Archive for March 2012
Complexity Engineering encompasses a set of approaches to engineering systems which are typically composed of various interacting entities often exhibiting self-behaviours and emergence. The engineer or designer uses methods that benetit from the findings of complexity science and often considerably differ from the classical engineering approach of “divide and conquer”. This article provides an overview on some very interdisciplinary and innovative research areas and projects in the field of Complexity Engineering, including synthetic biology, chemistry, articial life, self-healing materials and others. It then classies the presented work according to five types of nature-inspired technology, namely: (1) using technology to understand nature, (2) nature-inspiration for technology, (3) using technology on natural systems, (4) using biotechnology methods in software engineering, and (5) using technology to model nature. Finally, future trends in Complexity Engineering are indicated and related risks are discussed.
Stephen Downes – What this talk is about – it’s called “Education as Platform” – is the idea of exploring some of the experiences we’ve had with massive open online learning, and exploring some of the criticisms that we’ve experienced, some of the criticisms that we’ve seen, and trying to understand what elements of the design are working and what elements of the design are not working, and to use this understanding to try to advance our perspective on the way online learning is proceeding and should proceed.
Now, a couple of caveats, and they’re not in the slides, but I do want to bring these out. One of the caveats is the idea of education as solving mobility problems, social problems, employment problems, poverty problems, and I think it works the other way around. I don’t see education as being the means to solve these problems. I don’t think it’s an automatic thing. I know it’s a really good selling point for education generally and online learning in particular, but I don’t think that the root of social problems lies in a lack of education, and I don’t think that the solution will be there.
This literature review is one of four produced under The NFER Research Programme, as part of the From Education to Employment theme. Collectively, they identify strategies for assisting young people at risk of becoming not in education, employment or training (NEET) to make effective post-16 transitions into learning or employment. The reviews build upon recent NFER research identifying three discrete sub-categories of NEET young people:
- Open to learning’ NEETs – most likely to re-engage in education.
- Sustained’ NEETs – characterised by a negative experience of school, high levels of truancy and exclusion, and low academic attainment.
- Undecided’ NEETs – similar to ‘open to learning’ NEETs but dissatisfied
with available opportunities.
This first review explores successful approaches to re-engaging young people with education and training at a general level, as well as at the level of these different NEET sub-categories. It identifies the importance of a coordinated approach of national and local policies and highlights practice-level methods for both preventing young people from becoming NEET, and reintegrating those that are NEET into work, further education or training.
First, we don’t want to operate in a hierarchy, where decisions have to make their way up to the top and then back down. We’re a lattice or a network, not a hierarchy, and associates can go directly to anyone in the organization to get what they need to be successful.
Second, we try to resist titles. We have a lot of people in responsible positions in the organization, but the whole notion of a title puts you in a box, and worse, it puts you in a position where you can assume you have authority to command others in the organization. So we resist this.
Third, our associates, who are all owners in the company, self-commit to what they want to work on. We believe that rather than having a boss or leader tell people what to do, it’s more powerful to have each person decide what they want to work on and where they can make the greatest contribution. But once you’ve made your commitment as an associate, there’s an expectation that you’ll deliver. So there are two sides to the coin: freedom to decide and a commitment to deliver on your promises.
And fourth: Our leaders have positions of authority because they have followers. Rather than relying on a top-down appointment process, where you often get promoted because you have seniority, or are the best friend of a senior executive, we allow the voice of the organization to determine who’s really qualified to be a leader, based on the willingness of others to follow.
Gore is a strikingly contradictory company: a place where nerds can be mavericks; a place that’s impatient with the standard way of working, but more than patient with nurturing ideas and giving them time to flourish; a place that’s humble in its origins, yet ravenous for breakthrough ideas and, ultimately, growth. Gore’s uniqueness comes from being as innovative in its operating principles as it is in its diverse product lines. And in its quietly revolutionary way, it is doing something almost magical: fostering ongoing, consistent, breakthrough creativity.
