Explaining Invasive Ant Scenarios Using Emotion
Modeling
Joshua Wainer, Dusan Jan, and MinHee Kwon
Introduction
Native ant species play important roles in the
stability of their ecosystems, acting as sources of
food for many animals, natural predators for
some, and facilitators in the life cycles of many
plants. When native ant species are displaced or
killed off by an invasive ant species such as
Linepithema humile (Argentine ants), the latter
rarely fills the same biological niches or assumes
the same ecological roles as the former.
Subsequently, ecosystems that were once stable
are usually made unstable by the arrival of an
invasive ant species [7]. While the phenomenon
of an invading species disrupting a local
ecosystem is as old as biology itself, the exact
mechanisms through which such upheaval takes
place are rarely understood. However, recent
research [2] has helped to explain the processes
and interactions behind the successful invasions
of L. humile into areas with Mediterranean
climates, such as California. Because this area
owes much of its economic success to its
agriculture which in turn is dependent on the
stability of local ecosystems, a proper
understanding of the processes that drive
invasive ant behaviors is critical. To this end, we
will attempt to use a simple computational model
of emotions to synthesize both the local
interactions among invasive and native ant
species as well as the overall displacement of the
latter by the former.
Related work
There are many papers that have dealt with
interspecific ant interactions. Holway found that
invasive Argentine ants exhibit fewer
intraspecific conflicts than their native
counterparts which leads to higher population
densities, more foraging activity, and greater
brood production, which presumably contribute
to their successful invasions [8]. Later, he found
while Argentine ants used chemical compounds
as well as physical aggression when fighting
with native ants, the key to their successes in
fights lay in their numerical superiority.
Furthermore, Argentine ants were unique in that
they were both good fighters as well as good
foragers, while most native ants had a negative
correlation between the two traits [9]. Davidson
had noted the same phenomenon in other
invasive species and found that this could be
attributed to factors such as using carbohydrates
to replace nitrogen in their diets, the practices of
polydomy and polygyny, and/or the formation of
supercolonies [13]. Human and Gordon were the
first to suggest importance in the Argentine ant’s
ability to balance foraging success with fighting
success [10], but their later work suggests that
aggressive behavior does not correlate at all with
foraging success among any ant species. Instead,
it supports the idea that success at displacing
other ant species was positively correlated with a
high frequency of initiating interspecific
interactions [2].
Additionally, there have been many papers that
have attempted to recreate or mathematically
model various behaviors of ants. Sumpter and
Beekman showed that an ant’s response to a
pheromone trail is nonlinear with respect to the
amount of pheromone deposited and seems to
suggest an activation threshold in ants for
following a trail. Additionally, the amount of
pheromone that ants laid on a trail directly
correlated with the level of quality of the food
source, and ants tended to follow a trail that had
the most ants following it [11]. Chialvo and
Millonas also studied the probabilities of ants
moving in random directions as well as
following a given pheromone trail and found that
the empirically-determined probabilities enabled
the formation of “well-defined” trails [14].
Work has also been done that shows the validity
of using emotions to drive the behavior of
agents. Scheutz showed the validity of using
emotions such as “anger” and “fear” as driving
factors behind the actions of many agents [12] as
well as how displaying aggression is beneficial
for social groups of agents[15].
Methods
To test whether an emotional model of ant
behavior could synthesize the ants’ interspecific
interactions and the overall result of an invasion
by Argentine ants, we constructed a simulated
environment in which two species of ants had
their nests randomly placed within a 2D world.
With all the ants of a given species starting at
their nests, they began foraging for food at the
same time. At random intervals, user-specified
chunks of food would appear somewhere in the
ants’ world. When the ants found food, they
would try to take it back to their nest and store it,
but if they had an interaction with another ant of
a different species, there was a possibility that
this would not happen. Operating under the
assumption that a species of ant would either die
out or be displaced in order to find food
elsewhere when they gathered significantly less
food than another species living in the same
environment, we monitored how much food each
species gathered in a given simulation.
Our simulation recreated both the behaviors
common to all ants as well as the interspecific
interactions found between various ant species.
