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Systems Thinking: A Comprehensive Approach to Complex Socio-Technical Systems, Lecture notes of Computer Science

An outline for the ms-5060 systems thinking course. It covers various concepts of systems thinking, including systems thinking stories, illities, robustness and flexibility, emergent systems, systems dynamics, networks, simulation, decision-making in complex systems, optimization, data analysis, and experimentation. The document emphasizes the importance of an integrative approach to problem-solving in highly complex and interconnected socio-technical systems.

Typology: Lecture notes

2015/2016

Uploaded on 07/15/2016

ramandeep_arora
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Systems Thinking

MS-

Revision

Course Outline

  • Systems Concepts
    • Introduction to systems thinking
    • Systems thinking stories
    • Illities
    • Special focus on Robustness and Flexibility
    • Emergent Systems and Swarm intelligence
  • Systems Modelling and Analysis
    • Systems Dynamics
    • Networks
    • Simulation
    • Decision-making in complex systems
    • Multi Objective Optimization
    • Data analysis
    • Experimentation

System thinking stories

  • Some class stories:
    • Cricket versus basketball metrics
    • Toilets (footfalls as a proxy for measuring cleanliness, safety, usability, etc.)
    • Fixing congestion in a road
    • Waiting for the elevator
    • Deaths from safety road regulations. No free right turn.
    • Minimum wage (Video)
    • The electric car
    • The restaurant that looked at its trash
    • Sendhil Mullainathan’s ted talk: (Video)
    • Puzzle of Motivation (Dan Pink): (Video)

System thinking stories

  • What is common in these stories:
    • Needs to be about metrics (modelling effort), or methods of analysis, solving a problem, or achieving an objective
    • Have many interconnected parts, ideally with lots of interactions
    • Improvement in any one part need not necessarily mean improvement to the whole system. Individual performance is not system performance
    • Metrics/results/behaviour associated with individual parts need not be very representative of the whole system
    • It might not make any sense to modularize the system into parts. It might be suboptimal to break down the system into parts

Illities

  • The old illities:
    • Quality
    • Safety
    • Usability
    • Reliability

The new illities:

  • Flexibility
    • Reconfigurability
    • Redesign
      • Evolvability (ability to change its core DNA, the system’s very purpose)
      • Adaptability (ability to change in response to external stimuli)
      • Modularity (ability to be modular and therefore facilitate plug and play)
      • Agility (ability to change quickly)
      • Scalability (ability to grow to accommodate more volume)
      • Extensibility (ability to grow to accommodate new functions)
  • Interoperability – closely linked to compatibility and modularity
  • Resilience
  • Reliability
    • Maintainability
    • Availability
  • Durability
  • Robustness
  • Sustainability
  • Testability

illities

  • Focus on difference between operability and usability
  • Focus on the nuances of different types of Flexibility through redesign
  • Focus on difference between Reliability, durability and robustness
  • De Weck, Olivier L., Daniel Roos, and Christopher L.

Magee. Engineering systems: meeting human needs in a complex

technological world. MIT Press, 2011.

  • Great reference:
  • http://strategic.mit.edu/docs/es_book_004_proof.pdf

Illities special focus: flexibility

Emergence

  • The phenomena where larger entities, patterns, regularities arise

through interactions among smaller and simpler entities that

themselves do not exhibit such properties

  • The idea that in many complex environments, structures, behaviours

and properties seem to emerge even though there is no blueprint for

individual agents to follow

  • Individual agents self-organizing in an environment to form complex

behaviour

Emergence continued

  • Inspiring various management/academic approaches:
    • Genetic algorithms
    • simulated annealing
    • ant crawl optimization
    • Ensemble methods
    • Delphi Method
    • Prediction markets
    • Agent based modelling and problem solving
  • Reference:
  • Miller, Peter. "Swarm theory." National Geographic 212.1 (2007): 127
  • Surowiecki, James. The wisdom of crowds. Random House LLC, 2005.

System dynamics

  • Properties of a system the Systems Dynamics tries to model
    • Systems are dynamic not static. To understand them we need to model them over time not at one snapshot.
    • They are primarily governed by feedback mechanisms. Shower example.
    • They are typically non-linear
    • History dependent (A particular decision could have a permanent impact on the system for a long time to come)
    • Adaptive (decisions and behaviour are context specific and change over time)
    • Counterintuitive (cause and effect are too far apart in time and space, simple solutions rarely have the intended effect (minimum wage example))
    • Trade-offs between long run and short run response (time delays, nonlinearity, etc.)

Networks

  • Network theory basics
  • Understanding of node (vertices) connected by edges (links or arcs)
  • Adjacency matrix and incidence matrix
  • Node Metrics: Degree of a node, clustering co-efficient of a node,

Betweeness of a node,

  • Graph metrics: K-regular graph, Distance, Diameter, Degree distributions,

Degree correlations, and Network resilience

  • References: (Look at the presentations for lectures 7 and 8)
  • http://ocw.mit.edu/courses/engineering-systems-division/esd-00-

introduction-to-engineering-systems-spring-2011/lecture-notes/

Simulation

  • Introduction: What is it? Why and where is it useful?
    • Advantages, Disadvantages and differences between
      1. Experimentation
      2. Modelling
        1. Mathematical modelling
        2. Simulation modelling
  • Where do we simulate
    • Systems Dynamic models
    • Agent Based Modelling (important to understand the difference between systems dynamics and agent based modelling)
    • Anywhere there is stochasticity
    • Even when there is no stochasticity
  • References: Excel sheet shared with the class

Optimisation, data analysis and

experimentation

Experimentation

  • Create the data
  • Build a model from data
  • Optimize the model to find the optimal inputs

Data Analysis

  • Supervised learning
  • Mostly converting data to model

Optimisation

  • Given a model what are the inputs that give me the best output

Optimisation, data analysis and

experimentation

  • Multiobjective optimisation: The concepts of pareto/efficient frontiers (Dominated versus Non-dominated solutions)
  • Data analysis: The problem of multicollinearity (correlated input variables in a regression problem). Correlation versus causation the ice-cream story
  • Experimentation: Formal experimentation involves systematic, purposeful changes to input variables in an attempt to gain knowledge about the system and/or find the ideal settings that result in the best output.
  • Experimentation: The only real form of causal research

http://en.wikipedia.org/wiki/Design_of_experiments

  • Esther du Flo talk: http://www.ted.com/talks/esther_duflo_social_experiments_to_fight_poverty?la nguage=en