Hybrid System Science Methods: Some Observations Nathaniel Osgood Agent-Based Modeling Bootcamp for Health Researchers August 25, 2011
System Science Methodologies: Highly Complementary • Different modeling methodologies seek to answer different types of questions • No one system science methodology offers a replacement for the others • Significant synergies can be secured by using combinations of methodologies to address the same problem – As cross-checks on understanding where two or more can be applied – Exploiting competitive advantages
Multi-Framework Modeling • We have found the use of multiple frameworks highly effective – Co-evolving multiple models for • Cross-validation • Asking different sorts of questions • Revealing new questions to answer – Within a single model • Dealing with questions at different scales • Improving robustness of models • Allowing for representation & changing of factors that are otherwise ignored
Reminder:Multiple Model Types Social Network Analysis Agent- System Based Dynamics <Birth Rate> Pregnant Normal Normal and Weight Mothers Pregnancies to Underweight Pregnancies of with No GDM Non-Overweight Mother Modeling Normal Weight Weight Non-Overweight History Developing GDM Deaths Women Completion of Pregnancy to Non-Overweight State Pregnancy Duration Pregnant Women Shedding Obesity Developing Persistent Developing Obesity Overweight/Obesity Normal Weight Pregnant with GDM Individuals Developing Pregnancies Developing T2DM GDM from Mother with GDM History Pregnant Pregnancies to Overweight Overweight erweight Babies Born to Completion of Mothers with No Mother Developing GDM regnant Normal Weight Overweight Pregnancy to GDM History Mothers Overweight State Completion of GDM Pregnancy Pregnancies of bies Born from Oveweight Babies Born Overweight other with from T2DM Mothers Overweight Women Deaths GDM Pregnant Women with GDM that Continue on to Overweight Individuals Developing T2DM Postpartum T2DM Pregnancies for Pregnant with T2DM Women with GDM History of GDM Pre-Existing History of Women with History of T2DM Deaths GDM GDM Developing T2DM New Pregnancies from Completion of Non-GDM Completion of Pregnancy for Mother with T2DM Mother with T2DM Pregnancy for Woman with History of GDM Pregnant with T2DM Deaths from Non-T2DM Women with History of GDM High later gr ow th Consequence1 Expand capacity PL Low later growth Consequence2 High ear ly growth 1-PL High later gr ow th PE Consequence3 No expansion PL Low later growth Consequence4 Expand capacity 1-PL High later gr ow th Consequence5 Discrete Expand capacity PL Low later growth Consequence6 Decision Low early growth 1-PL High later gr ow th 1-PE Consequence7 No expansion Event PL Low later growth Consequence8 Initial Decision Analysis 1-PL High later gr ow th Consequence9 Expand capacity PL Modeling Low later growth Consequence10 High ear ly growth 1-PL High later gr ow th PE Consequence11 No expansion PL Low later growth Consequence12 No expansion 1-PL High later gr ow th Consequence13 Expand capacity PL Low later growth Consequence14 Low early growth 1-PL High later gr ow th 1-PE Consequence15 No Expansion PL Low later growth Consequence16
Multiple Model Social Network Types Analysis Agent- System Based Dynamics Modeling Discrete Decision Event Analysis Modeling
SNA Can Facilitate ABM • Social network statistics that be used to formulate synthetic networks • Identify patterns for calibration & investigation • Cross-checks on ABM simulation findings • Network visualization • Highlighting diverse settings for contact
Challenges in Using Data from SNA in ABM • SNA can provide an extremely valuable source of data to use for grounding ABM network structure • It is relatively easy to get networks from software like Pajek into software like AnyLogic or Repast • The bigger issue here is that we need to represent the hypothesized “true” spread of infection over the network – To do this, we need to represent the hypothesized underlying network that lies behind – Even the best of SNA data is highly incomplete (e.g. due to asymmetries in case-contact data, sampling in snowball sampling)
SNA Providing Context For ABM
Example Network Structure
Multiple Modeling Types System Dynamics Agent- Based Modeling Social Network Analysis
