Who Really Suffers When You Don't Share Your Ideas at Work Worried that someone at work might be stealing your good ideas? Relax. It doesn't happen as often as you think. A study in the current issue of the Academy of Management Journal discovered employees have nothing to gain from hiding their insights from co-workers, and just end up hurting themselves by doing so. The study's authors said employees should reconsider and be careful about hiding knowledge from their peers, because what goes around comes around. "More specifically, employees who intentionally hide more knowledge seem bound to receive such selfish behavior in return from their co-workers, which will ultimately hurt them and decrease their creativity," the researchers wrote in the study. One of the paper's authors, Matej Cerne of Ljubljana University in Slovenia, said certain workplaces encourage this behavior. "But, given the lack of emphasis on individual rewards in such settings, there is little incentive to hide knowledge," he said.
Coevolving Innovations | Systems Thinking and Futures Studies (Systems Thinking Ontario, 2013-02-21) The pre-reading of Emery (1967), “The Next Thirty Years: Concepts, Methods and Anticipations” was introduced as a challenging article for the second meeting of Systems Thinking Ontario on Feb. 21, 2013. The theme for the evening was “Systems Thinking and Future Studies”, so there was some irony in looking backwards to 1967 to have a discussion on looking forward. In my role as reviewer in Systems Thinking Ontario sessions, I would prefer to try to stick to the text rather than adding editorializing. However, since this Emery (1967) article is particular rich, I tried to provide some additional context to make the reading easier. Fred Emery is especially known for his work with the Tavistock Institute for Human Relations, as one of the pioneers of the field we know today as organization science, including organization development and organization design. In I. Essentially, the challenge is that human beings can shape their futures, and not just be passive participants in the changes. 1.
The Programmer Behind Heartbleed Speaks Out: It Was an Accident The Internet bug known as Heartbleed was introduced to the world on New Year's Eve in December 2011. Now, one of the people involved is sharing his side of the story. Programmer Robin Seggelmann says he wrote the code for the part of OpenSSL that led to Heartbleed. Seggelmann told the Sydney Morning Herald that the actual error was "trivial," but that its impact was clearly severe. Heartbleed is a vulnerability in the encryption that many sites use to ensure that your communications can't be intercepted. As the name suggests, OpenSSL is open-source, which makes it attractive to many services, big and small, as an easily implemented security tool. Although anyone can contribute to OpenSSL — either by contributing code or reviewing it to spot vulnerabilities like Heartbleed — few actually do. Although anyone can contribute to OpenSSL — either by contributing code or reviewing it to spot vulnerabilities like Heartbleed — few actually do. For now, most sites affected have patched the bug.
"Situational Futuring" and 44 Mind-Stretching Scenarios to Learn How to Use It Last week I got into a discussion with a friend about the concept of self-contained water. If you think in terms of picking up a bottle of water, only without the bottle, you get the picture. Rocks are self-contained, baseballs are self-contained, so why can’t we devise some way to make water self-contained? As an example, if water itself could be used to form a somewhat hardened skin around a small quantity of water, we could create 100% consumable water with zero waste. An industrial design team in London has come the closest with something called “Ooho,” a blob-like water container made out of an edible algae membrane. As we imagine our way through this design problem, many more questions come to light. Even a container made of water will get dirty, so how do we clean the dirt from the side of a solid water container? More importantly, what is the optimal size for a self-contained water container? Maybe we don’t actually eat or drink the container. Situational Futuring 2. 3. 4. 5. 6.
A Closer Look at Transformation: Collective Intelligence | Frank Diana's Blog Next up in this transformation series is the seventh enabler: Collective Intelligence. One of the key themes throughout this transformation series is the clear movement from an enterprise entity to an extended enterprise of stakeholders. This extended enterprise – or what I alternatively call value ecosystem – increases complexity and requires a new management approach to be effective. I use the term collective intelligence as an umbrella phrase that combines the critical need for both collaboration and analytic excellence. Collective intelligence allows us to harness the efforts, knowledge and brainpower of a community. Thanks to advances in technology, individuals, groups and computers can collectively act more intelligently than ever before. Value ecosystems complicate collaboration and exacerbate the diffusion of knowledge – I described the drivers of value ecosystems as part of this transformation series in an earlier Post. Extended Enterprise Value Ecosystems Forcing Functions: Mr.
Form, Patterns, Emptiness and Stuckness This post first appeared on my companion site Transformational Tools for Body Energy and Mind This one isn’t a pot-boiler. However it’s utterly profound and utterly a meta-pattern about life and next to every tool in TTEM. We live in a world of form – ie persistent patterns. However excess form is rigidity, stuckness. About twenty years ago I was interested in the work of the Santa Fe institute on complexity. this great idea of life existing at the edge of chaos (mine often feels that way (ha ha)). The criterion of whether you age or whether you grow is whether you become more fixed or more flowing. Your body will inevitably get more rigid – but even then that can be hugely minimised. Before we get too excited, enamoured of charts and commit the errors of derivative pricing (taking nice simple mathematical models and equating them to reality) we need to back up a bit. To take an example TTEM was stuck for many years by outdated website software. Anyway what does this all mean to you?
