IS THERE ANY ‘LAW OF REQUISITE VARIETY’ IN...

Dealing with Complexity in Society:

From Plurality of Data to Synthetic Indicators

September 17th and 18th, 2015 1

Jan W. Owsiński Systems Research Institute, Polish Academy of Sciences

Newelska 6, 01-447 Warszawa

[email protected]

IS THERE ANY ‘LAW OF REQUISITE

VARIETY’ IN CONSTRUCTION OF INDICES

FOR COMPLEX SYSTEMS?

mailto:[email protected]

Opening Session

The modelling project

The variables & indicators

The ways to synthesise

The purpose & the policy instruments

Jan W. Owsiński Dealing with Complexity in society 2

OUTLINE

1. The modelling project in the capital region of

Poland

2. The nature of variables & indicators

3. The diverse ways to synthesising

4. The purpose & the policy instruments

5. The requisite variety at work…

Opening Session






The modelling project in Masovia (1)

# In 2011-2013 a modelling project was carried out, commissioned by the

Masovian Bureau of Regional Planning (Masovia: capital province of

Poland)

# The project was financed from a program, supported by structural

funds of the EU, called Development Trends of Masovia

# The specifications for the modelling project included 19 domains of

modelling and the time horizon of 2025. It was assumed that

municipalities shall be the basic objects in the project

# The work was done by a team from the Systems Research Institute

and the Institute of Geography and Spatial Organization, both of the

Polish Academy of Sciences (some 15 persons in total)

Opening Session







# Within the 19 domains specified in the commission, altogether some

70 variables / indicators were formulated, along with more than 20

auxiliary variables / indicators

# For all of these, mostly relatively simple forecasting models, based on

various prerequisites, were developed

# In a vast majority of cases, the models were specified (forms,

parameters) for municipality types, but actually run for each of the

municipalities (315 in total) in annual steps until 2025

# A computer application was developed, along with a database, to run

the models for various assumptions („scenarios”), elaborated within

the project

Opening Session







1. Population Population numbers, various

demographic indicators

Birthrate, mortality and

migration scenarios

2. Social

capital

NGOs, sports clubs members,

art circle members + synthetic

indicator

For municipalities, but

also for counties,

separately + synthetic

3. Wealth of

municipalities

Own and total revenues, capital

expenditures, etc. + auxiliary

Model types for

municipality types

4. Municipality

investments

Revenues, capital expenditures,

debt, propensity to invest

An interactive model

5. Intellectual

capital

University graduates & students,

foreign capital firms + synthetic

indicator

Indicators at different

resolution levels +

synthetic one

6. Labour

market

Demand for labour (GDP);

auxiliary: productivity

Based on variables from

other domains

Opening Session







7. Social

exclusion

Synthetic indicators, based

on domains 1, 5, 14, 15

Gini-like measure is applied for

wages

8. Quality of

life

Single synthetic indicator

based on domains 3, 5, 7,

9, 15, 16, 17

Relative indicator, based on 7

variables (some of them

synthetic)

9. Technical

infrastructure

Shares of inhabitants using

water supply, sewage and

water treatment

Models for municipality types

and levels attained

10. External

investments

Value of external

investments per capita

Based on variables from other

domains

11. Inno-

vativeness

Composite indicators,

based on domains 3, 5, 13

Two indicators, for two levels

of resolution

12. Information

society

Based on 11 and internet in

schools

Two indicators (school levels

in municipalities and counties)

Opening Session







13. Fixed

assets

Assets of public bodies,

companies, total, per capita

Auxiliary: investments

14. Enterprise

& employment

Several derived indicators

and auxiliary variables

Models for municipality types

15. Transport

accessibility

Travel time based indicator of

population number

Based on road and

settlement networks

16. Quality of

environment

Synthetic indicator based on

several (+auxiliary) variables

Includes variables from

domains 17 and 19

17. Sustainable

development

Synthetic indicator based on

several auxiliary variables

Models for auxiliary variables

(e.g. car number)

18. GDP value Global and local GDP

dynamics

Simple macroeconomic

models with six scenarios

19. Scale&rate

of urbanisation

Several indicators based on a

couple of variables

Some variables from other

domains

Opening Session







-- a single run of the population model for a selected scenario

(births, mortality, migrations) produces 315 municipalities x 15

years x 15 age groups x two sexes x indicator categories (5) =

more than 700 000 numbers, constituting a forecast

-- altogether some 100 models (variables / indicators + auxiliary

variables) were developed, varying as to their complexity, many

of them featuring variants (parameter-wise) for the types of

municipalities, so that we deal with several hundred different

effective models

-- there are altogether more than 100 of feasible scenarios,

which can be selected by the user

-- all this costed the commissioning agency some 160 000 €…

Opening Session






The variables and indicators (1)

The variables (quantities) appearing in the models, can be very

roughly classified into:

-- original real-life variables (population number, number of

businesses, number of computers in schools,…)

-- first-order indicators (population density, forest share of

municipality area, university graduates per 1 000 inhabitants,…)

-- second-order indicators (GDP per capita, own revenues of

municipality per capita, employment per business,…)

-- composite variables (variables, defined effectively as models,

based on other variables)

-- synthetic indicators (these are no longer „models”…)

Opening Session







The issues:

-- selection of magnitudes and their interpretations

-- availability of data, their formal definitions and

interpretations

-- precision of measurement and degree of „truth”

(certainty)

-- validity and possibility of verification of models

-- in case of synthetic indicators: can we check the

meaning?

