1 / 41 Michael Beer & Gabe Mythen How to Study Risk Michael Beer & Gabe Mythen University of...

1 / 41Michael Beer & Gabe Mythen

How toStudy Risk

Michael Beer & Gabe MythenUniversity of LiverpoolInstitute for Risk & Uncertainty


MULTI-DISCIPLINARY CHALLENGE & OPPORTUNITY

Research and Education in the Institute for Risk & Uncertainty

Exploration of risk in the intersection of 8 disciplines

Architecture

Engineering Environmental Sciences

Economics and Finance

Financial andActuarial Mathematics

Computer Science

Psychology

Social Sciences

Primary goalsresearch and develop new risk and uncertainty theories, methodologies andapplications (e.g. for vague and imprecise information, expert knowledge, etc.)

deliver postgraduate education and training at Master and PhD levels inRisk and Uncertainty (multi-disciplinary Master Programme and PhD projects)

provide research, consultancy and training services to Government, Businessand Industry (e.g. software, courses, consulting, tailored graduates-PhD etc.)




Research focus areas

uncertainty quantification and robustness assessmentmethodologies & techniques for estimation, processing, simulation,assessment of risk; tools and software for industry (COSSAN-X)

financial & economical network analysis and banking risksinvestment risks, stability and failure risk on all kinds of systems; earthquake and tsunami risk; insurance matters

communication of risktranslating the understanding of risk between disciplines, societies and authorities; high stake consequences, terrorism, disaster management

environmental risk and mitigationclimate change, droughts, floods, landslides, earthquakes; energy networks; nuclear system failure & mitigation; supply system reliability

project risks and managementrisk and uncertainty from human action and natural environment (climate change) to construction processes and operation of products


Network for Uncertainty and Risk Quantification

Reliable computing,Verified numerical simulation

Computer Science

Environmental Sciences

Climate change,storms, floods, …,

load patterns

Economics

Systemic banking risks

Financial Mathematics

Investment risks

Engineering

Reliability, Robust design

Global collaboration with academic andindustrial partners: e.g. Swiss Re, Car industry, …

Generate synergy on a common mathematicaland computational basiswith different but associated application areas




GENERAL SITUATION

Risk & Uncertainty in Engineering

» close to reality» numerically efficient

Endeavor

numerical modeling − physical phenomena, structure, and environmentprognosis − system behavior, hazards, safety, risk, robustness,

economic and social impact, ...

Deterministic methods

deterministicstructural parameters Reality

deterministiccomputational models

Uncertainty ?

Consequences?Variability ?

Vagueness ?

Indeterminacy ?

Fluctuations ?

Imprecision ?

Ambiguity ?


SOME QUESTIONS

ofimprecise and rare data

Is thereliability analysisstill reliable ?

Is it safe ?statistical analysis

Effects on Pf ?

reliabilityanalysis

F(x)

model

Pf

~

~

set of plausible s

[Pf,l, Pf,r]imprecisionreflected in Pf

Sensitivity of Pf to imprecision ?



How precise will it be ?

modeling, quantification, processing, evaluation, interpretation ?

SOME QUESTIONS



FUZZY RANDOM VARIABLES

fuzzy realizations generated by elementary events

x(5)~

x(4)~

x(3)~

(x)

1.0

0.0

1

2

3

4

5

realizations

fuzzy numbers

x X =

random elementary events

original Xj

» real-valued random variable X that is completely enclosed in X

~

representation of X» fuzzy set of all possible originals Xj

~

random -level sets X

x(2)~

x(1)~

-discretization

X: F()~

Mathematical model



FUZZY PROBABILITIES IN RELIABILITY ANALYSES

structural parametersFailure probabilityFuzzy parameters

X~

acceptable Pf

Pf0

i

µ(x)

1

x

Pf

~

probabilistic model parameters

acceptableparameter interval

sensitivityof Pf w.r.t. x

0

i

µ(Pf)

1

mapping

X~

Pf

~

coarse specifications of design parameters & probabilistic models

attention to / exclude model options leading to large imprecision of Pf

acceptable imprecision of parameters & probabilistic models

indications to collect additional information

definition of quality requirements robust design



EXAMPLE 1 RELIABILITY ANALYSIS

Fixed jacket platformimprecision in the models for» wave, drag and ice loads» wind load» corrosion» joints of tubular members» foundation» possible damage




Probabilistic model (After R.E. Melchers)

» c(t,E) corrosion depth» f(t,E) mean value function» b(t,E) bias function» (t,E) uncertainty function (zero mean Gaussian white noise)» E collection of environmental and material parameters

c t E b t E f t E t E, , , ,

f(t,E)b(t,E)

Modeling ?

