This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
What’s NBIC(S) 2.0?– Return to the Planet of the NBIC(S) 2.0 – “Nib-Iks-Two-Oh”– Why NIBC(S)-2? Why add social?– A Few Slides from NBIC-1 (The lens of “work” – “Z-Theory”)– What has changed in 10 years?
• Social Media & Business (Collaborate)• 3D Printing (Even More Real Now)• Google Glasses (WorldBoard)• Drones (Telerobotics)
What’s next?– Energy 2.0: 30 stories building – robotically built in 10 days, recycled in 10 days– Manufacturing 2.0: Cars built as part of a local recycling service, self driving– Agriculture 2.0: Urban farming and local food – the pendulum swings back– Education 2.0: Universities=living-labs/smarter cities (“knowledge burden”); EdX & TEDx– Life 2.0: CAD for bacteria; Search for life & intelligence out there succeeds– Smart 2.0: SYNAPSE & Watson, SIRI, Wolfram Alpha, etc.
What’s important?– Whole Service Systems (individuals, family, cities, etc.)– Measures: Innovativeness, Equity, Sustainability, Resilience– Policymaking: Balance WTA & IWL policies (global grand challenges, X-Prizes)– Education: T-shaped people
50 Years: Information technology connecting islands of information (created by people) into larger networks
Converging Technologies for Improving Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Scienceby Mihail C. Roco (Editor), William Sims Bainbridge (Editor)
0.00E+00
5.00E+16
1.00E+17
1.50E+17
2.00E+17
2.50E+17
3.00E+17
1989 1991 1993 1995 1997 1999
transistors
About 10 billion transistors made per second in 2004, doubling each 18 monthsWorldwide Production of Transistors on all ICs (Source: NSF)
Growth rates for:
Nano: Transistors made per second
Bio: Gene sequenced per second, Cell divisions observed per second,fMRI regions scanned per second
Info: Bytes storage made per second
Cogno: Emails per second, IM per second Google searchers per second
Towards facilitated coevolution of capabilities… (an hypothesis)
Collaborate(incentives)
Augment(tool)
Automate(self-service)
Delegate(outsource)
Tool SystemHuman System
Service provider helpsthe client by doing some
of it for them(in a custom way)
Service provider helpsthe client by doing all
of it for them(in a standard way)
The choice tochange work practicesrequires answeringfour key questions:
- Should we? (Business Value)- Can we? (Technology)- May we? (Governance)- Will we? (Work Priorities)
Incent People(Social systems with intentional agents)
Harness Nature(Technology systems with stochastic parts)
43
21
Z
Collaborate(1970)
Augment(1980)
Delegate(2000)
Automate(2010)
Experts: High skill people on phones Tools: Less skill with FAQ tools Market: Lower cost geography (India) Technology: Voice response system
Example: Call Centers
Bootstrapping: Douglas Engelbart, Coevolution, and the Origins of Personal Computingby Thierry Bardini “Increasing our collective capabilities to address complex, urgent problems by improving improvement”
FOXP2 and the Evolution of Language, by Alec MacAndrewhttp://www.evolutionpages.com/FOXP2_language.htm
…Detective story from a family with slurred speech to genes that influence brain development and enable speech (Speech pathology, linguistics, genetics, embryogenesis, neurophysiology, anthropology, primate evolution, etc.)
