Digital-Twin model of Sejong City – Source model code & data

We only shared model codes, excluding private data and simulation engine codes.
The followings are brief reasons for the items we cannot share.

1. Residence address data
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NOMAD is an agent-based model of firm location choice between two aggregate regions (“near” and “off”) under logistics uncertainty. Firms occupy sites characterised by attractiveness and logistics risk, earn a risk-adjusted payoff that depends on regional costs (wages plus congestion) and an individual risk-tolerance trait, and update location choices using aspiration-based satisficing rules with switching frictions. Logistics risk evolves endogenously on occupied sites through a region-specific absorption mechanism (good/bad events that reduce/increase risk), while congestion feeds back into regional costs via regional shares and local crowding. Runs stop endogenously once the near-region share becomes quasi-stable after burn-in, and the model records time series and quasi-stable outcomes such as near/off composition, switching intensity, costs, average risk, and average risk tolerance.

Cultural group selection model used to evaluate the conditions for agents to evolve who have other-regarding preferences in making decisions in public good games.

An empirical ABM of smallholder decisions in times of drought stress.

The simulation model SimPLS shows an application of the PLS agent concept, using SEM as empirical basis for the definition of agent architectures. The simulation model implements the PLS path model TAM about the decision of using innovative products.

We model the relationship between natural resource user´s individual time preferences and their use of destructive extraction method in the context of small-scale fisheries.

We extend the Flache-Mäs model to incorporate the location and dyadic communication regime of the agents in the opinion formation process. We make spatially proximate agents more likely to interact with each other in a pairwise communication regime.

This model aims to mimic human movement on a realistic topographical surface. The agent does not have a perfect knowledge of the whole surface, but rather evaluates the best path locally, at each step, thus mimicking imperfect human behavior.

I added a discounting rate to the equation for expected values of defective / collaborative strategies.
The discounting rate was set to 0.956, the annual average from 1980 to 2015, using the Consumer Price Index (CPI) of Statistics Korea.

This model was design to test parameters that affects the number of people shot during mass shooting. This basic formulation places a gunman in a crowd and allows the users to manipulate parameters of the gunman.