No abstract

Natural resource agencies are responsible for managing specific aspects of the environment through the development and implementation of policies. Computational advances have emerged in recent years that provide opportunities for simulating the influence that agency structure has on policy outcomes, particularly those stemming from the area of network theory and analysis. However, there remains a need for methods that can measure and visualize the confounding effects that multiple agency characteristics may impose on policy implementation. The complex interactions amongst these factors require an approach that can evaluate these factors in relation to one another and provide a way to abstract meaningful findings that can be useful for both scientists and agencies to consider for future policy development. In this study, we present a network simulation modeling approach that (1) builds upon existing conceptualizations of bureaucrat decision-making within agency networks, (2) uses network theory to construct idealized natural resource agency networks that can be used to evaluate how agency structure influences policy implementation, and (3) visualizes simulation results to better understand how bureaucrat behaviors and relationships in concert with agency structure influence policy outcomes. Using this approach, we demonstrate how different aspects of decision-making by bureaucrats interact with the spatial constraints of institutional networks to influence policy outcomes. The network modeling and visualization methods presented here offer an alternative approach in the policy science toolbox that can help

Type of Report and Period Covered Sponsoring Agency Code Supplementary Notes AbstractUnderstanding the travel behaviors of individuals who use public transit is essential for enhancing the performance, sustainability and efficiency of public transportation. Contemporary methods for collecting data on transportation behavior are focused on manual or automated procedures for counting the number of individual passengers entering or exiting transit vehicles. While such methods provide useful data for understanding transit demand throughout a network, they ignore the important details of how passengers travel to and within a network as well as their personal experiences during their commute, all of which can enrich the ability of transit agencies to provide sustainable transportation. To address this issue, there has been a proliferation of location-based services (LBS) that allow for new methods of data collection involving passengers volunteering data about their commute. In this light, passengers engage in a crowdsourcing effort to generate data about experiences across the network. This project's objective is to implement and test specific LBS in a bus transit network to better understand their potential and limitations for improving the crowdsourcing of travel behavior data.17.