Business Complexity as

diversity and ubiquity of knowledge embedded in product

When we think of products in knowledge terms,

markets take on a different meaning

Diversity = variety (n.) , multiplicity, assortment, differencesUbiquity = existing everywhere at once, or seeming to be; Ubiquity is a synonym for omnipresence, the property of being present everywhere

Didi Sugandi, Nov 2011dsugandi@gmail.com

QUESTIONS - from a case studied

What are you Curious about?

How do you channel your Creativity?

What is your personal definition of Courage?

What are you Committed to?

How can we make our products & services more Intuitive?

How do we do what we do more Intelligently

What Inspires you and how do you inspire others?

What new possibilities are you CREATING

at Our Organization?

and then there are numbers of persons, units, teams, groups, divisions (etc. , whatever)

Just an example:

ARCHITECTURAL & INTERIOR DESIGNSPA CONSULTANCY

PR/MARKETINGDESIGN R&DPRODUCTSIT/SYSTEMS

H.R/BENEFITS/INCENTIVES

etcetera…..etcetera…..

POSSIBILITY IS COMBINATION OF “ALL THE THINGS”

Curiosity - What are you Curious about?

Creativity - How do you channel your Creativity?

Courage - What is your personal definition of Courage?

Commitment - What are you Committed to?

Intuition - How can we make our products & services more Intuitive?

Intelligence - How do we do what we do more Intelligently

Inspiration - What Inspires you and how do you inspire others?

Possibility discovery is the discovery (or matrix) of :

Knowledge embedded in person(s)

AND (or versus)

Knowledge embedded in product(s)

That’s what we mean by “ALL THE THINGS”

Business or Economic Complexity: (Diversity and Ubiquity) of Knowledge embedded in Product(s)

What are things made out of? One way of describing the business or economic world is to say that things are made with machines, raw materials and labor. Another way is to emphasize that products are made with knowledge; products consist of chunks of “productive knowledge”

When we think of products in knowledge terms, markets take on a different meaning. Markets allow us to access the vast amounts of knowledge that are scattered among the people of the world.

Products are vehicles for knowledge, but embedding knowledge in products requires people who possess a working understanding of that knowledge.

a unit of knowledge type called “productive knowledge”

Modern societies can amass large amounts of productive knowledge because they

distribute bits and pieces of it among its many members. But to make use of it, this

knowledge has to be put back together through organizations and markets. Thus,

individual specialization begets diversity at the national and global level. Our most

prosperous modern societies are wiser, not because their citizens are

individually brilliant, but because these societies hold a diversity (varieties) of

knowhow and because they are able to combine and recombine it to create a

larger variety of smarter and better products.

During the past two centuries, the amount of productive knowledge we hold

expanded dramatically. However, this was not an individual phenomenon. It

was a collective phenomenon. As individuals we are not much more capable

than our ancestors, but as societies we have developed the ability to make all

things that we have now – and much, much more.

The amount of knowledge that is required to make a product can vary

enormously from one good to the next. Most modern products require more

knowledge than what a single person can hold.

Productive knowledge_cont. 1

For a society to operate at a high level of total productive knowledge, individuals must

know different things. Diversity of productive knowledge, however, is not enough. In

order to put knowledge into productive use, societies need to reassemble these

distributed bits through teams, organizations and markets.

Accumulating productive knowledge is difficult. For the most part, it is not

available in books or on the Internet. It is embedded in brains and human

networks. It is tacit and hard to transmit and acquire. It comes from years of

experience more than from years of schooling. Productive

knowledge, therefore, cannot be learned easily like a song or a poem. It requires

structural changes. Just like learning a language requires changes in the

structure of the brain, developing a new industry requires changes in the

patterns of interaction (and relation) inside an organization or society.

The amount of knowledge embedded in a society, however, does not depend mainly on

how much knowledge each individual holds. It depends, instead, on the diversity of

knowledge across individuals and on their ability to combine this knowledge, and

make use of it, through complex webs of interaction.

J.L. Lemke (a semiotician): human make meaning in two fundamentally complementary opposite ways:

• TYPOLOGICAL, by classifying things into mutually exclusive categories (Language operates this way)

• TOPOLOGICAL, by distinguishing variations of degree (rather than kind) along various continua of

difference.

Productive knowledge_cont. 2

Expanding the amount of productive knowledge available in a company involves

enlarging the set of activities that the company is able to do. This process, however, is

tricky. Industries cannot exist if the requisite productive knowledge is absent, yet

accumulating bits (“chunks”) of productive knowledge will make little sense in

places where the industries that require it are not present. This “chicken and egg”

problem slows down the accumulation of productive knowledge. It also creates important

path dependencies. It is easier for companies to move into industries that mostly

reuse what they already know, since these industries require adding modest amounts

of productive knowledge. By gradually adding new knowledge to what they already

know, companies economize on the chicken and egg problem. That is why we find

empirically that companies move from the products that they already create to others

that are “close by” in terms of the productive knowledge that they require.

