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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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The #changing nature of work | Harold Jarche | #sharing

The #changing nature of work | Harold Jarche | #sharing | e-Xploration | Scoop.it

Via juandoming
luiy's insight:

First of all, it is becoming obvious that the fundamental nature of work is changing as we transition into a post-job economy. The major driver of this change is the automation of procedural work, especially through software, but increasingly with robots. The drivers behind the post-job economy are also changing our work structures. Organizations will need to become more networked, not just with information technology, but how knowledge workers create, use, and share knowledge. This new workplace also will require different leadership that emerges from the network and temporarily assumes control, until new leadership is required. Giving up control will be a major challenge for anyone used to the old ways of work. An important part of leadership will be to ensure that knowledge is shared. But moving to a knowledge-sharing organizational structure will be difficult, because of the knowledge sharing paradox; which is that the more control is exerted, the less knowledge is shared. All of these challenges need to be addressed, and rather quickly, as software continues to eat jobs, and income disparities get wider.

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Collaboration is the New Competition

Five ways to drive large-scale social change by working cooperatively.

Via ddrrnt, Complexity Digest
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ddrrnt's curator insight, January 12, 2013 2:19 AM

Leaders and organizations are acknowledging that even their best individual efforts can't stack up against today's complex and interconnected problems. They are putting aside self-interests and collaborating to build a new civic infrastructure to advance their shared objectives. It's called collective impact and it's a growing trend across the country. (...)

While collaboration is certainly not a foreign concept, what we're seeing around the country is the coming together of non-traditional partners, and a willingness to embrace new ways of working together. And, this movement is yielding promising results.

... five lessons for driving large-scale social change through collaboration:


  1. Clearly define what you can do together: As Dana O'Donovan of the Monitor Institute has noted, many organizations find collaboration to be messy and time consuming. From the very beginning, you must develop clarity of purpose and articulate, "What can we do together that we could not do alone?" (...)
  2. Transcend parochialism: Even the most well intended collaboration is often crippled by parochialism. Individual organizations earmark their participation and resources for activities that perfectly align with their own work or they use the collaboration platform as a way to get other participants to fund their own priorities. (...)
  3. Adapt to data: The complex, multidisciplinary problems that many collaborative projects tackle do not have easy fixes. These challenges require continuous learning and innovation and the use of real-time data to help participants understand what is and isn't working. Adjustments must be made on the fly. (...)
  4. Feed the field: You have an obligation to share what you learn — both the results and the methods for achieving them. Living Cities has long understood the value that our member institutions get by learning and working together. (...)
  5. Support the backbone: In our experience, progress is best achieved when a "backbone organization," keeps the group's work moving forward. Staff at these organizations ensure that work is completed between meetings, track data, enable adaptation, disseminate knowledge, and build buy-in and ownership from all participants.(...)

Ben Hecht

Ben Hecht is President & CEO of Living Cities, an organization that harnesses the collective knowledge of its 22 member foundations and financial institutions to benefit low income people and the cities where they live.



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Asset Management Tools for #Change: Social Network Analysis | #SNA #KM

Asset Management Tools for #Change: Social Network Analysis | #SNA #KM | e-Xploration | Scoop.it
Asset Management Tools for Change: Social Network Analysis
luiy's insight:

SOCIAL NETWORK ANALYSIS (SNA)


SNA is a methodology for determining and analyzing relationships between people in order to show how information flows and decisions are made, ultimately investigating how work gets done. This enables managers and teams to understand:

 

Who the prominent players are and whom others depend on to solve problems and provide technical information. Who do people turn to for advice? The actual nature of the communication network in reality, demonstrating how communications actually occur regarding work related issues and who is central to these communications. This illustrates both informal collaborative relationships and holes within the structures. Whether subgroups emerged that are disconnected or partially connected to the core. Which individuals are isolated and limited in their roles or, conversely, who faces a situation of overload.  

SNA is a means to analyze the informal organization beyond the organizational chart. The analysis allows managers and teams to visualize and understand the myriad of relationships that can either facilitate or impede information flow, decision processes and knowledge creation. Thus, mapping opportunities and constraints in invoking change within the organization.

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Swarm intelligence - Wikipedia

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1]

SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems.

Swarm intelligence (SI) is the collective behaviour of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1]
SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.
The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems.


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