Erez Hatna and Itzhak Benenson
Journal of Artificial Societies and Social Simulation 18 (4) 15

Abstract: The Schelling model is a simple agent-based model that demonstrates how individuals’ relocation decisions can generate residential segregation in cities. Agents belong to one of two groups and occupy cells of rectangular space. Agents react to the fraction of agents of their own group within the neighborhood around their cell. Agents stay put when this fraction is above a given tolerance threshold but seek a new location if the fraction is below the threshold. The model is well-known for its tipping point behavior: an initially random (integrated) pattern remains integrated when the tolerance threshold is below 1/3 but becomes segregated when the tolerance threshold is above 1/3. In this paper, we demonstrate that the variety of the Schelling model’s steady patterns is richer than the segregation–integration dichotomy and contains patterns that consist of segregated patches of each of the two groups, alongside areas where both groups are spatially integrated. We obtain such patterns by considering a general version of the model in which the mechanisms of the agents' interactions remain the same, but the tolerance threshold varies between the agents of both groups. We show that the model produces patterns of mixed integration and segregation when the tolerance threshold of an essential fraction of agents is either low, below 1/5, or high, above 2/3. The emerging mixed patterns are relatively insensitive to the model’s other parameters.

Chathika Gunaratne, Erez Hatna, Joshua M. Epstein and Ivan Garibay
Journal of Artificial Societies and Social Simulation 26 (2) 7

Abstract: The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. This work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.

Joshua M. Epstein, Ivan Garibay, Erez Hatna, Matthew Koehler and William Rand
Journal of Artificial Societies and Social Simulation 26 (2) 10

Abstract: This is a guest editors' statement accompanying the publication of a special issue on "Inverse Generative Social Science", published in volume 26, issue 2, 2023 of JASSS-Journal of Artificial Societies and Social Simulation"

Erez Hatna, Jeewoen Shin, Katelynn Devinney, Julia Latash, Vasudha Reddy, Beth Nivin, Alyssa Masor and Sharon K. Greene
Journal of Artificial Societies and Social Simulation 27 (3) 2

Abstract: Large outbreaks of Shigella sonnei among children in Haredi Jewish (ultra-Orthodox) communities in Brooklyn, New York have occurred every 3–5 years since at least the mid-1980s. These outbreaks are partially attributable to large numbers of young children in these communities, with transmission highest in child care and school settings, and secondary transmission within households. As these outbreaks have been prolonged and difficult to control, we developed an agent-based model of shigellosis transmission among children in these communities to support New York City Department of Health and Mental Hygiene staff. Simulated children were assigned an initial susceptible, infectious, or recovered (immune) status and interacted and moved between their home, child care program or school, and a community site. We calibrated the model according to observed case counts as reported to the Health Department. Our goal was to better understand the efficacy of existing interventions and whether limited outreach resources could be focused more effectively. We evaluated how well disseminating hand washing education in child care programs can reduce the number of infected children. The model indicated that intervention efficacy may be as high as 24% when all intervention parameters are at optimal values but only approximately 7% for a more realistic, less stringent scenario. We ranked intervention parameters according to their permutation importance using a random-forest regression analysis. The most important parameter was the minimum number of reported cases in a child care program that triggers a visit to disseminate hand washing education, followed by the use of non-antibacterial soap in hand washing education, the number of additional visits to child care programs, and the probability of successfully obtaining information on child care program attendance via patient interview. Additional strategies should be considered, such as working with community partners to assist with hand hygiene education at facilities during an outbreak.