Rzevsky agent models of large systems

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Masha Rudnichenko

Technology Education

Modelling Large Complex Systems Using Multi-Agent Technology George Rzevski Professor Emeritus, Complexity and Design, The Open University, UK Chairman, Knowledge Genesis Group, London, UK

Seven Criteria of Complexity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Classification of Systems RANDOM SYSTEMS COMPLEX SYSTEMS SYSTEMS IN EQUILIBRIUM ALGORITHMS & CLOCKS Uncertainty Total uncertainty Limited uncertainty No uncertainty No uncertainty Behaviour Random Emergent Planned, Designed Programmed Norms of behaviour Total freedom Limited freedom No freedom Plan, design Instructions Organization None Self-organizing Organized Structured Control None Self-control Centralized control No need for control Changes Random changes Co-evolution Small deviations temporary None Operating point None Far from equilibrium Equilibrium None

Where does Complexity Come From? ,[object Object],[object Object]

Stepwise Increase in Market Complexity Agricultural Economy Industrial Economy Knowledge Economy Agricultural Economy

Emergence of Exceedingly Complex Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Co-Evolution of Society, Economy & Technology Urban Society Industrial Economy Mass Production Technology Global Society Knowledge Economy Distributed Digital Technology Rural Society Agricultural Economy Elementary Tools land capital knowledge/ information KEY RESOURCES digital networks motorways & railways village roads DISTRIBUTION STAGES SCOPE local regional global SUCCESS FACTORS efficiency economy of scale adaptability

Evolution of English Language Chaucer Shakespeare

Evolution of Science Einstein Prigogine Newton

Evolution of Computing Monolithic algorithms Swarms of interacting agents + ontology

How Can We Model Complexity? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Models of Complex Systems must be Complex ,[object Object],[object Object],[object Object],[object Object],[object Object]

Technology for Modelling Complexity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

How Agent-Based Software Works? ,[object Object],[object Object],[object Object],[object Object]

Agent Based Models of Complex Systems Ontology Conceptual knowledge Data Virtual World Software Agents Modified State Data Factual knowledge Current State Complex Real System Current state Event Next state

Partitioning of Large Systems to Prevent Instability Social Unit 1 Social Unit 2 Social Unit 3 Social Unit 4

Emergent Intelligence ,[object Object],[object Object]

Multi-Agent Models in Commercial Use: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Architecture of the LEGO World of Agents Retail Agents Product Agents Order Agents Warehouse Agents Delivery Agents P1 P2 P3 P4 P5 P6 P7 P8 Enterprise Agent

A Family of Space Robots robot 3 robot 4 robot 5 robot 1 robot 2

Intelligent Geometry Compressor Stage 1 Agent Stage 2 Agent Stage 3 Agent Stage 4 Agent Coordinating Agent

Intelligent Logistics Network Supplier 1 store transporter transporter store store transporter Intelligent parcels Intelligent parcels Intelligent parcels Destination 1 Destination 2 store

Newtonian (Deterministic) Worldview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Complexity Science Worldview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Rzevsky agent models of large systems

1. Modelling Large Complex Systems Using Multi-Agent Technology George Rzevski Professor Emeritus, Complexity and Design, The Open University, UK Chairman, Knowledge Genesis Group, London, UK

2. COMPLEXITY

4. Classification of Systems RANDOM SYSTEMS COMPLEX SYSTEMS SYSTEMS IN EQUILIBRIUM ALGORITHMS & CLOCKS Uncertainty Total uncertainty Limited uncertainty No uncertainty No uncertainty Behaviour Random Emergent Planned, Designed Programmed Norms of behaviour Total freedom Limited freedom No freedom Plan, design Instructions Organization None Self-organizing Organized Structured Control None Self-control Centralized control No need for control Changes Random changes Co-evolution Small deviations temporary None Operating point None Far from equilibrium Equilibrium None

6. Stepwise Increase in Market Complexity Agricultural Economy Industrial Economy Knowledge Economy Agricultural Economy

8. Co-Evolution of Society, Economy & Technology Urban Society Industrial Economy Mass Production Technology Global Society Knowledge Economy Distributed Digital Technology Rural Society Agricultural Economy Elementary Tools land capital knowledge/ information KEY RESOURCES digital networks motorways & railways village roads DISTRIBUTION STAGES SCOPE local regional global SUCCESS FACTORS efficiency economy of scale adaptability

9. Evolution of English Language Chaucer Shakespeare

10. Evolution of Science Einstein Prigogine Newton

11. Evolution of Computing Monolithic algorithms Swarms of interacting agents + ontology

12. Modelling Complex Systems

13.

14.

15.

16.

17. Agent Based Models of Complex Systems Ontology Conceptual knowledge Data Virtual World Software Agents Modified State Data Factual knowledge Current State Complex Real System Current state Event Next state

18. Partitioning of Large Systems to Prevent Instability Social Unit 1 Social Unit 2 Social Unit 3 Social Unit 4

19.

20. Examples

21.

22. Architecture of the LEGO World of Agents Retail Agents Product Agents Order Agents Warehouse Agents Delivery Agents P1 P2 P3 P4 P5 P6 P7 P8 Enterprise Agent

23. A Family of Space Robots robot 3 robot 4 robot 5 robot 1 robot 2

24. Intelligent Geometry Compressor Stage 1 Agent Stage 2 Agent Stage 3 Agent Stage 4 Agent Coordinating Agent

25. Intelligent Logistics Network Supplier 1 store transporter transporter store store transporter Intelligent parcels Intelligent parcels Intelligent parcels Destination 1 Destination 2 store

26. Philosophy of Complexity

27.

28.

Rzevsky agent models of large systems

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