might go something like this: “It’s been a long time since my mother and I have had proper food. I’ve been struggling to find any kind of work so I can supplement my mom’s social security… A real pizza would certainly lift our spirits”. Anybody can then fulfil the order which is then marked on the site with a badge saying “got pizza’d”, often with notes of thanks.

That raises an interesting question. What kinds of requests are most successful in getting a response? Today, we get an answer thanks to the work of Tim Althoff at Stanford University and a couple of pals who lift the veil on the previously murky question of how to ask for a favour—and receive it.

They analysed how various features might be responsible for the success of a post, such as the politeness of the post; its sentiment, whether positive or negative for example; its length. The team also looked at the similarity of the requester to the benefactor; and also the status of the requester.

Finally, they examined whether the post contained evidence of need in the form of a narrative that described why the requester needed free pizza.

Althoff and co used a standard machine learning algorithm to comb through all the possible correlations in 70 per cent of the data, which they used for training. Having found various correlations, they tested to see whether this had predictive power in the remaining 30 per cent of the data. In other words, can their algorithm predict whether a previously unseen request will be successful or not?

It turns out that their algorithm makes a successful prediction about 70 per cent of the time. That’s far from perfect but much better than random guessing which is right only half the time.

So what kinds of factors are important? Narrative is a key part of many of the posts, so Althoff and co spent some time categorising the types of stories people use.

They divided the narratives into five types, those that mention: money; a job; being a student; family; and a final group that includes mentions of friends, being drunk, celebrating and so on, which Althoff and co call ‘craving’.

Of these, narratives about jobs, family and money increase the probability of success. Student narratives have no effect while craving narratives significantly reduce the chances of success. In other words, narratives that communicate a need are more successful than those that do not.

 “We find that clearly communicating need through the narrative is essential,” say Althoff and co. And evidence of reciprocation helps too.

(Given these narrative requirements, it is not surprising that longer requests tend to be more successful than short ones.)

So for example, the following request was successful because it clearly demonstrates both need and evidence of reciprocation.

“My gf and I have hit some hard times with her losing her job and then unemployment as well for being physically unable to perform her job due to various hand injuries as a server in a restaurant. She is currently petitioning to have unemployment reinstated due to medical reasons for being unable to perform her job, but until then things are really tight and ANYTHING would help us out right now.

I’ve been both a giver and receiver in RAOP before and would certainly return the favor again when I am able to reciprocate. It took everything we have to pay rent today and some food would go a long ways towards making our next couple of days go by much better with some food.”

By contrast, the ‘craving’ narrative below demonstrates neither and was not successful.

“My friend is coming in town for the weekend and my friends and i are so excited because we haven’t seen him since junior high. we are going to a high school football game then to the dollar theater after and it would be so nice if someone fed us before we embarked :)”

Althoff and co also say that the status of the requester is an important factor too. “We find that Reddit users with higher status overall (higher karma) or higher status within the subcommunity (previous posts) are significantly more likely to receive help,” they say.

But surprisingly, being polite does not help (except by offering thanks).

That’s interesting work. Until now, psychologists have never understood the factors that make requests successful, largely because it has always been difficult to separate the influence of the request from what is being requested.

The key here is that everybody making requests in this study wants the same thing—pizza. In one swoop, this makes the data significantly easier to tease apart.

An important line of future work will be in using his work to understand altruistic behaviour in other communities too…

Ref: : How to Ask for a Favor: A Case Study on the Success of Altruistic Requests”

The Secret Science of Retweets

Emerging Technology From the arXiv: “If you send a tweet to a stranger asking them to retweet it, you probably wouldn’t be surprised if they ignored you entirely. But if you sent out lots of tweets like this, perhaps a few might end up being passed on.

