Systems Science and ModelingGautam Sanka

Systems Science• 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

Modeling• 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

Types of questions• 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)

Available Modeling Techniques

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)

Social Network Analysis• Relationships between individuals, groups,

agencies, geographical locations• Nodes are the groups and links are relationships• Centrality and see hidden networks

Known

Unknown

SNA in Prevention Science• 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,

• 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

• 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

System Dynamics• 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

SD Diagram for Suicide Terrorists and Culture of Martyrdom

Level of Grievance

Public Opinion· Necessity· Legitimacy

Ideology

Population(non-terrorists)

Culture of Martyrdom

Suicide Terrorists

Terrorists

Occupation Policy

Opinion Leaders

??

State

CulturalResources

Media

Level (Stock)

Rate

Auxiliary Variables

• SD simulations consist of equations that can be solved forward in time:

Statet+1 = Statet + Ratet

where Ratet = f(Statet-1, … ,State0)

• Drawbacks• Macro-model of a system• Qualitative approach

• Many variables that cannot be quantified

Agent Based Modeling• 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

Example• 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

Track best Maize plots for pueblos

• 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

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Agent-based Threat Anticipation (TAP) Model (Los Alamos: E. Mackerrow)

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

Model V&V• 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

The End• Questions?