Coevolving Innovations

… in Business Organizations and Information Technologies

At U.C. Berkeley in the 1960s, Christopher Alexander, Horst Rittel and C. West Churchman could have had lunch together.  While disciplinary thinking might lead novices to focus only on each of pattern language, wicked problems and the systems approach, there are ties (as well as domain-specific distinctions) between the schools.

Circa 1968-1970: Christopher Alexander, Horst Rittel, West Churchman

Circa 1968-1970: Christopher Alexander, Horst Rittel, West Churchman

West Churchman joined Berkeley in 1957, and initiated master’s and doctoral programs in operations research at the School of Business Administration.   From 1964 to 1970, Churchman was associate director and research philosopher at UC Berkeley’s Space Sciences Laboratory, directing its social sciences program.  After his retirement in 1981, Churchman taught in the Peace and Conflict Studies program for 13 years.

Horst Rittel came to the Berkeley College of Environmental Design in 1963, the same year that dean William Wurster recruited Christopher Alexander.  In 1973, Rittel split his time between Berkeley and the architecture faculty at the University of Stuttgart, where he founded the Institut für Grundlagen der Planung.

Christopher Alexander became a cofounder of the Center for Environmental Structure at Berkeley in 1967, gradually moving outside of the university by 2000.

The tie between Churchman and Rittel are well-documented, in a 1967 article in Management Science.

Professor Horst Rittel of the University of California Architecture Department has suggested in a recent seminar that the term “wicked problem” refer to that class of social system problems which are ill-formulated, where the information is confusing, where there are many clients and decision makers with conflicting values, and where the ramifications in the whole system are thoroughly confusing. The adjective “wicked” is supposed the describe the mischievous and even evil quality of these problems, where proposed “solutions” often turn out to be worse than the symptoms. [p. B-141]

October 14th, 2017

Posted In: pattern language, systems

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Making my dissertation relevant to non-academics calls for a change in style.  An invitation to speak at the Open Data Häme workshop, following announcement of funding by the European Regional Development Fund, gave a venue to unveil some normative theory-building from my research potentially useful in the real world.

This talk was fewer slides, and more talk.  With 9 content slides to cover in about an hour, the agenda was:

  • 1. Why does open data mean open sourcing (with commercial potential)?
  • 2. When did open data begin? What’s the history?
  • 3. How do behaviours change with open innovation learning?

The slides had been posted on the Coevolving Commons in advance of the event.

The slides has now been matched up with the digital audio recording, for viewing as a web video.  Another voice in the mix is Minna Takala, as a Senior Advisor at Häme Regional Council.

The audio recording was exceptionally clear, and is downloadable (so boosted volume is probably unnecessary).

Audio
Digital audio
(1h03m08s)
[20170810_Hame_Ing mp3] (58MB)
[20170810_Hame_Ing 3db mp3] (volume boosted 3db, 58MB)
[20170810_Hame_Ing 6db mp3] (volume boosted 6db, 58MB)

Alternatively, downloadable video files may be better for people on the move.

Video H.264 MP4 WebM
Digital video
(1h03m08s)
[Hame_Ing_HD  m4v]
(HD 325Kbps 238MB)
[20170810_Hame_Ing_nHD m4v]
(nHD 109Kkps 97MB)
[20170810_Hame_Ing webm]
(HD 470Kbps 212MB)
[20170810_Hame_Ing nHD webm]
(nHD 177Kbps 80MB)

The first part of the talk places open data in the larger context and trend towards the behaviour of open sourcing, and open innovation. Open sourcing enables visibility into system internals, in contrast with private sourcing that makes internals opaque.  The rise of open sourcing became more noticeable with the advent of open source licensing in software, but can generalized outside of technology with an example of raising and catching salmon.

September 2nd, 2017

Posted In: innovation

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For the Quantitative Methodologies for Design Research (定量研究方法) course for Ph.D. students at Tongji University in spring 2017, Susu Nousala invited me to join the team of instructors in collaborative education in Shanghai.  Experts were brought in during the course to guide the graduate students.

My participation in the course over two days had three parts:  (a) preparing a lecture outline; (b) orienting the students; and (c) equipping the students with tools.

(A) Preparing a lecture outline

While I’m comfortable with the mathematics underlying statistical analysis, I have a lot of practical experience of working with business executives who aren’t.  Thus, my approach to working with data relies a lot on presentation graphics to defog the phenomena.  While the label of data science began to rise circa 2012, I’ve had the benefit of practical experience that predates that.

Today's APL

AGSS: A Graphical Statistical System (1994)

In my first professional assignment in IBM Canada in 1985, data science would have been called econometrics.  My work included forecasting country sales, based on price-performance indexes (from the mainframe, midrange and personal computer product divisions) and economic outlooks from Statistics Canada.  Two years before the Macintosh II would bring color to personal computing, I was an early adopter of GRAFSTAT: “An APL system for interactive scientific-engineering graphics and data analysis” developed at IBM Research.  This would eventually become an IBM program product by called AGSS (A Graphical Statistical System) by 1994.