(time-dependent corrosion depth, uniform)




dimensions» height: 142 m» top: 27 X 54 m» bottom: 56 X 70 m

Monte Carlo simulation with importance sampling and response surface approximation

loads, environment» T = 15C, t = 5 a» random: wave height, current, yield stress, and corrosion depth c(t,E)» beta distribution / interval for b(.) [0.8,1.6]

Reliability analysis

Failure probability

7.0 8.0 9.0 10.0

Beta (q=r=1)

Beta (q=r=2)

Interval

Pf (×10-7)

9.499.60 9.73

7.0 8.0 9.0 Pf [107]

9.739.499.60beta, CI

beta, CIIinterval

b(.) = 1.0

Fixed jacket platform

Nb = 114Nb = 2000

NPf = 5000



EXAMPLE 2 COLLAPSE SIMULATION

fuzzy random variables» material behavior

fuzzy stochastic structuralanalysis

» Monte Carlo simulation with response surface approximation

σ

εεuε2ε1

σ2

σ1

f(σ1)~

f(ε2)~

f(εu)~

fuzzy random function

X(σ1, ε2, εu)~

fuzzy probability distribution

function F(t) for failure time~

t = τ, criticalfailure time

t0

1P(t<τ)~

(P(t<τ))

Controlled demolition of a store house

(DFG Research Unit 500)



RESUMÉ

How to study risk

rapidly growing dimension and complexity of problems

need for a change in strategy to approach risk

Multi-disciplinary challenge & opportunity

key is multi-disciplinary communication & collaboration

great opportunitiesand mutual benefits

small effort

Let’s contributeto the evolution of

research approaches.


.... The Second Half

Policy Impacts of the Turn to Risk

Risk as a Technology of Control

Limits to Risk in the Policy Arena

Example: Counter Radicalisation


Uses and Abuses of Risk

Tracing the Risk Turn

Political Discourse

Media Representation

Theoretical Application

Policy Formation

Ideology and Power


Recent Research

Impacts of Counter-Terrorism Policies on Muslim Minority Groups

Mythen, G. (2012) ‘Contesting the Third Space? Identity and Resistance Amongst Young British Pakistanis’ British Journal of Sociology, 63(3)

Mythen, G., Walklate, S. and Khan, F. (2012) ‘Why Should We Have to Prove we’re Alright?’ Counter-Terrorism, Risk and Partial Securities’, Sociology, 46(4)

Mythen, G. (2012) ‘Who Speaks for Us?’ Counter-terrorism, collective attribution and the problem of voice’, Critical Studies on Terrorism, 5(3): 1-16.


Present Research (with Walklate and Peatfield)

Counter-Radicalisation: Discourses, Evidence and Elisions

Which discourses of risk are present in political debates about radicalisation?

Who is thought to be at risk from radicalisation and why?Which risk-based strategies are utilised in policy to counter radicalisation?How reliable is the evidence surrounding the process of radicalisation?


Managing Risk: Context

Nichtwissen

Imperfect Knowledge

Centrality of Terrorist Risk

Combating Terrorism

Pre-emptive Interventions


‘The ultimate deadlock of risk society ... resides in the gap between knowledge and decision: there is no one who really knows the global outcome - at the level of positive knowledge, the situation is radically ‘undecideable’ - but we none the less have to decide ... so risk society is provoking an obscene gamble, a kind of ironic reversal of predestination: I am accountable for decisions which I was forced to make without proper knowledge of the situation’

(Beck, 1999:

78)


David Cameron (2010): Munich Security Conference

‘The biggest threat that we face comes from terrorist attacks, some of which are, sadly, carried out by our own citizens... we should acknowledge that this threat comes in Europe overwhelmingly from young men who follow a completely perverse, warped interpretation of Islam, and who are prepared to blow themselves up and kill their fellow citizens’.