Opening Session







The objectives & purposes:

-- to know what is going on – a selection of „most important”

variables in „cognitive” terms

-- to assess the situation from a broader perspective, but still

within a well-defined domain (e.g. the joint meaning of a

couple of variables, pertaining to the same subject matter)

-- to assess the situation from a really broad perspective

(„quality of life”, „sustainable development”,…)

-- to determine the ways to act, the policy levers and the

undertakings meant to achieve definite goals…

Opening Session







The summary:

-- sometimes a commonsense assessment is more valuable

than a million data items and a study worth a million…

-- sometimes there is an intuition as to „what it all boils down

to…”

-- sometimes there are variables that could be picked up and

(relatively easily) measured, which shed light on a much

wider landscape of phenomena

-- but beware of both these hunch-guesses and the academic

immovable truths…: keep to your ground knowledge

Opening Session






The ways to synthesise (1)

The summary:

-- synthesising can be easy: when variables pertain to the

same subject, are similarly measured, and correlated (A) -- synthesising can be challenging: when variables are [only]

interconnected, like through a model (often the question

arises: if there is a model, why synthesise?) (B) -- synthesising can be difficult: when variables are of widely

differing character, and the meaning of the resulting indicator

is doubtful… (C)

Opening Session







Case (A): synthesising can be [relatively] easy: when

variables pertain to the same subject, are similarly measured,

and correlated

An example:

9. Technical infrastructure: shares of municipality inhabitants with

access to (i) water supply; (ii) sewage; (iii) water treatment (in %):

# generally very high correlations (exceeding 0.85, even close to 0,98)

# classification into municipality types (kinds of needs…)

# upper limit of 100% (saturation) – many entities already there…

## choices: (a) average score; (b) minimum of the three – accounting for

municipality type; (c) weighted score, e.g.: w1=0.45, w2=0.45, w3=0.1

Opening Session







Case (B): synthesising can be challenging: when variables

are [only] interconnected, like through a model (often the

question arises: if there is a model, why synthesise?)

Examples:

6. Labour absorption capacity = GDP / productvity, both of which are

separately modelled

15. Transport accessibility = number of inhabitants who can reach a given

destination (distance+ population) withina given time limit

3. Wealth of the municipalities:

DWMit=DWMit-1+aSATDWMi + (1-a)a2typ(WWPAt – WWPAt-1);

a simple econometric model superposing trend, with an increment in the

trend, and the influence of an important independent variable

Opening Session







Case (C): synthesising can be difficult: when variables are

of widely differing character, and the meaning of the resulting

indicator is doubtful

Examples:

6. Quality of life: JZM = f(DWM,SWM,ZWS, [WOD,KAN,OCZ], DTR, [JSP, UWP,

UWS1]) = (DWM# + SWM# +ZWS#+(WOD+KAN+OCZ)#/3 + DTR#+

(JSP+UWP+UWS1)#/3))/6; a weighted sum of the unitarised variables, assumed to

contribute to quality of life of the inhabitants

11. Innovativeness: WIGit = f(SWM, PKZ, WWPA, WMI) = (SWMit* + PKZit* +

WWPAit* + WMIit*)/4; similarly as above…

16. Quality of natural environment: JSPit = f(ZIEL, ROLG, GZL, AUTO, GZB) =

ZIEL’i + ROLG’i - dGZL(typ)i – (GZL#it + AUTO#it + GZB(scen)#it)/3; ibidem

Opening Session







Some trivial conclusions:

-- the situations (may) differ very widely, both in statistical

and in substantive terms

-- each case must be considered separately, with application

of statistical and „theoretical” reasoning

-- it is highly probable that a given synthetic indicator turns

out to be poorly justified (apples and oranges summed up)

-- to see landscape, one has to dispose of three dimensions

(cognitive needs)

-- an important aspect is constituted by the „purpose” (the

required number & character of dimensions)

Opening Session






The purpose and the policy instruments (1)

What do we need an indicator for? Whether

and why should we go for a single, all-

embracing indicator?

(The answer „to simplify” is incorrect.)

If the purpose is policy making, then very

rarely one dimension suffices (e.g.

determining tax levels or fees).

In most cases, for practical reasons, more

than one indicator is needed.

Opening Session






The purpose and the policy instruments (2)

Even if we have only one policy instrument (e.g. subsidy), it

is not true that we need just one indicator.

This is because the instrument can almost always be used in

a variety of manners (e.g. subsidy to different areas of socio-

economic life or for different kinds of undertakings).

Thus, instead of trying to „aggregate everything”, a matching

should be sought between the diversity of encountered

situations and the possibility of diversifying policy execution.

The matching shall, definitely, very rarely be „perfect”,

anyway…

Opening Session






The law of requisite variety at work

The postulate of „matching” the indicators to the policy

instruments and their use gives rise to a different kind of

problem, involving the multidimensional scaling and other

data analysis techniques (clustering) and the MCDM

techniques.

Data / variables

Policy

instruments &

their use

MDS & other data

analysis

techniques The dimensionality and

granularity required,

and transformations

What is

measured, how

and why?

MCDM methods

Can we take a

rational decision?

The decision

Opening Session






The law of requisite variety at work?

But that is an entirely different

story…

So,

for now, thank you very much for

your attention,

and/or, indeed, for your patience…

Opening Session

Title Section 1

Title Section 2

Title Section 3

Title Section 4

First Author et al. Dealing with Complexity in society

22

OUTLINE

2. Title Section 2

Title Subsection 1

Title Subsection 2

…….

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