Bill Gore threw out the rules. He created a place with hardly any hierarchy and few ranks and titles. He insisted on direct, one-on-one communication; anyone in the company could speak to anyone else. In essence, he organized the company as though it were a bunch of small task forces. To promote this idea, he limited the size of teams — keeping even the manufacturing facilities to 150 to 200 people at most. That’s small enough so that people can get to know one another and what everyone is working on, and who has the skills and knowledge they might tap to get something accomplished — whether it’s creating an innovative product or handling the everyday challenges of running a business.
Read also: The Un-CEO
In the early 1980s chilean biologists Humberto Maturana and Francisco Varela spawned a revolution in the way scientists think about life. They felt that there were fundamental problems in our current understanding of life, and proposed the new concept of autopoiesis, coining a word from the Greek to mean “self-producing”. They defined an autopoietic system as a network of processes that produces its own components in a feedback loop, and is distinct from its environment. Let’s unpack this to see how it works.
With this new understanding of the nature of life, we can view the global networks with a fresh eye. The world of information and ideas in fact precisely matches the definition of autopoiesis: it produces its own components through a network of processes involving feedback loops. Information and ideas are generated in the minds of people and the circuits of computers. They do not come from nowhere—they are created from the raw material of other information and ideas that have been read, heard, or received as input.
Read also: Living Networks
Resilience has, in the past four decades, been a term increasingly employed throughout a number of sciences: psychology and ecology, most prominently. Increasingly one ﬁnds it in political science, business administration, sociology, history, disaster planning, urban planning, and international development. The shared use of the term does not, however, imply uniﬁed concepts of resilience nor the theories in which it is embedded. Diﬀerent uses generate diﬀerent methods, sometimes diﬀerent methodologies. Evidential or other empirical support can diﬀer between domains of application, even when concepts are broadly shared.
Towards this end, the review centers on three resilience frameworks, of increasing complexity: Engineering Resilience (or “Common Sense” resilience); Systems Resilience, called Robustness in economics; and Resilience in Complex Adaptive Systems. As one might expect, with simplicity comes ease of measurement and management; with complexity comes accuracy. Nevertheless, even simple approaches can generate novel insight, and complex approaches can translate into concrete action.
Systems thinking as a modern approach for problem solving was revived after WWII even though it had been an ancient philosophy. We can track systems thinking back to antiquity. Differentiated from Western rationalist traditions of philosophy, C. West Churchman often identified with the I Ching as a systems approach sharing a frame of reference similar to pre-Socratic philosophy and Heraclitus. In the paper we will compare the evolutionary system of consciousness, which was presented in Tun calendar at Maya Indians and contemporary systems theory and systems thinking, which is nothing else but highly evolved human consciousness in society. The Mayan numerical system and long count units has been proven as one of the most accurate systems for describing the present and future of the civilization in which we have all evolved. We will present Mayan nine-level pyramids system that represents the evolutionary system – the consciousness, which nowadays shows the actual level of human consciousness. Deriving from all described we will show the main systems principles, discussed by contemporary systems authors and Mayan systems principles, which differs only in one expression – they named a “big picture” as “the divine plan”. The final results can be perfectly implied to the society we live in. Seeing the world from the big picture point of view is reaching a level of awareness, where linear thinking is replaced by system thinking. Maya explained that the civilization will achieve the system of conscious cocreation. We can claim that linear thinking guides us to a limited consciousness, whereas systems thinking opens the possibilities of conscious co-creation for the benefits of sustainable society and future of the planet.
Decentralized problem solving works better on some problems than others. According to an article from SEO Theory, swarms work in situations that involve discovery, testing, and comparing results. Swarm Theory works less effectively for creative processes like innovation, except perhaps as a broad directional pointer. A Swarm cannot paint the next Mona Lisa. Decentralized problem solving also has preconditions. Individual agents must act on a very simple heuristic, and all agents must be capable of making the same very simple judgments. In decentralized problem-solving, there must be some mechanism by which the group communicates and evaluates information. Swarm theory has drawbacks, even when it does provideefficient consensus. First, consensus is by no means perfect. Evolutionarily speaking, it is quite possible that humans are supposed to refine their individual sensibilities and expertise, so as to provide higher-quality intelligence to the group. Also, there must be a motivation to contribute. As systems become more complex, certain subgroups will be more motivated than others, which will increase the weight their input and introduce bias. Read