With ABM (agent-based model simulations, researchers can observe the dynamics of agents, the collective, and the interrelating environment, in relation to policy. ABM simulations are well suited for capturing relationship connections and interaction processes from heterogeneous agents in operation during the policy process. ABMs allow for generating models and policy scenarios that can identify and show leverage points of policy drivers and policy regulators of what works and how it works in policy. Policy levers are not enacted in isolation. ABMs, if effectively applied, extend the limits of traditional input-output policy research providing insight into processes, mechanisms, and agent interactions in the mysterious policy black-box.
ABMs are built based on theories, assumptions,
rules, algorithms, and data. Developing an ABM
starts with assumptions about agents, agent behavior, and the environment. Computer simulations then create scenarios with the capacity to reveal the dynamic consequences about the policy from the model’s assumptions. ABM is a methodology that can transcend traditional policy research, while capturing the dynamics of simple, complicated, complex and chaotic systems. This Guidebook provides an overview and a simple step-by-step “how to” use ABMs effectively in policy research.
Liz Johnson formerly worked as a news anchor, reporter, and PR specialist. She currently works with the Complex Systems Institute in Charlotte, NC. She has been conducting research and publishing on complex systems for over six years in the areas of policy, nanotechnology, human-centrism for species survival, education, sports, agent-based modeling, AI, hybrid engineering, qualitative research, and policy theory. She approaches research problems combining qualitative, quantitative, network science, and agent based modeling methodologies. In addition, she teaches critical thinking and courses on complexity internationally at conferences and academic institutions. Johnson co-founded the Journal on Policy and Complex Systems and serves as the Managing Editor. She holds masters in human development/ learning and ethics/applied philosophy, as well as a doctorate in educational policy and leadership.
ABMs are built based on theories, assumptions,
rules, algorithms, and data. Developing an ABM
starts with assumptions about agents, agent behavior, and the environment. Computer simulations then create scenarios with the capacity to reveal the dynamic consequences about the policy from the model’s assumptions. ABM is a methodology that can transcend traditional policy research, while capturing the dynamics of simple, complicated, complex and chaotic systems. This Guidebook provides an overview and a simple step-by-step “how to” use ABMs effectively in policy research.
Liz Johnson formerly worked as a news anchor, reporter, and PR specialist. She currently works with the Complex Systems Institute in Charlotte, NC. She has been conducting research and publishing on complex systems for over six years in the areas of policy, nanotechnology, human-centrism for species survival, education, sports, agent-based modeling, AI, hybrid engineering, qualitative research, and policy theory. She approaches research problems combining qualitative, quantitative, network science, and agent based modeling methodologies. In addition, she teaches critical thinking and courses on complexity internationally at conferences and academic institutions. Johnson co-founded the Journal on Policy and Complex Systems and serves as the Managing Editor. She holds masters in human development/ learning and ethics/applied philosophy, as well as a doctorate in educational policy and leadership.
