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Browsing posts in: Modelling

Processing Simulations Part II – Cellular Automata


Continued revision, here are some examples of cellular automata used for simulation.

Cellular automata consists of a grid of cells, a neighbourhood around each cell, a set of rules as to how what happens in a cells neighbourhood affects the cell and a set of states that a cell can take on.

One of the most well-known of these models is Conway’s Game of Life

This has the basic rules
Any live cell with fewer than two live neighbours dies, as if caused by under-population.
Any live cell with two or three live neighbours lives on to the next generation.
Any live cell with more than three live neighbours dies, as if by overcrowding.
Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.

The ways in which this simulation evolves complex patterns provide a good example of emergence and self-organisation.

Below is my own simple cellular automata examples of three cities – all starting with a different amount of resources. These cities then grow according to a number of rules based on proximity to neighbours. One can observe that the city on the left grows much faster, the middle sometimes never grows at all, and the right slowly – the left eventually enveloping them all.


Processing Simulations Part I – ABM


This post will cover some of the examples I have worked on in terms of simulating urban phenomena in Processing as part of my MRes coursework.

Diffusion-Limited Aggregation
For a city to exist people must have proximity to one another. New entrants to the city will need to connect to those already there. However, individual values dictate their locational choice. This model holds these values as seeking as much personal space as possible – so that they wish to live as far away from others as possible but still remain connected and within the city’s area. Here a diffusion-limited aggregation (DLA) rule has the particles (here, individuals) undertaking a random walk until they connect with a static particle. The simulation begins with one static particle in the centre and you can see the pattern begins to form dendritic structure similar to those seen in some settlements and transportation networks.

Agent-based models
As touched on in my other post, agent-based models can be used to describe system behaviours from the bottom-up, where simple behavioural rules work together to create a phenomena more, or different, to the sum of their parts. Here the agents are performing basic behaviours of avoiding each other and moving towards a goal. One can see the effect the agents have on eachother reaching their goal. One can start to observe how these models can be used to simulate the effects of congestion and crowding.


3D environments for agent-based models


As part of my coursework at CASA we are being introduced to some of the latest 3D visualisation technologies and experimenting with how they might be used in a cities research context. Below are some examples of what can be achieved in a short period of time with these software packages.

The moving parts of these visualisations can be defined as agents. Agents with programmed behaviours and decision-trees can, in part, attempt to re-create and predict the appearance of complex real-world phenomena. This process can be defined as emergence – when complex systems arise from relatively simple interactions.

My very first thoughts when experimenting with these programs was – what is the difference between this and the complex behaviours we experience in video games or in movies (such as the large, generated crowds in Lord of the Rings)?

The first and most obvious of these differences is the purpose. Agent-based and other models are designed to provide scientific prediction of future, real-world events. Computer games are constructed primarily for enjoyment / entertainment (though can sometimes seem very realistic).

In video games, the human player takes control of the model and what effects occur, whereas in ABMs the input is largely derived from the data and defined conditions. In the real world simulations, these are based on theories of human (or other agents’) behaviour, while video game agents these behaviours will be based on plot points, be largely fictional and generally better looking.

Where these might collide is placing humans within the agent-based simulation – such as through immersive gaming experiences offered by the Occulus Rift. It is also interesting to think about emergent behaviour of humans interacting with eachother virtually within video game environments – such as in massively-multiplayer games.

1) Blocks following a terrain and avoiding a teapot
Uses: 3D Studio Max

Simple Agents in 3D Studio Max from Oliver Lock on Vimeo.

2) Gravity simulation of particles on a generated city-scape
Uses: Greeble, 3D Studio Max

Simple Agents in 3D Studio Max II from Oliver Lock on Vimeo.

3) Pedestrian movements in a built environment
Uses: CityEngine, 3D Studio Max

3) Pedestrians walking through a building
Uses: CityEngine (3D Model), 3D Studio Max

CityEngine model with 3DS Max Pedestrian Flows from Oliver Lock on Vimeo.

A quick example of these put into more complex, real-world practice is this stadium evacuation produced by Redfish.

4) Agent-Based Model of Crowd Dynamics During Disaster Evacuation

Stadium Evacuation from stephen guerin on Vimeo.

In terms of sharing these models, I recently discovered P3D which allows you to share very clean 3D models in your browser. Integrating simple ABMs into these would be a great way to communicate their results.