How come? What makes somebody retweet information from a stranger? That’s the question addressed today by Kyumin Lee from Utah State University in Logan and a few pals from IBM’s Almaden research center in San Jose….by studying the characteristics of Twitter users, it is possible to identify strangers who are more likely to pass on your message than others. And in doing this, the researchers say they’ve been able to improve the retweet rate of messages sent strangers by up to 680 percent.
So how did they do it? The new technique is based on the idea that some people are more likely to tweet than others, particularly on certain topics and at certain times of the day. So the trick is to find these individuals and target them when they are likely to be most effective.
So the approach was straightforward. The idea is to study the individuals on Twitter, looking at their profiles and their past tweeting behavior, looking for clues that they might be more likely to retweet certain types of information. Having found these individuals, send your tweets to them.
That’s the theory. In practice, it’s a little more involved. Lee and co wanted to test people’s response to two types of information: local news (in San Francisco) and tweets about bird flu, a significant issue at the time of their research. They then created several Twitter accounts with a few followers, specifically to broadcast information of this kind.
Next, they selected people to receive their tweets. For the local news broadcasts, they searched for Twitter users geolocated in the Bay area, finding over 34,000 of them and choosing 1,900 at random.
They then a sent a single message to each user of the format:
“@ SFtargetuser “A man was killed and three others were wounded in a shooting …” Plz RT this safety news”
So the tweet included the user’s name, a short headline, a link to the story and a request to retweet.
Of these 1,900 people, 52 retweeted the message they received. That’s 2.8 percent.
For the bird flu information, Lee and co hunted for people who had already tweeted about bird flu, finding 13,000 of them and choosing 1,900 at random. Of these, 155 retweeted the message they received, a retweet rate of 8.4 percent.
But Lee and co found a way to significantly improve these retweet rates. They went back to the original lists of Twitter users and collected publicly available information about each of them, such as their personal profile, the number of followers, the people they followed, their 200 most recent tweets and whether they retweeted the message they had received
Next, the team used a machine learning algorithm to search for correlations in this data that might predict whether somebody was more likely to retweet. For example, they looked at whether people with older accounts were more likely to retweet or how the ratio of friends to followers influenced the retweet likelihood, or even how the types of negative or positive words they used in previous tweets showed any link. They also looked at the time of day that people were most active in tweeting.
The result was a machine learning algorithm capable of picking users who were most likely to retweet on a particular topic.
And the results show that it is surprisingly effective. When the team sent local information tweets to individuals identified by the algorithm, 13.3 percent retweeted it, compared to just 2.6 percent of people chosen at random.
And they got even better results when they timed the request to match the periods when people had been most active in the past. In that case, the retweet rate rose to 19.3 percent. That’s an improvement of over 600 percent.
Similarly, the rate for bird flu information rose from 8.3 percent for users chosen at random to 19.7 percent for users chosen by the algorithm.
That’s a significant result that marketers, politicians, news organizations will be eyeing with envy.
An interesting question is how they can make this technique more generally applicable. It raises the prospect of an app that allows anybody to enter a topic of interest and which then creates a list of people most likely to retweet on that topic in the next few hours.
Lee and co do not mention any plans of this kind. But if they don’t exploit it, then there will surely be others who will.
Ref: : Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information”

The Collective Intelligence Handbook: an open experiment

Michael Bernstein: “Is there really a wisdom of the crowd? How do we get at it and understand it, utilize it, empower it?
You probably have some ideas about this. I certainly do. But I represent just one perspective. What would an economist say? A biologist? A cognitive or social psychologist? An artificial intelligence or human-computer interaction researcher? A communications scholar?
For the last two years, Tom Malone (MIT Sloan) and I (Stanford CS) have worked to bring together all these perspectives into one book. We are nearing completion, and the Collective Intelligence Handbook will be published by the MIT Press later this year. I’m still relatively dumbfounded by the rockstar lineup we have managed to convince to join up.

It’s live.

Today we went live with the authors’ current drafts of the chapters. All the current preprints are here:

And now is when you come in.

But we’re not done. We’d love for you — the crowd — to help us make this book better. We envisioned this as an open process, and we’re excited that all the chapters are now at a point where we’re ready for critique, feedback, and your contributions.
There are two ways you can help:

  • Read the current drafts and leave comments inline in the Google Docs to help us make them better.
  • Drop suggestions in the separate recommended reading list for each chapter. We (the editors) will be using that material to help us write an introduction to each chapter.

We have one month. The authors’ final chapters are due to us in mid-June. So off we go!”