We need a “map” (“a topology”: showing relative positions, continuity and

connectivity, variations of degree (instead of kind) along various continua of difference)

that captures the similarity of products in terms of their knowledge requirements.

This topology (map) provides “paths or continuations” through which productive

knowledge is more easily clustered (= agglomerated or accumulated). We call this

map, or network, the product space, a thought landscape and use it to locate each

person or company, illustrating their current productive capabilities (i.e. knowledge)

and the products that lie nearby (or overlapped).

How do we go (=find the links, find the continuations)

from what a company makes (=knowledge embedded in product) to what a company knows (=knowledge embedded in people, persons)?

If making a product requires a particular type and mix of knowledge, then the

companies that make the product reveal having the requisite knowledge.

From this simple observation, it is possible to extract a few implications that can

be used to construct a measure of business complexity.

This measures (“states”) are neither states of natural-process nor states of

knowledge; they are objective probability measures. An objective probability

measure is the formal expression of an objective indefiniteness.

An objective indefiniteness entails that the values of certain observables are

extrinsic (possessed only because they are indicated) rather than intrinsic

(indicated only because they are possessed).

This dependence on value-indicating facts is not a dependence on anything

external to the free-standing reality that owes nothing to

observers, information, or our interventions into the course of natural-process.

amount of knowledge that a company has is expressed in the diversity and ubiquity of the products that it makes

First, companies whose persons and organizations possess more knowledge

have what it takes to produce a more diverse set of products. In other words, the

amount of embedded knowledge that a company has is expressed in its

productive diversity, or the number of distinct products that it makes.

Second, products that demand large volumes of knowledge are feasible

only in the few places where all the requisite knowledge is available. We

define ubiquity as the number of companies that make a product. Using this

terminology, we can observe that complex products –those that contain many

personbytes of knowledge–are less ubiquitous.

The ubiquity of a product, therefore, reveals information about the volume of

knowledge that is required for its production. Hence, the amount of knowledge

that a company has is expressed in the diversity and ubiquity of the

products that it makes.

A game of scrabble is a useful analogy (1)

In scrabble, players use tiles containing single

letters to make words. For instance, a player can

use the tiles R, A and C to construct the word CAR

or ARC. In this analogy, each product is

represented by a word, and each capability,

(=module of embedded knowledge), is

represented by a letter.

Our measure of business complexity corresponds

to estimating what fraction of the alphabet a

player possesses (=sum of his/her

knowledge), knowing only how many words

he/she can make, and how many other players

can also make those same words (“product”).

We assume that each player has plenty of copies

of the letters they have.

A game of scrabble is a useful analogy (2)

Players who have more letters will be able

to make more words.

So we can expect the diversity of words

(products) that a player (company) can

make to be strongly related to the number

of letters (capabilities) that he (it) has.

Long words will tend to be rare, since they can only be put together by players

with many letters. Hence, the number of players that can make a word tells

us something about the variety of letters each word requires: longer words

tend to be less ubiquitous, while shorter words tend to be more common.

Similarly, ubiquitous products are more likely to require few capabilities, and less

ubiquitous products are more likely to require a large variety of capabilities.

From Scrabble back to Diversity and Ubiquity

If these players can only make a few other words, then it is likely that

rarity explains the low ubiquity. However, if the players that can make

these rare words (“products”) are, in general, also able to put

together many other words (“products”), then it is likely that the low

ubiquity of the word (“product”) reflects the fact that it requires a

large number of letters (“capabilities”) and not just a few rare ones.

Diversity and ubiquity are, respectively, crude approximations of the variety of

capabilities (i.e. knowledge) available in a person or a company or required by

a product. Both of these mappings are affected by the existence of rare letters

(scarcity), such as Q and X. For instance, players holding rare letters will be able to

put together words (=“product”) that few other players can make, not because they

have many letters (=“knowledge”), but because the letters that they have are rare.

This is just like rare natural resources, such as uranium or diamonds, etc.

Yet, we can see whether low ubiquity originates in scarcity or complexity

by looking at the number of other words (other “products”) that the makers

of rare words (“products”) are able to form.

Diversity and Ubiquity corrects each other

Diversity can therefore be used to correct the information carried by

ubiquity, and

Ubiquity can be used to correct the information carried by diversity.

