How to Study Risk

1. How toStudy Risk Michael Beer & Gabe Mythen University of Liverpool Institute for Risk & Uncertainty

2. Multi-disciplinary challenge & opportunity Research and Education in the Institute for Risk & Uncertainty Exploration of risk in the intersection of 8 disciplines Architecture Computer Science Social Sciences Environmental Sciences Engineering Psychology Economics and Finance Financial and Actuarial Mathematics Primary goals research and develop new risk and uncertainty theories, methodologies and applications (e.g. for vague and imprecise information, expert knowledge, etc.) · provide research, consultancy and training services to Government, Business and Industry (e.g. software, courses, consulting, tailored graduates-PhD etc.) · deliver postgraduate education and training at Master and PhD levels in Risk and Uncertainty (multi-disciplinary Master Programme and PhD projects) ·

3. Multi-disciplinary challenge & opportunity Research and Education in the Institute for Risk & Uncertainty Research focus areas uncertainty quantification and robustness assessment · methodologies & techniques for estimation, processing, simulation, assessment of risk; tools and software for industry (COSSAN-X) financial & economical network analysis and banking risks · investment risks, stability and failure risk on all kinds of systems; earthquake and tsunami risk; insurance matters communication of risk · translating the understanding of risk between disciplines, societies and authorities; high stake consequences, terrorism, disaster management environmental risk and mitigation · climate change, droughts, floods, landslides, earthquakes; energy networks; nuclear system failure & mitigation; supply system reliability project risks and management · risk and uncertainty from human action and natural environment (climate change) to construction processes and operation of products

4. Research and Education in the Institute for Risk & Uncertainty Multi-disciplinary challenge & opportunity Network for Uncertainty and Risk Quantification Computer Science Reliable computing, Verified numerical simulation Economics Engineering Systemic banking risks Reliability, Robust design Financial Mathematics Investment risks Generate synergy on a common mathematical and computational basis with different but associated application areas Environmental Sciences Global collaboration with academic and industrial partners: e.g. Swiss Re, Car industry, … Climate change, storms, floods, …, load patterns

5. General Situation Vagueness ? Uncertainty ? Imprecision ? Indeterminacy ? Fluctuations ? Ambiguity ? ? Consequences Variability ? Risk & Uncertainty in Engineering Endeavor numerical modeling − physical phenomena, structure, and environment · prognosis − system behavior, hazards, safety, risk, robustness, economic and social impact, ... » close to reality » numerically efficient Deterministic methods deterministic computational models deterministic structural parameters · · Reality

6. Some Questions Risk & Uncertainty in Engineering of imprecise and rare data statistical analysis Is it safe ? ~ F(x) set of plausible s model reliability analysis ~ Pf Is the reliability analysis still reliable ? [Pf,l, Pf,r] imprecision reflected in Pf Effects on Pf ? Sensitivity of Pf to imprecision ?

7. Risk & Uncertainty in Engineering Some Questions How precise will it be ? modeling, quantification, processing, evaluation, interpretation ?

8. fuzzy Random Variables ~ ~ ~ ~ ~ x(2) x(4) x(3) x(1) x(5) original Xj » real-valued random variable X that is completely enclosed in X · ~ Risk & Uncertainty in Engineering Mathematical model fuzzy realizations generated by elementary events · random elementary events (x)    1.0 fuzzy numbers 5 ~  4 X:   F() 3 2 1 0.0 ~ representation of X » fuzzy set of all possible originals Xj · x X =  realizations -discretization random -level sets X ·

9. Fuzzy probabilities in reliability analysEs ~ ~ ~ ~ Pf Pf X X Risk & Uncertainty in Engineering Fuzzy parameters Failure probability structural parameters · probabilistic model parameters · acceptable Pf µ(x) µ(Pf) 1 1 acceptable parameter interval sensitivity of Pf w.r.t. x i i mapping  x Pf 0 0 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

10. example 1  Reliability Analysis Risk & Uncertainty in Engineering Fixed jacket platform imprecision in the models for » wave, drag and ice loads » wind load » corrosion » joints of tubular members » foundation » possible damage ·

