Micro-targeted vs stochastic political campaigning agent-based model simulation. Written by Toby D. Pilditch (University of Oxford, University College London), in collaboration with Jens K. Madsen (University of Oxford, London School of Economics)

The purpose of the model is to explore the various impacts on voting intention among a population sample, when both stochastic (traditional) and Micto-targeted campaigns (MTCs) are in play. There are several stages of the model: initialization (setup), campaigning (active running protocols) and vote-casting (end of simulation). The campaigning stage consists of update cycles in which “voters” are targeted and “persuaded” - updating their beliefs in the campaign candidate / policies.

NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

This proof-of-concept model explores the effects of how social and natural factors are incorporated (factor configuration) in environmentally induced migration. It is built in a conceptual environment where five regions are located in a row.

This model/program presents a “three industry model” that may be particularly useful for macroeconomic simulations. The main purpose of this program is to demonstrate a mechanism in which the relative share of labor shifts between industries.

Care has been taken so that it is written in a self-documenting way so that it may be useful to anyone that might build from it or use it as an example.

This model is not intended to match a specific economy (and is not calibrated to do so) but its particular minimalist implementation may be useful for future research/development.

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Chicago’s demographic, neighborhood, sex risk behaviors, sexual network data, and HIV prevention and treatment cascade information from 2015 were integrated as input to a new agent-based model (ABM) called the Levers-of-HIV-Model (LHM). This LHM, written in NetLogo, forms patterns of sexual relations among Men who have Sex with Men (MSM) based on static traits (race/ethnicity, and age) and dynamic states (sexual relations and practices) that are found in Chicago. LHM’s five modules simulate and count new infections at the two marker years of 2023 and 2030 for a wide range of distinct scenarios or levers, in which the levels of PrEP and ART linkage to care, retention, and adherence or viral load are increased over time from the 2015 baseline levels.

This model simulates different seeding strategies for information diffusion in a social network adjusted to a case study area in rural Zambia. It systematically evaluates different criteria for seed selection (centrality measures and hierarchy), number of seeds, and interaction effects between seed selection criteria and set size.

The BASAR model aims to investigate different approaches to describe small-scale farmers’ decision-making in the context of diversified agroforestry adoption in rural Rwanda. Thereby, it compares random behaviour with perfect rationality (non-discounted and discounted utility maximization), bounded rationality (satisficing and fast and frugal decision tree heuristics), Theory of Planned Behaviour, and a probabilistic regression-based approach. It is aimed at policy-makers, extension agents, and cooperatives to better understand how rural farmers decide about implementing innovative agricultural practices such as agroforestry and at modelers to support them in selecting an approach to represent human decision-making in ABMs of Social-Ecological Systems. The overall objective is to identify a suitable approach to describe human decision-making and therefore improve forecasts of adoption rates and support the development and implementation of interventions that aim to raise low adoption rates.

Scholars have written extensively about hierarchical international order, on the one hand, and war on the other, but surprisingly little work systematically explores the connection between the two. This disconnect is all the more striking given that empirical studies have found a strong relationship between the two. We provide a generative computational network model that explains hierarchy and war as two elements of a larger recursive process: The threat of war drives the formation of hierarchy, which in turn shapes states’ incentives for war. Grounded in canonical theories of hierarchy and war, the model explains an array of known regularities about hierarchical order and conflict. Surprisingly, we also find that many traditional results of the IR literature—including institutional persistence, balancing behavior, and systemic self-regulation—emerge from the interplay between hierarchy and war.

PSoup is an educational program in which evolution is demonstrated, on the desk-top, as you watch. Blind bugs evolve sophisticated heuristic search algorithms to be the best at finding food fast.

A reimplementation of the Wedding Ring model by Francesco Billari. We investigate partnership formation in an agent-based framework, and combine this with statistical demographic projections using real empirical data.