Here’s what’s in the book:

Chapter 1. Introduction
Thomas W. Malone (MIT) and Michael S. Bernstein (Stanford University)
What is collective intelligence, anyway?
Chapter 2. Human-Computer Interaction and Collective Intelligence
Jeffrey P. Bigham (Carnegie Mellon University), Michael S. Bernstein (Stanford University), and Eytan Adar (University of Michigan)
How computation can help gather groups of people to tackle tough problems together.
Chapter 3. Artificial Intelligence and Collective Intelligence
Daniel S. Weld (University of Washington), Mausam (IIT Delhi), Christopher H. Lin (University of Washington), and Jonathan Bragg (University of Washington)
Mixing machine intelligence with human intelligence could enable a synthesized intelligent actor that brings together the best of both worlds.
Chapter 4. Collective Behavior in Animals: An Ecological Perspective
Deborah M. Gordon (Stanford University)
How do groups of animals work together in distributed ways to solve difficult problems?
Chapter 5. The Wisdom of Crowds vs. the Madness of Mobs
Andrew W. Lo (MIT)
Economics has studied a collectively intelligent forum — the market — for a long time. But are we as smart as we think we are?
Chapter 6. Collective Intelligence in Teams and Organizations
Anita Williams Woolley (Carnegie Mellon University), Ishani Aggarwal (Georgia Tech), Thomas W. Malone (MIT)
How do the interactions between groups of people impact how intelligently that group acts?
Chapter 7. Cognition and Collective Intelligence
Mark Steyvers (University of California, Irvine), Brent Miller (University of California, Irvine)
Understanding the conditions under which people are smart individually can help us predict when they might be smart collectively.

Chapter 8. Peer Production: A Modality of Collective Intelligence
Yochai Benkler (Harvard University), Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington)
What have collective efforts such as Wikipedia taught us about how large groups come together to create knowledge and creative artifacts?

Saving Big Data from Big Mouths

Cesar A. Hidalgo in Scientific American: “It has become fashionable to bad-mouth big data. In recent weeks the New York Times, Financial Times, Wired and other outlets have all run pieces bashing this new technological movement. To be fair, many of the critiques have a point: There has been a lot of hype about big data and it is important not to inflate our expectations about what it can do.
But little of this hype has come from the actual people working with large data sets. Instead, it has come from people who see “big data” as a buzzword and a marketing opportunity—consultants, event organizers and opportunistic academics looking for their 15 minutes of fame.
Most of the recent criticism, however, has been weak and misguided. Naysayers have been attacking straw men, focusing on worst practices, post hoc failures and secondary sources. The common theme has been to a great extent obvious: “Correlation does not imply causation,” and “data has biases.”
Critics of big data have been making three important mistakes:
First, they have misunderstood big data, framing it narrowly as a failed revolution in social science hypothesis testing. In doing so they ignore areas where big data has made substantial progress, such as data-rich Web sites, information visualization and machine learning. If there is one group of big-data practitioners that the critics should worship, they are the big-data engineers building the social media sites where their platitudes spread. Engineering a site rich in data, like Facebook, YouTube, Vimeo or Twitter, is extremely challenging. These sites are possible because of advances made quietly over the past five years, including improvements in database technologies and Web development frameworks.
Big data has also contributed to machine learning and computer vision. Thanks to big data, Facebook algorithms can now match faces almost as accurately as humans do.
And detractors have overlooked big data’s role in the proliferation of computational design, data journalism and new forms of artistic expression. Computational artists, journalists and designers—the kinds of people who congregate at meetings like Eyeo—are using huge sets of data to give us online experiences that are unlike anything we experienced in paper. If we step away from hypothesis testing, we find that big data has made big contributions.
The second mistake critics often make is to confuse the limitations of prototypes with fatal flaws. This is something I have experienced often. For example, in Place Pulse—a project I created with my team the M.I.T. Media Lab—we used Google Street View images and crowdsourced visual surveys to map people’s perception of a city’s safety and wealth. The original method was rife with limitations that we dutifully acknowledged in our paper. Google Street View images are taken at arbitrary times of the day and showed cities from the perspective of a car. City boundaries were also arbitrary. To overcome these limitations, however, we needed a first data set. Producing that first limited version of Place Pulse was a necessary part of the process of making a working prototype.
A year has passed since we published Place Pulse’s first data set. Now, thanks to our focus on “making,” we have computer vision and machine-learning algorithms that we can use to correct for some of these easy-to-spot distortions. Making is allowing us to correct for time of the day and dynamically define urban boundaries. Also, we are collecting new data to extend the method to new geographical boundaries.
Those who fail to understand that the process of making is iterative are in danger of  being too quick to condemn promising technologies.  In 1920 the New York Times published a prediction that a rocket would never be able to leave  atmosphere. Similarly erroneous predictions were made about the car or, more recently, about iPhone’s market share. In 1969 the Times had to publish a retraction of their 1920 claim. What similar retractions will need to be published in the year 2069?
Finally, the doubters have relied too heavily on secondary sources. For instance, they made a piñata out of the 2008 Wired piece by Chris Anderson framing big data as “the end of theory.” Others have criticized projects for claims that their creators never made. A couple of weeks ago, for example, Gary Marcus and Ernest Davis published a piece on big data in the Times. There they wrote about another of one of my group’s projects, Pantheon, which is an effort to collect, visualize and analyze data on historical cultural production. Marcus and Davis wrote that Pantheon “suggests a misleading degree of scientific precision.” As an author of the project, I have been unable to find where I made such a claim. Pantheon’s method section clearly states that: “Pantheon will always be—by construction—an incomplete resource.” That same section contains a long list of limitations and caveats as well as the statement that “we interpret this data set narrowly, as the view of global cultural production that emerges from the multilingual expression of historical figures in Wikipedia as of May 2013.”
Bickering is easy, but it is not of much help. So I invite the critics of big data to lead by example. Stop writing op–eds and start developing tools that improve on the state of the art. They are much appreciated. What we need are projects that are worth imitating and that we can build on, not obvious advice such as “correlation does not imply causation.” After all, true progress is not something that is written, but made.”

