Each of the Webpage links below is a version of a Nova model designed
to run in a Web browser. Each model is expressed in a Novascript
variant that is equivalent to the script produced by the Nova Desktop
*capture* process. A Nova runtime engine written entirely in
Javascript is used to exectute the model in the client browser.

*Nova Online* uses HTML 5 controls for input. Visualization
components designed to interface with *Nova Online* models
(graphs, tables, agentviewers, etc.) have been built using the
D3 platform. A rudimentary
set of such visualizers is used here; future work will incorporate
more advanced use of the D3 platform.

The first two are models used in published research.

**Ebola Epidemic Model**- An agent-based model described
in Getz, et. al, "Tactics and Strategies for Managing Ebola
Outbreaks and the Salience of Immunization",
*Computational and Mathematical Methods in Medicine*, Volume 2015 (2015), Article ID 736507, http://dx.doi.org/10.1155/2015/736507. **Agent-based Model using a Genetic Algorithm**- This is
a stochastic, agent-based model to study how genetic traits and
experiential changes in the state of agents and available resources
influence individuals' foraging and movement behaviors. It is
described fully in Getz, et. al., "Panmictic and Clonal Evolution on a Single
Patchy Resource Produces Polymorphic Foraging Guilds",
*PLOS ONE*, in press.

The following are drawn from the Nova Model Library.

**Double Exponential Growth**- This model shows 2 instances of an exponential
growth population model with different growth rates. It is
implemented as two "chip" instances of a single Nova submodel with
growth-rate inputs.
**Lotka**- Lotka-Volterra predator-prey model.
**Diffusion**- In this version of diffusion,
temperature flows between neighboring cells at a rate proportional
to the difference in temperature between the cells.
**Life**- This model implements Conway's Game of Life on a
bounded 50x50 board. The initial configuration can be altered by
left-clicking on a cell to change its color and dragging that cell
to change the colors of neighboring cells.
**Fire Spread**- Each cell in this model is one of
three states: burnable-green (0), burning-red (1) or burned-black
(2). If a burnable cell has a burning neighbor, it will become burning
in the next generation with the probability given by the slider's
value. Each burning cell burns for exactly 1 generation, and then
changes to burned. Each cell can also be part of a firewall (state
3). Firewalls (blue) remain in a fixed state and can prevent fires
from spreading. The initial configuration can be altered by
left-clicking on a cell to change its color and dragging that cell to
change the colors of neighboring cells.
**Agent Motion Model**- This model contains agents
moving over a toroidal landscape. Agents may die and disappear, and
new agents are born and added to the landscape, with the same
probability (0.2).
**Agent/Cell Motion Model**- This model demonstrates the
interaction between agents and cells. Agents move across a toroidal
landscape As them move they cause the color of the cells over which
they run to gradually change from green to white.
**Agent SIR**- In this agent-based world individuals
are either infected (red) or susceptible (green). Agents move one
step at at time in a random walk except that they cannot traverse
the blue barriers. Any susceptible agent within 1 unit of an
infected agent becomes infected. Agents randomly recover based on
the Recovery Probability set by the user.
**Agent SIR II**- A second, "cuter" version of Agent SIR.
**Measles or Flu SIR model**- Another agent-based version of SIR, with controls for different properties. Comparison graphing is also supported.
**Network Diffusion (Grid Version)**- This model demonstrates
diffusion of a quantity through a directed network. The quantity
moves among nodes in the network only along established, directed
links between two nodes. The simple rules that drive this diffusion
lead to intertesting patterns related to the topology, density and stability of the network (derived from Stonedahl & Wilensky, 2008). This version places the nodes in a grid and limits connections to
nearest neighbors only.
**Network Diffusion (General Version)**- Same as
the previous model, only without the grid limitation. Uses d3.force-directed
graph layout (Bostok, http://github.com/mbostok/d3/wiki) to display the network.
**Network SIR Model**- The model demonstrates the spread
of a virus through a network. Although the model is somewhat
abstract, one interpretation is that each node represents a
computer, and we are modeling the progress of a computer virus (or
worm) through this network. Each node may be in one of three states: susceptible, infected, or resistant. In the academic literature such
a model is sometimes referred to as an SIR model for
epidemics. (derived from Stonedahl & Wilensky, 2008).
**Localized Network SIR Model**- In this
version of the previous model a network of 400 nodes is divided into
4 "localities" of 100 nodes each. Within each locality the nodes
form a grid with as many as 8 immediate neighbors. Local connections
are only possible within immediate neighborhoods. In addition, a
small number of "non-local" connections join nodes in different
localities
**Susceptible/Infected/Recovered Simulation Games**- set of three "circle" games which demonstrate the spreading of infection and recovery from that infection.

last modified July 11, 2015 by rms@cs.oberlin.edu