Modeling Humans: The Art and Science of Capturing Complex Behavior

Humans are not billiard balls following simple physical laws; they are diverse, social, adaptive, and often unpredictable. Modeling such behavior requires a blend of art and science.

Scott Page emphasizes that no single theory fully captures human behavior across contexts. The rational-actor model assumes individuals maximize utility given constraints. While useful as a benchmark, it often fails to predict actual behavior due to psychological biases like loss aversion and hyperbolic discounting.

Rule-based models offer an alternative. Fixed rules, such as 'buy when prices drop 10%,' provide simplicity, while adaptive rules evolve based on experience, capturing learning and ecological rationality. These models reflect how people adjust behavior in response to social and environmental feedback.

The micro-macro loop illustrates how individual actions aggregate into collective patterns, which in turn influence individuals, creating dynamic social systems.

Many-model thinking encourages using multiple behavioral models to capture this complexity, acknowledging that different models may apply in different contexts or levels of analysis.

This approach informs better policy design, market predictions, and social interventions by embracing human complexity rather than oversimplifying.

For more on this topic, see Scott Page’s detailed discussions and related articles on sobrief.com and bookey.app, which delve into the interplay of psychology, economics, and modeling.[[0]](#__0)[[2]](#__2)