session 4 – trade‐off analyses: what are the best methods and … · 2018. 3. 13. · session 4...

Session 4 – Trade‐off analyses: what are the best methods and how do we satisfy

stakeholder needs?

Vista consultation workshop, Vienna, 23 October 2017 1

…but first a bit of Vista context…


Introduction: trade‐offs in Vista



Vista consultation workshop, Vienna, 23 October 2017

ScenarioScenarioFactor 1

Val1 Val2 Val3 Val4

Factor 3

Val1 Val2 Val3

Factor 2

Val1 Val2

Factor 4

Val1 Val2 Val3 Val4

Model

Metric

s con

solid

ation

Metric

s con

solid

ation

KPIs Stakeholder 1

KPI1 KPI2 KPI3 KPI4

KPIs Stakeholder 2

KPI1 KPI2 KPI3

KPIs Stakeholder 3

KPI1 KPI2

Scen

arios a

nalyser

Scen

arios a

nalyser

Trade‐offs

Factor 1

Val1 Val2 Val3 Val4

Factor 3

Val1 Val2 Val3

Factor 2

Val1 Val2

Factor 4

Val1 Val2 Val3 Val4

4


Vista’s WP5 Impact trade‐off timeframe: First tasks started after summer 2017 Initial assessment early next year and final report by summer 2018


Trade‐offs ‐ how do we satisfy stakeholder needs?


Trade‐offs working definition

What do we understand by a trade‐off?

Elements needed:

i. At least twomeasurable variablesii. Variables are expected to be related somehowiii. That relation is an inverse relation

Easiest example ever:

variable + other variable = constant value


Trade‐offs: food for thought

“At least twomeasurable variables” ‐ Only dealing with quantitative variables

“Variables are expected to be related somehow” ‐ A common context is needed

“That relation is an inverse relation” ‐ A sense or order is required for both variables



“What gets measured gets managed.”Probably by Peter Drucker, or just a trendy meme

However, do not fall in the (incorrect) corollary: “What can’t be measured isn’t worth managing” just because:



The need of a common context

• Usually in ATM trade‐offs are defined between KPAs, still need to be put on a common scenario

• Most often macro and micro trade‐offs are ignored, moving from individual metrics trade‐offs to high‐level or system‐wide implications

• Some trade‐offs may occur at different time scales, short term or long term objectives

• Trade‐offs as a result of factors, regulatory or business, background or foreground.



Inverse relation and relative terms

Often different stakeholders have different perception on same variables

• Certain KPAs are more relevant for some actors, e.g. some actors might not even consider other KPAs

• Micro trade‐offs may lead to different trade‐offs at a macro level, e.g. individual metric worsening for a system‐wide improvement

• Time scale is also subject to perception strategic trade‐offs, e.g. assume current losses for expected future gains.



The many facets of a trade‐off

• Stakeholders, different perspectives• Variables selection, expected relationships• System scale and impact, from micro to macro• Temporal scope, form the short to long term• Effect of changes in the system, factors


Trade‐offs ‐ what are the best methods?


Trade‐offs

How to determine trade‐offs:

a. A priori trade‐offs, expected or interesting to be analysed

b. A posteriori trade‐offs, extracted from results exploration


A priori trade‐offs

1. Expert analysis, e.g. this is may happen or be of interest2. Hypothesis testing, e.g. H0 = “variables A and B are inversely (co‐)related”


A posteriori trade‐offs

Knowledge discovery,

1. Consider all possible relations,2. Determine common context; scenarios, factors, time‐frames, etc. 3. Apply variable reduction techniques. 4. Discard no statistically relevant relations.5. Compute (co‐)relation matrix in the remainder variable.6. Find any statistically significant inverse (co‐)relations.


Theoretical approaches

Vista consultation workshop, Vienna, 23 October 2017

Approach Pros Cons

Correlations(linear dependencies)

‐Fast‐Easy to understand

‐Does not imply causality

Expected utility and prospect theory

(Bayesian networks)

‐Creates probability mapsdependencies

‐hard to determine‐assumes

Granger causality(linear time series predictor)

‐Clear discerns ‐Need large time series‐ Limited to linear

predictors

Artificial Neural Network causality

(ANN time series predictor)

‐not limited to linear predictors

‐Need even larger timeseries

Precursor‐successor analysis(chain of events)

‐Determines causes and effects

‐Knock‐on effects tree

‐Hypothesis testing could be challenging

Replace (co‐)relations with any of the following:

17

Trade‐offs

A priori trade‐offs, a. Relations are known or assumed (expertise)b. Relations are quantified and verified (modelling)

A posteriori trade‐offs, a. Relations are quantified (modelling)b. Relations remain yet to be explained (expertise)


Warm up questions

Q1. Are we using the right methods for assessing trade‐offs?

Q2. What are the key stakeholder needs that are not being met?

Q3. How might these needs change in the future, e.g. under regulatory change?

Q4. How should we decide on performance trade‐offs (between or within stakeholders) for priority consideration?


session 4 – trade‐off analyses: what are the best methods and … · 2018. 3. 13. · session 4...

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