The behaviors equally prevalent in all species
included randomly foraging, following a trail of
pheromones to food, and returning with food to
the nest. These behaviors were modeled as a set
of states in every simulated ant, with foraging for
food set as a default behavior. If our simulated
ants came across a pheromone trail laid by their
own species, they would follow such a trail away
from their nests since it would be laid down with
the intention of leading ants to food [1]. Once the
ants found food, they would take it directly back
to their nests while laying down a trail of
pheromones for other ants of their own species to
follow.
While the above behaviors occur without any
communication at all among the ants, there is a
specific set of behaviors that take place only
when two or more ants interact with each other.
Such behaviors include biting, lunging, running,
and antennating (communicating through the
touching of antennae), and can be grouped into
three categories of behavior: aggressive,
retreating, and neutral. These behaviors are
found with varying degrees of frequency among
different species of ants [2], though there are
protocols of interaction that all ants seem to
comply with:



Anger
Fear
ants will never run away when they
begin an interaction with another ant,
but ants will usually run away when
approached by another ant.
ants will respond with aggressive
behavior if and only if such behavior is
first displayed toward them
ants will begin an interaction with
aggression more often when they are
close to a large number of ants of their
own species [3]
Stimulus
Not acting
aggressively
Interacting with
an ant of a
different species,
regardless of who
initiated the
interaction
Decay
Time, acting
aggressively
Time, co-location
with ants of same
species
Table 1: Operational Laws of Ants' Emotions
To drive these behaviors, we used a simple
computational model of emotions originally
conceptualized by Sloman [4] and later
developed by Velasquez. In this model, an
emotion is simply a value that changes over time
due to stimulation (increasing value) and decay
(decreasing value). Emotions also have
saturation limits to ensure that they do not
exceed a maximum value as well as activation
thresholds that determine when a given emotion
causes a particular behavior to activate [5].
Considering that there is no correlation between
the frequency of displayed aggression by a
species and the outcome of interactions between
ants [2], we separated interspecific interactions
into two components: to willingness to begin an
interaction, and the willingness to act
aggressively. We decided to call the emotions
driving such behaviors “fear” and “anger”,
respectively, though it is important to note that
the dynamics behind these emotions are not
meant to be similar to the mechanisms behind
the human emotions of the same names. The
operational laws behind the two emotions are
described in Table 1. It is important to note that
while the decay law for fear is drawn from
scientific literature [3], the other laws were made
to provide stability for the emotional values.
In addition to modeling the ants’ internal
behaviors and drives, special attention was paid
to environmental variables that could affect the
outcome of the simulation, such as the location
of the random food drops, their frequency, the
size of the world, the number of ants simulated,
and the duration of a given simulation.
Specifically, if food was dropped closer to one
species’ nest than another, this would give the
closer species an unfair advantage in gathering a
large amount of food, thus skewing our data.
Additionally, if food were dropped too
frequently, the ants were very likely to go for
food source closest to them and were unlikely to
interact with the other species at all, skewing our
data again. Furthermore, if too few or too many
ants were placed in a world of a particular size,
this would result in findings that were not
gathered under the same conditions as those in
the study whose results we were attempting to
recreate. In short, because any of the above
circumstances would invalidate our findings, we
had to take extra care in tuning these variables.
The abovementioned rules, algorithms, and
techniques were all implemented in Swarm, an
open-source toolkit used for simulating the
individual behaviors of many independent agents
to generate emergent group behaviors [6]. The
toolkit features two separate modes of operation,
visual and batch, which allow for faster, easier
debugging and impressive demonstrative
capabilities as well as decreased running time for
simulations, respectively. Additionally, the
toolkit offers the choice of programming in
either Java or Objective-C, a variant of the C
language developed by Apple. We opted for the
latter choice, as there are more programs that
demonstrate the full capabilities of Swarm in
Objective-C.
Use of experiments in
tuning the model
After we had the basic framework of the
simulation in place we had to fit the model to the
data reported in field observations. We had to
ensure that that the model generalizes well for
parameters outside the ones observed in the field
and that the match for the observed ones was as
close as possible.
