Date of Award
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
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.
Leezer, Jason, "Simulating Realistic Social and Individual Behavior in Agent Societies" (2009). Computer Science Honors Theses. 23.