Effective Decision Making

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Description:
Decision making and problem solving define how we perform in business but research shows that most of us are poor at using data and probabilities to deal effectively with unfamiliar situations. This workshop will explore common pitfalls and examine how simple techniques and the growing use of machine intelligence will help us make better decisions and communicate them persuasively.
Prerequisites:
No specific pre-requisite courses.
Objectives:
Having completed this workshop the participants will:
Be aware of the common pitfalls that cause us to misread information and how to overcome them
Understand common decision making techniques and processes
Have an informed perspective on the role, benefits and limitations of machine intelligence in decision making
Be able to use proven techniques to influence others to 'buy into' their decisions
Decisions and popular techniques:
Typical situations for decision making in business
The anatomy of decision making
Common Techniques
Force field Analysis
De Bono's PMI structure; Plus- Minus = Interesting
Decision Trees
Kepner-Tregoe
Kipling's 5W1H
Common pitfalls and how to avoid them - at the organisational level:
How information travels in an organisation
Silos and goal conflicts
Group think
Company culture and management style
Common pitfalls and how to avoid them - at the individual level:
Inherent biases and blind spots
Perception and bias
Base rates and the law of small numbers (aka the Gambler's Fallacy)
Cognitive Dissonance (rationalising and reframing as in Denning's Appalling Vista)
Prospect Theory (the perception of gains and losses)

Kahneman and Tversky's heuristic errors
Representativeness
Availability
Anchoring
Useful Tests:
Occam's razor
Reductio ad absurdum
Pre-mortem
The most important concept in decision making - Materiality
Factfulness as an antidote to Fake Views:
Hans Rosling's ten reasons we're wrong about the world and why things are better than you think
Financial Modelling - a practical illustration of machine and human collaboration:
How do machine and human intelligence differ?
Using the Cynefin Framework and Known/Unknown Matrix to explore the differences

Financial Modelling - a practical illustration of machine and human collaboration
What should be hard coded into the model
What is best left to human judgement
How the two combine

What are the relevant trends in, and likely boundaries to, machine intelligence in business?
Communicating Decisions Persuasively:
Influencing
Framing (90% chance of survival or 10% chance of dying)
People don't choose between things they choose between descriptions of things
Covering all the angles - FACERAP