Simulations use computer models to operationalize the real world and create a computational analogue of it. Simulations allow for thought experiments, allowing the resarcher to experiment with different mechanisms, scenarios, and what-if questions. Simulations are especially valuables when the research seeks to explain complexity — tipping points, catastrophes, and threshold effects. Agent-based models (ABM), based on the description of mechanisms at the individual level, are suited to explain social phenomena and their temporal dimension. Recently, ABM began to be applied widely in the social science, producing large-scale models, and models designed to engage with or inform policy and to address policy resistance in fields as criminality (GLODERS), terrorsim (PROTON) and migration. ABM can be seen as a cross between sociology and distributed artificial intelligence. In an ABM, actors in a system are separately represented as autonomous agents within a computer program and the interactions of the actors as explicit messages between the agents. The agents are given rules that govern their behavior, including their interaction with each other and with their environment through time. The ABM then traces out individual trajectories and population-level patterns, as they are generated from the behaviours and the interactions of the agents in a bottom up fashion. Such an approach provides a formal mapping from individual-level assumptions up to coevolving population-level dynamics and captures the structures that emerge from the nonlinear interactions among individual agents. Not only are ABSs powerful tools for modelling social dynamics, but they also offer a number of potential advantages to a decision maker. By making explicit the assumptions, key pathways, and uncertainties involved (along with the mapping of all three of these onto potential outcomes), ABM enhance the decision maker’s understanding of a system and its dynamics under various conditions. Such models can also be especially useful tools when performing real-world experiments to inform policy choice is difficult, overly expensive, time- consuming, unethical, or impractical. An additional advantage offered by such models is in their ability to uncover and assess the impacts of solutions to a given problem, usually related to supporting decision making, politic, regulation or management activities. Application of ABM involves stakeholders deeply and continuously in the modelling work through participatory modelling.
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