1. Decision Intelligence Course
2. Decision Intelligence
- Decision intelligence is a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design model align execute monitor and tune decision models and processes. Those disciplines include decision management (including advanced nondeterministic techniques such as agent-based systems) and decision support as well as techniques such as descriptive diagnostics and predictive analytics.
- In short: Decision Intelligence turns provided information into better actions.
3. Decision doesn’t necessarily dictate desired outcomes
- The most important lesson in decision making is understanding the difference between decision and outcome.
4. Decision:
- Decision is irrevocable allocation of resources.
5. Outcome:
- Outcome is how things turn out later or a consequence of a decision.
6. Right decision paradox
- It is possible to make the right decision and end up with a wrong outcome.
7. Biases:
- Outcome bias
- Outcome bias arises when a decision is based on the outcome of previous events without regard to how the past events developed.
- Survivorship Bias
- Survivorship bias or survival bias is the logical error of concentrating on entities that passed a selection process while overlooking those that did not.
- Hindsight bias
- Hindsight bias is a type of cognitive bias that causes people to convince themselves that a past event was predictable or inevitable.
- Confirmation Bias
- Confirmation bias is the tendency to search for interpret favor and recall information in a way that confirms or supports one's prior beliefs or values.
- Novelty Bias
- also referred to as the Novelty Effect describes a tendency to place undue emphasis on new or novel things.
8. The Paradox of Choice
- The paradox of choice suggests that an abundance of options actually requires more effort to choose and can leave us feeling unsatisfied with our choice.
9. Process for decision making using data intelligence:
- User asks for help
- Define desired outcome before moving to any further steps proposing some examples of what the outcomes of provided decision could be
- If user doesn’t know what his desired outcome is help him to define it by guiding him step by step using real life practical examples
- If user knows what is his desired outcome go to the next step
10. Define performance goals and process goals that can help to achieve desired outcome.
11. Identify default actions that user could take and give user recommendation on how to avoid common biases affecting decision making for specific analyzed case (outcome survivorship hindsight confirmation novelty)
12. Come up with alternative outcomes for the decision and based on possibilities propose the best decision making technique or framework for a given problem.
13. Walk user step by step through the framework until user makes a final decision or decides to exit look in your data and over internet for data that can support user decision making process
14. In case user is not able to make a decision due to problem that prevents him from doing so propose to use one of the problem solving techniques or root cause analysis frameworks
15. Identifying outcome and goal setting
- Outcome goal – actual goal you want to achieve it can be vague and hard to measure but should be the north star.
- Performance goal – goal that’s measurable and mostly under user control or at least influence contributing to the outcome goal
- Process goal – measurable and fully under user control clearly defined
16. Problem Solving in Decision Making
- Decision makers can reach a blockage in the decision process when there is an unresolved problem that cannot be easily resolved.
- In such case problem solving techniques and root cause analysis frameworks can be used such as:
- 5 why’s
- Pareto chart
- Ishikawa fishbone diagram
- Fault tree analysis
- Scatter diagram if data is available
17. Frameworks supporting business and professional decision making
- SWOT analysis (only supported by PEST or PESTEL)
- Decision Matrics
- Pareto Analysis
- Decision Tree
- Cost-benefit analysis
18. Other valuable sources & definitions
- Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions.
- Prediction tools increase productivity—operating machines handling documents communicating with customers.
- Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete.
19. The Pareto Chart
- is a very powerful tool for showing the relative importance of problems. It contains both bars and lines where individual values are represented in descending order by bars and the cumulative total of the sample is represented by the curved line.
20. Decide about the Christmas or gifts for other occasions.
21. The science of gift giving
- Gift giving is an important social custom that has many layers to it. It is a representative microcosm of many social constructs—identity social norms similarity obligatory rituals reciprocity and so on. A gift giver has several objectives: to satisfy the recipient to signal their own status to represent the status of the relationship and so on. In addition to balancing all this add into the mix the paradox of choice—the feeling of paralysis that arises when we’re faced with too many options. Gift giving is an art but it calls for science too.
- If you are struggling to cross those last few names off your Christmas list might I complicate your life just a little bit with a few more factors you should consider? Behavioral science can offer us a few evidence-based tips on how to pick the right gift for somebody.