Agent-Based Modeling Facilitates SNA • Exploring dynamic hypotheses to explain SNA patterns • Formulating ideas for SNA metrics that could be highly effective (discriminatory) for identifying at-risk individuals • Understanding dynamic implications of given network structure • Understanding implications of changing network structure • Evaluating SNA-informed interventions (e.g. SNA-metric prioritized contact tracing) • Examining impact of additional collection of SNA data (e.g. , more complete contact tracing) • Positing possible pieces of missing structure in SNA network
ABM To Explain Emergent Patterns Uncovered via SNA A. Al-Azem, Social Network Analysis in Tuberculosis B. Control Among the Aboriginal Population of Manitoba2006
Multiple Modeling Types System Dynamics Agent- Based Modeling Social Network Analysis
System Dynamics Supporting ABM • Description of continuous individual-level evolution • Deriving calibrated parameter estimates for low- level model • Focusing AB exploration • Qualitative diagramming of – Interactions at a particular scale – Hypothesized drivers underlying emergent behaviour
Multiple Modeling Types System Dynamics Agent- Based Modeling Social Network Analysis
Agent-Based Modeling in Support of SD • Cross-validating SD aggregation: Evaluating importance of – Stratification by heterogeneities – Stochastics – Network dynamics • Giving insight into feedbacks to depict • Investigating specialized interventions – e.g. Interventions that depend on individual history, network position, etc. • Use to determine parameters for SD model
Multi-Scale ABM-SD Hybrid Strategies • Agent Based & System Dynamics – System Dynamics within ABM: Agent behaviour described w/stocks & flows (optionally, within SD tools) – ABM within System Dynamics: Agents drive some flows – Using Qualitative Methods of System Dynamics (Group Model Building, Causal Loop diagram) to elicit understanding for an agent-based model • DES & ABM – Agents associated with Entities – Entity presentation dependent on Agent State – Agent evolution dependent on entity Treatment
System Dynamics & Individual-Based Modeling • Individual-based models can be created using – Traditional System Dynamics software • Small populations: – Separate stocks for each individual – Hand-drawn connections • Larger Populations – Subscripting stocks by population member – Binary network matrices – Stock & flows in other dynamic modeling software • e.g. in AnyLogic – System Dynamics methodology • Feedback-centric reasoning • Process-based work
Network Embedded Individuals Virion Production Rate Mean of Viral Load Virion Production Rate if 1 Person Per Contact Virions Rate of Neighbors Non Quantized Infection Mean Viral Load <Population Size> Virus Load Virion Clearance Virion Production From Infected Cells Likelihood Density of Mean Virion Uninfected Cell Infection by Single Virion Lifetime Replentishment Rate Per Infected CellVirion Uninfected Infected Production Rate Cells Cells infected cell death New Cell Uninfected Cell Infections by CTLs rate which infected cells Replentishment Uninfected Cell Infected Cell are killed by CTLs Death death <Population Size> Mean Infected Mean Uninfected Mean Uninfected Cells <Population Size> Cell Lifetime Mean Infected Cell Cells Lifetime Mean CTL CTL CTLs responsiveness immune response to lifespan CTL turnover infected cells
Individual-Based Model in Vensim All of these stocks & their associated flo Population member (via population-mem
Population-Member Subscripting
Example Interactions between Global & Local Levels A Global Level (Aggregate, Cross Population) Factor!
Example Individual-Level Risk Factors An Individual-Level Risk Factor Another Individual-Level Risk Factor (here, represented categorically, but we could Represent it as a continuous variable – e.g. cumulative smoke exposure, some estimate of cumulative physiologic damage from smoke, a moving average of smoke exposure, etc.)
Impact of Risk Factors on Individual Dynamics
Multiple Modeling Types System Dynamics Agent- Based Modeling Social Network Analysis
System Dynamics in Support of SNA • Examining (aggregate) impact of SNA-driven feedbacks • Understanding dynamic implications of certain levels of contact tracing • Dynamics within an individual • Coupled individual dynamics • Identifying key parameters for SNA to examine – E.g. Recognizing importance of a small amount of contact tracing
Multiple Model Social Network Types Analysis Agent- System Based Dynamics Modeling Discrete Decision Event Analysis Modeling
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