Types of Decision Problem and Applications of Decision Support and Analysis Simon French

2 Types of Decisions

3 Players Decision Makers Experts Science Forecasts of what might happen Stakeholders Values Accountabilities and responsibilities Analysts Process expertise

4 Strategy Pyramid (1) Strategic Tactical Operational

5 Strategy Pyramid (2) Strategic Tactical Operational Instinctive (recognition primed) unstructured, long time spans of discretion very structured, short time spans of discretion

6 Planned, Orderly Activities Strategic thinking ….. Tactical thinking …. Implementation Operational, structured decision making Strategic, unstructured decision making

7 Responsive Activities & Emergent Strategy Immediate response …… regain of control Instinctive, (rehearsed?) decision making Strategic, unstructured decision making

8 The interplay between rationalistic and emergent strategy Rationalistic decision making brings coherence to parts of the strategy So decision analysis is usually made against background of some inconsistency and in recognition that this will continue Savage’s ‘small world’

9 Organisational Levels Strategic  Corporate Strategic Tactical  General Operational  Operational Instinctive  Hands-on Work (recognition primed)

10 Levels of Decision Support Level 0:Acquisition, checking and presentation of data, directly or with minimal analysis, to DMs Level 1:Analysis and forecasting of the current and future environment. Level 2:Simulation and analysis of the consequences of potential strategies; determination of their feasibility and quantification of their benefits and disadvantages. Level 3:Evaluation and ranking of alternative strategies in the face of uncertainty by balancing their respective benefits and disadvantages.

11 Business Intelligence Data Mining DSS by levels and domains Domain of Activity Level of Support Hands-on work OperationalGeneralCorporate Strategic Level 3 Level 2 Level 1 Level 0 EIS AI/Expert Systems OR models Forecasting Decision Analysis Soft modelling

12 Cynefin: a Welsh habitat D. Snowden (2002). "Complex acts of knowing - paradox and descriptive self- awareness." Journal of Knowledge Management 6 pp

13 Cynefin and decision making categorise and respond Sense and respond probe, sense, respond act sense respond

14 Cynefin and solutions Databases expert systems, neural nets, deterministic optimisation data assimilation and fitting then optimisation Judgement collaboration knowledge mgmt Explore and seek insight Evaluation and validation data driven Evaluation and validation judgement based

15 Cynefin and statistics Repeatable events Unique events Events? Estimation and confirmatory statistics exploratory statistics

16 Cynefin and investigation Experiments and trials Case studies and surveys

17 Do preferences exist? DeFinetti famously said –“Probabilities do not exist” Do preferences exist? or better –When do preferences come into existence?

18 Cynefin and Values Repeatable events Unique events Events? Values/preferences understood & rehearsed Values/preferences not fully understood

Applications: Simpler than you think! Simon French

20 Decision support means Helping the decision makers and the other players understand  Working at their cognitive level Need simple models usually to convey ideas Analysts may need complex models but more likely they need diagnostics for simple models Paradoxically decision support and analysis drives to simplicity Requisite modelling Start simple and build in necessary complexity until there is sufficient understanding to ‘make the decision’

21 Chernobyl The world’s worst nuclear accident Complex event at a complex time in Soviet Union’s history Many people affected Vast swathes of land contaminated

22 Hierarchy used in 5 th Conference

23 Decisions based on Intervention Levels Measure of Dose Above this level, relocation would be advised and offered Below this level, there would be little need to do anything except reassure the population In between these levels, many countermeasures would be implemented to clean up the area and protect the population

24 Details of the Countermeasure Strategies

25 Framing Issues Imagine that you are a public health official and that an influenza epidemic is expected. Without any action it is expected to lead to 600 deaths. However, there are two vaccination programmes that you may implement: Programme A would use an established vaccine which would save 200 of the population. Programme B would use a new vaccine which might be effective. There is a 1/3rd chance of saving 600 and 2/3rds chance of saving none.

26 Framing Issues Imagine that you are a public health official and that an influenza epidemic is expected. Without any action it is expected to lead to 600 deaths. However, there are two vaccination programmes that you may implement: Programme A would use an established vaccine which would lead to 400 of the population dying. Programme B would use a new vaccine which might be effective. There is a 1/3rd chance of no deaths and 2/3rds chance of 600 deaths.

27 Pareto Plots

28 Sensitivity analysis

29 Chernobyl The ‘world’ was a complex as it comes The analysis and presentation was really rather simple –And hugely effective.

30 Fast and Frugal aids Simple heuristics have been shown to help substantially reduce psychological biases For instance, Gigerenzer has shown that ‘frequency’ presentations can reduce the issue of ‘forgotten base rates’

31 Probabilities as frequencies

32 Other fast and frugal ideas Consider the opposite –Challenge your thinking –Calibrate yourself against past decisions Over-define some parts of the model –Beware of framing effects

33 Other fast and frugal ideas Consider the opposite –Challenge your thinking –Calibrate yourself against past decisions Over-define some parts of the model –Beware of framing effects Positive emotions encourage divergent thinking –Brainstorm and formulate issues when you are happy!

34 Applications of decision support and analysis is usually about bringing together various simple ideas to help decision makers evolve their understanding, preferences and beliefs.

35 The process of decision analysis Formulate Evaluate Review Requisite? Decide Yes No

36 Business Intelligence Data Mining DSS by levels and domains Domain of Activity Level of Support Hands-on work OperationalGeneralCorporate Strategic Level 3 Level 2 Level 1 Level 0 EIS AI/Expert Systems OR models Forecasting Decision Analysis Soft modelling

37 Linear programming models Huge and complex But actually rather simple with respect to the world Algorithms are complex (though idea is easy) But models are simple to explain in principle

38 Business Intelligence and Analytics Is data mining based on simple or complex models Algorithms are complex But representation to managers is usually simple –Flags and warnings saying ‘check this!’