Digital Humanitarians

New book by Patrick Meier on how big data is changing humanitarian response: “The overflow of information generated during disasters can be as paralyzing to humanitarian response as the lack of information. This flash flood of information when amplified by social media and satellite imagery is increasingly referred to as Big Data—or Big Crisis Data. Making sense of Big Crisis Data during disasters is proving an impossible challenge for traditional humanitarian organizations, which explains why they’re increasingly turning to Digital Humanitarians.
Who exactly are these Digital Humanitarians? They’re you, me, all of us. Digital Humanitarians are volunteers and professionals from the world over and from all walks of life. What do they share in common? The desire to make a difference, and they do that by rapidly mobilizing online in collaboration with international humanitarian organizations. They make sense of vast volumes of social media and satellite imagery in virtually real-time to support relief efforts worldwide. How? They craft and leverage ingenious crowdsourcing solutions with trail-blazing insights from artificial intelligence.
In sum, this book charts the sudden and spectacular rise of Digital Humanitarians by sharing their remarkable, real-life stories, highlighting how their humanity coupled with innovative solutions to Big Data is changing humanitarian response forever. Digital Humanitarians will make you think differently about what it means to be humanitarian and will invite you to join the journey online.
Clicker here to be notified when the book becomes available. For speaking requests, please email”

Eight (No, Nine!) Problems With Big Data

Gary Marcus and Ernest Davis in the New York Times: “BIG data is suddenly everywhere. Everyone seems to be collecting it, analyzing it, making money from it and celebrating (or fearing) its powers. Whether we’re talking about analyzing zillions of Google search queries to predict flu outbreaks, or zillions of phone records to detect signs of terrorist activity, or zillions of airline stats to find the best time to buy plane tickets, big data is on the case. By combining the power of modern computing with the plentiful data of the digital era, it promises to solve virtually any problem — crime, public health, the evolution of grammar, the perils of dating — just by crunching the numbers.

Or so its champions allege. “In the next two decades,” the journalist Patrick Tucker writes in the latest big data manifesto, “The Naked Future,” “we will be able to predict huge areas of the future with far greater accuracy than ever before in human history, including events long thought to be beyond the realm of human inference.” Statistical correlations have never sounded so good.

Is big data really all it’s cracked up to be? There is no doubt that big data is a valuable tool that has already had a critical impact in certain areas. For instance, almost every successful artificial intelligence computer program in the last 20 years, from Google’s search engine to the I.B.M. “Jeopardy!” champion Watson, has involved the substantial crunching of large bodies of data. But precisely because of its newfound popularity and growing use, we need to be levelheaded about what big data can — and can’t — do.

The first thing to note is that although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful. A big data analysis might reveal, for instance, that from 2006 to 2011 the United States murder rate was well correlated with the market share of Internet Explorer: Both went down sharply. But it’s hard to imagine there is any causal relationship between the two. Likewise, from 1998 to 2007 the number of new cases of autism diagnosed was extremely well correlated with sales of organic food (both went up sharply), but identifying the correlation won’t by itself tell us whether diet has anything to do with autism.