11. example 1  Reliability Analysis Risk & Uncertainty in Engineering Probabilistic model (After R.E. Melchers) (time-dependent corrosion depth, uniform) · » 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 Modeling ? b(t,E) f(t,E)

12. example 1  Reliability Analysis Beta (q=r=1) 9.60 9.73 Beta (q=r=2) 9.49 Interval Pf (×10-7) 7.0 8.0 9.0 10.0 Risk & Uncertainty in Engineering Fixed jacket platform dimensions » height: 142 m » top: 27 X 54 m » bottom: 56 X 70 m loads, environment » T = 15C, 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 Monte Carlo simulation with importance sampling and response surface approximation · Failure probability Nb = 2000 Nb = 114 beta, CI 9.60 beta, CII 9.73 9.49 interval b(.) = 1.0 NPf = 5000 7.0 8.0 9.0 Pf [107]

13. example 2  Collapse Simulation σ σ2 σ1 fuzzy probability distribution function F(t) for failure time · ~ εu ε ε1 ε2 ~ 1 P(t<τ) ~ ~ ~ f(σ1) f(ε2) f(εu) (P(t<τ)) fuzzy random function X(σ1,ε2,εu) ~ 0 t t = τ, critical failure time Risk & Uncertainty in Engineering (DFG Research Unit 500) Controlled demolition of a store house fuzzy random variables · » material behavior fuzzy stochastic structural analysis · » Monte Carlo simulation with response surface approximation

14. Resumé How to study risk Multi-disciplinary challenge & opportunity rapidly growing dimension and complexity of problems · need for a change in strategy to approach risk · key is multi-disciplinary communication & collaboration · great opportunities and mutual benefits small effort Let’s contribute to the evolution of research approaches.

15. .... 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

16. Uses and Abuses of Risk Tracing the Risk Turn Ideology and Power • Political Discourse • Media Representation • Theoretical Application • Policy Formation

17. 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.

18. 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?

19. Managing Risk: Context Nichtwissen Imperfect Knowledge Centrality of Terrorist Risk Combating Terrorism Pre-emptive Interventions

20. ‘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)

21. 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’.

22. Combating Terrorism

23. Pre-emptive Interventions Detention Without Charge Control Orders Section 44 and Stop Search Powers Glorification of Terrorism

24. The Terrorist as Tabula Rasa? • Vulnerable? Naive? • Marginalized? Neuropathological? • Anomic? Brain-washed?

25. The Problem of Radicalisation • Axial in Security Policy • Imprecise Definition • Stakeholders and Turf-markers • An Industrial Pursuit?

26. 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)

27. 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)

28. 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)

29. Sources of Evidence • Classified Reports and Intelligence Services Data • Court Data • Interviews with Radical Activists, Convicted Terrorists and ‘de-radicalized’ Individuals

30. Silber and Bhatt (2007)

31. 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)

32. 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)

33. Preventing Violent Extremism • A ‘Hearts and Minds’ Approach? • Building Partnerships • Challenging Radicalism • Identifying ‘at risk’ Individuals • Early Intervention

34. The Channel Project • Teachers, Youth Workers, Health Professionals • Preventing ‘Recruitment’? • 1120 Vulnerable Individuals: 90% Muslims • Surveying Suspect Populations • The ‘biggest spying programme in Britain in modern times’ ?

35. Problems and Issues • Industry Without Evidence? • Merged Assumptions: What Goes on Before the Bombs Go Off? • Illusion of Understanding • Emphasis on Theological Contortions • Fixation with Muslim Extremism*

36. Unknowable or Overlooked?

37. Elephants in the Room? • Western Imperialism • Neo-Colonialism • Foreign Policy • Military Interventions • Racism, Discrimination and Inequality

38. 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’.

39. 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?

40. Conclusions • Primary Empirical Foundation • Policy Based Evidence Making? • Climate of Suspicion • Restricting Expression • Iatrogenic Policy Making*

41. 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).