Metaphor Computer Systems workstation

Metaphor Computer Systems workstation

In 1988, I had an assignment where data science would have been called marketing science.  I was sent to California to work in the IBM partnership with Metaphor Computer Systems. This was a Xerox PARC spin-off with a vision that predated the first web page on the World Wide Web by a few years.  These activities led me into the TIMS Marketing Science Conference in 1990, cofounding the Canadian Centre for Marketing Information Technologies (C2MIT) and contributing chapters to The Marketing Information Revolution published in 1994.

This journey led me to appreciate the selection and use of computer-based tools for quantitative analysis.  Today, the two leading platforms in “Data Science 101” are Python (a general purpose language with statistical libraries), and the R Project for Statistical Computing (a specialized package for data analysis and visualization).  Both are open source projects, and free to download and use on personal computers.  I tried both.  R is a higher level programming language more similar to the APL programming language that gets work done more quickly.  For statistical work, I recommend R over Python (although APL is a theoretically better implementation).

Intro to R Programming, Big Data University

Intro to R Programming, Big Data University, Feb. 22, 2017

Since I live in Toronto, I attended the February session of Data Science with R – Bootcamp in person, at Ryerson University.  There, I was watched Polong Lin leading a class through R using the Jupyter notebook, both in (i) an interactive version, and (ii) a printable version.  Students had the choice to either follow Polong (i) actively, in a step-by-step execution in the Cognitive Class Virtual Lab (formerly called the Data Scientist Workbench) with a cloud-based R session through their web browsers, or (ii) passively, reading the static printable content.

August 26th, 2017

Posted In: universities

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The theme of “New Developments of Systems Thinking: From IoT to AI” at the Tenth International Symposium on Service Systems Science presented an opportunity to look at changes currently happening with contemporary technologies.  For a short talk, my agenda focused on three assertions:

  • 1. Open innovation learning, through open sourcing while private sourcing, has grown from 2001 to become mainstream
  • 2. Significant Internet of Things, cloud platforms and cognitive computing initiatives involve commercial and noncommercial contributors
  • 3. Creators, makers and remixers should consciously choose and declare conditions for derivative works

The relevance of the research for my dissertation (currently in review at Aalto University) became a frame for examining IoT, cloud and cognitive.  With both commercial and noncommercial contributors working alongside each other, content creators and makers should think ahead to conditions they wish to place on others who may derive from their works.  The previously posted slides on the Coevolving Commons have been synchronized with the digital audio recording.

The lecture and subsequent questions-and-answers are available online as web video.

For those who just want to listen, downloadable audio files (some with digitally boosted volume) are an option.

Audio

June 26th, 2017

Posted In: services, systems, technologies

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For the “Understanding Systems & Systemic Design” course in the program for the Master of Design in Strategic Foresight and Innovation at OCAD University, the lecture slides were the same for both the full-time cohort on March 8 and part-time cohort on March 9, while the oral presentation varied.  The target, in about 90 minutes, was to cover at least 4 of 5 sections, from:

  • 1. Architecting ↔ designing
  • 2. Service systems ← production systems
  • 3. Affordances ↔ pattern language
  • 4. Ecological anthropology ← teleology
  • 5. Inquiring systems ↔ methods

The students were alerted that some of the arrows in the section headings were double-headed, and some were single-headed — with specific meanings.  For each day, the classroom audio was recorded.  That digital audio has now been synchronized with slides that had previously been posted on the Coevolving Commons.

This session was #8 of 15 lectures for the OCADU SFI students.  They had already done some basic reading on systems approaches.  Since they were working towards a Major Research Project (a lighter weight form of a thesis) for their Master of Design degree, my overall agenda for this lecture was to have them reflect on acts of representation.   Systems have already been represented to them in a variety of forms:  textually, orally and visually.  For their Major Research Projects, they would be creating detailed representations, as ways of having their audience appreciate the in-depth study of the world and issues selected for the term.

June 7th, 2017

Posted In: design, pattern language, services, systems

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Teaching methods in a master’s class is different from lecturing on theory.  There’s more emphasis on how, with why subsequently provided as the need for that arises.  Since I had given a dense 20-minute theoretical talk in the month earlier, the invitation from Satu Teerikangas to the program in International Service Business Management was an opportunity to stretch out at a more leisurely pace with students, as they’re preparing for thesis work.

The 3 hours class was conducted in parts:

  • (A) Introductory lecturing for 85 minutes on …
    • 1. Architecting versus designing
    • 2. Alexandrian example → services
  • (B) Faciliated learning, for 55 minutes, with an …
    • 3. Exercise:  trying out pattern language
  • (C) Contextual lecturing for 23 minutes, on …
    • 4. Systems thinking + service systems
    • 5. Ignorance and errors

The classroom interaction was recorded in audio, and is complemented by slides that had been posted on the Coevolving Commons.

coevolving.com/commons/20161202-service-systems-thinking-generative-pattern-language

For people who prefer the real-time experience of being in a classroom, video and audio are provided, below.

January 13th, 2017

Posted In: pattern language, services, systems

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