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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.
* Harassed state(Hw): represent a woman that is affected by one potential homicide.
* Report state(R): represent a woman that is affected by one potential homicide but she made a public report.
* Dead state(F): represent a femicide.

For man only two states are defined:

* Wholesome (W): represent a man that doesn’t harass woman.
* Potential homicide state (H): represent a man that harass and kill women.

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:

* HARASSMENT-TO-FEMICIDE-PROBABILITY (hf): Is the probability that a man who is harassing a woman murders her.
* REPORTED-TO-FEMICIDE-PROBABILITY (rf): It is the probability that a report doesn’t work at all and the homicide is committed anyway.
* HARASSMENT-PROBABILITY (h): The likelihood that a harassment will occur to a safety woman.
* REPORTED-TO-SAFETY-PROBABILITY (rs): Is the likelihood that, after a woman made a report, can be saved thanks to police efficiency and become a safety woman again.
* REPORT-HARASSMENT-PROBABILITY (rh): Represent the probability that a woman reports a harassment behavior by a man.
* TURN-TO-HOMICIDE-PROBABILITY (th): The worst probability, here the model represents the likelihood of creation of a killer from a normal man.
* TURN-TO-WHOLESOME-PROBABILITY (tw): As opposed to TURN-TO-HOMICIDE-PROBABILITY, the probability that a homicide will become a wholesome man.

### Agent-based 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.
They can be turned into a POTENTIAL-MURDERER with a probability that it can changes manually. They are represented with the green color.
On the other hand, the men can be turned into a WHOLESOME man with a probability that it can changes manually.

#### Women agents

Women are represented initially with the red color and their status are SAFETY.
Changes in a woman's status depend on the interaction with men:

If a man with POTENTIAL-MURDERER 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)
* REPORTED status represented by yellow color(if her current status is HARASSED)
* SAFETY status represented by blue color again(if her current status is REPORTED)
* or just be killed from a HARASSED or REPORTED status and their agents just disappear.

### 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:

![homicide-percentage](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.
Press GO button to stop the simulation and see the results.

Sliders:
* POPULATION define the initial population.
* REPORT-HARASSMENT-PROBABILITY: it changes the probability that a woman reports her harassment state.
* REPORTED-TO-FEMICIDE-PROBABILITY: it changes the probability that a harassed woman who has reported, could be killed.
* TURN-TO-FEMICIDE-PROBABILITY: it changes the probability that a man becomes a potential murderer.

The are some monitors to show some general data:
* POTENCIAL-HOMICIDE-COUNTER
* HARASSMENT COUNTER
* REPORT COUNTER
* FEMICIDE COUNTER
* EFFECTIVE REPORTS
* MEN NUMBER
* WOMEN NUMBER

Finally there are two graphics which shows the change of states in women over time based on agent-based 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 %REPORT-HARASSMENT-PROBABILITY. How does the overall degree of femicides change?

Try different values for %REPORTED-TO-FEMICIDE-PROBABILITY. How much does police effectiveness really affect in cases of harassment? Does it really ensure the lives of women?

Try different values for %TURN-TO-FEMICIDE-PROBABILITY. 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’’
http://www.soychile.cl/Santiago/Sociedad/2017/09/10/486568/Femicidios-2017-38-habia-denunciado-contra-su-agresor-y-15-tenia-una-cautelar-vigente.aspx
soychile.cl. Noticias locales de todas las regiones de Chile

* ‘Country by country: the map showing the tragic figures of femicides in Latin America’ http://www.bbc.com/mundo/noticias-america-latina-37828573
Noticias - BBC News Mundo - BBC.com

* ‘Latin America, the most violent region for women: there are at least 12 femicides a day’
https://www.nodal.am/2017/11/america-latina-la-region-mas-violenta-las-mujeres-al-menos-12-femicidios-diarios/
NODAL - Portal informativo de noticias de América Latina y el Caribe

* ‘Femicide in Chile: 2017 ends with more registered cases than last year’
http://www.emol.com/noticias/Nacional/2017/12/28/888956/Femicidios-en-Chile-El-2017-finaliza-con-mas-casos-que-el-ano-pasado.html
Emol.com - El sitio de noticias online de Chile

* ’10 years of femicide in Argentina: definitions and figures of violence against women’
https://www.infobae.com/tendencias/2018/03/08/10-anos-de-femicidio-en-la-argentina-definiciones-y-cifras-de-la-violencia-contra-las-mujeres/
Infobae: Últimas Noticias de Argentina y del Mundo

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