An ICT system for decision-making under  severe uncertainty in disaster management   Tina Comes,  Michael Hiete, Frank Sch...
Outline <ul><li>Strategic decision-making in disaster management </li></ul><ul><li>Distributed generation of scenarios </l...
Strategic decision-making  in disaster management Selection of best mitigation measure Problem: Uncertainty in Consequence...
Determining the consequences of a decision <ul><li>Consequences  </li></ul><ul><ul><li>for each mitigation measure </li></...
Distributed generation of scenarios Consequences  of decision alternative a Expert 1 Expert 2 Expert 3 Expert 4 Expert N …...
Construction of the Causal Map <ul><li>Attributes: Operationalisation of objectives </li></ul>Expert Z b l i CM Expert A b...
Generation of scenarios b 1 i b k i b 2 i b 3 i -… b j i b m i … … <ul><li>successively: determine possible states for eac...
Evaluation of scenarios <ul><li>per mitigation measure, a set of scenarios is generated  </li></ul><ul><li>evaluation of e...
Disaster management example <ul><li>Situation: </li></ul><ul><li>derailment of freight train, leakage in chlorine tank (pr...
Construction of Causal Map Information request Attribute Tree Health Effort Impact on society # ill in hospital  exposed a...
Generation of scenarios Flow of information Number of ill in hospital in  area A exposed Decision alternative: Shelter are...
Exemplary results <ul><li>Selected  sources of uncertainty: </li></ul><ul><li>success  of chlorine transfer </li></ul><ul>...
Conclusion and Outlook <ul><li>Integration of scenarios and multi-criteria evaluation </li></ul><ul><ul><li>consideration ...
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COMES Tina - An ICT system for decision making under severe uncertainty in disaster management

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An ICT system for decision making under severe uncertainty in disaster management

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COMES Tina - An ICT system for decision making under severe uncertainty in disaster management

