1. Urban Encounters: The game of real life Eamonn O’Neill University of Bath Department of Computer Science Vassilis Kostakos University of Madeira / Carnegie Mellon University CHI 2008 Proceedings Works In Progress 1

2. Outline Introduction Modelling human encounter in urban space The game of real life Describing the cell dynamics Modelling life, death and survival Discussion Conclusion and ongoing work 2

3. Introduction(1/2) A game that helps us model urban encounter Developing models of human movement and patterns of encounter in cities Aim: – a systemic understanding of cities and urban life – use this understanding to aid in the development of urban pervasive applications 3

4. Introduction(2/2) “The Game of Real Life” is an extension of Conway’s Game of Life User plays game on the mobile device and the game gathers empirical data by Bluetooth 4

5. Modelling human encounter in urban space Models can be broadly categorised into three levels: – Macro – Meso – Micro Applications of encounter – Delay Tolerant Networks – a mobile application 5

6. The game of real life(1/4) Understanding the patterns of urban encounters The Game of Life as a basis for simulating how people encounter and interact with each other 6

7. The game of real life(2/4) The game of life: – Survival – Death – Life 7

8. The game of real life(3/4) The game of real life: – The playing board becomes an undirected graph – Cells are not limited to two states 8

9. The game of real life(4/4) Address two issues: – define the set of neighbours that a cell can have at iteration n of the game. – define the rules that drive life, death and survival. 9

10. Describing the cell dynamics Data-gathering mobile application that utilises Bluetooth Implements a version of the Game of Real Life in order to motivate users to keep it running on their mobile devices. 10

11. Modelling life, death and survival(1/2) A cell represents a specific mobile Bluetooth device The number of neighbouring devices is given by the results of a Bluetooth discovery scan The cell’s state may decrease or increase Analysed a week’s data from three participants From this data we derived a set of rules for changing the cells’ state 11

12. Modelling life, death and survival(2/2) Identified as optimum the following rules: – Under-crowding is 2 or fewer neighbouring Bluetooth devices – Desirable number of neighbouring devices is 3-5 – Over-crowding is 6 or more neighbouring devices 12

13. Modelling life, death and survival(1/2) Adding memory to the cell 13

14. Modelling life, death and survival(2/2) 14

15. Discussion(1/2) Inevitably most of the time was spent on death (states 0 and 1) and state 6, which was the maximum possible. The states between the two extremes typically act as buffers and are occupied only temporarily 15

16. Discussion(2/2) Identified that adding memory to the cells produces graphs with smaller and fewer fluctuations. 16

17. Conclusion and ongoing work(1/2) Describe extensions to the game that make it more closely resemble human encounters Present a mobile application that acts both as a game for users and a data collection tool. Cell spend most time on the extreme states Equipping each cell with memory enables it to predict its state by utilising its own memory 17

18. Conclusion and ongoing work(2/2) This paper provides the groundwork for developing such an account. Attempting to determine the asymptotic behaviour of our model as influenced by different set of values for life and death Ultimately develop a mathematical account of cell dynamics that closely matches the encounters recorded by our Bluetooth scanners. 18