“With enough eyeballs, all bugs are shallow”
“With a large enough smart mob, all inferences are shallow”
Relationship oriented computing tools Amazon – Recommendation system
E-Bay – Reputation system
Google – Relevancy ranking
The Symbolic Species: The Co-Evolution of Language and the Brainby Terrence W. Deacon
First transatlantic telesurgery – September 2001Roundtrip 14,000 km, time lag 200 milliseconds
Doctor: United States Patient: France
Flesh and Machines: How Robots Will Change Usby Rodney Brooks “The brains of people in poorer countries will be hired to control the physical-labor robots, the remote-presence robots, in richer countries. The good thing about this is that the persons in that poorer country will not be doing the dirty, tiring work themselves. It will be relatively high-paying and desirable to work for many places where the economy is poor. Furthermore, it will provide work in those places with poor economies where no other work is available.” (146-147)
“This Parker Hannifin emissions filter, a crankcase vapor coalescer, is made out of PPSF (polyphenylsulfone), a rapid prototyping material from Stratasys. Parker Hannifin bolted this filter onto a 6.0-liter V8 diesel engine block, and then let the engine run for about 80 hours to test filter-medium efficiency. The prototype filter did just fine. It collected blow-by gases containing 160°F oil, fuel, soot, and other combustion by-products. It didn’t leak. And except for some staining, the filter didn’t appear to have degraded at all.” By Lawrence S. Gould
Rapid Manufacturing: The Technologies and Applications of Rapid Prototyping and Rapid Toolingby S. S. Dimov, Duc Truon Pham
BUILDING BONES. A rat's skull regenerates better with a new bone-promoting scaffold (left) than with a less-sophisticated scaffold (right).F.E. Weber/University Hospital Zurich
Services in an economy drive up human capability growth
BusinessServices
Public Administration
ExtractiveSector
ManufacturingSector
InfrastructureServices
TradeServices
Social/personalServices
Consumer
Developing nations that invest in government services, health and educationservices, financial and business services, transportation services, utility services,communication services, and wholesale and retail services (growth of their service economy) create large populations of service labor – removing “un-freedoms,” doing valuable work for others. (see Amartya Sen, Development as Freedom)
Financial & informationProfessional & business
Government
Education & healthcareLeisure & hospitality
Wholesale & retailTransportation & warehousingUtilities & communication
Example: medical, legal,and IT work in India
Development as Freedomby Amartya Sen
1998 Nobel PrizeWinner Economics
Source: Dorothy I. Riddle (1986) Service-Led Growth. Praeger, NY
Production is measure of results or “goals achieved”Production per capita (Y) as a function of output per worker (L) and capital assets per worker
(K) and investment per worker (I)
Investment drives technology progress and improves the efficiency of labor; accumulates over time as capital assets
Today: Six billion people (L) with the capital assets created by one hundred billion people throughout history (K) and innovation investments (I) to increase efficiency of L, K, and I
Innovation impact will be realized in terms of…More workers (L): Healthy – healthcare services
More capital assets (K): Wealthy – financial services, retail services, transportation services
More investment (I): Wise – education services, information services, financial services
What you may not know… IBM led in the creation of Computer Science departments at universities
Now IBM is working toEstablish Service Science
The biggest costs were in changing the organization. One way to think about these changes is to treat the
Organizational costs as an investment in a new asset. Firms make investments over time in developing a new
process, rebuilding their staff or designing a neworganizational structure, and the benefits from theseInvestments are realized over a long period of time.”Eric Brynjolfsson, “Beyond the Productivity Paradox”
From old paradigm business-technology-work coevolution to a new paradigm of facilitated coevolution of capabilities to address complex, urgent problems (maybe – if we can get collectively smart enough, fast enough; problems or challenges are coevolving with capabilities)
Work evolutionImproved collaboration (communications & coordination)
Towards facilitated coevolution of capabilities… (an hypothesis)
Collaborate(incentives)
Augment(tool)
Automate(self-service)
Delegate(outsource)
Tool SystemHuman System
Service provider helpsthe client by doing some
of it for them(in a custom way)
Service provider helpsthe client by doing all
of it for them(in a standard way)
The choice tochange work practicesrequires answeringfour key questions:
- Should we? (Business Value)- Can we? (Technology)- May we? (Governance)- Will we? (Work Priorities)
Incent People(Social systems with intentional agents)
Harness Nature(Technology systems with stochastic parts)
43
21
Z
Collaborate(1970)
Augment(1980)
Delegate(2000)
Automate(2010)
Experts: High skill people on phones Tools: Less skill with FAQ tools Market: Lower cost geography (India) Technology: Voice response system
Example: Call Centers
Bootstrapping: Douglas Engelbart, Coevolution, and the Origins of Personal Computingby Thierry Bardini “Increasing our collective capabilities to address complex, urgent problems by improving improvement”
FOXP2 and the Evolution of Language, by Alec MacAndrewhttp://www.evolutionpages.com/FOXP2_language.htm
…Detective story from a family with slurred speech to genes that influence brain development and enable speech (Speech pathology, linguistics, genetics, embryogenesis, neurophysiology, anthropology, primate evolution, etc.)