Second, big data can work well as an adjunct to scientific inquiry but rarely succeeds as a wholesale replacement. Molecular biologists, for example, would very much like to be able to infer the three-dimensional structure of proteins from their underlying DNA sequence, and scientists working on the problem use big data as one tool among many. But no scientist thinks you can solve this problem by crunching data alone, no matter how powerful the statistical analysis; you will always need to start with an analysis that relies on an understanding of physics and biochemistry.

Third, many tools that are based on big data can be easily gamed. For example, big data programs for grading student essays often rely on measures like sentence length and word sophistication, which are found to correlate well with the scores given by human graders. But once students figure out how such a program works, they start writing long sentences and using obscure words, rather than learning how to actually formulate and write clear, coherent text. Even Google’s celebrated search engine, rightly seen as a big data success story, is not immune to “Google bombing” and “spamdexing,” wily techniques for artificially elevating website search placement.

Fourth, even when the results of a big data analysis aren’t intentionally gamed, they often turn out to be less robust than they initially seem. Consider Google Flu Trends, once the poster child for big data. In 2009, Google reported — to considerable fanfare — that by analyzing flu-related search queries, it had been able to detect the spread of the flu as accurately and more quickly than the Centers for Disease Control and Prevention. A few years later, though, Google Flu Trends began to falter; for the last two years it has made more bad predictions than good ones.

As a recent article in the journal Science explained, one major contributing cause of the failures of Google Flu Trends may have been that the Google search engine itself constantly changes, such that patterns in data collected at one time do not necessarily apply to data collected at another time. As the statistician Kaiser Fung has noted, collections of big data that rely on web hits often merge data that was collected in different ways and with different purposes — sometimes to ill effect. It can be risky to draw conclusions from data sets of this kind.

A fifth concern might be called the echo-chamber effect, which also stems from the fact that much of big data comes from the web. Whenever the source of information for a big data analysis is itself a product of big data, opportunities for vicious cycles abound. Consider translation programs like Google Translate, which draw on many pairs of parallel texts from different languages — for example, the same Wikipedia entry in two different languages — to discern the patterns of translation between those languages. This is a perfectly reasonable strategy, except for the fact that with some of the less common languages, many of the Wikipedia articles themselves may have been written using Google Translate. In those cases, any initial errors in Google Translate infect Wikipedia, which is fed back into Google Translate, reinforcing the error.

A sixth worry is the risk of too many correlations. If you look 100 times for correlations between two variables, you risk finding, purely by chance, about five bogus correlations that appear statistically significant — even though there is no actual meaningful connection between the variables. Absent careful supervision, the magnitudes of big data can greatly amplify such errors.

Seventh, big data is prone to giving scientific-sounding solutions to hopelessly imprecise questions. In the past few months, for instance, there have been two separate attempts to rank people in terms of their “historical importance” or “cultural contributions,” based on data drawn from Wikipedia. One is the book “Who’s Bigger? Where Historical Figures Really Rank,” by the computer scientist Steven Skiena and the engineer Charles Ward. The other is an M.I.T. Media Lab project called Pantheon.

Both efforts get many things right — Jesus, Lincoln and Shakespeare were surely important people — but both also make some egregious errors. “Who’s Bigger?” claims that Francis Scott Key was the 19th most important poet in history; Pantheon has claimed that Nostradamus was the 20th most important writer in history, well ahead of Jane Austen (78th) and George Eliot (380th). Worse, both projects suggest a misleading degree of scientific precision with evaluations that are inherently vague, or even meaningless. Big data can reduce anything to a single number, but you shouldn’t be fooled by the appearance of exactitude.

FINALLY, big data is at its best when analyzing things that are extremely common, but often falls short when analyzing things that are less common. For instance, programs that use big data to deal with text, such as search engines and translation programs, often rely heavily on something called trigrams: sequences of three words in a row (like “in a row”). Reliable statistical information can be compiled about common trigrams, precisely because they appear frequently. But no existing body of data will ever be large enough to include all the trigrams that people might use, because of the continuing inventiveness of language.

To select an example more or less at random, a book review that the actor Rob Lowe recently wrote for this newspaper contained nine trigrams such as “dumbed-down escapist fare” that had never before appeared anywhere in all the petabytes of text indexed by Google. To witness the limitations that big data can have with novelty, Google-translate “dumbed-down escapist fare” into German and then back into English: out comes the incoherent “scaled-flight fare.” That is a long way from what Mr. Lowe intended — and from big data’s aspirations for translation.