In the experiments conducted by Human and
Gordon the maximum numbers of ants involved
in interactions at baits were about 20 to 50 of
each species. With this in mind we decided to
test our model for 25, 50, 75 and 100 ants of
each species. In the initial tuning we focused on
a specific pair of ants, the Argentine ants
Linepithema Humile (LH) and one of the native
species Messor Andrei (MA). The reports by
Human and Gordon indicate that LH initiated
75% of all interactions. Of the interactions
initiated by LH, 90% of them were initiated
aggressively. MA on the other hand initiated
aggressively only 47% of the time.
The first factor we tried to control was the ratio
of initiated interactions. Our initial model
increased fear on interaction with other species,
but only decayed it based on time. A simple
mathematical model indicated that using a ratio
of 3:1 for fear decay of HA and MA should give
the desired effect, but it was unclear how reliable
it was as many parameters depended on
interactions between the two species. After
tuning the decay parameters for 25 ants per
species we checked how well the behavior
generalized with higher numbers. We found that
the ratio was out of bounds at higher
populations. The problem was in the fact that
fear decay was the same in all scenarios while
fear increase scaled with number of interactions.
This coupled with a relatively high saturation
threshold used for fear put MA in constant state
of fear. To counterbalance this we had to create a
decay mechanism that would scale as the
population increases. We decided to reduce fear
with interactions with ants of same species.
While this achieved the desired property of
having decay scale with population size it is also
supported by literature which suggests that ants
that are alone and away from their colony are
less prone to interact with other ants. Using this
additional decay mechanism and lower saturation
limit for fear (twice the value of activation
threshold) we achieved results that are much
more even across different population sizes.
While there were still some variations it was
nowhere close to the spread we observed before
(before ratios ranged up to several hundred).
Table 2 shows the number of initiated
interactions for each species and the ratio for
each of the 4 colony sizes. While the ratio did
not fit exactly to the desired 3:1 we left the fine
tuning until after other aspects of the simulation
were finalized.
25 ants per species
LH
MA
Ratio
993
670
1042
641
1096
746
1461
567
953
795
830
700
836
613
583
465
1050
797
917
479
Average
Deviation
976.1
225.86
647.3
118.55
50 ants per species
LH
MA
Ratio
3802
1731
2844
1523
3852
1294
2479
1915
2686
1710
2647
1700
2805
1595
2530
1857
2350
1706
2746
1873
1.48
1.63
1.47
2.58
1.2
1.19
1.36
1.25
1.32
1.91
1.54
0.43
2874.1
524.46
1690.4
185.04
2.2
1.87
2.98
1.29
1.57
1.56
1.76
1.36
1.38
1.47
75 ants per species
LH
MA
Ratio
5748
2860
7099
3114
4897
3029
6693
3410
7246
2596
6440
3557
6732
3085
6169
2998
5456
2931
5277
2679
1.74
0.51
6175.7
801.99
3025.9
295.02
2.01
2.28
1.62
1.96
2.79
1.81
2.18
2.06
1.86
1.97
2.05
0.32
100 ants per species
LH
MA
Ratio
10461
4505
9185
4665
8706
4991
8254
4598
11971
3904
10386
5520
13102
3941
9331
4942
9853
4530
10010
3995
10125.9
1474.48
4559.1
516.66
2.32
1.97
1.74
1.8
3.07
1.88
3.32
1.89
2.18
2.51
2.27
0.55
Table 2: Number of initiated interaction between HA and MA in 10000 cycles
The tuning of anger was easier as the value was
influenced mainly by decisions of the ant itself,
so a mathematical model was more accurate.
Having an anger increase of A when initiating
interactions in a neutral manner, r being the
desired ratio of aggressively initiated
interactions, t being average time between
encounters initiated and T total elapsed time then
total anger increase is A*T/t*(1-r) and total
anger decay is T. These two have to be balanced
so the anger increase should be set to A=t/(1-r)
to achieve the desired ratio. The variations
resulting from changes in anger from reactive
behavior also have some minor impact, but
experiments confirmed that setting the anger
increase in this way achieved the desired ratio
and was quite stable.
