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Agent-Based Modelling And Organisational Structure

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Presentation on theme: "Agent-Based Modelling And Organisational Structure"— Presentation transcript:

1 Agent-Based Modelling And Organisational Structure Dr. Tony Dekker ( ) 10 May 2002

2 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 3 The FINC Methodology Int N Force (Activity) Intelligence (Information) Network C2 (Command and Control, Decision-Making) Conceptual delays on network links need calibration C2 FF FF Int NN N N N NN

4 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 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 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 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 8 Experiment 1: the metrics coordination coefficient (coord) Hierarchical Centralised information flow coefficient (info) intelligence coefficient (intel) Hierarchical with Info Sharing Negotiation Centralised with Info Sharing Distributed Negotiation with Info Sharing Distributed with Info Sharing

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

10 1010 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 11 Experiment 2: the results 95% of variance in performance predicted using only the intelligence coefficient

12 1212 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: Need support at (1,9) Found target at (3,7) I am at (4,5)

13 1313 Implementation: Behaviour Hierarchy Java Class Hierarchy Agents have “slots” for different behaviours Behaviour code manipulates agents internal map, etc.

14 1414 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 1515 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 1616 Any Questions? ?


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