Our knowledge base

Appreciating that humans are two minds in one

Getting humans to work together

Humans tell stories

Going beyond human norms

Humans are meaning-seeker and meaning-makers

Humans long to belong

Humans are wired to look for intentionality

Humans create worlds from concepts

Human thinking follows feeling

Human imagination is unique

Intuition is fast and mostly good enough

Human desires are layered and emergent

Humans knowledge is group knowledge

Humans are cause and effect thinkers

When the non-obvious becomes obvious - Aha!

Humans are creative because we’re curious about the world

Variability is both good and bad

Thinking as spatial and based on movement

How humans construct reality

Know when to explore versus exploit

The downside of uncertainty

Cognitive biases as the price of energy efficient cognition

We are our choices

Key ChatGPT Use Cases

Collaborative Problem-Solving with ChatGPT

Customization and Fine-tuning of ChatGPT

Limitations of ChatGPT

Probabilistic Nature of ChatGPT and the Role of Temperature

Understanding Accuracy in ChatGPT

Reinforcement Learning with Human Feedback

The Technology Behind ChatGPT

The best of both worlds: Human-machine complementarity

Relationships with AI

Bias, prejudice, and hate from machines

Four types of machine learning

How humans experience AI

How machines learn, a primer on machine learning

The basics of artificial intelligence

How to responsibly use the framing effect to influence people

How to tell stories with data

How to convince others of your decision

How to protect against the sunk cost fallacy

How to protect against the framing effect

How to protect against hindsight bias

How to protect against overconfidence

How to protect against availability bias

How to protect against anchoring bias

How to protect against loss aversion

How to protect against confirmation bias

How to understand your biased thinking

How to understand fast and slow thinking

How to make a group decision

How to prioritize solutions

How to generate solutions

How to know your current solution is not the best solution

How to know you have enough data to make a decision

How to synthesize information for decision making

How to triangulate analysis

How to use a decision tree

How to use a decision matrix to make a decision

How to use logic trees to analyze a decision

How to use technology adoptions curves in forecasting

How to analyze maximum market size

How to use the MECE principle to analyze a decision

How to use Occam’s razor to make a decision

How to use the 80:20 ru​​le to make decisions

How to make predictions from data

How to conduct a statistical test

How to understand the different ways you might be wrong

How to spot confounding factors

How to determine causation from correlation

How to understand statistical significance

How to design an experiment to test an hypothesis

How to use statistical tests in decision making with data

How to spot patterns in data

How to understand randomness

How to define a hypothesis

How to use statistical and data mining techniques to find patterns, trends, and insights in data

How to understand errors

How to use dispersion statistics to understand data

How to use descriptive statistics to understand data

How to balance data and intuition when making decisions

How to collect and analyze data

How to prepare your data for analysis

How to structure data analysis by asking questions

How to break a problem into knowns, unknowns, and unknowable parts

How to think like an analyst

How to make decisions that take emotions into account

How to reduce the negative impacts of emotions in decision-making

How to understand the impact of your emotions

How to make data-driven decisions

How to design learning rules in complex systems

How to avoid unintended consequences when designing incentives in a complex human system

How to design incentives in a complex system

How to find the point of leverage in a problem system

How to use design abduction to respond to complex business problems

How to define the levels in system that represents a complex business problem

How to draw a systems map to represent a complex business problem

How to design a solution to a complex problem

How to apply principles from complexity science to complex business problems

How to recognize a complex problem

How to recognize a complicated problem

How to recognize a simple problem

How to recognize adaptation in business systems

How to recognize heterogeneity in business systems

How to recognize feedback loops in business systems

How to recognize non-linear relationships in business systems

How to recognize emergent properties in business systems

How to recognize interconnected business systems

How humans make problems more complex

How machine learning makes problems more complex

How digitization makes problems more complex

How to recognize a complex system

Human choice and our right to a future tense