# Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem
## 7 Experimental Results  
As all experimentation is strongly dependent on the particular values of many model variables, proving any definitive validation is beyond the scope of the present work. Our goal is to show a possible mechanism by which privacy-induced emotions may be integrated in a trust model, enabling a futuristic interaction between unmanned automated elements and humans, both transporting objects. The role of emotions is not specifically designed to improve the rewards obtained by the agents. Although emotions affect these rewards, they are only intended to mimic human behavior, causing reactions that are as realistic as possible according to psychological theories and previously reported practical approaches to these theories, as explained in Section 1. Using the problem definition presented in Section 6, we compare two alternatives with our emotional trust model (denoted as _emotionaltrust_):  
* A trust model without emotions (denoted as _noemotions_): the current feeling does not modify how much trust is required to propose and to accept a proposal for delegation of a task (happy and surprised emotions decrease the trust requirement, whereas sadness, anger and fearful emotions increase the trust requirement);
* No trust model at all (denoted as _nottrust_): No agent trusts any other agent, and no cooperation takes place (no objects are delegated to other agents to be carried towards their destinations). All agents carry the initially assigned objects to their destinations by themselves, without any way to decrease the corresponding delays. This is the worst case, serving as the benchmark to show the improvement achieved by the other alternatives. All comparisons are repeated 100 times to decrease the variability caused by the initial random locations of objects and agents. The comparison is measured in two ways. First, we determine how these three models obtain rewards when the number of idling agents changes. When the number of initially idle agents increases, the possibility of cooperation increases (fewer delays and the avoidance of more privacy issues are possible). Figure 6 shows the results obtained in this first comparison, according to which we can conclude that:  
* The rewards for any alternative can be expected to increase as the percentage of idle agents increases; however, rewards appear to reach a saturation point between 20% and 40%, beyond which no significant increase is perceived;
* With the independence of the percentage of idle agents, the no-trust alternative obtains considerably fewer rewards than the other two alternatives (no emotions and emotional trust);
* When the percentage of idle agents is very low (20%), only a few boxes can be delegated, causing results that significantly differ from those obtained with greater percentages. This is especially true for the no-trust alternative;  
Figure 5: Ongoing situation in which carrier agents move to the destinations of their boxes. * Except when the percentage of idle agents is very low (20%), the use of emotions slightly increases the rewards obtained by agents (the emotional trust alternative slightly outperforms the no-emotions alternative). Therefore, in these cases, using emotions in the trust model causes some improvement. However the difference is minimal;
* When the the percentage of idle agents is very low (20%), the use of emotions clearly leads to fewer rewards, with an apparent decrease in the quality of decisions made by the trust model (the no-emotion alternative clearly outperforms the emotional trust alternative). In the second comparison, we determine how these three models obtain rewards when the privacy probability changes. Privacy probability represents the probability of a box or a cell becoming a privacy issue for a particular agent. The privacy of the box is constant and fixed from the moment the assignment takes place (both initially and for delegation), whereas the privacy of a cell is computed whenever a meeting takes place. For both parameters, the same probability value is used.