Systemic Perspective
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This document provides an overview of taking a systemic perspective to understand a situation. It discusses establishing a boundary, identifying patterns of behavior over time through graphs or diagrams, mapping the network of interactions through models like causal loop or stock and flow diagrams, understanding different stakeholder perspectives, challenging assumptions, and identifying leverage points for change. It then provides an example of applying this perspective to a situation involving an organization called Magic Connections that is struggling to maintain support resources.
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Policy Making and Modeling in a
Complex World
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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
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Instrumental Approach
Representational Approach
Agent-based Simulation Models
• Agent-based simulation is presenting an optimal approach to address
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• Agents interact through a network
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• Interactions can be unpredictable
• Common mistakes in modeling complex systems
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• Compartmentalization
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• Instrumental
• Representational
• Agent-based modeling
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1. Transaction processing systems are used at the operational level to record daily transactions like sales and orders. Management information systems are used at the middle level and provide periodic summary reports of transactions.
2. Executive support systems are used by senior managers and focus on long term strategic issues affecting the organization over several years. Decision support systems use analytical models and interactive "what if" analysis to support decision making.
3. Expert systems emulate human expertise in a specific domain through use of knowledge bases and inference engines. They are used to diagnose problems and provide consultative advice.
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This document discusses decision making and decision support. It covers Simon's four phases of decision making: intelligence, design, choice, and implementation. It defines rationality and bounded rationality. It also discusses how decision support systems can support each phase of decision making through tools like data analysis, modeling, scenario analysis, and knowledge management systems. New technologies like mobile devices and tablets are also changing how decisions can be supported.
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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
.
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Systemic Perspective
- 1. Systemic Perspective Necessary and Sufficient Activities
- 2. Typical Approach Graphic from Corporate Sigma by Anwar El-Homsi & Jeff Slutsky
- 3. Systemic Approach Graphic from Corporate Sigma by Anwar El-Homsi & Jeff Slutsky
- 4. Systemic Perspective Situation Patterns of Behavior Network of Interactions Stakeholder Perspectives Establish a Boundary Challenge Assumptions Leverage Points
- 5. Series Videos • Elaborate on each activity • Develop understanding • Example systemic perspective • For a specific situation • Guide for skills development
- 7. Situation • Situation / System Dilemma • Results from multiple interactions • Interactions connect with experience • Provides context for relevance • Frame description for guidance
- 8. Situation (cont…) • Perspectives vary • Frame purpose of investigation • Frame description for communication
- 9. Magic Connections • Rapidly growing & needing resources • Support hiring and training resources • Other organizations raiding resources • Support struggling to maintain readiness
- 10. Systemic Perspective Patterns of Behavior
- 11. Patterns of Behavior • Situation may appears as an event • Describe relevant changes over time • Consider a relevant time frame • Plotting graphs is great • Freehand frequently is useful
- 14. Systemic Perspective Network of Interactions
- 15. Network of Interactions • Reality is complex • Models simplify & promote learning • Situation – relevant vs connected • All models are wrong, some models are useful! • Useful models promote learning
- 16. Types of Models • Qualitative - view interactions • Causal Loop Diagram • Quantitative – used in simulations • Stock & Flow Diagram • Develop a model with communication in mind
- 18. Stock & Flow Diagram
- 21. Stakeholder Perspectives • Who influences and who is influenced • Stakeholders may have different perspectives • Collaborative iterations valuable • What’s applicable depends on complexity and coherence
- 22. Jackson’s Framework • Systems • Simple – few, stable, highly structured • Complex – numerous, loosely structured • Participants • Unitary – similar values and beliefs • Pluralist – compatible interests • Coercive – few common interests
- 24. Magic Connections • Customers • Corporate Leadership • Support Management • Non-Support Management • Pluralist and simple in this context
- 25. Systemic Perspective Establish a Boundary
- 26. Boundary • Identify regions of responsibility • Aligned to stakeholders • May be multiple boundaries • For the relevant stakeholder • Inside the boundary is “the System” • Outside boundary is “the Environment”
- 30. Challenge Assumptions • Decisions based on assumptions • Mental models based on experience • Surface and challenge • Potential invalid assumption
- 31. Mental Models
- 34. Leverage Points • Constants, parameters, numbers • Buffers, stabilizing stocks • Structure of stocks & flows • Length of delays relative to change • Negative feedback loop strength • Positive feedback loop gain
- 35. Leverage Points (con’t) • Structure of information flow • Rues of the system • Power to change or evolve structure • Goal of the system • Mindset of the system • Transcend paradigms
- 36. Magic Connections • Consider the model • Possible leverage points • Develop a strategy for action • Implement the strategy • Monitor progress
- 38. Alternatives • Policy for no resource raiding • Migrate to self-service support • Engineer support requirements out of the products • Expand Support’s responsibility • The best answer is…
- 39. Systems Thinking World Join us on LinkedIn at http://www.linkedin.com/groups?gid=2639211