Over the past 50 years, the Pentagon’s Defense Advanced Research Projects Agency (DARPA) has produced an unparalleled number of breakthroughs. Arguably, it has the longest-standing, most consistent track record of radical invention in history.

Its innovations include the internet; RISC computing; global positioning satellites; stealth technology; unmanned aerial vehicles, or “drones”; and micro-electro-mechanical systems (MEMS), which are now used in everything from air bags to ink-jet printers to video games like the Wii.

Though the U.S. military was the original customer for DARPA’s applications, the agency’s advances have played a central role in creating a host of multibillion-dollar industries.

The DARPA model has three elements:

Ambitious goals.

The agency’s projects are designed to harness science and engineering advances to solve real-world problems or create new opportunities. At Defense, GPS was an example of the former and stealth technology of the latter. The problems must be sufficiently challenging that they cannot be solved without pushing or catalyzing the science. The presence of an urgent need for an application creates focus and inspires greater genius.
 

Temporary project teams.

DARPA brings together world-class experts from industry and academia to work on projects of relatively short duration. Team members are organized and led by fixed-term technical managers, who themselves are accomplished in their fields and possess exceptional leadership skills. These projects are not open-ended research programs. Their intensity, sharp focus, and finite time frame make them attractive to the highest-caliber talent, and the nature of the challenge inspires unusual levels of collaboration. In other words, the projects get great people to tackle great problems with other great people.
 

Independence.

By charter, DARPA has autonomy in selecting and running projects. Such independence allows the organization to move fast and take bold risks and helps it persuade the best and brightest to join.

The work in Pasteur’s Quadrant doesn’t exist on road maps. It results in discoveries that upset the current trajectory and can destroy an existing business. Expecting the research organization executing against the road map to simultaneously deliver breakthrough innovations that challenge the road map is unrealistic. Instead, companies should create a small, dedicated independent organization to work in Pasteur’s Quadrant. They should take to heart the lesson that the U.S. government learned from the launch of Sputnik: The best way to prevent surprise is to create it. And if you don’t create the surprise, someone else will.

It is a pretty lengthy article filled with a lot of good info..   

Read the rest of the article here

 

 

"THE CLOTHESLINE PARADOX"

TIM O'REILLY is the founder and CEO of O'Reilly Media, Inc., a leading computer book publisher. O'Reilly Media also hosts conferences on technology topics, including the O'Reilly Open Source Convention, the Strata series of conferences on big data, and Tools of Change for Publishing. O'Reilly Media's Maker Media unit publishes Make Magazine and operates Maker Faire, the world's largest gathering of DIY hardware enthusiasts and entrepreneurs. O'Reilly AlphaTech Ventures is a leading early-stage venture capital firm.

I've been thinking a lot lately about a piece I read in Stuart Brand's, CoEvolution Quarterly back in 1975. It's called the "Clothesline Paradox." The author, Steve Baer, was talking about alternative energy.


The thesis is simple: You put your clothes in the dryer, and the energy you use gets measured and counted. You hang your clothes on the clothesline, and it "disappears" from the economy. It struck me that there are a lot of things that we're dealing with on the Internet that are subject to the Clothesline Paradox. Value is created, but it's not measured and counted. It's captured somewhere else in the economy.
 

I started thinking about this first in the area of open-source software, or for that matter, the Web. You think about how much value Tim Berners-Lee created and how he didn't actually capture very much of it. It was captured by companies like Google, Apple, Twitter, and Facebook. You also think about the other extreme, where companies like Goldman Sachs managed to extract a great deal of value from the economy, but as the 2008 financial crisis demonstrated, they did so while actually destroying value for the overall economy. So that got me thinking about how value creation and value capture are not the same thing. Our economics tends to measure value capture. If we're going to get 21st century economic policy right, or even just correctly model what's working and why, we have to start moving to a model that measures value creation rather than value capture.

It is thought-provoking. Read the rest of the article here
 

 

My recent webinar about  5 unique Things about Large-Scale Scrum 

Check out this  youtube link

 

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Misconception: There Is No Possible Formal Theory to Evaluate Development Process; It Is All Opinion or ‘Religion’

(Excerpts from Scaling Lean and Agile  By Larman and Vodde) 
 

This is a misconception at the root of many conflicts regarding approaches to development processes and improvement. From this view one will logically claim that lean or agile principles (or indeed, any process) are only opinion-based or someone’s definition of “best practices.” However, there are formal models that can be applied to understand and evaluate a process or work system with people:

•  queueing theory

•  control theory

•  information theory

•  game theory
   

  These developed in the late 1800s through mid-1900s in physics, economics, and communications to understand and improve the behavior of systems—with variability, nonlinearity, information and request flows, autonomous actors, and other complex or chaotic behaviors. Knowledge work, such as the non-repetitive discovery work of product development, is variable and involves information flows and people making decisions. It may be modeled, understood, and improved with insights from these theories. They provide a set of mathematically grounded models to understand if a particular system is likely to improve or degrade long-term value throughput.
   

• Queueing theory—Deals with systems with variability, workers, and queues with requests. Useful to evaluate product development options in work package size, cycle time, and worker utilization

 

• Control theory—Deals with dynamic systems with feedback (cybernetics) and their control. Useful to evaluate the impact of open-loop versus double-loop feedback management strategies, such as defining the requirements at the start of development and controlling toward that goal, versus other options.

 

• Information theory—This was originally developed in the context of communication systems, but the insights are more broadly applicable to general data analysis and information feedback.

 

• Game theory—This deals with the decisions people make in the context of cooperation and competition with others; organized into cooperative and noncooperative games. Cockburn has framed product development as a cooperative game

 

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