Date of Award


Document Type

Thesis open access


While the value of simulations as a tool in the natural sciences has been realized for quite some time, its

potential in the social sciences is only beginning to be explored. A class of simulations used to study social behavior

and phenomena is known as social simulations. One particular type of social simulation is known as agent based

social simulation. Here agents are used to model social entities such as people, groups and towns. A purpose of

these models is to reproduce realistic behavior in the simulation which is then used to draw conclusions about the

corresponding real world entities. However reproducing realistic behavior is a difficult task. This is in part due to

the fact that human actions and interactions do not adhere to well defined rules. A successful solution to this

problem must reproduce realistic individual decision making as well as realistic social interactions.

We propose two models. First, a model for producing realistic decision making is based off human

intuition and deliberation. This model is tested in the Iterative Ultimatum Game and Bargaining Game. It is shown

that when agents use both intuitive and deliberative decision making they make decisions similar to those of

human subjects.

Next we propose a realistic model for social interactions. Our agents remain selfish and are able to break

relationships in order to maximize their utility. It is shown that when agents are able to break unrewarding

relationships that a Pareto‐optimum strategy arises as the social convention. In addition we conclude the rate and

amount of Pareto‐optimum strategy that arises is dependent on the network structure when the networks are

dynamic and the rate is independent of the network structure when the networks are static.