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 orderingPreference

Cultural beliefs

Normative beliefs

Growth needs

deficiency needs

sense

act

generate

select pla

n

update

inte

rpre

t filter

plan select

direct

R

urges, stress

select

direct

ove

rru

le

stimuli

explicit

implicit

BeliefsResponseIntentionsDesiresGoalsEgo

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

Þ 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

Gamesessions

Data,conclusions

Test design

Experimentalsetup

Gamingsimulation

Agentmodeling

Agent-BasedModel

Modelvalidation

Modelruns

Validationresults

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 anAgent-Based Model of

Culture’s Consequences for Trade

Saskia Burgers, Gert Jan Hofstede,

Catholijn Jonker, Tim Verwaart

September 9-10, 2010 - 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 withCultural Differentiation

Gert Jan Hofstede,

Catholijn M. Jonker, Tim Verwaart

September 9-10, 2010 - 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

partialmulti-agent simulation

partialmicro

simulations

Compositional Cross-Validation

Example in Trade

• Trust & Tracing game to simulate trade chains

Producers Middlemen ConsumersRetailersProducers Middlemen ConsumersRetailers

Decision model within agent

determinetrade goal

selecttrade partner

negotiate

deliver

monitor and enforce

update beliefs

determinetrade goal

selecttrade partner

negotiate

deliver

monitor and enforce

update beliefs

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