A Multi-Methodological Framework for Analysing Crisis Management and Low Intensity Conflicts...

A Multi-Methodological Framework for Analysing Crisis Management and Low Intensity Conflicts

Advanced Research Workshop, Velingrad, Bulgaria, 21–25 Oct. 2006

Stein Malerud

Norwegian Defence Research Est.

Outline

• Introduction– OR at FFI– Multi-methodology

• Multi-methodological framework• Case study• Our findings

OR at FFI

• OR is mainly used for– Long term planning– Materiel investment support– Analysis support to operations/HQs

• Before: Planning for War – National territorial defence– The enemy was known and predictable– Mostly ”hard” OR

• Today: Crisis Management and Low Intensity Conflicts (LIC)– International engagements in different coalitions– Threats are multiple and unclear– Focus on risk and uncertainty– More use of ”soft” OR

Research Objectives

• Strengthen our ability to

– analyze crisis management and LIC

– analyze complex human systems

– manage uncertainty

• Gain experiences with assembling different methods in multi-methodologies (combining soft and hard OR methods)

• Some research questions

– Do there exist combinations of methods that work particular well together?

– Are there any “rules of thumb” for selecting and combining methods?

– How can uncertainty be treated as an integrated part of the analysis process?

Multi-Methodology

• Crisis management and LIC can be characterised as messy problems*: – ambiguity about objectives – uncertainty about outcomes– great risk if things turn out badly

• Combining complementary soft and hard OR methods in multi-methodologies seems to be an useful approach to messy problems

• Multi-methods – three perspectives**– Real world problems are inevitable multi-dimensional– Addressing a problem is usually not one single event, but a process

divided into a number of phases – Use of methods that overlap, i.e. solving the same problem with

different methods

*Pidd (2003): Tools for thinking. Wiley** Mingers (2001): Rational Analysis for a problematic world revisited. Wiley

Outline



Methodological Framework

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling

Choice ofAlternative

PreferenceModelling

MCDA model

SystemModelling

Problem Structuring• Problem structuring – “what to do” before “how to do it”• Involvement of stakeholders• A facilitator chairs the workshops

• Elements of Soft Systems Methodology (*)– Problem formulation– Rich picture– CATWOE and Root definitions

Desired output• Specification of objectives of the study• Understanding of the domain• Identification of stakeholders• Elicitation of values and evaluation criteria• Decision alternatives• Key uncertainties and predetermined trends

C Client

A Actors

T Transformation

W Weltanschauung

O Owner

E Environment

(*) Checkland (1999): Systems thinking, systems practice. Wiley.

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

Scenario Development and Planning

• Scenarios – Describe plausible alternative futures– Comprise external and uncontrollable uncertainties

• “Bound” the space of uncertain factors – How many scenarios are sufficient?

• Scenarios are test conditions for decision alternatives

• Method: Elements of Scenario Planning* combined with Morphological Analysis– ”Driving forces” identified– A handful of scenarios developed ()

• plausible and internally consistent• different – reflecting main uncertainties• relevant – able to separate the alternatives

(*) van der Heijden (2005): Scenarios – The art of strategic conversation. Wiley.

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

Scenario Planning

Review of the decision alternatives• Relevancy• Robustness

• Other alternatives

Decisionalternatives

Outcomes

Structure

Review

Uncertainties

Ideas and insight Scenarios


Outcomes

Structure

Review

Uncertainties


Multi Criteria Decision Analysis (MCDA) (1)

• Decision based on more then one criteria• Not a “mathematical optimal” solution,

but the most preferred alternative• Structured and traceable approach

• Three main categories*:– Value and utility function methods– Outranking– Goal and reference point methods (mathematical

programming)

• Main ingredients– Value/goal model– Preference modelling– Evaluation and prioritisation

# exposures(persons)

Shore length(km)

Restitution time(years)

Damage index Loss(NOK)

Damage

Recreation Environment Industry

Visibility Shore BirdsMarine

mammalsFish Tourism Fishery

Otherindustry

Load LengthVulner-ability

Preserv.value

Vulner-ability

Preserv.value

FishingFish

farmingVulner-ability

Preserv.value


Shore length(km)



