Agent-Based Modelling And Organisational Structure

1. Agent-Based Modelling And Organisational Structure http://www.acm.org/~dekker/LA.ppt http://www.acm.org/~dekker/FINCX http://www.acm.org/~dekker/FINCY http://www.acm.org/~dekker/FINCZ Dr. Tony Dekker ( dekker@ACM.org ) 10 May 2002

2. Introduction • The FINC methodology for analysing organisational structures • Do FINC metrics predict organisational performance? • Two simulation experiments • What happened • Java implementation: how we did it • Intelligent agents & behaviour hierarchy

3. The FINC Methodology • Force (Activity) • Intelligence (Information) • Network • C2 (Command and Control, Decision-Making) • Conceptual delays on network links need calibration N N C2 C2 Int N N N N Int F F C2 N N F F

4. The FINC Metrics • Information flow coefficient (tempo superiority) low is good • = average path length (intelligence -> force) • Coordination coefficient (coordination superiority) low is good • = average path length (force -> force) • Intelligence coefficient (information superiority) high is good • = SUM (relevant area * (intelligence quality / path length)) Effective quality

5. The FINC Hypothesis • These metrics can predict organisational performance • i.e. better metrics mean the task gets done better • Test this using agent-based simulations • (later follow with real-world studies)

6. Experiment 1: the scenario • A “SCUD Hunt” • 4 SCUD missiles (white) • Information from 1 satellite and 4 surveillance aircraft (green) • Information of varying quality: “ghost”missiles (grey) • 4 strike aircraft (blue) • Several headquarters (red) • 8 possible architectures

7. Experiment 1: the scenario • A “SCUD Hunt” • 4 SCUD missiles (white) • Information from 1 satellite and 4 surveillance aircraft (green) • Information of varying quality: “ghost”missiles (grey) • 4 strike aircraft (blue) • Several headquarters (red) • 8 possible architectures

8. Experiment 1: the metrics intelligence coefficient (intel) Distributed with Info Sharing Negotiation with Info Sharing Distributed Centralised with Info Sharing Negotiation coordination coefficient (coord) Hierarchical with Info Sharing Centralised Hierarchical information flow coefficient (info)

9. Experiment 1: the results Poor Sensors Fair to Good Sensors Slow Tempo Balance information & coordination superiority Information superiority is most important Moderate Tempo Balance all three kinds of superiority Fast Tempo Balance information & tempo superiority

10. Experiment 2: the scenario • Hierarchical organisation of 16 companies • Try to locate 5 “targets” and manoeuvre forces towards them • World contains obstacles (green) • 3 planning strategies • 4 possible architectures • Varying communications quality

11. Experiment 2: the results 95% of variance in performance predicted using only the intelligence coefficient

12. Implementation: Intelligent Agents & Messages • Network of agents can be any graph • Agents co-operate on the same goal • Agents pass messages • Agents have internal map and path planner: I am at (4,5) Found target at (3,7) Need support at (1,9)

13. Implementation: Behaviour Hierarchy • Java Class Hierarchy • Agents have “slots” for different behaviours • Behaviour code manipulates agents internal map, etc.

14. Implementation: Dynamic Instantiation • New Behaviour classes produced regularly • Initial agent network from CAVALIER network editing tool • CAVALIER specifies a text string (class name plus arguments) for each agent “slot” • E.g. “Followgoal, A, B, C, D” means move towards average of A, B, C, D positions • Agent simulation environment instantiates behaviour objects using dynamic object creation in Java’s reflection package

15. Summary • FINC methodology for analysing organisational structures • Do FINC metrics predict performance? • Experiments 1 & 2 • Excellent prediction of performance in simulations • Implementation: Intelligent Agents & Messages • Behaviour Hierarchy & Dynamic Instantiation

16. Any Questions? • ?