To measure which species was more successful
we looked at ratio of food collected for each
species. Since the total amount of food could
vary between simulations the difference was not
the right measure. Also to get a linear measure
for the result variable we applied log to the ratio
of food collected. A result of 0 means that both
species collected the same amount of food,
positive values indicate HA collected more food
and negative values indicate MA collected more
food. Looking at the collected simulation data it
was clear that there was no significant
difference between foraging success of the two
species.
At this point we started refining the simulation in
order to minimize the effects on foraging success
by variables that we were not interested in. By
looking at the visual runs of the simulation it
seemed that the most significant factor
influencing foraging success was distance of
food drops to the nest. To eliminate this bias we
After we had the desired local behaviors in place
we started collecting data on foraging success of
the two species. Table 3 shows the food collected
after 10000 cycles in the first iteration of the
simulation design.
Average
Deviation
25 ants per species
HA
MA
Result
2.66E+03 3.73E+03 -1.48E-01
3.89E+03 1.69E+03 3.63E-01
2.59E+03 1.97E+03 1.19E-01
1.35E+03 2.18E+03 -2.10E-01
2.74E+03 1.83E+03 1.76E-01
3.76E+03 2.90E+03 1.13E-01
1.01E+03 4.54E+03 -6.53E-01
3.29E+03 3.48E+03 -2.45E-02
1.30E+03 1.89E+03 -1.61E-01
2.89E+03 2.62E+03 4.22E-02
2.55E+03 2.68E+03 -3.82E-02
1019.64
963.38
0.28
Average
Deviation
50 ants per species
HA
MA
Result
3.73E+03 4.57E+03
-0.09
5.51E+03 3.30E+03
0.22
3.29E+03 7.93E+03
-0.38
3.23E+03 5.55E+03
-0.23
2.87E+03 3.47E+03
-0.08
3.94E+03 4.77E+03
-0.08
6.61E+03 2.45E+03
0.43
5.38E+03 2.99E+03
0.26
3.55E+03 7.19E+03
-0.31
2.95E+03 4.28E+03
-0.16
4.11E+03 4.65E+03 -4.31E-02
1277.08
1795.39
0.26
Average
Deviation
100 ants per species
HA
MA
Result
4.19E+03 6.09E+03
-0.16
5.78E+03 4.94E+03
0.07
6.78E+03 5.34E+03
0.1
7.26E+03 9.21E+03
-0.1
6.69E+03 4.83E+03
0.14
7.03E+03 5.54E+03
0.1
6.92E+03 3.58E+03
0.29
6.72E+03 3.34E+03
0.3
3.78E+03 7.98E+03
-0.32
9.87E+03 5.01E+03
0.29
6.63E+03 5.65E+03
0.07
8.51E+03 2.97E+03
0.46
7.24E+03 4.39E+03
0.22
5.87E+03 7.95E+03
-0.13
9.05E+03 5.69E+03
0.2
4.71E+03 4.79E+03
-0.01
9.00E+03 6.03E+03
0.17
8.29E+03 4.49E+03
0.27
6.47E+03 6.79E+03
-0.02
4.08E+03 3.33E+03
0.09
6.74E+003 5.40E+003
0.1
1695.42
1644.07
0.19
Table 3: Food collected with random food dropping
Average
Deviation
25 ants per species
LH
MA
Result
2.33E+03 6.92E+02
0.53
2.42E+03 3.23E+03
-0.13
1.60E+03 1.66E+03
-0.02
2.29E+03 2.24E+03
0.01
1.34E+03 2.05E+03
-0.19
1.41E+03 1.33E+03
0.03
1.11E+03 7.20E+02
0.19
1.32E+03 1.71E+03
-0.11
1.90E+03 1.69E+03
0.05
2.17E+03 2.77E+03
-0.11
0.03
0.21
50 ants per species
LH
MA
Result
5.91E+03 4.71E+03
0.1
2.82E+03 4.39E+03
-0.19
2.61E+03 2.55E+03
0.01
2.83E+03 1.96E+03
0.16
2.55E+03 3.70E+03
-0.16
4.77E+03 4.00E+03
0.08
2.67E+03 3.47E+03
-0.11
3.20E+03 2.73E+03
0.07
2.69E+03 2.74E+03
-0.01
