What is the Standing Ovation Model?
The Standing Ovation Model asks a simple question: when a performance ends and a few people stand, will the whole audience follow, or will the ovation fizzle? Each audience member forms a private judgment of the show, then adjusts based on what the people they can see are doing. John Miller and Scott Page used it in 2004 as a teaching example for agent-based modeling. The same audience, with the same information, can produce opposite outcomes depending on who sees whom and who moves first.
When to use it
Use the model when actors combine their own judgment with what they see others doing. Examples include states joining a sanctions regime, depositors withdrawing funds, accounts amplifying a narrative, and banks exiting a jurisdiction. It won't give you a point prediction. What it gives you is a structured answer to the question of how fragile or self-reinforcing a cascade is, and which actors' early moves matter most.
How to use this tool
- State the key question and define what "standing" means, such as "publicly announces suspension of participation."
- Choose a mode: the classic auditorium of rows and seats, or custom actors, where you specify who observes whom.
- Set private judgment: how good the "show" is, the bar each actor needs to act alone, and how much perception varies.
- Set conformity and early movers: how many visible neighbors it takes to switch, and who moves first.
- Run many simulations to see the spread of outcomes, the time to tipping and an influence ranking of actors. Replay any single run step by step.
- Run sensitivity sweeps and compare scenarios, then write the bottom line and the indicators to watch.
Common pitfalls
- Reading one run as the answer. Outcomes vary from run to run, so look at the spread.
- Guessing visibility. Who watches whom drives the results, so base it on evidence.
- Calling it a forecast. It explores how a cascade could unfold; it doesn't predict that it will.
Related
- Run the model in Python with
SimulateStandingOvationModel. - Indicators turns the early moves that matter into a watch list.
- Schelling Segregation Model shows how local preferences tip a whole system.
- Stakeholder Map identifies the actors and their influence before you model them.