Agent-Based Modeling Showcases the Impact of Combined Interventions on Reducing Meat Consumption
May 05, 2026
A new study by researchers at the Johns Hopkins Center for a Livable Future (CLF) evaluates the effect of food retail interventions designed to reduce meat consumption. By applying a systems modeling approach to create an agent-based model (ABM), the researchers simulated the potential efficacy of various intervention scenarios in reducing meat consumption within the population context of Baltimore City, Maryland. Their analysis demonstrates that the concurrent implementation of combined interventions has greater effect in reducing meat consumption compared to individual interventions alone.
The study, “Evaluating select factors and mechanisms influencing meat consumption in Baltimore City: an agent-based modeling study,” was published online in BMC Public Health on February 19, 2026. ABMs are computer-simulated models that allow researchers to better understand decision-making behaviors of virtual agents, which can represent individuals or groups of people. Although ABMs have been used in other food consumption and purchasing related analyses, this study features a novel ABM application accounting for the complex and interconnected factors—including individual characteristics and sociocultural and external factors—that shape meat consumption behaviors. Given the study’s systems-driven approach, CLF brought together a team of experts from different disciplines to collaborate and conduct this analysis.
The virtual agents were defined as individuals, 18 years and older, who could independently decide which food to consume for dinner, including meat. Baltimore City was selected as the case study for this ABM because of its socioeconomic and racial diversity. The team’s previous experience with Baltimore City’s food environment and corresponding data. Dinner was also chosen as the focus of this simulation because past research shows greater meat consumption during dinner as opposed to breakfast or lunch.
Several parameters were integrated into the ABM including agent characteristics, food preferences, food environment, and systems-level driving factors such as exposure and access to food and marketing and outreach, among others. The researchers used publicly available datasets such as the American Community Survey (ACS) and the National Health and Nutrition Examination Survey (NHANES) to create a synthetic population of 596,377 adult agents from Baltimore City.
A key takeaway from this analysis is that implementing combined interventions generates greater impacts in reducing meat consumption than single-strategy approaches. When compared to baseline, the combined non-meat push (intervention scenario 4) yielded the largest reduction in meat consumption in this simulation, with a 12% decrease. Although reduced meat consumption was the focus of this study, the analysis shed light on other non-meat alternatives that agents shifted toward in their dinner meal choices. When available non-meat options are increased (intervention scenario 3), agents preferred dairy-based alternatives like eggs and cheese.
This study also highlights the potential efficacy of the intervention scenarios by race and income. Across all scenarios, the largest change in meat consumption was among non-Hispanic White and Asian populations. Reduced meat consumption among these two groups was most evident when the availability of non-meat options was increased (intervention scenario 3) and for the combined non-meat push (intervention scenario 4). In comparison, meat consumption reduction was least observed across all scenarios among non-Hispanic Black populations and those with a household income between $25k-$55k.
“Although Baltimore City was our selected case study, it is important to recognize that our agent-based model can be applicable to similar urban populations beyond Baltimore,” says Rebecca Ramsing, one of the authors of this study and senior program officer at the Johns Hopkins Center for a Livable Future. “Applying a systems approach in our efforts to understand meat consumption behaviors enables us to better evaluate the effectiveness of meat reduction programs, policies, and combined efforts and to create tailored approaches that better address the unique aspects of various populations.”
“Evaluating select factors and mechanisms influencing meat consumption in Baltimore City: an agent-based modeling study” was co-authored by Caitlin Misiaszek, Gary Lin, Jamie Harding, Daphene Altema-Johnson, Yeeli Mui, Kenjin Chang, Karen Bassarab, Kate Clancy, Anne Palmer, Atif Adam, and Rebecca Ramsing.
This work was supported by funding from the Santa Barbara Foundation.