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CompositeCollectiveDecisionMaking

by Tomer J. Czaczkes (Submitted: 12/13/2015)

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## WHAT DOES THIS MODEL DO?
This model models the collective foraging of an ant colony in an enviroment with multiple semi-permenant and replenishing food sources. This may, for example, aphid colonies, flowers, or extra-floral nectaries. The model is designed to explore how two types of information - social (in the form of pheromone trails) and private (in the form of route memories) affect colony level foraging.
The model was used to provide the data for:
Czaczkes TJ, Czaczkes B, Iglhaut C, Heinze J. 2015. Composite collective decision-making. Proceedings of the Royal Society B: Biological Sciences 282.

## HOW IT WORKS

See the electronic supplementary material of the original paper for a complete ODD (overview, design, details) protocol of the model.

## HOW TO USE IT

To the left of the world view (big black box) are the controls and settings:
-Set up or reset the model by clickign the 'setup' button.
-Start the model by clicking the 'go' button. To make the model take only one time step, click 'go-once'
-feedercount sets the number of food sources
-random-feeder-locations? sets whether the food sources are randomly or non-randomly placed
-pheromone on? / memory on? activates/deactivates pheromone and memory respectivly
-CNF? activates a crowding negative feedback effect, as used in Czaczkes (in prep). This effect causes ants which have met another ant to reduce their pheromone deposition.
-patience-level - how many time-steps ants wait at an empty feeder before becoming disappointed and walking off
-phero-decay-rate - how rapidly the pheromone decays
-StDev - how 'curvy' the correlated random walk of the ants is - the higher, the curvier
-shuffle-every-X-ticks - how often the enviroment changes.
-click 'shuffle' to cause an enviromental change

The the right of the word view are monitors and a plot, which allows the model to be examined. These are self explanatory. There is also a toggle which causes the pheromone to be visualised.

## EXTENDING THE MODEL

One might consider making the enviroment change over time - perhaps the location or quality of feeders might change every so often. How do memory and pheromone trails enable ants to cope with such an enviroment?

Why is there no down-side to having a less flexible memory? I reckon its because the negative and positive effects of long memories cancel out. negative = keep going back to overexploited food. Positive: don't immediately give up on underexploited food. two types of errors: give up when you shouldn't (type 1 error) and keep visiting an overexploited feeder (type 2 error).
We can think of this as a tradeoff between type 1 and type 2 errors. We could test this by making one type of error more costly. For example, if every time a type 2 error is made the total amount of food the colony collected is reduced by one. We then expect colonies to have more total food when they make less type 2 errors, so when they avoid visiting empty feeders, so when they make more memory switches, so when max-memory = 1.

## RELATED MODELS

The Crowding Negative Feedback effect used is based on one developed by T.J. Czaczkes. See: Czaczkes TJ. 2014. How to not get stuck – negative feedback due to crowding maintains flexibility in ant foraging. Journal of Theoretical Biology 360:172–180.

## CREDITS AND REFERENCES
This model was written by T.J Czaczkes and B. I. Czaczkes
In order to cite this model please cite the original paper

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