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## WHAT IS IT?
This model simulates the stalker behavior that a man can take against a woman in the society, even reaching a femicide in some cases.
The femicide is only the result of a long activity of harassment against a woman. Most cases caused by women that don’t perform public actions before the tragedy. There is statistics that shows a significant decrease of femicide probability when reporting properly.
## HOW IT WORKS
This project considers two types of modeling, one model based on agents and the other one based on differential equations.
For both models four states are defined in which each woman can be. Those are:
* Safety state(S): represent a woman that isn’t affected by any potential homicide.
For man only two states are defined:
* Wholesome (W): represent a man that doesn’t harass woman.
We relate each of these states as follows
![states](https://i.imgur.com/WpNEVtD.png)
We also define probabilities of transition between the different states, which are:
* HARASSMENTTOFEMICIDEPROBABILITY (hf): Is the probability that a man who is harassing a woman murders her.
### Agentbased model
The model is initialized with a certain POPULATION of similar proportions of women and men.
#### Men agents
Men are represented initially with the blue color and their status are WHOLESOME.
#### Women agents
Women are represented initially with the red color and their status are SAFETY.
If a man with POTENTIALMURDERER status cross with a woman there are a probability that she can turned into:
* HARASSED status represented by pink color(if her current status is SAFETY)
### Based in differential equations
Each of the states in which a woman or man can be are represented by a ‘stock’ on the Systems Dynamics Modeler and each equation establishes the transition from one state to another based on a probability. In the Systems Dynamics Modeler each transition can be represented with a ‘Flow’.
The equations are:
![equations](https://i.imgur.com/JErIvbo.png)
Here we can see that the flows between each variable are represented by the transitions between each state, where there is a defined probability.
The basic idea is that for each equation (representing a state) there are inflows and outflows (except for femicides since there is no exit from this state), where, in the inputs, the probability is multiplied by the state from which it comes the flow and in the outputs, the probability is multiplied by the same state.
An exception occurs in the transition between the state 'S' and the state 'Hw' where, in addition to the probability h of harassment, we must take into account the percentage of homicides that exist, because if there are no homicides, this transition must be zero. We do this through the expression:
![homicidepercentage](https://i.imgur.com/liYASE9.png)
## HOW TO USE IT
The total number of individuals in the simulation is controlled by the slider POPULATION (initialized to vary between 10–500), which must be set before SETUP is pushed.
Click the SETUP button to set up the agents. There are a random number of men and woman (blue and red agents respectively) and they are located on a random patch.
Click the GO to start the simulation. The agents will start to move on a similar direction simulating a typical walking of human behavior.
Sliders:
The are some monitors to show some general data:
Finally there are two graphics which shows the change of states in women over time based on agentbased model and the differential equations model.
## THINGS TO NOTICE
As time goes by, the female population is exponentially decreasing because of femicides that occur. But, as the probability of harassed women reporting increases, it can notice the huge decrease in total femicides. It may be obvious, since a report means less likelihood of the homicide being carried out. But, in our society, the probability of a woman reporting the harassment is extremely low, so the focus of this model is to be aware that reporting harassment is extremely important to avoid tragedies like this.
In other hand, as the probability of a man became a potential homicide, it can notice that a higher value implies a greater number of potential homicidal men, so more femicides will happen.
## THINGS TO TRY
Try different values for %REPORTHARASSMENTPROBABILITY. How does the overall degree of femicides change?
Try different values for %REPORTEDTOFEMICIDEPROBABILITY. How much does police effectiveness really affect in cases of harassment? Does it really ensure the lives of women?
Try different values for %TURNTOFEMICIDEPROBABILITY. How can a socially sexist or sick population affect the number of femicides?
Try different values of POPULATION. How does the initial occupancy density affect the number of femicides? How does it affect the time it takes for the model to finish?
## EXTENDING THE MODEL
Based on the simplicity on this model when women change form ‘harassment state’ to ‘report state’. (a probability) It can be interesting to find out what are the reasons why a woman doesn’t report that she is suffering from harassment situations and incorporate them on the model. In this way the model could go deeper in terms of situations that women suffer in silence.
On the other hand, the probability that a man will become a homicide could be given by some factors that would also allow to go deeper and understand what is the cause and the possible actions that could be achieved to decrease this probability and, with it, the amount of femicides.
## NETLOGO FEATURES
To simulate a human walking behavior, the agents move with a heading that changes between 10 and 20 respect its current heading.
## CREDITS AND REFERENCES
To obtain some probabilities, we rely on information from Latin American news blogs that contain graphs showing the behavior of femicides in their respective countries:
* ‘Femicides 2017: 38% of the murdered women had denounced their aggressor’’
* ‘Country by country: the map showing the tragic figures of femicides in Latin America’ http://www.bbc.com/mundo/noticiasamericalatina37828573
* ‘Latin America, the most violent region for women: there are at least 12 femicides a day’
* ‘Femicide in Chile: 2017 ends with more registered cases than last year’
* ’10 years of femicide in Argentina: definitions and figures of violence against women’

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