### Project progress report 02. Systemic approach and causal loop model

A causal loop model has been developed in order to help understand the complex systemic structure of poverty in all its dimension. System diagramming is here a loose term used to describe the activity of conceptually representing and visualizing a system in its constitutive elements: the elements, the relationships and the system boundary distinguishing what does and does not belong to the set.
The assumption of this qualitative exercise is that poverty, and its dimensions, are the result of the dynamics between a wide variety of factors from macro-politic, to the personal behavioral patterns.
The key element of the visualization are the factors and the variables. They are the environment attributes and characteristics that have an influence level of poverty.

A Relevance issue was a criterion for deciding which factors belonged to the system. In this case, relevance was determined by a open discussion between the students and the board of the course.
The system has been visualized in a particular format: a causal loop model (or diagram).

In a causal loop model, the system’s elements (factors, variables) are represented by boxes, and the causal relationships between two variables are represented by arrows. The variable at the tail of the arrow has a causal effect on the variable at the point. In addition, a distinction can be made between positive and negative causal relationships. A positive causal relationship implies that both variables will change in the same direction: if variable, a (at the tail) increases, then also variable b (at the point) will increase (and if a decreases, then b decreases). A negative relationship, on the other hand, implies that variables change in oppositedirections (if a increases b will decrease and if a decreases b will increase).
The causalities discussed so far are linear causalities (from a to b). Circular causalities (e.g. from a to b and from b to a) in systems maps are called feedback loops. They are an important feature of causal loop models because they help to explain the dynamic behavior of the system.