This EArly-concept Grant for Exploratory Research (EAGER) project will generate simulated data to model the behaviors of the interdependent beef production system and transportation infrastructure in southwestern Kansas, with due consideration of key social and economic factors. This process involves: i) modeling of the system as a multilayer network; ii) designing of an agent-based model incorporating all collected data and considering key social and economic aspects; iii) assessment of interdependencies between the beef production and transportation infrastructures, iv) evaluation of different scenarios and their impact on the infrastructure performance. The generated data will be organized and publicly shared with the final goal of increasing the understanding of these coupled systems. Benefits of this work include improved understanding of how to prevent and contain risks to these systems, thus contributing to the goal of greater safety and economic viability. Mentoring and training of a graduate student in the conduct of interdisciplinary research is an important component of the research. Project data and software will be publicly shared through websites and results disseminated through academic conferences and journals. The Center for Engagement and Community Development of Kansas State University will be utilized to distribute project results to target stakeholder audiences through the organization of a workshop in a critical location for the beef industry.
From a theoretical perspective, the expected outcomes will provide novel insights into the structural characteristics and the interdependencies of coupled infrastructures. New methods and models will be developed and made publicly available for scientists in the field to use in other regions and other contexts. From an application perspective, the data generated in this project will advance current knowledge of the beef production and transportation systems. Furthermore, the significant economic and social aspects of these interdependent systems will provide foundational elements for cross-disciplinary analysis in the domains of network science and social organization.
