Agent-based Simulation

Agent-based Simulation is an individual-based method of computer-aided modeling and simulation that has evolved during the last 30 to 40 years from various disciplines such as chaos theory, game theory, artificial intelligence, complex systems, multi-agent systems, evolutionary programming and cellular machines.

Today, the model paradigm of Complex Adaptive Systems (CAS) or Complexity Science, developed by the Santa Fe Institute in the U.S., is established as a powerful modeling tool for analyzing complex and dynamic systems and predicting their behavior. Examples of applications can be found in the modeling of stock and financial markets, markets with competition, ant colonies, in brain research or the analysis of the immune system. It is considered as the leading paradigm to allow a completely new view of market dynamics, due to the failure of the standard tools of macroeconomics in the financial crisis. In the holistic view of a complex-dynamic system, the main criteria are evolution, emergence of new patterns, adaptation, self-organization and complexity.

Characterization of complex-dynamic systems (Source: New England Complex Systems Institute)


The modeling approach of Agent-based Simulation is based upon small entities (agents) and their decision-making or action. The system behavior results from the behavior of the individual agents with each other and their environment. If this results in effects on the system level that are not directly derived from the decision algorithms of the individuals, then new structures and patterns emerge - the micro-behavior of individuals determines the macro-behavior of the system. In addition, agent capabilities such as adaptation, learning and memory can be programmed. Agents can thereby be key players in global markets, people in social networks or people in purchasing or crisis situations.

population dynamics
Illustration of a model of population dynamics


Agent-based model of a social network (Source: Argonne National Laboratory)

Classic questions of Executive Management

Typical questions of Executive Management that are addressed through the methodology of Agent-based Simulation in a real world entrepreneurial context are, for example:

  • Scenario analysis of market dynamics and technology including customer behavior and buying decisions
  • Sustainability considerations for the protection of EBIT growth
  • Strategic business planning
  • Strategic asset management
  • Support of planning processes in strategic and operational management, illustration and optimization of processes
  • Risk assessment
  • Innovation and production management

Typical application areas of Agent-based Simulation

Today, typical application areas for Agent-based Simulation are, for example:

  • Macroeconomics
  • Financial and energy markets
  • Market and strategy simulation
  • Ecological systems and environmental
  • Health service
  • Defense
  • Urban and population dynamics, epidemiology
  • Political systems and social networks
  • Social science
  • Supply chain management and production systems

GLEAMviz - The Global Epidemic and Mobility Model from FuturICT.

Advantages of Management Flight Simulators

Simulation models that are based on the methodology of Agent-based Simulation have the following advantages for Executive Management in increasingly dynamic future markets:

  • Forecast future customer behavior and their buying decisions
  • Analysis of new products and brands in established or emerging markets
  • Holistic capture and transparent presentation of highly complex decision situations in the strategy area
  • Avoidance of tacit misunderstandings by disclosing the different perceptions of different decision-makers
  • Development and simulation of business scenarios, customers and competitors to make uncertainty about the future tangible
  • Identification of the levers for value-enhancing corporate governance in the face of high uncertainty and risks
  • Modeling of alternative strategies and testing by simulation before they are implemented in the business
  • Evaluation of sustainable business strategies in the context of innovation and in competitive situations
  • Implementation of early-warning systems to indicate market changes, market position, and to monitor strategic decisions and operational actions in real time