• We encounter ‘problems’ everyday

• Selecting a technology
• Deciding how to allocate effort or resources
• Choosing which task to start

• Yet, we seldom examine our approach to problem-solving and decision making

• We can learn specific techniques that enable a better method and more reliable outcomes

• Research in cognitive sciences show what humans do extraordinarily well

• Classification – identifying an object and its attributes
• Generalization – recognizing the attributes of similar objects
• Specialization – recognizing the similarity of certain objects
• Cause – identifying how one event triggers another
• Patterns – recognizing similar aggregate objects

• These principles support more sophisticated ontologies in almost every discipline, from art to software engineering

• These basic characteristics of cognition support specific techniques for how we understand a problem

• Humans are very good at:

• Making lists – creating a list of tasks
• Giving examples – ‘Al’s, Susan’s or Figaro’s?’
• Finding exceptions – proofreading a document
• Ranking – identifying important requirements
• Sequencing – determining the steps for testing
• Visualization – creating a wireframe
• Comparisons – finding a similar technical architecture

• Strengths can also lead to certain weaknesses in problem-solving and decision making

• Confirmation bias – weighting evidence that supports your preconception

• Anecdotal bias – weighting personal experience over broader information

• Experience bias – every solution is like previously successful solutions

• Superiority bias – well-formed solutions are intrinsically correct; also the Dunning-Krugar Effect describes how competence and confidence are inversely proportional. - John Cleese on Stupidity