4.60E+03 3.95E+03
0.07
0
0.12
75 ants per species
LH
MA
Result
4.01E+03 4.26E+03
-0.03
4.68E+03 3.60E+03
0.11
4.17E+03 5.14E+03
-0.09
6.17E+03 6.15E+03
0
5.14E+03 4.87E+03
0.02
4.87E+03 6.16E+03
-0.1
4.75E+03 3.09E+03
0.19
4.45E+03 3.35E+03
0.12
6.03E+03 5.16E+03
0.07
4.32E+03 3.23E+03
0.13
0.04
0.1
100 ants per species
LH
MA
Result
3.77E+03 4.51E+03
-0.08
5.99E+03 3.77E+03
0.2
4.56E+03 5.71E+03
-0.1
2.90E+03 2.28E+03
0.11
3.91E+03 4.50E+03
-0.06
4.57E+03 3.97E+03
0.06
5.66E+03 4.47E+03
0.1
7.14E+03 7.27E+03
-0.01
6.73E+03 5.73E+03
0.07
4.16E+03 4.23E+03
-0.01
0.03
0.1
Table 4: Food collected with equidistant food locations
made all food drops appear at locations
equidistant to nests of both species. Table 4
shows the results after this change.
While the deviation reduced by this change, the
results were still chancy (paired t-test of log
values not significant). Careful examination of
the simulations revealed that most of the
interactions that happened were by chance and in
most cases the species collected the food from
different sides of the food patch, rarely
interacting until the food was almost gone.
Since literature reported that ants patrol the areas
around food locations we decided to implement
this behavior as we believed it would have a
significant impact on number of interactions
between the two species at the foraging sites. We
examined several possible algorithms for
patrolling and we finally decided to use a model
where an ant randomly chooses a direction where
there is food present and walks in that direction
for 2 simulation cycles. The algorithm had a nice
property that the ants walked randomly along the
food patch, but were not stopped by small gaps
in food location. The ants would patrol the area
for 5 cycles before returning to nest with the
food. Table 5 shows the results after we
implemented the patrolling behavior.
Average
Deviation
P value
25 ants per species
LH
MA
Result
2.03E+03 2.08E+03
-0.01
1.63E+03 5.86E+02
0.44
3.11E+03 1.92E+03
0.21
4.62E+02 1.75E+03
-0.58
2.22E+03 1.16E+03
0.28
4.19E+03 1.10E+03
0.58
1.08E+03 1.51E+03
-0.15
2.62E+03 1.19E+03
0.34
7.34E+02 1.58E+03
-0.33
1.95E+03 1.96E+03
0
1.53E+03 1.10E+03
0.14
2.05E+03 9.98E+02
0.31
1.70E+03 2.09E+03
-0.09
1.24E+03 1.07E+03
0.06
1.27E+03 1.64E+03
-0.11
1.78E+03 1.46E+03
0.09
1.64E+03 1.28E+03
0.11
1.34E+03 1.66E+03
-0.09
1.99E+03 9.97E+02
0.3
1.68E+03 2.95E+03
-0.24
0.06
0.28
0.3240
50 ants per species
LH
MA
Result
2.83E+03 1.86E+03
0.18
2.31E+03 2.88E+03
-0.1
2.07E+03 3.38E+03
-0.21
3.44E+03 2.70E+03
0.11
3.63E+03 1.91E+03
0.28
4.63E+03 1.87E+03
0.39
2.56E+03 3.78E+03
-0.17
4.41E+03 2.90E+03
0.18
1.56E+03 1.39E+03
0.05
4.65E+03 3.06E+03
0.18
3.76E+03 3.44E+03
0.04
2.65E+03 2.01E+03
0.12
1.37E+03 1.44E+03
-0.02
3.23E+03 1.96E+03
0.22
3.11E+03 2.10E+03
0.17
3.11E+03 1.41E+03
0.34
3.71E+03 3.21E+03
0.06
3.08E+03 1.73E+03
0.25
4.36E+03 2.10E+03
0.32
4.26E+03 1.83E+03
0.37
0.14
0.17
0.0020
At this point the paired t-tests showed significant
results for all tests except in the 25 ant case.