Damage




Otherindustry


Preserv.value

Vulner-ability

Preserv.value

FishingFish


Preserv.value

Damage




Otherindustry


Preserv.value

Vulner-ability

Preserv.value

FishingFish


Preserv.value

*Belton and Stewart (2002):Multiple criteria decision analysis. Kluwer

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

MCDA (2)

• Input: – Values, goals and evaluation criteria– Decision alternatives– Initial understanding of preferences – Uncertainties– Constraints

• Some challenges: – Find the “right” method– More than one stakeholder– Obtain simple and transparent models– Manage uncertainty – internal and external

Decision Making Using Scenarios

• How to aggregate results from MCDA to identify the most preferred alternative over the scenario space?

• Different approaches:– Consequences of all criteria and alternatives in

each scenario (assume that preferences do not change)– Meta criterion by combining criteria and scenario– ...

• Which system are the best? Some famous decision rules– Best guaranteed payoff – maximin (Wald)– Optimism – pessimism index (Heurwicz) – Minimise regret (Savage)– Highest average payoff (Laplace)– ...

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

ProblemStructuring

ScenarioPlanning

ConsequenceAnalysis

Goal/valuemodelling


PreferenceModelling

MCDA model

Outline


• Multi-methodological framework• Case study• Our findings/conclusion

Oil-Spill Preparedness Study

• Analyzing measures to reduce the risk of oil pollution in the northern parts of Norway

• Objective: Prevent oil exposure of environmentally valuable areas

• Problem: Utilize today’s system most effectively

• System: The combined effort of tugboats and oil-spill preparedness equipment

• Limitation: Consequences only, i.e. an oil-spill has occurred

• Decision alternatives

a) No action (benchmark)

b) Fight oil-spill on site

c) Tow to shore (emergency harbour or beaching point)

d) Tow to sea (away from vulnerable areas)

Scenario Development and Planning

• Driving forces identified • Morphological analysis to ensure consistency• Three scenarios developed

1

2 3

Waveheight

(m)

Windspeed

(m/s)

On-shorewind direction

(North = 360o)

Leakagerate(m3/h)

Shipcondition

Total amount ofoil (m3)

Dist. to vulnerable areas (nm)

315–344

285–314

High

(> 6)

255–284Very high(> 500)

> 150.000(Large tanker)

Medium-high

(2,5–6)

Storm

(> 20)

225–254High(100–500)

Sinking within hours

50.000– 150.000 (Medium tanker)

Long(> 10)

Medium

(0,5–2,5)

Gale

(10–20)

195–224Medium(50–100)

Sinking within days

5.000– 50.000(Small tanker)

Medium(3– 9)

Low

(0–0,5)

Breeze

(0–10)

165–194Low

(0–50)

Not sinking0–5.000 (Bunkers)

Short(0–2)

Waveheight

(m)

Windspeed

(m/s)

On-shorewind direction

(North = 360o)

Leakagerate(m3/h)

Shipcondition

Total amount ofoil (m3)

Dist. to vulnerable areas (nm)

315–344

285–314

High

(> 6)

255–284Very high(> 500)

> 150.000(Large tanker)

Medium-high

(2,5–6)

Storm

(> 20)

225–254High(100–500)

Sinking within hours

50.000– 150.000 (Medium tanker)

Long(> 10)

Medium

(0,5–2,5)

Gale

(10–20)

195–224Medium(50–100)

Sinking within days

5.000– 50.000(Small tanker)

Medium(3– 9)

Low

(0–0,5)

Breeze

(0–10)

165–194Low

(0–50)

Not sinking0–5.000 (Bunkers)

Short(0–2)

Scenario 1 Scenario 2 Scenario 3Scenario 1 Scenario 2 Scenario 3Scenario 1 Scenario 2 Scenario 3

MCDA model – Goal Hierarchy

(*) Value hierarchy based on the Norwegian oil spill combat strategy analysis, 1980


Shore length(km)



