1. The Contemporary Interactions of Nocturnal Bipedal Mammals Stephen Zylka

2. The Contemporary Interactions of Nocturnal Bipedal Mammals YES!
NO. (These aren't even mammals.)

3. Project Idea Recently I went dancing over the weekend,
my mind racked trying to come up with projects.
Then, I looked around me!

4. Dancing! Intrigue! Fighting!

5. Can I model this? What do people do at the bar? Meet/Lose people
Buy Drinks
Flirt/Fight
Dance

6. I can model this! Meet/Lose people Buy Drinks
Flirt/Fight
Dance
Crowd Dynamics in confined spaces
Matching based on attractiveness/inebriation

7. How can I model this?
3D isn't really necessary. (People can't fly. Thankfully.)
Overhead map-view of different spaces should work
Should be able to model different behaviours for similar agents simply

8. Crowd Dynamics What kind of layouts work better than others?
Things to consider:
Placement of washrooms, bars, exits, seating areas, etc with relation to the dance floor.
What if there is more than one bar/washroom/etc?
How comfortable are individuals getting close to one another.
Are there good techniques to avoid bottlenecks/traffic jams?

9. Crowd Dynamics
So how to simulate this?
Patches! Agents!

10. Crowd Dynamics Each patch colour will represent a different “area.”
Thirsty!
Each patch colour will represent a different “area.”
Agents will have different locational goals based on a randomized (or calculated?) state.

11. Crowd Dynamics
I will begin by modelling layouts of several clubs I've been to, comparing real-life bottlenecks to simulated ones.
I will make an attempt at improving these layouts based on observed data.
From there, hypothetical layouts can be produced and tested.

12. We can simulate this mathematically... right?
Agent Matching
People tend to get together and break it off at the bar!
We can simulate this mathematically... right?

13. A Caveat: People are COMPLICATED!
We will need to simplify their behaviour significantly.
Each agent will have a given “attractiveness” value a.
Assume Agent X and Y are interacting, where X.a > Y.a.
Then Y will attempt to win over X. X will accept Y's offer with probability 1.0 – (X.a – Y.a) / (attractivenessMax).
A potential variable that could affect this: The inebriation of Y, potentially reducing their “standards” and making matches more likely.

14. A Caveat: People are COMPLICATED!
We will need to simplify their behaviour significantly.
If Y is rejected, it will be forced away. Consistent rejection could lead to a fight!
Fights will influence nearby patrons, and have the participating members ejected from the scenario.

15. A Caveat: People are COMPLICATED!
We will need to simplify their behaviour significantly.
Once matched, couples will behave as a single unit until either they are split up, or they leave together.
Couples inspire others and will have an impact on nearby patrons as well!

16. Alright Then.
So we've made this simulatable. However there's still a lot of optional variables we could consider:
Inebriation levels impacting nearby patrons.
Bouncer/Busser agents among the crowd and their impact
Different ways couples may split up
Factors that will make a patron stay/leave
Different types of areas, interactions within them
Seating
Washrooms
VIP
Further suggestions?

17. Questions I hope to answer:
Will the layout of an establishment have a significant impact on the behaviour of the patrons?
Am I able to semi-realistically simulate a dance club?
What kind of optional behavioural patterns will have the most significant impact on the patrons?
Will these simulations alter my or anyone's views on dance clubs in general? (Probably not :D)

18. Timeline?
March 7
Basic layout structure with several dierent layouts hard coded, basic patron behaviour. (Dance floor, bar, "standing around" behaviours functional.)
March 21
Differing behavioural patterns randomized within bar patrons resulting in (hopefully) interesting results. Bouncers implemented to dispose of "unruly" patrons. (Fighting, too inebriated, etc.)
April 6
Patron coupling implemented, hopefully resulting in interesting pairing/breaking dynamics using the characteristics of each individual patron.
April 12
Final results compiled, final report submitted.

19. Questions?