We can take this process a step further by correcting diversity using a measure

of ubiquity that has already been corrected by diversity and vice versa. In fact,

we can do this an infinite number of times using mathematics. This process

converges after a few iterations and represents our quantitative measures of

complexity.

DIVERSITY is related to the number of products that a player is connected to.

It is equal to the number of links that this player has in the network (map)

UBIQUITY is is related to the number of players that a product is connected to.

It is equal to the number of links that this product has in the network (map)

Note that those two are about “person(s)” and “product(s)” in a continua

Possibilities are created by ways of relation & interaction

What new possibilities are you CREATING at OurCompany?

Curiosity -

What are you

Curious

about?

Creativity -

How do you

channel your

Creativity?

Courage -

What is your

personal

definition of

Courage?

Commitment -

What are you

Committed

to?

Intutition -

How can we

make our

products &

services

more

Intuitive?

Intelligence -

How do we

do what we

do more

Intelligently

Inspiration -

What Inspires

you and how

do you

inspire

others?

1

2

3

4

5

6

7

8

9

10

Interaction of individuals, possessing different knowledge and different

views, is what constitutes the life of thought. The growth of reason is a

social process based on the existence of such differences. It is of its

essence that its results cannot be predicted, that we cannot know which

views will assist this growth and which will not. — Friedrich A. Hayek

Topologize! - to make

tacit knowledge

more explicit

“Group priority

instant assessment” matrix

a tool “to map” proximities

(closeness, similarity etc)

between and among

“player’s/actor’s knowledge”

and“knowledge inside products”

distinguishing variations of

degree (rather than kind)

along various

continua of difference

continua of difference : thinking topologically (not typologically)

"Hijau" adalah nilai/makna yang ada dalam kisaran di antara hal yang

sudah termasuk hijau, sampai dengan nilai/makna yang mau jadi

bukan hijau.. Sebuah harga dalam ekonomi juga demikian. Kebenaran

juga hal seperti itu. Sebuah konsep atau atribut bisa tidak dipandang

sebagai "titik" atau garis tak ber-entitas dalam ruang dan waktu, tetapi

dipandang sebagai suatu kisaran (range), suatu keberlanjutan (continua), -

dipandang sebagai variations of degree along continua of difference.

Entitas adalah relasi atribut-atribut, relasi konsep-konsep.. suatu

rangkaian relationships, suatu kisaran nilai, suatu continuum. Mulai dari

yang paling "rendah" sampai paling "tinggi". Sesuatu nilai yang terletak

diantara yang (mulai) masuk akal sampai di batas hampir ke luar, di luar

akal. Kisaran nilai/makna di antara yang "terendah" mulai bisa dicerap (or

perceived) sampai “tertinggi” bisa dicerap.

Pairwise Comparison (pembandingan perpasangan) – Why

1. Berangkat dari prinsip bahwasanya: tiap hal, masing-masing

mempengaruhi dan dipengaruhi oleh setiap dan masing-masing hal

lain – “exchange value of each and every other thing” atau “bilateral

exchange ratios” - Contoh dalam ekonomi: price is “bilateral exchange

value of each commodity for every other commodity”

2. COMBINING: menemukan pilihan kombinasi “beberapa hal dari segenap

hal” misalnya “dua hal dari segenap hal”

3. WHY COMBINING?.. Because we need to prioritize (rank) capabilities

(knowledge) which we can readily transformed into product(s)

4. Prosedur ini adalah computing with collaborative minds. Berpikir

otonom dan sekaligus kolaboratif. Berangkat dari pikiran

individual, subjektif, menemukan (men-discover) pikiran kolektif/objektif.

Tanpa pendiktean/diktator.

5. Prosedur ini sebaiknya dilakukan secara iteratif, periodik/ reguler.

Appendices, next following slides

• Some explanations / reasoning about increase in complexities when or if relationship and or interaction increases

- Between persons

- Between tasks (assuming 1 task per person)

The number of possible interactionsThe number of possible interactions can be computed in the following way.

Let n be the number of subordinates reporting to a supervisor. Then, the number

of possible relationships of direct single type which exist in the organization

is identical with the number of organizational members: n.

The number of possible interactions between organizational members is

n (n - 1), and

the number of possible task interactions (assuming one task per person) is

n (n-1)2.

The figure provides a good impression of the dilemma of coordinating work. A

more realistic example for a single person can illustrate this. A manager having 3

subordinates and adding a fourth, faces 4 additional direct relationships to

monitor, i.e. that each new subordinate increases the number of direct

interactions with (n+1). This figure does not even consider the fact of possible

group interactions, which would boost the total number of possible interactions

exponentially.

Number of possible person relationships & task interactions

exchange value

of each

for

every other thing

Thou shall not, alone eat the fruit of knowledge.Share it with others.

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