  1. 1. An ICT system for decision-making under severe uncertainty in disaster management Tina Comes, Michael Hiete, Frank Schultmann 3rd International Disaster and Risk Conference Risk, Disasters, Crisis and Global Change – From Threats to Sustainable Opportunities 30 May - 3 June 2010, Davos, Switzerland
  2. 2. Outline <ul><li>Strategic decision-making in disaster management </li></ul><ul><li>Distributed generation of scenarios </li></ul><ul><li>Evaluation of scenarios </li></ul><ul><li>Example </li></ul><ul><li>Conclusion </li></ul>
  3. 3. Strategic decision-making in disaster management Selection of best mitigation measure Problem: Uncertainty in Consequences and Evaluation Consequences Evaluation for each mitigation measure Multiple objectives Preferences Decision Makers and Stakeholders Experts Prognoses Situation description Decision Ranking of measures
  4. 4. Determining the consequences of a decision <ul><li>Consequences </li></ul><ul><ul><li>for each mitigation measure </li></ul></ul><ul><ul><li>with respect to each objective </li></ul></ul><ul><li>Heterogeneous information on various aspects of present situation and future development </li></ul><ul><ul><li>quality (expertise and reliability), </li></ul></ul><ul><ul><li>uncertainty (deterministic, probabilistic, fuzzy, not quantifiable), </li></ul></ul><ul><li>Boundary Conditions </li></ul><ul><ul><li>limited time to make a decision </li></ul></ul><ul><ul><li>actors possibly geographically dispersed </li></ul></ul><ul><ul><li>bounded availability of experts </li></ul></ul><ul><ul><li>risk of information overload </li></ul></ul>Aim: Generation of relevant, consistent, coherent, plausible scenarios  distributed scenario construction with Causal Maps (CMs)
  5. 5. Distributed generation of scenarios Consequences of decision alternative a Expert 1 Expert 2 Expert 3 Expert 4 Expert N … <ul><li>Configuration: </li></ul><ul><ul><li>Service Oriented Architecture: experts specify Services and Input  local Causal Map (CM) </li></ul></ul><ul><ul><li>Negotiation Protocols for identification of best timely available expertise </li></ul></ul>Scenario Expert I Expert II Expert III Expert IV … x 1 x 2 x 3 x j x i x n
  6. 6. Construction of the Causal Map <ul><li>Attributes: Operationalisation of objectives </li></ul>Expert Z b l i CM Expert A b k i b j i identification of best timely available expert CM <ul><li>for each impact factor b identify relevant experts </li></ul><ul><li>merge local CMs to global CM with nodes </li></ul><ul><li>determine local CMs </li></ul>attribute c 2 attribute c 3 attribute c 4 attribute c n-1 attribute c n … attribute c 1 … b A i b Z i b l i b k i b j i
  7. 7. Generation of scenarios b 1 i b k i b 2 i b 3 i -… b j i b m i … … <ul><li>successively: determine possible states for each node conditioned on the states of its predecessors </li></ul>Mitigation measure <ul><li>uncertainty: expert determines most relevant possible states </li></ul><ul><li> generation of scenarios by branching </li></ul>attribute c 2 attribute c 3 attribute c 4 attribute c n-1 attribute c n … attribute c 1 … b 1 i b 2 i b 3 i … b m i b 1 i b k i b 2 i b 3 i … b j i b m i … … attribute c 2 attribute c 3 attribute c 4 attribute c n-1 attribute c n … attribute c 1 … b k i b j i …
  8. 8. Evaluation of scenarios <ul><li>per mitigation measure, a set of scenarios is generated </li></ul><ul><li>evaluation of each scenario with techniques from (deterministic) Multi-Attribute Value Theory </li></ul>R Attribute Tree Criteria criterion 1 … criterion p x 2 ( a 2 ) … x n ( a 2 ) x 1 ( a 2 ) x n-1 ( a 2 ) s 1 ( a 2 ) Attributes weighing and aggregation <ul><li>Risk of information overload </li></ul><ul><li>Scenario Selection </li></ul><ul><li>Aggregation of Results </li></ul>x 2 (a 1 ) … x n (a 1 ) x 1 (a 1 ) x n-1 (a 1 ) s 1 ( a 1 ) normalisation with value functions x 2 (a) … x n (a) x 1 (a) x n-1 (a) S 1 (a) … … x 2 (a) … x n (a) x 1 (a) x n-1 (a) S 1 (a) x 2 (a) … x n (a) x 1 (a) x n-1 (a) S 1 (a) x 2 (a 1 ) … x n (a 1 ) x 1 (a 1 ) x n-1 (a 1 ) s M1 ( a 1 ) x 2 (a) … x n (a) x 1 (a) x n-1 (a) S 1 (a) x 2 (a) … x n (a) x 1 (a) x n-1 (a) S 1 (a) x 2 (a) … x n (a) x 1 (a) x n-1 (a) S 1 (a) x 2 ( a 2 ) … x n ( a 2 ) x 1 ( a 2 ) x n-1 ( a 2 ) s M2 ( a 2 )
  9. 9. Disaster management example <ul><li>Situation: </li></ul><ul><li>derailment of freight train, leakage in chlorine tank (provisionally covered). </li></ul><ul><li>chlorine must be transferred to transportation tanks </li></ul><ul><li>risk: damage of tanks or pipe can lead to release of large quantities of chlorine </li></ul>uncertain developments Time Threat current situation <ul><li>Mitigation </li></ul><ul><li>measures: </li></ul><ul><li>evacuation, </li></ul><ul><li>sheltering, </li></ul><ul><li>do nothing </li></ul><ul><li>Criteria: </li></ul><ul><ul><li>health, </li></ul></ul><ul><ul><li>effort, </li></ul></ul><ul><ul><li>impact on society </li></ul></ul><ul><li>measured by 39 attributes </li></ul>
  10. 10. Construction of Causal Map Information request Attribute Tree Health Effort Impact on society # ill in hospital exposed and sheltered # residents exposed and sheltered Criteria R Attributes Plume: shape & concentration Source term Weather Areas affected Population distribution in area A Hospitals distribution in area A Hospitals exposed in area A Leak size Chemical Vessel size Number of ill in hospital in area A exposed Mitigation measure: Shelter area A Number residents in area A exposed
  11. 11. Generation of scenarios Flow of information Number of ill in hospital in area A exposed Decision alternative: Shelter area A Number residents in area A exposed Plume: shape & concentration Source term Weather Areas affected Population distribution in area A Hospitals distribution in area A Attribute Tree Health Effort Impact on society # ill in hospital exposed and sheltered # residents exposed and sheltered Criteria R Attributes Hospitals exposed in area A Leak size Chemical Vessel size Source term Plume: shape & concentration Number of ill in hospital in area A exposed Mitigation measure: Shelter area A Number residents in area A exposed Areas affected Population distribution in area A Hospitals distribution in area A Hospitals exposed in area A # ill in hospital exposed and sheltered # residents exposed and sheltered
  12. 12. Exemplary results <ul><li>Selected sources of uncertainty: </li></ul><ul><li>success of chlorine transfer </li></ul><ul><li>residual amount of chlorine in tank </li></ul><ul><li>weather </li></ul>Evaluation R(s) Scenarios for Alternatives Evacuation (E), Sheltering (S) and Do Nothng (N) results for best and worst scenarios
  13. 13. Conclusion and Outlook <ul><li>Integration of scenarios and multi-criteria evaluation </li></ul><ul><ul><li>consideration of uncertainties in a transparent manner </li></ul></ul><ul><ul><li>detailed evaluation with respect to multiple objectives </li></ul></ul><ul><li>Distributed generation of scenarios with Causal Maps </li></ul><ul><ul><li>distributed information processing </li></ul></ul><ul><ul><li>relevance as rationale for information filtering </li></ul></ul><ul><ul><li>consistency </li></ul></ul><ul><ul><li>interdependencies and cause-effect-chains </li></ul></ul><ul><li>Further tests and development of our approach with users from emergency management authorities </li></ul><ul><ul><li>presentation of results </li></ul></ul><ul><ul><li>visualisation methods </li></ul></ul><ul><li>Dynamics of decisions </li></ul><ul><ul><li>scenario updates </li></ul></ul><ul><ul><li>sequential decision-making </li></ul></ul>
  14. 14. Thank you for your attention.

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