Wait, we almost forgot one last problem: the hype….

Brainlike Computers, Learning From Experience

The New York Times: “Computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head.

The first commercial version of the new kind of computer chip is scheduled to be released in 2014. Not only can it automate tasks that now require painstaking programming — for example, moving a robot’s arm smoothly and efficiently — but it can also sidestep and even tolerate errors, potentially making the term “computer crash” obsolete.

The new computing approach, already in use by some large technology companies, is based on the biological nervous system, specifically on how neurons react to stimuli and connect with other neurons to interpret information. It allows computers to absorb new information while carrying out a task, and adjust what they do based on the changing signals.

In coming years, the approach will make possible a new generation of artificial intelligence systems that will perform some functions that humans do with ease: see, speak, listen, navigate, manipulate and control. That can hold enormous consequences for tasks like facial and speech recognition, navigation and planning, which are still in elementary stages and rely heavily on human programming.

Designers say the computing style can clear the way for robots that can safely walk and drive in the physical world, though a thinking or conscious computer, a staple of science fiction, is still far off on the digital horizon.

“We’re moving from engineering computing systems to something that has many of the characteristics of biological computing,” said Larry Smarr, an astrophysicist who directs the California Institute for Telecommunications and Information Technology, one of many research centers devoted to developing these new kinds of computer circuits.

Conventional computers are limited by what they have been programmed to do. Computer vision systems, for example, only “recognize” objects that can be identified by the statistics-oriented algorithms programmed into them. An algorithm is like a recipe, a set of step-by-step instructions to perform a calculation.

But last year, Google researchers were able to get a machine-learning algorithm, known as a neural network, to perform an identification task without supervision. The network scanned a database of 10 million images, and in doing so trained itself to recognize cats.

In June, the company said it had used those neural network techniques to develop a new search service to help customers find specific photos more accurately.

The new approach, used in both hardware and software, is being driven by the explosion of scientific knowledge about the brain. Kwabena Boahen, a computer scientist who leads Stanford’s Brains in Silicon research program, said that is also its limitation, as scientists are far from fully understanding how brains function.”