“With enough eyeballs, all bugs are shallow”
“With a large enough smart mob, all inferences are shallow”
Relationship oriented computing tools Amazon – Recommendation system
E-Bay – Reputation system
Google – Relevancy ranking
The Symbolic Species: The Co-Evolution of Language and the Brainby Terrence W. Deacon
First transatlantic telesurgery – September 2001Roundtrip 14,000 km, time lag 200 milliseconds
Doctor: United States Patient: France
Flesh and Machines: How Robots Will Change Usby Rodney Brooks “The brains of people in poorer countries will be hired to control the physical-labor robots, the remote-presence robots, in richer countries. The good thing about this is that the persons in that poorer country will not be doing the dirty, tiring work themselves. It will be relatively high-paying and desirable to work for many places where the economy is poor. Furthermore, it will provide work in those places with poor economies where no other work is available.” (146-147)
“This Parker Hannifin emissions filter, a crankcase vapor coalescer, is made out of PPSF (polyphenylsulfone), a rapid prototyping material from Stratasys. Parker Hannifin bolted this filter onto a 6.0-liter V8 diesel engine block, and then let the engine run for about 80 hours to test filter-medium efficiency. The prototype filter did just fine. It collected blow-by gases containing 160°F oil, fuel, soot, and other combustion by-products. It didn’t leak. And except for some staining, the filter didn’t appear to have degraded at all.” By Lawrence S. Gould
Rapid Manufacturing: The Technologies and Applications of Rapid Prototyping and Rapid Toolingby S. S. Dimov, Duc Truon Pham
BUILDING BONES. A rat's skull regenerates better with a new bone-promoting scaffold (left) than with a less-sophisticated scaffold (right).F.E. Weber/University Hospital Zurich
Perhaps technology can help search for improvements in the reconfigurations space…Blue Gene, as its name suggests, is aimed at the drug-development market. Scientists hope eventually to model how proteins fold – a process that is important in designing drugs that can block cancer cells and other diseases.Computational organization theory and agent-based computational economicsare potential future directions.
Emergence of Service ScienceUnderstand service phenomena to better optimize across three levels impacted by rise of service economy
Economic goals at three levels1. Nations: Maximize GDP / Capita per year
Note nations have many other goals, including environment, health, education, defense, quality of life for citizens, high-skill, high-pay jobs, etc.
2. Businesses: Maximize Revenue / Employee and Profits / Employee per year
Note increasingly businesses are adding additional values, such as sustainable environment, work-life balance, etc.
3. Individuals: Maximize Income / Time
Note in a survey of US information technology workers, base-pay rated fourth in overall goals, behind challenge, stability, and flexibility of work experience.
Economic goals are achieved by four plans, productivity level is the key attribute1. Follow demand: Migrate labor to high productivity industries/offerings/jobs where demand exceeds supply
2. Create demand/value innovation: Invent new high productivity industries/offerings/jobs
3. Repair supply: Invest to transform low productivity industries/offerings/jobs(skills); including leap-frog productivity strategy
4. Protect supply: Invest to protect low productivity industries/offerings/jobs(skills) in an effort to buy time, and if lucky catch next wave
The study of value innovation & labor productivity are important to service scienceHistorically, what has determined the rise in demand for particular types of services? What types of innovation have led to a rise in the demand for
particular types of services? What types of innovation have led to a rise in labor productivity in particular industries?
