I investigate what big factors in environments, and in the social and cognitive properties of people, lead to patterns of organizational structure such as hierarchies. I'm currently a PhD candidate at Utah State University, co-advised by Dr. Stefani Crabtree and Dr. Jacob Freeman. I use agent-based models, network analysis, and statistical learning techniques.
As someone training to be a computational social scientist, I am interested in hierarchy and social power from a perspective of collective computation, and I am fascinated by processes managing collective action problems in complex systems.
As an interdisciplinary scientist and concerned earthling, I am also interested in more practical topics touching upon organizational structures and complex systems: What organizations and institutions do we need for the climate change challenge we face? What organizational transitions are needed for social movements to create new organizations with functional permanence, and is that even a good idea? At first these may seem like questions at the global scale. But they are also questions for small businesses, community organizations, local chapters of social movements, university extension programs, and neighborhood cooperatives.
You can contact me at email@example.com
Currently in review, this project simulates simple command hierarchies--the kind of hierarchy represented in an organization chart--in changing environments to understand the performance tradeoffs inherent in adopting a hierarchical structure. The model reveals that environments that vary locally can cause unavoidable tension between the views of front-line workers and managers, or local offices and head offices; even perfect agents find themselves in an inevitable computational dilemma. The best organizational strategy to manage this dilemma is continuing to provide manager input while enabling some degree of worker autonomy.
I wrote an appendix on making colorblind-friendly agent-based models for Iza Romanowska, Colin Wren, and Stefani Crabtree's book, which evolved naturally from modifying a number of models in the book to create colorblind-friendly versions. Helping work on the book really opened my eyes to the need for better accessibility and usability in agent-based modeling tools and models themselves. I would like to improve upon this appendix--perhaps writing a whitepaper or a mini-book--by considering a broader scope of accessibility challenges with agent-based models and modeling. If you're interested in this too, send me an email!
Check out the whole book, it rocks: Agent-Based Modeling for Archaeology: Simulating the Complexity of Societies.
Social identity is a key component of political polarization. This agent-based model project builds on work done with an international team that came together at Santa Fe Institute's Complexity Interactive. In that project, we looked at how agent tolerance, network connectivity, and opinion adjustment affected polarization. We're now adding a social identity module that enables agents to use cues (e.g., the clothes someone is wearing) to infer the party identity of the other agent. How does the ability of agents to create co- and cross-partisan stereotypes affect the polarization dynamics of the original model?
Scholars in the social sciences define hierarchy differently, which makes the comparison and integration of research findings on hierarchy quite challenging. This project uses text mining methods, working with Google Scholar and Zotero, to collate thousands of papers that define the term hierarchy into a searchable database. The papers are then coded by the type of definition used to provide insight into how the ontology of hierarchy has developed, and how we may want to distinguish between definitions in the future.
This study compares the organizational structures of U.S. federal agencies over 17 years to better understand how structures vary among agencies and over time. Organizational structure matters in all organizations, including in U.S. federal agencies. However, few works have robustly identified defining factors that distinguish agency organizational structures, and no work has extended this analysis to stability of types over time. To address this gap, I perform a longitudinal analysis of federal agency organizational structures from 2004 to 2021 using Fedscope federal employee workforce data.
The Fremont people of the Great Basin grew more maize--and grew in population--during a period of climate stability from AD 750 to AD 1050. What decision strategies did they employ, and what factors did they consider, as they increased maize agriculture in the region? In collaboration with Judson Finley of Utah State University and Erick Robinson of Boise State University, I am building models that include a number of important environmental and socio-cognitive factors in an attempt to understand the increase and collapse in Fremont maize agriculture. As more evidence emerges about the extent of Fremont settlements, and the importance of their surrounding hydrological regimes, agent-based modeling provides a crucial tool in understanding the behavior of Fremont within a coupled human-environmental system.
This experiment uses a game in which players can gobble up resources that slowly regenerate. These are rival goods, so taking them both denies them to other players, and reduces the ability of the environment to regenerate more resources. How does enabling hierarchy, vis-a-vis an appointed player leader, change the behavior of groups dealing with these resource dilemmas? This experiment uses Amazon Mechanical Turk to recruit participants. In collaboration with Jacob Freeman of Utah State University and Tam Nguyen of LinkedIn.