The Standing Ovation model proposed by Miller and Page (2004) illustrates a familiar decision-making problem: at the end of a performance the audience begins to applaud. The applause builds and a few members of the audience decide to stand up in enthusiastic recognition. In this situation every other member of the audience must decide whether to join the standing individuals in their ovation, or else remain seated. It is not a trivial decision; imagine, for example, that you initially decide to stay down quietly but then find yourself surrounded by people standing and clapping vigorously. It seems plausible that you may feel awkward, change your mind and end up standing up, saving yourself a significant dose of potential embarrassment. Analogously, you probably wouldn't enjoy being the only person standing and clapping alone in the middle of a crowded auditorium of seated people.

1. Standing Ovation Problem
Reproduced in MASON
by Talha OZ
May 2014

2. Overview
• What is SOP ?
• Why SOP ?
• Is it a good fit for ABM?
• SOP in MASON – Base Model
– Demo
• SOP in MASON – Extensions
– Demo
• Conclusion
• Discussion & Thanks

3. What is SOP?
• The problem of SO is whether to join the
standing individuals in their ovation, or else
remain seated

4. Why?
• Project suggestions
– 123. *Formation and evolution
of a standing ovation in an
audience
• Introduced by Miller & Page
– The book I reviewed
• Not in MASON
– As a classic, it deserves

5. Is it a good fit for ABM?
• Heterogeneity
– asocial/consistent, confused/reversed
• Autonomy: decide individually
• Environment: seat locations
• Interaction: neighbor decisions
• How economics students had modeled:
1. si(q) = q + εi if T1< si(q) then stand up (εi is for heterogeneity)
2. if α>T2 then everyone stands up (α is ratio of people standing)

6. SOP in MASON – Base Model
• 20x20 grid, 400 agents
• Initial personal preference
• Neighborhood: 2 sides + 3 seats in the front row
– Vision/distance can be increased
• Asynchronous updating (Random ordering)
• Decision-making: Joining the majority
– What if == ?

7. SOP in MASON - Extensions
• Converts visualized; still closer to the actual
• Noise/Realistic; prevents locks !
– 1% asocial; consistent
– 1% confused; reversing

8. Free Parameters
• Initial Preference
– Standing probability
• Vision / Distance for neighborhood
– d=1 (5 neighbors), d=2 (9 neighbors)
• Confused Ratio
– 1% reversing
• Asocial Ratio
– 1% not influenced

9. Results

10. Conclusion
• SOP is a very nice exploratory model to study
the emergence of descriptive norms*
• Initial personal preference has no effect
• Vision increase leads to faster convergence
• Coloring the converts leads to better perception
• Asocial & confused individuals prevent locks
• *: A descriptive norm is a behavioral rule that individuals follow when
their empirical expectations of others following the same rule are met
(Muldoon et al., On the emergence of descriptive norms)

11. Discussion & Thanks
• My first ABM projects (PD, Game Theory)
• My first model in MASON (Standing Ovation)
• Book reviews (CAS, Miller & Page)
• Thank you all !
– CSS610 Classmates
– Randy Casstevens
– Robert Axtell