Already empirical evidence indicates that effective IT-enabled productivity gains requires aligning technology, business/value, and organizational culture innovations. People can resist change or help accelerate change depending on the alignment of all three factors.
As economic goals are achieved, wealth increases, and increasingly other goals take priority, hence the value of economic transactions will not simply be measured in financial terms, side-effects matter. Economic transactions will need to maximize value add beyond financial metrics.
Service Science is the study of business methods to create and capture value, technology tools to re-engineer processes, and organizational culture practices to incent and align people, and their collective impact on effectiveness and efficiency in the performance of services work.
Recent studies of IT Productivity Paradox indicate that technology tools, business methods, and organizational culture must align to achieve return on investment for IT.
The services industry must be viewed as a collection of interacting systems, where the history of the systems (legacy) matters as much as new events in understanding what should, can, may, and will happen next.
Effectiveness means working on the right things that matter to the business and efficiency means doing the work according to best practices. Productivity depends on both effective and efficient performance.
Services are typically simultaneously produced (by the provider) and consumed (by the client). The provider and the client can each be individuals, organizations, or automated systems.
Is about accelerating innovation…By improving technology and organizations and work
Service science to accelerate the coevolution of business-technology-work innovations
Is not about assessing risks… Is not about betting on the future...
Things That Make Us Smart: Defending Human Attributes in the Age of the Machineby Donald A. Norman Examples: Watch, Writing; Metric: Symbols & Models
Survival of the Smartest: Managing Information for Rapid Action and World-Class Performanceby Haim Mendelson, Johannes ZieglerMetric: Awareness, Decisions, Communication, Focus, Infrastr.
Privacy violations (social issues) Unequal access (social issues) Censorship (social issues) Mischief and crime (social issues) Environmental damage (systemic issues) Glitches and out of control (systemic issues) Overload (cognitive, social, and systemic issues) Also alienation, narrowing, deceit, degradation,
intrusion, inequality, etc. (and many more issues associated with new technologies of all sorts throughout the history of humans which is also (incidentally) the history of technology & organizations)
The Future Does Not Compute: Transcending the Machines in Our MidstSee NetFuture (http://www.netfuture.org/) by Steve Talbott ([email protected])(also see Chapter 7of Andy Clark “Natural-Born Cyborgs” titled “Bad Borgs?”))
But if you want to bet, check out Longbets.org, one of a growing number of websites dedicated to betting on the future
Many bets such as Featured Bet on 20040208: Douglas C. Hewes predicts: "By 2025 at least 50% of all U.S. citizens residing within the United States will have some form of technology embedded in their bodies for the purpose of tracking and identification."read the argument »
Stuart Brand, author of “The Clock of the Long Now”Founder, Longbet.org
Capability evolution = things that make us smart (our organizations & tools)Growth of capabilities to create and achieve goals, intentionally and parsimoniously
Growth of win-win games over win-lose; higher payoffs; lower risks; lower maintenance (entropy)
Growth of capabilities to sense, communicate, decide, act; Growth of capabilities to bud and scale
Slowly: In the past 12 billion years (2 million years), evolution has been driving what has been making things (humans) smarter
Rapidly: In the past 200 years, organizations have been driving what has been making us smarter – business-technology-work coevolution
Citizen - 230 years ago it was government – rise of modern democracy (intangible - sustainable freedom)
Worker - 150 years ago it was business – rise of modern managerial firm (intangible - efficient value)
Consumer – 80 years ago buy more than make; Shareholder – 20 years ago; upside for growth of businesses
Very Rapidly: In the past 50 years, information technology has been driving what has been making us smarter – service economy dominates
Only in the last fifty years with the discovery of DNA (bio), creation of digital computing technology (info), ability to manipulate matter at the atomic scale (nano), and rapid advancement of cognitive science to better understand human thought processes (cogno) has information processing in natural, social, and technological substrates been perceived as “converging” – discoveries in one area leading to advances/applications in the others
Shadows in the Sun, by Wade Davis“Ethnosphere: It's really the sum total of all the thoughts, beliefs, myths, and institutions brought into being by the human imagination. It is humanity's greatest legacy, embodying everything we have produced as a curious and amazingly adaptive species.”
