Master of Science / Department of Psychological Sciences / Kimberly Kirkpatrick / The present experiments examined the effect of different uncertain-reward magnitudes (i.e., gains and losses) on global and local probabilistic choice behavior in rats. In two experiments, rats were given a choice between a variable-amount certain outcome that delivered 2 or 4 pellets and a variable-amount uncertain outcome that probabilistically delivered a larger reward. In Experiment 1, the larger uncertain outcome was always 11 pellets and different groups received 1, 2, or 4 pellets for the uncertain small reward. In Experiment 2, the uncertain small reward was always 4 pellets and different groups received 6, 9, or 11 pellets for the uncertain large reward. In both experiments, the rats increased their uncertain choice behavior with the probability of uncertain food. In Experiment 1, the magnitude of the uncertain small outcome affected choice behavior; there was no such effect of the uncertain large reward magnitude in Experiment 2. The group differences in choice behavior suggest that the expected value of the certain choice served as a reference point distinguishing uncertain gains and losses, and that the rats exhibited differential sensitivities to such outcomes. As some extant theoretical frameworks of choice behavior seem unable to account for all of the present data, a possible mechanism for the present results is proposed. These results emphasize the importance of identifying the choice outcomes that constitute gains and losses in animals such that the effects of prior uncertain gains and losses on subsequent choice behavior can be adequately and comprehensively understood.

Works on semiotic dynamics and language as a complex adaptive system in general has been an important lane of research over the last decade. In this study, the mean-field naming game model developed in the course of the pioneering research programme of Luc Steels and colleagues is modified to include probabilistic word choice based on weighted lists of words, instead of either deterministic or totally random word choice based on (ordered) sets of words. The parameters&rsquo / interaction and this interaction&rsquo / s effect on time of convergence of the system and size of individual lexicons over time are investigated. The classical model is found to be a special case of this proposed model. Additionally, this model has more parameters and a larger state space which provides additional room for tweaking for time- or space-optimization of the convergence process.

abstract: In the middle of the 20th century in the United States, transportation and infrastructure development became a priority on the national agenda, instigating the development of mathematical models that would predict transportation network performance. Approximately 40 years later, transportation planning models again became a national priority, this time instigating the development of highly disaggregate activity-based traffic models called microsimulations. These models predict the travel on a network at the level of the individual decision-maker, but do so with a large computational complexity and processing time requirement. The vast resources and steep learning curve required to integrate microsimulation models into the general transportation plan have deterred planning agencies from incorporating these tools. By researching the stochastic variability in the results of a microsimulation model with varying random number seeds, this paper evaluates the number of simulation trials necessary, and therefore the computational effort, for a planning agency to reach stable model outcomes. The microsimulation tool used to complete this research is the Transportation Analysis and Simulation System (TRANSIMS). The requirements for initiating a TRANSIMS simulation are described in the paper. Two analysis corridors are chosen in the Metropolitan Phoenix Area, and the roadway performance characteristics volume, vehicle-miles of travel, and vehicle-hours of travel are examined in each corridor under both congested and uncongested conditions. Both congested and uncongested simulations are completed in twenty trials, each with a unique random number seed. Performance measures are averaged for each trial, providing a distribution of average performance measures with which to test the stability of the system. The results of this research show that the variability in outcomes increases with increasing congestion. Although twenty trials are sufficient to achieve stable solutions for the uncongested state, convergence in the congested state is not achieved. These results indicate that a highly congested urban environment requires more than twenty simulation runs for each tested scenario before reaching a solution that can be assumed to be stable. The computational effort needed for this type of analysis is something that transportation planning agencies should take into consideration before beginning a traffic microsimulation program. / Dissertation/Thesis / M.S. Civil Engineering 2010