The future of value-seeking in two inequalities

How well do we understand the future? How well do we understand “value-seeking” and these two inequalities?

Transformation > Experience > Data > Software > Hardware

Wisdom > Understanding > Knowledge > Information > Data

Some service science researchers are trying to deepen our understanding of these two inequalities to understand better the future of value-seeking in business and society.

Let’s examine each in turn, and identify some relevant literature…

(1) Transformation > Experience > Data > Software > Hardware

The three best references to decode the TEDSH inequality are…

Pine BJ, Gilmore JH (1999) The experience economy: work is theatre & every business a stage. Harvard Business Press.
(this explains why Experience > Data, and if your read the last chapter why Transformation > Experience)

Domingos P (2015) The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books.
(this explains why Data > Software)

Gelernter D (1993) Mirror worlds: Or the day software puts the universe in a shoebox… How it will happen and what it will mean. Oxford University Press.
(this explains why Software > Hardware; and really XaaS – Everything as a Service – everything as an information/data service)

The gist is people want value co-creation experiences that are also capability co-elevation transformations.   Think about healthcare, education, and governance – healthy, wealthy, and wise…

In sum, read:

Spohrer JC, Engelbart DC (2004) Converging technologies for enhancing human performance: Science and business perspectives. Annals of the New York Academy of Sciences. May 1;1013(1):50-82.


(2) Wisdom > Understanding > Knowledge > Information > Data

The classic to understand WUKID, or sometimes reversed as DIKUW:

Ackoff, RL (1989) From data to wisdom. Journal of applied systems analysis. 16(1):3-9.

A more recent good attempt at WUKID sometimes described in the other order as DIKUW is this piece:
dos Santos Sarraipa JF (2013) Semantic Adaptability for the Systems Interoperability (Doctoral dissertation, Universidade Nova de Lisboa).
…. though I think the interpretations and conclusions are off a bit, the input research is quite useful in the above.

Closer to reality is this line of thinking I believe:

Data is about entities doing measurement (interactions with the world – one part of reality is partially reflected in another part of reality – beginning of models – mirror worlds)

Information is about entities doing communications (interactions with the social world – entities with storage/dynamic memory/models)

Knowledge is about entities doing reasoning that build more knowledge to change interaction outcomes (circular reasoning I know – this is where purpose comes in)

Understanding is about entities doing discovery that builds models for building knowledge faster and more accurately/truth-seeking (learning – this is where proto and scientific method comes in to create better explanations)

Wisdom is about entities resolving conflicts in ways that also benefit future generation, including but not limited to existential challenges and questions (learning – ????).

Nobody fully understands intelligence or wisdom yet.  Does ???? = value-seeking.   I don’t think so.  Not unless value-seeking is simply about entities doing rapid rebuilding from scratch.  Why rapidly rebuilding from scratch?  I can think of know other capability that leads to more efficiently and systematically exploring large design spaces for modularizing the construction of emergent layers of reality to discovery new modes of interaction and new types of capabilities.  This is a big open question.

Without trying to make sense of the direction of evolution, this is the classic paper about systems that learn:
March JG (1991) Exploration and exploitation in organizational learning. Organization science. Feb;2(1):71-87.

One attempt at understanding the direction of evolution for smarter service system entities is this work, which I consider a classic when combined with Doug Engelbart’s augmentation evolution work:
Wright R (2001) Nonzero: The logic of human destiny. Vintage.

Of course, wisdom (and WUKID) is required to address Wicked Problems…

The classic reading on Wicked Problems is this…

Churchman, CW  (1967) Wicked Problems.  Guest Editorial, Management Science. 14(4):B-141-B-146.

This is also good for Wicked Problems and Design Thinking for Social Entrepreneurs/Innovators
Kolko J (2012) Wicked Problems: Problems Worth Solving: An introduction to wicked problems. AC4D.

In Conclusion

In the cognitive era, as individuals and institutions learn to build, understand, and work with digital cognitive systems, the bigger picture is the process of intelligence augmentation (IA), which is based in part on AI (Artificial Intelligence) work from computer science.

IA is the human journey and story….  it is about both people (organizations-institutions) and machines (technology-infrastructure) as the environment shaping the evolution of entities.   This story is the story of what Kline calls the socio-technical systems design loop — or what Spohrer and other service science colleagues call the evolution from smarter service systems to wiser service systems….

Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press.
(this explains service science and T-shapes in terms of what Kline calls the socio-technical system design loop and multidisciplinary thinking)

Norman DA (1993)Things that make us smart: Defending human attributes in the age of the machine. Basic Books.
(this explains augmented intelligence very well – perhaps the best I have seen from a cognitive science and design perspectives)

Another classic foundational paper, and service science was almost called “augmentation science” – after  conversation with Doug Engelbart
Engelbart DC (1962) Augmenting human intellect: a conceptual framework.  SRI Summary Report AFOSR-3223, Prepared for: Director of Information Sciences, Air Force Office of Scientific Research, Washington DC, Contract AF 49(638)-1024, SRI Project No. 3578 (AUGMENT,3906,).

In sum, the future in two inequalities:

Transformation > Experience > Data > Software > Hardware

Wisdom > Understanding > Knowledge > Information > Data

Some service science researchers are trying to deepen our understanding of these two inequalities to shift the study of from smarter service systems to wiser service systems.

Learning systems that can learn to play “better” games create an evolving ecology of social entities.   The evolving ecology of service system entities with capabilities, constraints, rights, and responsibilities is moving into the future – in part driven by the two value-seeking inequalities above.


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