Damage




Otherindustry


Preserv.value

Vulner-ability

Preserv.value

FishingFish


Preserv.value

Calculating Consequences

Scenario

Oil spill preparednesssystem• Equipment• Tugboats• Emergency harbours

InputInput

OutputOutputMC simulationMC simulation

No. of exposed areas The “optimal” decision

Spread of oil spillSystem performance

HighMediumLowNone

HighMediumLowNone

1

2

3

HighMediumLowNone

HighMediumLowNone

1

2

3


Shore length(km)



Damage




Otherindustry


Preserv.value

Vulner-ability

Preserv.value

FishingFish


Preserv.value


Shore length(km)



Damage




Otherindustry


Preserv.value

Vulner-ability

Preserv.value

FishingFish


Preserv.value

Damage




Otherindustry


Preserv.value

Vulner-ability

Preserv.value

FishingFish


Preserv.value

Decision model

Stochastic:• Wind vector• Wave height

The alternative that contribute most to reduce the environmental damage is chosen ‘

Outline



Summing Up• Three perspectives on multi-methodology

1. Real world problems are multi-dimensional2. Analysis process divided into different phases3. Use of overlapping methods

• A multi-methodological framework for the second perspective has been developed– Problem structuring using elements of SSM– Scenario development and planning supported by morphological

analysis– Monte Carlo simulation model for calculating consequences – MCDA model for evaluation and prioritisation of decision

alternatives

• Managing uncertainty by: PSM, scenarios, simulation and sensitivity analysis

• The framework has been applied in a case study addressing the oil-spill emergency preparedness system

Our Findings (1)

• Multi-methodologies seems to be useful for analysing messy problems– Combining “soft” and “hard” methods

• SSM, Scenario planning and MCDA seem to work well together

• We claim: – that this is a suitable framework to support decision making and

planning under uncertainty, however, – It is a rather comprehensive approach, and should be adjusted to

the actual problem situation

• Choice of MCDA methods and/or other “harder” OR methods should be viewed in light of– the three perspectives on multi-methodologies– identified problems from the problem structuring process

Our Findings (2)

• PSM should be emphasised introductorily– SSM provides a flexible and adaptable toolbox– First steps of SSM are well suited for problem structuring– Provide useful information and knowledge to the rest of the

analysis process

• Driving force method combined with morphological analysis provide a structured approach for scenario construction

• Scenario planning combined with MCDA– Scenario planning useful for a pre-evaluation of decision

alternatives– MCDA brings structure and traceability to the decision process– Useful for managing external uncertainties

Further Work

• Study peace and low intensity conflicts– Understand the complexity and dynamics– Study decision models to support planning and for direct

support to operations

• Managing uncertainty

• Multi-methodological framework– Decision theory (including game theory)– Simulation (Agent based simulation)– Optimisation – Soft OR methods

Questions?

Reserve

Uncertainty – Terminology

• Strict uncertainty: no information about the likelihood of various states of nature

• Subjective probabilities: enough information (prior and/or observations) to estimate probabilities of the states of nature (predictability)

• Internal: uncertainties related to the modelling and analysis process

• External: uncertainties related to the environment

• Controllable: uncertainties under control of DM• Uncontrollable: uncertainties outside the control of the DM

Decision Making under Uncertainty

• Controllable uncertainties: should be included in the decision alternatives

• Uncontrollable uncertainties: use methods in the table

Methods Strict uncertainty

Subjective prob.

External Internal

PSM X X X X

Scenario X (X) X

Simulation (X) X X

Utility X X

Split criteria X X

Fuzzy X X X

Sensitivityanalysis

X X X X

Experiences from the Case Study

• SSM provided a good starting point – development of rich picture and CATWOE– Formulation of the decision problem

• Using scenario planning combined with MCDA– Bring structure to the decision process– Explicit discussion of uncertainties– Learning process

• A comprehensive and time consuming process• Methods (soft and hard) should be fitted to the problems

identified and formulated

Some Preliminary Results

• Distribution of decision alternatives in each scenario

• Is any of the alternatives stochastic dominant (first, second, third)?

• The effectiveness of today’s oil-spill preparedness system (given optimal decision-making in each situation)

• The multi-methodological framework seems to work well for this case study.

Scenario Planning


Outcomes

Structure

Review

Uncertainties


A Multi-Methodological Framework for Analysing Crisis Management and Low Intensity Conflicts...

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