Rational drug development requires managing enormous complexity. Pharmaceutical companies are beginning to differentiate themselves on the power of their information technology platforms. IT Platform intellectual property is likely to be more valuable than content (gene sequences, metabolic pathways, protein structures, etc.)
DNA 40,000 genes (approx.100 million bases) represent less than 3% of the genome (approx. 3 billion bases). The function of the remaining 97% remains elusive.
alternative splicing turns 40,000 genes into 500,000 messages
post translational modification turns 500,000 messages into 1.5 million proteins
1.5 million proteins interacting in complex networks create hundreds of millions of metabolic pathways
hundreds of millions of pathways influenced by the environment and stochastic processes create 6 billion different individuals
Personalized Pharmaceuticals
RNA Protein Pathways Phenotype
Drugs treat phenotypes
Historically, 220 targets have generated $3trillion of value. Industrialized genome sequencing has created a target rich, lead poor environment that will slowly reverse over the next several years as in-silico biology drives the discovery of new lead compounds.
DNA to Phenotype = 300 terabytes per person x 6 billion persons = 1800 billion terabytes of data
Healthy
e.g., Modafinil enhances wakefulness and vigilance
50 Years: Information technology connecting islands of information (created by people) into larger networks
Converging Technologies for Improving Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Scienceby Mihail C. Roco (Editor), William Sims Bainbridge (Editor)
0.00E+00
5.00E+16
1.00E+17
1.50E+17
2.00E+17
2.50E+17
3.00E+17
1989 1991 1993 1995 1997 1999
transistors
About 10 billion transistors made per second in 2004, doubling each 18 monthsWorldwide Production of Transistors on all ICs (Source: NSF)
Growth rates for:
Nano: Transistors made per second
Bio: Gene sequenced per second, Cell divisions observed per second,fMRI regions scanned per second
Info: Bytes storage made per second
Cogno: Emails per second, IM per second Google searchers per second
The evolution of business towards “On Demand e-Business”
Technology and business innovations are coevolving.Rapid business productivity improvements are driven by technology innovations.
Rapid technology improvements are driven by business investments.Moore’s “law” is as much a law of business investment as of technological possibilities.
(see http://almaden.ibm.com/coevolution) – two systems ratchet each other up.Characteristics of an on-demand e-business.
Adaptive Enterprise: Creating and Leading Sense-And-Respond Organizationsby Stephan H. Haeckel, Adrian J. Slywotzky
Knowledge in our minds is soft capability Knowledge in our genes, body, brains is hard capability Knowledge in our organizations is relationship capability However, in human and social systems attitudes, incentives, and
games are an element of the cognitive capabilities of the system Given a goal: land and safely return humans on Mars, one can
estimate how many resources would be required to achieve this goal given the cognitive capabilities of the system.
How does one compare the complexity of achieving different goals? How does one compare sensing, communications, decision making,
and execution performance?
Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence
by Andy Clark “…human cognitive evolution seems to involve the distinct way human brains repeatedly create and exploit various species of cognitive technology.” (pg. 78)
The science: nano-bio-cogno-socio-techno convergence:It’s all about information – encoding, processing, replicating – in different systems (ultimately all grounded in matter patterns)
System Encoding Processing Replicating
Nano Matter(Nature)
Atoms & Molecules
Universe to Atoms
Galactic, Solar, Planet Systems
Bio Life (Nature)
DNA Cells to Ecosystems
Evolution
Cogno Thought (Nature/Human)
Brains Neural Nets Evolution - Culture
Socio Culture (Human)
People Organizations Evolution - Culture
Techno Technology (Human)
Artifacts & Bits Computers Design-Factories
Examples: FOXP2, Complex Adaptive Systems (CAS) – Variability, Interaction, Selection
Coevolutio
n
Rapidly increasing rates of advancement in each system area is creating cross pollination
IBM’s business is helping customers transform their businesses. Services is now 50% of IBM, with rapid growth from strategic outsourcing, help desk, business consulting.
