Catholijn M. Jonker

1. Development and Application of Rich Cognitive Models and the Role of Agent-Based Simulation for Policy Making Catholijn M. Jonker

2. BRIDGE: Development and Application of Rich Cognitive Models for Policy Making Frank Dignum, Virginia Dignum, Catholijn M. Jonker

3. Policy • Policy introduction • Goal: noticeable change on the global level • Assumption: incentive for individuals to change behaviour to intended new behaviour • Influencers of individual’s behaviour • Dynamics of environment • Social circles (family, friends, work, culture …) • Personal circumstances

4. Example Policies • Anti-smoking ban: • Aim: Healthy (work) environment • Result? Less bar revenues, civil disobedience • VAT increases • Aim: More state revenues • Result? more black market, less revenues • Higher demands on hospital hygiene • Aim: Better health • Result? superbugs

6. Levels of simulation / models • Macro-level to measure policy effect • Model at macro level: • Averages over behaviour of individuals • Misses out on holistic effects • Micro-level to allow variation in behaviours • Requires rich cognitive models • Personality • Cultural differences • Local variation • Personal circumstances • Social circles

7. Micro-macro simulation: zoom-in/zoom-out approach

9. update B D sense Normative beliefs generate I Growth needs Cultural beliefs filter G act interpret E plan select personal ordering Preference deficiency needs Inference method Beliefs Response Intentions Desires Goals Ego The BRIDGE architecture plan select direct select explicit implicit overrule R direct urges, stress stimuli

10. Support for Policy Makers Old view Agent-based simulation view Policy maker first tries out the policy in the simulation Policy maker directly puts policy at work in the society.

11. When would ABM help? • Agent should show realistic human behaviour, with culture, social circles etc. • If we can build agents that react realistically to any policy, then we solved the strong AI problem! Agent-based simulation view Policy maker first tries out the policy in the simulation

12. Policy – Effect examples • Goal: reduce garbage heaps • Policy: garbage bags are taxed • Effect: people dump garbage in nature • Goal: Reduce “fat” from Ministry of Defense • Policy: Reduce budget • Effect: Minister announces Trade Fleet cannot be protected from pirates • Goal: Reduce risk of terrorist attacks • Policy: Forbid face covering clothing • Effect: Police officers refuse to enforce it

13. Our proposal • Identify stakeholders • Qualitative interviews with representatives of: • target population • implementers of policy • Possible implementations, possible reactions of targets, possible side effects • Interview experts in psychology and national cultures to create XML file to link possible reactions to personality, culture, and circumstances • Run simulations using XML file

14. Required Adaptations of Models • Additional info from interviewed people  • new actions and decision rules • Adapt existing decision rules when influenced by new actions • Run simulation possible side effects policy possible reactions

16. Caveats • Sensitivity analysis required of the • Basic agent model • Overall simulation model • Validation! • Cannot predict, only explore possibilities

17. Game design Theory, hypotheses Gaming simulation Test design Agent modeling Theorizing Experimental setup Agent-Based Model Validation results Game sessions Model runs Data, conclusions Real world observations Model validation

18. tests predictions based on Gaming simulation Theory implements mechanisms according to implements design of tests predictions based on validates mechanisms described by Computer simulation

19. September 9-10, 2010 - Treviso (Italy) Sensitivity Analysis of anAgent-Based Model ofCulture’s Consequences for Trade Saskia Burgers, Gert Jan Hofstede, Catholijn Jonker, Tim Verwaart

20. Sensitivity analysis • Generally considered “good modeling practice” • Actual parameter values are uncertain • A powerful tool in the process of model verification and validation • Specific problems arise when performing sensitivity analysis for agent-based models

21. Sensitivity analysis for ABM • Agent-based models may be very sensitive to parameter changes in particular parts of parameter space: • Nothing may happen in large areas in the joint parameter space • Areas may exist where the system responds dramatically to slight changes • Parameters may significantly interact • Sensitivity may be studied for aggregated individual level outputs

22. Influence of culture • Culture modifies parameter values in the decision functions • Describe culture based on Hofstede’s five dimensions of national cultures • Relational attributes have different significance in different cultures: • Group distance • Status difference • Interpersonal trust

23. The role of parameters • Which areas in parameter space result in realistic behavior? • In which areas of parameter space can tipping points occur? • Which parameters have significant effects for which outputs? • Which interactions between culture and other parameters are important? • Are the answers different between aggregate and individual level?

24. Results of sensitivity analysis (1/2) • For many of the parameter sets drawn at random, no transactions occur • No obvious regions in parameter space where transactions occur / no transactions occur • Logistic regression: discover the parts of parameter space where transactions occur • Zoom in on the regions in parameter space where interesting behaviouroccurs

25. Results of sensitivity analysis (2/2) • Parameters that have significant effects can be identified through meta-modeling, even for complex systems. However, the analysis is not straightforward. • When keeping culture constant, straightforward methods for sensitivity analysis can be applied. Results differ considerably across cultures. • Sensitivity of individual agents can differ considerably from aggregate level sensitivity.

26. September 9-10, 2010 - Treviso (Italy) Cross-validation of Multi-Agent Simulation withCultural Differentiation GertJan Hofstede, Catholijn M. Jonker, Tim Verwaart

27. Validation • Why: to combat under-determinism • model M explains the behaviour of a system S • Is M the only model to do so?

28. Cross-validation (Moss& Edmonds, 2005) • Compare statistics of • Agent-based simulation • Simulated system at aggregate level • Compare • Behaviour at individual level • Data from qualitative research

29. Human-like Agent behaviour • Complexity requires compositionality • Process model composed of sub-process models • Sub-models implement theories of different aspects of behaviour: • Negotiation, trust, deceit … • Moods, emotions, affect, …

30. Culture complicates matters • Social situations are culture-sensitive • Policies affect social situations • Policy making requires culture-sensitive modelling

31. Our proposal to approach validation • Complexity: Use compositionality • Validate sub-processes at lower compositional levels • Qualitative Data: Use gaming simulations • Played by humans for these sub-processes to gather data

32. Compositional Cross-Validation partial multi-agent simulation partial micro simulations Overall multi-agent simulation

33. Example in Trade • Trust & Tracing game to simulate trade chains

35. Decision model within agent

36. Conclusion • BRIDGE: rich cognitive agents & support for policy makers • Involve stakeholders to avoid strong AI problem • Sensitivity analysis • Game-based Compositional cross-validation Acknowledgements: • Frank Dignum, Virginia Dignum, Gert-Jan Hofstede, Tim Verwaart, Saskia Burgers