Agent-Based Modeling (ABM) is a relatively new computational modeling paradigm, is the modeling of phenomena as dynamical systems of interacting agents. Another name for ABM is individual-based modeling.

In general, when we build an ABM to simulate a certain phenomenon, we need to identify the actors first (the agents). We then need to consider the processes (rules) governing the interactions among the agents.

Agent Based Modeling (ABM) can be defined as an essentially decentralized, individual-centric (as opposed to system level) approach to model design. When designing an agent based model the modeler identifies the active entities, the agents (which can be people, companies, projects, assets, vehicles, cities, animals, ships, products, etc.), defines their behavior (main drivers, reactions, memory, states), puts them in a certain environment, establishes connections, and runs the simulation. The global behavior then emerges as a result of interactions of many individual behaviors. (ex: Marketing Candies)

Today’s companies and governmental organizations have accumulated large amounts of useful data in their CRM, ERP, and HR databases that are very much underutilized. Agent based modeling is a natural way to leverage that data and put it to work. As long as agent based models are essentially individual-based they can be populated with agents whose properties are real and read directly from a CRM system or from an ERP/HR database if you are modeling the dynamics of the human resources inside the organization. This gives you an easy, precise, and up to date way to model, forecast, compare scenarios, and optimize your strategy.

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