IBM 101 – The New (Post 1995) IBM EcosystemRevenue: $80+ Billion/YearEmployees: 320,000+, about 50% inside-US, 50% outside-USIBM Global Services, approx. 170,000 people in 120 countries
ODIS 101: On-Demand Innovation Services (ODIS) sets the stage for the next generation researcher – one that is closely tuned with real-world client issues to drive and validate innovations, technological-organizational-business perspectives. Requires new academic collaborations beyond technological.
– Quality-of-Life (QoL)• Less waste in flows (energy, materials, etc.)• More development of capabilities• Better governance and decision-making
Service Science, Service Systems & Measurement– Service = applying knowledge to benefit others (value-cocreation)– Service science studies human-serving systems (service systems)– Service systems = configure individuals, infrastructure, institutions, information– QoL matters, university & urban innovations matter to business & society– Business Measures = Productivity, Quality, Compliance, Innovation– Societal Measures = Innovativeness, Equity, Sustainability, Resilience
IBM is making long term investments to develop talent for the growth markets
Collaboration with UniversitiesIBM works with 5,000 universities and 10,000 faculties around the globe. We have joint initiatives and investments with universities in Vietnam, Malaysia, India, Russia, Brazil, Bulgaria, Egypt, China and Africa to encourage the training of skills required.
Government Partnerships
By helping governments to establish new national research facilities, we are helping to create new industries, helping to develop long terms skills curriculums like SSME.
Global Placements & MentoringTransferring knowledge and expertise to the growth markets is critical. One of the ways we do this is to move experts into the market to coach and train local teams.
Daniel Patrick Moynihan said nearly 50 years ago: "If you want to build a world class city, build a great university and wait 200 years." His insight is true today – except yesterday's 200 years has become twenty. More than ever, universities will generate and sustain the world’s idea capitals and, as vital creators, incubators, connectors, and channels of thought and understanding, they will provide a framework for global civil society.
Dr. James (“Jim”) C. SpohrerInnovation Champion & Director, IBM University Programs & open worldwide entrepreneurship research (IBM UPower) [email protected]
“Instrumented, Interconnected, Intelligent – Let’s build a Smarter Planet.” – IBM“If we are going to build a smarter planet, let’s start by building smarter cities” – CityForward.org“Universities are major employers in cities and key to urban sustainability.” – Coalition of USU
“Cities learning from cities learning from cities.” – Fundacion Metropoli“The future is already here… It is just not evenly distributed.” – Gibson
“The best way to predict the future is to create it/invent it.” – Moliere/Kay“Real-world problems may not/refuse to respect discipline boundaries.” – Popper/Spohrer
“Today’s problems may come from yesterday’s solutions.” – Senge“History is a race between education and catastrophe.” – H.G. Wells
“The future is born in universities.” – Kurilov“Think global, act local.” – Geddes
98
SERVICE(Value-Cocreation)
AS-ISTarget & Context
TO-BETarget &Context
Aspirations
Goals Constraints
Responsibilities
Needs
Wants OUTCOME
Target &Context
IF-REDONETarget &Context
Learning
Side Effects
Experience
Unintended Consequences
Gaps
InsightsSHAREDINFORMATION
Plans
Procedures
Flowcharts
Rules
Policies
Regulations
Templates
Schedules
Diagrams/ Schematics
Instructions
ORGANIZATIONS
Software
Applications
Equipment
InfrastructureTools
VehiclesHardware
TECHNOLOGY/ENVIRONMENT
Users
IntermediariesAgents
Managers
Customers
Employees
Engineers
Contractors
PEOPLE
Consultants
Buildings
Expectations
Relationships
Disputes
Suppliers
Competitors
GovernmentAgencies
Third PartiesBanks
Insurance
Web Communities
eBusinesses
Benefits
Sacrifices
Shareholders
Criminals
Prices
Value-cocreation from resource fusion (integration) and fission (specialization)
World as System of SystemsWorld (light blue - largest)Nations (green - large)States (dark blue - medium)Cities (yellow - small)Universities (red - smallest)
Cities as System of Systems-Transportation & Supply Chain-Water & Waste Recycling-Food & Products ((Nano)-Energy & Electricity-Information/ICT & Cloud (Info)-Buildings & Construction-Retail & Hospitality/Media & Entertainment-Banking & Finance-Healthcare & Family (Bio)-Education & Professions (Cogno)-Government (City, State, Nation)
Nations: Innovation Opportunities- GDP/Capita (level and growth rate)- Energy/Capita (fossil and renewable)
March, J.G. (1991) Exploration and exploitation in organizational learning. Organizational Science. 2(1).71-87.Sanford, L.S. (2006) Let go to grow: Escaping the commodity trap. Prentice Hall. New York, NY.
