Guilherme Pontes (Spatial Modeling)

Moods

Guilherme Pontes

Introduction

I created a model in order to show how interactions between people and both negative and positive external influences day by day can change the mood of these people. I tried to illustrate five different moods in the model. Mood 0 or red color symbolizes the most unhappy people, mood 1 or orange color symbolizes unhappy people, the mood 2 or yellow color symbolizes the normal or neutral people, mood 3 or blue color symbolizes happy people and the mood 4 or green color symbolizes the happiest people. Besides, the positive external influences are represented by a happy face in a green color and the negative external influences represented by a sad face in a red color.

Moods are emotional states, so they are always changing and this model attempts to represent how that change happens in real life. When a very happy person interacts with a very sad person they affect their moods and thus both are with a neutral mood. Another example is when two people very happy or green interact and both are still green. However, if after that they get in touch with a negative influence the mood of them will go down.

This model is important because mood can strongly influence in our most important choices, a person’s mood can influence how they process advertising, mood has been found to interact with gender to affect consumer processing of information, voluntary facial expressions, such as smiling, can produce effects on the body that are similar to those that result from the actual emotion, such as happiness. Articles such as from George, Jennifer M., Positive mood at work (as an affective state) was hypothesized to be significantly and positively associated with the performance of both extrarole and role-prescribed prosocial organizational behaviors (http://psycnet.apa.org/journals/apl/76/2/299/), from Apr 1991.”, from International Clinical Psychopharmacology, “Mood disorders in the general population represent an important and worldwide public health problem.(http://journals.lww.com/intclinpsychopharm/Abstract/1995/12004/Mood_disorders_in_the_general_population_represent.2.aspx) from December 1995”, from Denise B. Kandel, “moods could influence the thought process itself…our good mood could making us feel better (or worse) about potentially pushing, and therefore more (or less) likely to do it. (http://www.salon.com/2013/03/14/study_mood_often_influences_our_most_important_choices_partner) from Mar 14, 2013” prove what I said about my phenomenon’s problem.

Methods

In order to explain how the model works I will use the ODD protocol to make the model descriptions more understandable and the importance of the parameters. I used seven parameters in the model to change the amount of  population of most unhappy, unhappy, normal, gleeful and most gleeful people and also the amount of positive and negative influences.

Since my model is mainly based in interactions between people and they influence the mood of other people, the change in the amount of a certain population directly affects the model. For example, if I keep all my parameters with 15 people and add more 15 unhappy people, my model will have more people with bad mood because they will more people with bad mood influencing other people.

Other important parameters are the outside influences. The negative influences and the positive influences also directly affects the model. The negative influences can be bad grades, bad friendships, loss of money and etc. The positive influences can be a promotion in a company, the conquest of championship and etc.

My temporal resolution and spatial resolution vary according to the parameters that are in the model. The temporal resolution might be early in the morning if the user select a low number of unhappy and happy people. If the user select a large number of people circulating in this random place, it must be around the rush hour where we can find more people circulating in the streets. MY spatial resolution also varies according to my parameters. It may be a big city if there are many people circulating in the streets or a small town such as Eugene with few people.

The interaction among agents are all direct because they require close proximity in order to happen. The model is an abstract way to illustrate human communications being represented by contacts of the agents. The interaction is stochastic because the direction is random. However, through the random movement interaction happens.

Individuos belong to collectives that both effect the other individuos in another collectives. This happen because a collective is more or less influential based on the population of the collective.

Results

The model was developed to examine the interaction between people in the real life and how it can change their mood. In order to know the results, it is necessary to use the behaviourspace tool and create a new experiment. After this it is needed to vary the parameters, I chose four different amounts of population per each parameter and four different amount of negative influences and positive influences to measure the amount of unhappy people and happy people in the final of this study.

When there was more positive influences than negative influences, more people were getting happy because there was more good things happen such as listen to a good music, eat a chocolate, get good grades than bad things. When there was more negative influences comparing with the amount of positive influences, more people were getting sad and unhappy because probably things such as bad grade, unemployment and divorce was happening with the population.

In addition, the variation of the parameters showed me that the interaction between people is important to change their mood. To show some results, there are three different videos above:

https://www.youtube.com/watch?v=Hdmt7JGP2QI

The model with all parameters with the same amount of population. (Most Unhappy 15, Unhappy 15, Normal 15, Gleeful 15 and Most Gleeful 15)

 

https://www.youtube.com/watch?v=umIwffDqTiw

The model with the same 15 number of people per each mood but without positive influences, only with negative influences.

https://youtu.be/69rpIx-h2Jo

The model with the same 15 number of people per each mood but without negative influences, only with positive influences.

My model with parameters, plot and monitors.

Discussion and Conclusion

How I was expecting, the model and its parameters worked as I wanted to be. Everything that I wanted, I could see working in my model. I don’t think that it was a hard model, since it was a simple model, I could work on it and finish. The only problem that I had was with the results for the first time. I chose to do too many variations with my parameters what took me a long time and when I needed to my presentation I couldn’t show any screenshots and graphs.

The conclusion that I had with this model and results was that people are influenced by other people. So, in order to don’t lose a good mood or don’t be an unhappy person, people sometimes should avoid the contact with some people. If we understand that someone is a bad influence or know about the past of that person is not good, we should stop to talk or stop to have any connection with that kind of person.