development and application of rich cognitive models and the role of agent- based simulation for...
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![Page 1: Development and Application of Rich Cognitive Models and the Role of Agent- Based Simulation for Policy Making Catholijn M. Jonker](https://reader034.vdocument.in/reader034/viewer/2022042821/56649cb65503460f9497b87e/html5/thumbnails/1.jpg)
Development and Application of Rich Cognitive Models and the Role of Agent-
Based Simulation for Policy Making
Catholijn M. Jonker
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BRIDGE: Development and Application of Rich Cognitive
Models for Policy Making
Frank Dignum, Virginia Dignum, Catholijn M. Jonker
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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
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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
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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
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Micro-macro simulation: zoom-in/zoom-out approach
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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
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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
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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
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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
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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
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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
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Caveats
• Sensitivity analysis required of the – Basic agent model – Overall simulation model
• Validation!• Cannot predict, only explore possibilities
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Theorizing
Theory,hypotheses
Gamesessions
Data,conclusions
Test design
Experimentalsetup
Gamingsimulation
Agentmodeling
Agent-BasedModel
Modelvalidation
Modelruns
Validationresults
Game design
Real world observations
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Gaming simulation
Computer simulation
Theory
tests predictions based on
implements design of
implements mechanisms according to
validates mechanisms described by
tests predictions based on
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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)
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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
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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
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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
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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?
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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
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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.
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Cross-validation of Multi-Agent Simulation withCultural Differentiation
Gert Jan Hofstede,
Catholijn M. Jonker, Tim Verwaart
September 9-10, 2010 - Treviso (Italy)
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Validation
• Why: to combat under-determinism• model M explains the behaviour of a
system S– Is M the only model to do so?
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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
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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, …
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Culture complicates matters
• Social situations are culture-sensitive• Policies affect social situations• Policy making requires culture-sensitive
modelling
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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
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Overall multi-agent
simulation
partialmulti-agent simulation
partialmicro
simulations
Compositional Cross-Validation
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Example in Trade
• Trust & Tracing game to simulate trade chains
Producers Middlemen ConsumersRetailersProducers Middlemen ConsumersRetailers
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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
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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