The Rise of the Sharing Economy- PapyrusEditor By Lonnie Shekhtman Governments have their work cut out for them in keeping pace with innovation, especially as mobile, social and cloud technologies allow for new business models that, in the eyes of regulators, threaten consumer safety and incumbent industries. The most poignant current-day example of the tug-of-war between government and technology entrepreneurs is the legal quagmire many “sharing,” or “collaborative consumption,” companies face in the cities they operate. The problem, at least for home- and car-sharing services, is multifaceted: they’re agitating dozens of stakeholders, operating in uncharted territories, and legally indefinable. And indefinable is hard to regulate. You can’t talk about legal issues surrounding ‘sharing’ without talking about the industry’s ‘800-pound gorilla’: home rental service Airbnb. “Government is usually the last one to pick up on innovations,” Turner said. Or is it?
What if Universities were like Wikipedia? – Managing Turbulence A recent session at Educause apparently invoked Wikipedia and spoke to universities as agile organizations. The speaker wasn’t really suggesting that Wikipedia should be the model for the university of the future, but the abstracted concept was a little intriguing. Of course, Peter Drucker foretold the knowledge economy built with knowledge workers long before some of us were born, and I suspect his agile brain had glimmers of the knowledge management implications of Wikipedia around the same time. And, understandably, most academics keep their distance and steer toward more critically-accepted and stringently peer-reviewed resources. But Wikipedia made me think about knowledge in different ways. Knowledge as co-generative: maybe this is crowdsourcing on steroids. Knowledge for the sake of itself may be a penultimate goal. So can the University be a place of realized potential?
May the Best Model Win WIKIMEDIA, W.REBELA little friendly competition never hurt anyone, right? But can a healthy dose of rivalry actually solve major medical conundrums and, ultimately, spur innovation? That’s the motivating idea behind a series of open-source, Big Data computational challenges hosted by Sage Bionetworks and DREAM (Dialogue for Reverse Engineering Assessments and Methods) and an ever-increasing number of other companies looking to crowdsource the brightest minds in statistics, machine learning, and computational biology to develop better predictive models of disease. Though teams are pitted against each other in individual competitions, organizers say the challenges promote the kind of collaboration necessary to solve massive biological quandaries. Though teams from computational big hitters like IBM were early leaders, the winners were a small group from Columbia University’s School of Engineering led by electrical engineer turned computational biologist Dimitris Anastassiou.
Cultural Creatives 1.0: The (R)evolution | Watch the Full Documentary Online Featuring many key figures from Europe and the U.S., this is the first documentary film to look with scientific thoroughness at the world of Cultural Creatives. It shows that a great mass of people think differently from the way propagated by the media and promoted by the establishment. By the end of the film it becomes evident that this huge mass, were it to become aware of its power, could change the world. Because Cultural Creatives are unstoppable and their number is continuously rising, the values they champion could soon become core values for human civilization generally. Cultural Creatives are emerging without anybody organizing their presence, without anyone seeking to create political power from their existence, and without any group having any interest in them. So they are all here, among and around us: 80 million Cultural Creatives in the United States and 120 million in Europe, all with a similar mindset — the citizens of a new world.
Chinese Search Giant Baidu Thinks AI Pioneer Andrew Ng Can Help It Challenge Google and Become a Global Power Punk bands from Blondie to the Ramones once played in Broadway Studios, an age-worn 95-year-old neoclassical building surrounded by strip clubs in San Francisco’s North Beach. But early on this bright June morning, a different sort of rock star arrives. A small crowd attending a tech startup conference swarms around a tall, soft-spoken man in a blue dress shirt and navy suit who politely poses for photos. Andrew Ng, newly appointed chief scientist at Baidu, China’s dominant search company, is here to talk about his plans to advance deep learning, a powerful new approach to artificial intelligence loosely modeled on the way the brain works. The avid reception helps explain why Baidu has made Ng, 38, the linchpin of an effort to transform itself into a global force. Andrew Ng hopes to lure AI talent to Baidu’s new Silicon Valley research lab. As they look beyond China, Baidu and other Chinese companies find themselves on a collision course with the established U.S. Cool Things
Collaborative Intelligence – Knowledge Visualization, IBM Manay Eyes, visual analytics, Katy Borner, Zann Gill Collaborative Intelligence in Ecosystem Forecasting Ecosystem forecasting is supported by information visualization, e.g. Visualization of Data, Indicators, and Thresholds Collaborative Problem-Solving — Process Visualization & Management Navigation and Search — User Interface & Knowledge Management Frameworks Geospatial Visualization — Spatio-Temporal Representations Visualization of Data, Indicators, and Thresholds Outstanding visualization is the key to understanding how components interact in a complex system. Tim Nyerges reviews the challenge of visualizing sustainability in his paper: “Linked Visualizations in Sustainability Modeling: An Approach Using Participatory GIS for Decision Support.” Three visualizations representing sustainability issues: 1. Example of visual conceptual models developed for indicator analysis. In the directed graph above, nodes represent: Imagery in a Knowledge Framework.