ITS 832 Chapter 4 Policy Making and Modeling in a Complex World Information Technology in a Global Economy Professor Miguel Buleje Introduction • Policy Making and Modeling in a ComplexWorld • Complexity • Managing Complex Systems • Modelling for Complex Systems Complexity • System composed of multiple interacting elements • Possible behavioral states can combine in ways that are hard to predict • Many complex systems in the physical world • Adaptive capacity of organisms allow for long-term survival in complex systems • Complex Adaptive Systems (CAS) • Strong capacity to self-organize Double Pendulum Example Common Mistakes in Managing Complex Systems • Quantification • Policy is biased towards Quantifiable Variables. • Most often, monetary quantification. • Most often, solution selected would be favorable to the optimal economic outcome. • Commonly overlooks important non-quantifiable aspects • Compartmentalization • Second response by policymakers in trying to simplify complex system. • Attempts to simplify complex social systems • Large systems are split into smaller systems • High RISK to miss interactions between smaller systems • Spillover effect Complexity in Policy Making • Common approaches • Instrumental • Choosing between a set of possible policies • Evaluated based on past effectiveness • Requires • Large enough pool of available strategies • Effective assessment of effectiveness • Representational • Little more complication approach • Series of models • Each is assessed on its ability to predict observed behavior Instrumental Approach Representational Approach Agent-based Simulation Models • Agent-based simulation is presenting an optimal approach to address issue around complexity and policymaking. • Agent base models: • Represents individuals as separate computer models • Each model captures the behavior by each individual • Agents interact through a network • Distributed nature allows for realistic interactions, and makes this an attractive alternative. • SIMSOC • Simulated Society: use by universities and other groups to teach social sciences. • Large modeling projects repository, per inventory on the SIMSOC, in our space of policy making. Summary • Complex systems are difficult to model • Interactions can be unpredictable • Common mistakes in modeling complex systems • Quantification • Compartmentalization • Two common approaches to complex system modeling • Instrumental • Representational • Agent-based modeling .

1. ITS 832
Chapter 4
Policy Making and Modeling in a
Complex World
Information Technology in a Global Economy
Professor Miguel Buleje
Introduction
• Policy Making and Modeling in a ComplexWorld
• Complexity
• Managing Complex Systems
• Modelling for Complex Systems
Complexity
• System composed of multiple interacting elements
• Possible behavioral states can combine in ways that are hard
to
predict

2. • Many complex systems in the physical world
• Adaptive capacity of organisms allow for long-term
survival in complex systems
• Complex Adaptive Systems (CAS)
• Strong capacity to self-organize
Double Pendulum Example
Common Mistakes in Managing
Complex Systems
• Quantification
• Policy is biased towards Quantifiable Variables.
• Most often, monetary quantification.
• Most often, solution selected would be favorable to the
optimal economic
outcome.
• Commonly overlooks important non-quantifiable aspects
• Compartmentalization
• Second response by policymakers in trying to simplify
complex system.
• Attempts to simplify complex social systems
• Large systems are split into smaller systems

3. • High RISK to miss interactions between smaller systems
• Spillover effect
Complexity in Policy Making
• Common approaches
• Instrumental
• Choosing between a set of possible policies
• Evaluated based on past effectiveness
• Requires
• Large enough pool of available strategies
• Effective assessment of effectiveness
• Representational
• Little more complication approach
• Series of models
• Each is assessed on its ability to predict observed behavior
Instrumental Approach
Representational Approach

4. Agent-based Simulation Models
• Agent-based simulation is presenting an optimal approach to
address
issue around complexity and policymaking.
• Agent base models:
• Represents individuals as separate computer models
• Each model captures the behavior by each individual
• Agents interact through a network
• Distributed nature allows for realistic interactions, and makes
this an
attractive alternative.
• SIMSOC
• Simulated Society: use by universities and other groups to
teach
social sciences.
• Large modeling projects repository, per inventory on the
SIMSOC,
in our space of policy making.
Summary
• Complex systems are difficult to model
• Interactions can be unpredictable
• Common mistakes in modeling complex systems

5. • Quantification
• Compartmentalization
• Two common approaches to complex system modeling
• Instrumental
• Representational
• Agent-based modeling