Scientific Definition
A behavioral relaxation of Nash: players choose better strategies more often, but not always — errors are smooth, not binary.
Plain-English Definition
A behavioral relaxation of Nash: players choose better strategies more often, but not always — errors are smooth, not binary.
Feynman Explanation
Game theory with the assumption that everyone occasionally clicks the wrong button.
Core Principle
A behavioral relaxation of Nash: players choose better strategies more often, but not always — errors are smooth, not binary.
Mechanisms
Pending editorial review.
A behavioral relaxation of Nash: players choose better strategies more often, but not always — errors are smooth, not binary.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Market design, mechanism design, anywhere a Nash assumption keeps breaking in the wild.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Game theory with the assumption that everyone occasionally clicks the wrong button.
Examples
- McKelvey & Palfrey's QRE explains experimental deviations from sharp Nash predictions.
- Market design, mechanism design, anywhere a Nash assumption keeps breaking in the wild.
Pending editorial review.
Famous Experiments
Pending editorial review.
Design Principles
- When a 'rational' model keeps mispredicting, add noise to the strategy choice — not to the payoffs.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
Pending editorial review.
The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- When a 'rational' model keeps mispredicting, add noise to the strategy choice — not to the payoffs.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
A state where no player benefits from changing strategy unilaterally.
Systems maintain stability by self-regulating around a setpoint.
Subjects often converge to neoclassical predictions only after many rounds of feedback — not on the first try.
Systems that gain from disorder.
Approach motivation — pursuit of rewards and goals.
Avoidance motivation — sensitivity to punishment, uncertainty, and threat.
Map reinforcing (R) and balancing (B) feedback loops between variables to see system behavior.
A system with many interacting parts that learn and adapt.
Many interacting parts producing emergent behavior nobody designed.
Single-loop fixes the action. Double-loop questions the goal or model that produced it.
System 1 is fast, automatic, intuitive; System 2 is slow, effortful, deliberate.
Interdependent network of actors evolving together.