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

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

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

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

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

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

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

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

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

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

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

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 policy possible reactions possible side effects

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

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

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

Sensitivity Analysis of an Agent-Based Model of Culture’s Consequences for Trade Saskia Burgers, Gert Jan Hofstede, Catholijn Jonker, Tim Verwaart September 9-10, Treviso (Italy)

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

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

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

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?

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 behaviour occurs

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.

Cross-validation of Multi- Agent Simulation with Cultural Differentiation Gert Jan Hofstede, Catholijn M. Jonker, Tim Verwaart September 9-10, Treviso (Italy)

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

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

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, …

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

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

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

Example in Trade Trust & Tracing game to simulate trade chains

Decision model within agent

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