Markovian Agent Models: A Dynamic Population of Interdependent Markovian Agents

  • Andrea Bobbio
  • Davide Cerotti
  • Marco GribaudoEmail author
  • Mauro Iacono
  • Daniele Manini
Part of the Simulation Foundations, Methods and Applications book series (SFMA)


A Markovian Agent Model (MAM) is an agent-based spatio-temporal analytical formalism aimed to model a collection of interacting entities, called Markovian Agents (MA), guided by stochastic behaviours. An MA is characterized by a finite number of states over which a transition kernel is defined. Transitions can either be local, or induced by the state of other agents in the system. Agents operate in a space that can be either continuous, or composed by a discrete number of locations. MAs may belong to different classes and each class can be parametrized depending on the location in the geographical (or abstract) space. In this work, we provide a very general analytical formulation of an MAM that encompasses many forms of physical dependencies among objects and many ways in which the spatial density may change in time. We revisit recent literature to show how previous works can be cast in terms of this more general MAM formulation.


Agent-based model Spatially distributed systems Performance modelling 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrea Bobbio
    • 1
  • Davide Cerotti
    • 2
  • Marco Gribaudo
    • 2
    Email author
  • Mauro Iacono
    • 3
  • Daniele Manini
    • 4
  1. 1.Università del Piemonte OrientaleAlessandriaItaly
  2. 2.Politecnico di MilanoMilanItaly
  3. 3.Seconda Università di NapoliCasertaItaly
  4. 4.Università di TorinoTurinItaly

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