Risk in Complex Systems: From No Data to Big Data

Transcription

1 Risk in Complex Systems: From No Data to Big Data Patrick McSharry Head of Catastrophe Risk Financing, Smith School of Enterprise & the Environment, University of Oxford Carnegie, Mellon University in Rwanda

2 Risk in complex systems: from no data to big data Dr Patrick McSharry Smith School of Enterprise and the Environment Oxford MAN Institute of Quantitative Finance University of Oxford ICT Center of Excellence Carnegie Mellon University Contact: 9 th Oct 2014 Global Challenges Foundation & Future of Humanity Institute, Oxford University Oxford, UK

3 Nuclear Risk

4 Risk

5 Forecast uncertainty Honest forecasts are needed Can society handle probabilistic forecasts? Met Office says it will 'empower people to make their own decisions' New Met Office forecast system likely to mean 80% chance of confusion Tamara Cohen, Daily Mail, 10 November 2011.

6 Science and the media Met Office and the BBC: BBQ summer and Mild winter Climategate (2009) L Aquila earthquake: seven natural disaster experts sentenced to six years in jail for manslaughter Judge: assessment of risks connected to the seismic aclvity in the area around L'Aquila turned out to be completely vague, generic and ineffeclve

7 Source: Science and policymaking

8 Alarmist?

9 Reputation: climategate

10 Human experts versus machines Traditionally important decisions have been made by human experts Predictive analytics due to availability of data, computational resources and quantitative techniques Accounting for uncertainty is key for quantifying the confidence underlying the decision-making process

11 Big data Large quantities of data Structured and unstructured data Temporal and spatial Source: Mark Graham, OII Data on individuals, households, organisations Transactional data (searches, clicks, tweets, purchases, comments ) Data about what people actually did and what they are doing in real-time

12 Risk and Reward Big data involves considering potenlal rewards at the cost of privacy risk Data analylcs will help organisalons to berer understand potenlal rewards as well as associated risks This will facilitate achieving an oplmal balance between future risk and reward

13 Occam s razor Occam s razor (principle of parsimony): seek the simplest model that explains the data How complicated should a model be? How can we evaluate and compare models? What are the appropriate benchmarks? Examples: William of Occam studied theology at the University of Oxford from 1309 to 1321, but never completed his master's degree Parsimonious risk indices (catastrophes, climate) Diagnostics for decision-making

14 Global Trends By 2050, world population will be 9 billion with 70% living in cities Security of food, water and energy Today, 925 million people experience hunger, with food price spikes and volatility threatening the sustainability of global food security Interconnected risks: Rising food prices à Tunisian street vendor, Mohamed Bouazizi, sets himself on fire and dies à Ben Ali toppled from from power à Arab Spring

15 Model Risk Numerous major losses which were not adequately modelled Japan ($37bn); NZ quake ($12bn); Thai flood ($10bn) Vendor model changes Modelled versus real world & uncertainty Regulation associated with Solvency II and increased focus on 1 in 200 year events Compliance issues relating to model uncertainty and risk assessment

16 Systemic Risk Exposure is more interconnected than ever Sovereign risk, Arab Spring, Spatiotemporal extremes Supply chain risk (outsourcing, lean manufacturing, just in time inventory) Local and regional models are inadequate for assessing potential global losses Thailand flooding was an unexpected loss $10 bn (forecasted to reach $20 bn) Contingent business interruption Semiconductors, car manufacturing

17 Catastrophe Modelling Risk = Hazard x Exposure x Vulnerability Under certain assumptions, extreme value theory or simulation can be used to perform extrapolation in order to assess the probability of extreme losses. Loss from a 1 in 200 year event is estimated at $152 billion. Loss from Katrina was $90 billion.

18 Loss versus wind speed

19 Risk Forecasting Quantitative predictive modelling informed by qualitative analysis Fusion of scientific knowledge and empirical investigations based on data analysis Provide prospective rather than retrospective risk analysis Bridge gap between state of the art modelling and real-world decision making

20 Catastrophe risk Member of the Willis Research Network working on parsimonious models for catastrophe risk assessment Assessment of economic losses arising from disasters Demonstration of ability of atmospheric models to generate synthetic hurricanes Potential for global windstorm risk assessment Loss estimates from calibrated HIGEM model (solid) versus commercial CAT model (dashed).

21 Insufficient historical data Ongoing collaboration with EU partners in the SafeWind consortium Assessment of risk of extreme wind speed to measure resource Wind farms sites only have a few years of data Dynamical atmospheric models Reanalysis data (~ 50 years) Anastasiades & McSharry (2014). Wind Energy

22 Regulation: acceptable risk Q2 Pillar 1 of Solvency II will require (from 01- Jan-2016) insurers operating in EU to use a quantitative approach for calculating their solvency capital ratio (SCR). The SCR is the capital an insurer should hold to meet its obligations over the next year with a probability of at least 99.5%. This corresponds to having sufficient capital to withstand the loss from a 1 in 200 year event

23 Scenarios and model averaging Hellman: range [2e-4, 5e-3] Barrett: range [ , 0.07] President Obama & Gen.Powell: <0.001% Focus group to assign weights to all three models and combine this into a final distribution for the estimate Other scenarios should also be considered with new countries having nuclear weapons

24 Dynamical systems Intuitively, based on linear equilibrium systems, we expect shocks to die away Nonlinear dynamical systems (NDS) help us understand the impacts of shocks in the real world NDS tend to have multiple attractors (some safe for humans and some not) Moving from a safe attractor to an unsafe attractor may be detrimental and difficult to reverse

25 Logistic map: x n+1 = ax n (1-x n ) Bifurcation diagram McSharry & Smith, Physical Review Letters, 1999.

26 Critical Transitions Source:

27 Challenges Extreme events often cluster in time and space Independent analysis of risks may be misleading Using a Poisson assumption for a long memory process will underestimate the risk Correlated risks refer to a combination of hazards such as wind and rain Earthquake and tsunami, Japan (2011) cost $235 billion (World Bank) due to multi-hazards, nuclear crisis and loss of economic activity

28 Conclusions (1) PPPs to better understand risks by constructing open-access models, improving data quality and embracing forward-looking risk forecasting techniques; (2) Quantitative models can complement qualitative reasoning to generate scenarios (3) Parsimonious risk indices for communicating to decision-makers and policy-makers