1. Gautam Sanka

2. Analyze and Elucidate the behavior of complex systems Complex Systems Collection of interconnected elements (system) Behavior and Characteristics cannot be anticipated from Any one element in system Sum of the elements when considered separately Many interrelated connections between elements Feedback loops, externalities, nonlinear relationships Paper claims that it is suited very well for Prevention Sciences Using it to analyze target populations looking for risk factors, various environmental and social contexts

3. Reasons to do modeling and simulation We think linearly- complex systems are not linear Constrained in that we cannot imagine and explore all possibilities in a real system Cannot foresee cascading events as a result of an event Difficult to include Random events in mental Models Mental Models are too rudimentary Why do we Model INSIGHTS, not numbers. We want an explanation to why events can occur Predict Future events

4. Prevention Science Have a number of interventions and policy options and limited resources for implementing Pros/cons for each option Can some options work in tandem with each other Given X condition and Y condition Cigarette Example Gov. wants to increase tax on cigarette- Implications? Black Market trade Usage of other tobacco products Unhealthy dependence on tax revenue (unstable revenue)

5. more descriptive more process oriented “Accounting” and Data Models Statistical Modeling, Inductive Inferencing (Data Driven Models) Social Network Analysis (SNA) Systems Dynamics (SD) Agent-based Modeling/Complexity (ABMS)

6. Relationships between individuals, groups, agencies, geographical locations Nodes are the groups and links are relationships Centrality and see hidden networks Known Unknown

7. Most applied model in prevention research Example Project TND (Towards No Drug Abuse) Reduced youth substance use in short and long term Decades of Research-peers provide critical context To test,

8. TND Networked Students wrote Five Best friends, best person for group leader and this helped create a network on the computer Score developed to indicate substance use among each participant’s friend network

9. TND Network were less likely to sue substances compared to controls Youths with higher levels of substance use among friends were more likely to increase substance usage Ground Breaking research as proved that social networks were active elements in prevention efforts

10. Aggregates individual entities and continuous quantities into specific groups Simulation is prepared Exploration of questions about why systems behave the way they do and helps identify leverage points Tools Casual loop Diagrams- casual relationships Stock and Flow Models- simulate accumulations within a system over time

12. SD simulations consist of equations that can be solved forward in time: State t+1 = State t + Rate t where Rate t = f(State t-1, …,State 0) Drawbacks Macro-model of a system Qualitative approach Many variables that cannot be quantified

13. An agent is An individual with a set of attributes or characteristics Placed in an artificial environment A set of rules governing agent behaviors is made Responds to the environment Interacts with other agents Rich quantitative methodology that explores how certain components give rise to multi-layered phenomena

14. Can be used for many applications ranging from anthropology to health Heroin Effects in Denver (consumer, producer, distributor) Simulated roles, motives, behaviors and interactions of market participants Consumers and brokers are most complex Heroin addiction changed based on heroin usage, past experiences, transaction partners Police and homeless people-less complex Typical market conditions and reactions

15. Environmental conditions are put in the system- precipitation levels, ground water locations, climatic shifts Simulation is pretty accurate although the settlements are not as precise COPYRIGHT 2005 SCIENTIFIC AMERICAN, INC.

16. Objects learn and adapt based on their history, current state, and the states of other objects. Simulation is built upon many different instances of these object types, each with different attributes. The object architecture allows for flexibility: the PersonRole class, and its inherited subclasses, allow a construct where any one Person object can play multiple roles. Interfaces allow for specification of required actions that can be implemented differently, depending upon the type of object implementing the interface Objects in the TAP Model

17. Model must be verified and validated Verification Verifying that the model does what it is intended to do from an operational perspective Validation Validating that the model meets its intended requirements in terms of the methods employed and the results obtained

18. Questions?