Entities(Service Systems, both Individuals & Institutions)
Interactions(Service Networks,
link, nest, merge, divide)
Outcomes(Value Changes, both
beneficial and non-beneficial)
Value Proposition (Offers & Reconfigurations/
Incentives, Penalties & Risks)
Governance Mechanism (Rules & Constraints/
Incentives, Penalties & Risks)
Access Rights(Relationships of Entities)
Measures(Rankings of Entities)
Resources(Competences, Roles in Processes,
Specialized, Integrated/Holistic)
Stakeholders(Processes of Valuing,
Perspectives, Engagement)
Identity(Aspirations & Lifecycle/
History)
Reputation(Opportunities & Variety/
History)
prefer sustainable non-zero-sum
outcomes,i.e., win-win
win-win
lose-lose win-lose
lose-win
Spohrer, JC (2011) On looking into Vargo and Lusch's concept of generic actors in markets, or“It's all B2B …and beyond!” Industrial Marketing Management, 40(2), 199–201.
Where is the “Real Science” - wonders to appreciate?In the many sciences that study the natural and human-made worlds…
Unraveling the mystery of evolving hierarchical-complexity in new populations…To discover the world’s architectures and mechanisms for computing non-zero-sum
Entity Architectures (ЄN) of nested, networked Holistic-Product-Service-Systems (HPSS)
SSME: IBM Icon of Progress & IBM Research Outstanding Accomplishment– Internal 10x return: CBM, IDG, SDM Pricing & Costing, BIW COBRA, SIMPLE, IoFT, Fringe, VCR
• Key was tools to model customers & IBM better• Also tools to shift routine physical, mental, interactional & identify synergistic new ventures• Alignment with Smarter Planet & Analytics (instrumented, interconnected, intelligent)• Alignment with Smarter Cities, Smarter Campus, Smarter Buildings (Holistic Service Systems)
– External: More than $1B in national investments in Service Innovation activities
– External: Increase conferences, journals, and publications
– External: Service Science SIGs in Professional Associations
– External: Course & Program Guidelines for T-shaped Professionals, 500+ institutions
– External: National Service Science Institutions, Books & Case Studies (Open Services Innovation)
Service Research, a Portfolio Approach– 1. Improve existing offerings (value propositions that can move the needle on KPI’s)
– 2. Create new offerings (for old and new customers)
– I know/work with service research pioneers from many academic disciplines• I advocate for Service Science, Management, Engineering, and Design (SSME+D)
– Short-term: Curriculum (T-shaped people, deep in an existing discipline)– Long-term: New transdiscipline and profession (awaiting CAD tool)
• I advocate for SRII (“one of the founding fathers”)• Co-editor of the “Handbook of Service Science” (Springer 2010)
Other background (late 90’s and before)– Founding CTO of IBM’s Venture Capital Relations group in Silicon Valley
– Apple Computer’s (Distinguished Engineer Scientist and Technologist) award (90’s)
– Ph.D. Computer Science/Artificial Intelligence from Yale University (80’s)