Ten thoughts for the future

The Economist: “CASSANDRA has decided to revisit her fellow forecasters Thomas Malnight and Tracey Keys to find out what their predictions are for 2014. Once again they have produced a collection of trends for the year ahead, in their “Global Trends Report”.
The possibilities of mind control seem alarming ( point 6) as do the  implications of growing income inequality (point 10). Cassandra also hopes that “unemployability” and “unemployerability”, as discussed in point 9, are contested next year (on both linguistic and social fronts).
Nevertheless, the forecasts make for intriguing reading and highlights appear below.
 1. From social everything to being smart socially
Social technologies are everywhere, but these vast repositories of digital “stuff” bury the exceptional among the unimportant. It’s time to get socially smart. Users are moving to niche networks to bring back the community feel and intelligence to social interactions. Businesses need to get smarter about extracting and delivering value from big data including challenging business models. For social networks, mobile is the great leveller. Competition for attention with other apps will intensify the battle to own key assets from identity to news sharing, demanding radical reinvention.
2. Information security: The genie is out of the bottle
Thought your information was safe? Think again. The information security genie is out of the bottle as cyber-surveillance and data mining by public and private organizations increases – and don’t forget criminal networks and whistleblowers. It will be increasingly hard to tell friend from foe in cyberspace as networks build artificial intelligence to decipher your emotions and smart cities track your every move. Big brother is here: Protecting identity, information and societies will be a priority for all.
3. Who needs shops anyway?
Retailers are facing a digitally driven perfect storm. Connectivity, rising consumer influence, time scarcity, mobile payments, and the internet of things, are changing where, when and how we shop – if smart machines have not already done the job. Add the sharing economy, driven by younger generations where experience and sustainable consumption are more important than ownership, and traditional retail models break down. The future of shops will be increasingly defined by experiential spaces offering personalized service, integrated online and offline value propositions, and pop-up stores to satisfy demands for immediacy and surprise.
4. Redistributing the industrial revolution
Complex, global value chains are being redistributed by new technologies, labour market shifts and connectivity. Small-scale manufacturing, including 3D and soon 4D printing, and shifting production economics are moving production closer to markets and enabling mass customization – not just by companies but by the tech-enabled maker movement which is going mainstream. Rising labour costs in developing markets, high unemployment in developed markets, global access to online talent and knowledge, plus advances in robotics mean reshoring of production to developed markets will increase. Mobility, flexibility and networks will define the future industrial landscape.
5. Hubonomics: The new face of globalization
As production and consumption become more distributed, hubs will characterize the next wave of “globalization.” They will specialize to support the needs of growing regional trade, emerging city states, on-line communities of choice, and the next generation of flexible workers and entrepreneurs. Underpinning these hubs will be global knowledge networks and new business and governance models based on hubonomics™, that leverage global assets and hub strengths to deliver local value.
6. Sci-Fi is here: Making the impossible possible
Cross-disciplinary approaches and visionary entrepreneurs are driving scientific breakthroughs that could change not just our lives and work but our bodies and intelligence. Labs worldwide are opening up the vast possibilities of mind control and artificial intelligence, shape-shifting materials and self-organizing nanobots, cyborgs and enhanced humans, space exploration, and high-speed, intelligent transportation. Expect great debate around the ethics, financing, and distribution of public and private benefits of these advances – and the challenge of translating breakthroughs into replicable benefits.
7. Growing pains: Transforming markets and generations
The BRICS are succumbing to Newton’s law of gravitation: Brazil’s lost it, India’s losing it, China’s paying the price for growth, Russia’s failing to make a superpower come-back, and South Africa’s economy is in disarray. In other developing markets currencies have tumbled, Arab Spring governments are still in turmoil and social unrest is increasing along with the number of failing states. But the BRICS & Beyond growth engine is far from dead. Rather it is experiencing growing pains which demand significant shifts in governance, financial systems, education and economic policies to catch up. The likely transformers will be younger generations who aspire to greater freedom and quality of life than their parents.
8. Panic versus denial: The resource gap grows, the global risks rise – but who is listening?
The complex nexus of food, water, energy and climate change presents huge global economic, environmental and societal challenges – heating up the battle to access new resources from the Arctic to fracking. Risks are growing, even as multilateral action stalls. It’s a crisis of morals, governance, and above all marketing and media, pitting crisis deniers against those who recognize the threats but are communicating panic versus reasoned solutions. Expect more debate and calls for responsible capitalism – those that are listening will be taking action at multiple levels in society and business.
9. Fighting unemployability and unemployerability
Companies are desperate for talented workers – yet unemployment rates remain high. Polarization towards higher and lower skill levels is squeezing mid-level jobs, even as employers complain that education systems are not preparing students for the jobs of the future. Fighting unemployability is driving new government-business partnerships worldwide, and will remain a critical issue given massive youth unemployment. Employers must also focus on organizational unemployerability – not being able to attract and retain desired talent – as new generations demand exciting and meaningful work where they can make an impact. If they can’t find it, they will quickly move on or swell the growing ranks of young entrepreneurs.
10. Surviving in a bipolar world: From expecting consistency to embracing ambiguity
Life is not fair, nor is it predictable.  Income inequality is growing. Intolerance and nationalism are rising but interdependence is the currency of a connected world. Pressure on leaders to deliver results today is intense but so too is the need for fundamental change to succeed in the long term. The contradictions of leadership and life are increasing faster than our ability to reconcile the often polarized perspectives and values each embodies. Increasingly, they are driving irrational acts of leadership (think the US debt ceiling), geopolitical, social and religious tensions, and individual acts of violence. Surviving in this world will demand stronger, responsible leadership comfortable with and capable of embracing ambiguity and uncertainty, as opposed to expecting consistency and predictability.”

Global Collective Intelligence in Technological Societies

Paper by Juan Carlos Piedra Calderón and Javier Rainer in the International Journal of Artificial Intelligence and Interactive Multimedia: “The big influence of Information and Communication Technologies (ICT), especially in area of construction of Technological Societies has generated big
social changes. That is visible in the way of relating to people in different environments. These changes have the possibility to expand the frontiers of knowledge through sharing and cooperation. That has meaning the inherently creation of a new form of Collaborative Knowledge. The potential of this Collaborative Knowledge has been given through ICT in combination with Artificial Intelligence processes, from where is obtained a Collective Knowledge. When this kind of knowledge is shared, it gives the place to the Global Collective Intelligence”.