Combating Terrorism


Pre-emptive Interventions

Detention Without ChargeControl OrdersSection 44 and Stop Search PowersGlorification of Terrorism


The Terrorist as Tabula Rasa?

Vulnerable? Naive?Marginalized?

Neuropathological?Anomic? Brain-washed?


The Problem of Radicalisation

Axial in Security Policy

Imprecise Definition

Stakeholders and Turf-markers

An Industrial Pursuit?


Counter Radicalisation Industry?

‘Today, counter-radicalisation is a career, as young scholars enter the mini-industry of national security think-tanks, terrorism studies departments, law enforcement counterterrorism units and intelligence services to work on modelling radicalisation’

(Kundnani, 2012: 7)


Imprecise Definition

‘Radicalisation is the process of increasing readiness to pursue changes - possibly by undemocratic means - and to encourage others to do so’

(Dutch General Intelligence Service, 2007)


Explaining Radicalisation

An Ideological Process

(Silber and Bhatt)* A Misguided Religious Cause

(Gartenstein-Ross and Grossman)* A Psycho-Social Condition (Laqueur) An Assumed Identity (Wiktorowicz)


Sources of Evidence

Classified Reports and Intelligence Services Data

Court Data Interviews with Radical

Activists, Convicted Terrorists and ‘de-radicalized’ Individuals


Silber and Bhatt (2007)


Radicalisation: Theological Cause

Process of Incubation

Influence of Charismatic Leader(s)

Acceptance of Islamist Ideology

Exposure to Jihadi Propaganda

Convicted to Need for Violence (Gartenstein-Ross and Grossman, 2009)


Risk Modelling Radicalisation?

‘If a set of religious beliefs can be identified that terrorists share with a wider group of radicals, but which ‘moderate’ Muslims reject, then a model can be developed in which such beliefs are seen as ‘indicators’ of radicalisation, a point along a pathway to becoming a terrorist’.

(Kundnani, 2012: 11)


Preventing Violent Extremism

A ‘Hearts and Minds’ Approach?

Building Partnerships

Challenging Radicalism

Identifying ‘at risk’ Individuals

Early Intervention


The Channel Project

Teachers, Youth Workers, Health ProfessionalsPreventing ‘Recruitment’?1120 Vulnerable Individuals: 90% MuslimsSurveying Suspect PopulationsThe ‘biggest spying programme in Britain in modern times’ ?


Problems and Issues

Industry Without Evidence? Merged Assumptions: What Goes

on Before the Bombs Go Off?Illusion of UnderstandingEmphasis on Theological

ContortionsFixation with Muslim Extremism*


Unknowable or Overlooked?


Elephants in the Room?

Western Imperialism

Neo-Colonialism

Foreign Policy

Military Interventions

Racism, Discrimination and

Inequality


Siddique Khan: Martyrdom Video‘Your democratically elected governments continuously perpetrate atrocities against my people all over the world. And your support of them makes you directly responsible, just as I am directly responsible for protecting and avenging my Muslim brothers and sisters. Until we feel security, you will be our targets. And until you stop the bombing, gassing, imprisonment and torture of my people we will not stop this fight. We are at war and I am a soldier. Now you too will taste the reality of this situation’.


UK ICM Poll (2006): British Muslims

One fifth of respondents had ‘some sympathy with the feelings and motives of those that carried out the London attacks’

Representative of the Views of 350,000 British Muslims

Conflating ‘Radical’ Views and Violent Methods?


Conclusions

Primary Empirical Foundation

Policy Based Evidence Making?

Climate of Suspicion

Restricting Expression

Iatrogenic Policy Making*


Risk Reduction or Risk Production?

‘The question of whether anti-terrorism laws have in themselves become a significant factor in violent radicalisation in Europe remains an open question – a question with obvious policy implications’

(Dalgaard-Nielson, 2010: 800).

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