Since we introduced some changes to the
simulation we went back and retuned the fear
and anger values. We achieved ratios of initiated
interactions of 3.3 for 100 ants, 2.9 for 75 ants,
2.4 for 50 ants ant 1.7 for 25 ants. Our
observations suggest that main difference in
ratios came from the fact that at lower population
sizes there are more interactions by chance and
less interactions at foraging sites. Most
interactions by chance occur when both ants
have low fear value which makes them equally
likely to initiate the interactions, thus lowering
the ratio for lower population sizes. It would be
interesting to compare ratios for interactions at
foraging sites only, but we did not perform such
an analysis in our study.
After we had conclusive results for interaction
between LH and MA we tuned the parameters
for another native species, in this case
Camponotus Semitestaceus (CS) which is more
aggressive than MA (79% of initiated
interactions were aggressive). In simulations of
LH and CS the results were significantly in favor
of LH (p = 0.0001), which shows that aggression
75 ants per species
LH
MA
Result
4.89E+03 4.07E+03
0.08
6.57E+03 3.19E+03
0.31
4.26E+03 3.61E+03
0.07
3.84E+03 3.30E+03
0.07
5.03E+03 3.96E+03
0.1
4.77E+03 2.99E+03
0.2
3.79E+03 4.10E+03
-0.03
5.70E+03 3.67E+03
0.19
6.00E+03 3.81E+03
0.2
3.31E+03 3.62E+03
-0.04
8.90E+03 2.61E+03
0.53
5.67E+03 5.33E+03
0.03
4.13E+03 3.37E+03
0.09
2.75E+03 3.42E+03
-0.09
5.17E+03 3.43E+03
0.18
3.70E+03 4.48E+02
0.92
5.68E+03 3.55E+03
0.2
2.38E+03 2.69E+03
-0.05
7.35E+03 2.18E+03
0.53
4.49E+03 3.29E+03
0.14
0.18
0.24
0.0033
Table 5: Food collected with patrolling behavior
100 ants per species
LH
MA
Result
8.99E+03 2.67E+03
0.53
8.35E+03 1.94E+03
0.63
5.45E+03 1.92E+03
0.45
5.53E+03 2.97E+03
0.27
6.95E+03 3.49E+03
0.3
5.45E+03 2.94E+03
0.27
6.66E+03 3.25E+03
0.31
8.34E+03 4.42E+03
0.28
4.72E+03 4.56E+03
0.02
4.20E+03 4.71E+03
-0.05
6.75E+03 2.51E+03
0.43
9.20E+03 3.41E+03
0.43
5.95E+03 4.01E+03
0.17
4.35E+03 4.68E+03
-0.03
6.06E+03 2.66E+03
0.36
7.11E+03 4.09E+03
0.24
6.68E+03 4.20E+03
0.2
4.82E+03 3.46E+03
0.14
4.41E+03 2.37E+03
0.27
6.54E+03 3.95E+03
0.22
0.27
0.18
0.0001
levels do not play a big role in foraging success
as indicated in the literature. To verify this we
conducted another set of simulations between
MA and CS which only differed in aggression.
The results again indicated that aggresion does
not have a significant impact on foraging success
(p = 0.8634).
Conclusions
In this paper we presented an emotion-inspired
model for simulating local ant behavior and
showed that computer simulations confirm the
hypothesis presented by biologists. Comparisons
of foraging success in simulations between ants
of different species show that foraging success is
not related with frequency of aggression, but is
largely dependent on who initiates the
interactions. While the Argentine ants are also
numerically dominant in the intoduced areas,
these behavioral tendencies affect their success
as an invader to a greater degree. The fact that
their numbers are high further helps them to
explore space more effectively as well as
encounter and displace ants of native species
more frequently at food sources.
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