Systems thinking and the analysis of complex dynamical systems Flashcards
How can science for SD be used for informing policy goals?
1. What is our goal and when do we want to achieve it?
- reflect inltl’ consensus & public perception that change is needed
2. In measurable units
- developing the right indicators , data availability over time, possibility of linking indicators to models to explore & update policies
3. Where are we now?
- need for reliable and sufficient data, need for in-depth knowledge on the state of the system
4. What do we need to do/change to achieve it?
- effective policy requires knowledge on how the system will respond based on eg:
-> historic responses, process-based models of system, expert knowledge, scenario-based models…
–> Indicators: quantify goals, assess historic developments
–> Modelling: requires and contributes to system understanding
–> Models: used to explore future effects of policy options
What is system thinking?
a set of synergistic analytic skills
used to improve
- the capability of identifying and understanding systems
- predicting their behaviors
- devising modifications to them in order to produce desired effects
These skills work together as a system.
Causal Loop Diagram: Definition
An abstract representation of a specific process or system
System dynamics / dynamic structure of the system are represented by CLDs
Causal loop diagram: Construction
A causal loop diagram consists of four basic elements:
- the variables (“nouns”)
- the links between them (“verbs”)
- effects: the signs on the links (show how the variables are interconnected) : + / -
- feedback: the sign of the loop (which shows what type of behavior the system will produce): + / -
–> Reinforcing (+ leads to +) or balancing loop (+ and -)
How to construct it:
- Variables: Use nouns not verbs (i.e. costs not increasing costs), use variables describing quality (i.e. happiness not state of mind), use “positive” senses of names (i.e. growth not contraction)
- Think of all unintended consequences and outcomes possible for every course of action
- Make the goal explicit (ex. desired quality)
- Distinguish between perceived and actual state
- If a variable has multiple consequences, lump them into one term at first (ie. Stress –> Coping strategies)
- Differentiate between short and long term consequences
- Insert additional terms if need be to increase clarity
- Finish by “talking through the loop” or telling its story to make sure it makes sense
Causal loop diagram: Goals
Goals:
- By representing a problem or issue from a causal perspective, you can become more aware of the structural forces that produce puzzling behavior.
- to make our understanding of the interrelationships within a system’s structure more explicit
- to recognize the multiple, interdependent effects of our actions.
- to make a visual representation
Stock-flow diagram
An abstract representation of a specific process or system consisting of:
1. Stock: entities that accumulate or can be depleted (they continue to exist if time stops)
2. Flow: movement/flux of material into or from stock//Changes in stock, can increase or decrease (they disappear if time stops) also = rates or fluxes
Temporal aspect: info may only be for certain periods. Change per period = net flow
3. Parameters: can be used for various operations in the system
4. Consistent units of measurement: across the system
In calculus
Stocks are the equivalent of integrals
Flows are the equivalent of derivatives
Difference between Causal-loop diagrams & Stock-flow diagrams
CLD, SFD, Advantages of SFD, ex.
CLD:
- for communicating high-level views of a system, especially for non-experts
- understandable
- good first step
SFD:
- talk about rates of change
- higher level of rigor, more detailed
- include more variables (eg. stock and flow)
- differentiate between parts of the system
- gives an enhanced understanding of the system
- more subtle view of system
- units and relative magnitudes of system are specified
Advantages of SFD:
- each variable has to be thought of in detail to find its units of measure
- relationships between variables need to be clarified (for clear units)
- pushes you to discover new variables to make units match –> more detail and understanding overall!
- gives you the basics to create a computer model (because they use S&F & high level of detail)
- discovering counterintuitive dynamics
Ex:
- Birth rate, births and population –> more accurate representation
Usable knowledge - To Know
Crafting usable knowledge for SD - Clark, van Kerkhoff & Gallopin
Core lessons found using ICAP lense (Innovation system, Complex system, Adaptive system, Political systems)
To know:
- understanding the coproduction relationships through which knowledge making and decision making shape one another in the SESs that constitute the stage on which the drama of sustainable development is played out.
- IS: The need to work together with end-users and all actors in the system, adjusting to user, context, knowledge that is shaped to “fit”
- CS: You can’t just do 1 thing (i.e. unintended consequences); impacts are always context dependent (i.e. no panaceas); impacts may involve abrupt or irreversible changes
- AS: Novelty is always bubbling up; local conditions impact these novelties; SES dynamics shaping the future will be different than those from the past
- PS: knowledge is power; they will be percieved as taking sides; incentives they face in their choices will disproportionately reflect some parties and not others; how they treat knowledge of stakeholders will empower or disempower them
Usable knowledge - To Do
Crafting usable knowledge for SD - Clark, van Kerkhoff & Gallopin
To do:
- improving the capacity of the research community to put its understanding of coproduction into practice
Improve:
- Stakeholder collaboration: for understanding, for multi-scale applications; for the long-term, antidote to elitism, boundary work (bridging cultural divides).
- Knowledge governance: recognizing and reshaping the rules and norms governing the relationships of coproduction
- Social learning: favoring learning over knowledge for multidimensional & evolving understanding; research arenas as “safe spaces” for failure and errors; implemented methodologically; critically examining scientific institutions
- Researcher training: how to get researchers to learn this
What is Systems Thinking?
Introduction to Systems Thinking - D. Kim
Systems thinking is a way of seeing and talking about reality that helps us better understand and work with systems to influence the quality of our lives.
Has a vocabulary, is a language, is a set of tools for visually capturing and communicating about systems.
Understanding the how and why things happen in complex systems to better manage them.
Systems thinkers work from a central premise: If you don’t know how you’re pro- ducing certain outcomes, you’ll have great difficulty determining how to produce better outcomes! Causal loop diagrams give us better pictures & understanding, especially when done in groups.
What is a system & its characteristics
& Sust. Sci by D. Vries
Introduction to Systems Thinking - D. Kim
A system is any group of interacting, interrelated, or interdependent parts that form a complex and unified whole that has a specific purpose.
All parts are interrelated and independent.
Characteristics:
- Purpose acts as a predominant force in a system (purpose of the whole)
—>Mechanical systems’ purpose do not change
–> Living or natural or social systems’ purpose evolves & it is difficult to define; we don’t know what impacts our actions will have on the system; we often impose purpose on systems (eg. dogs = pets OR food)
- All parts must be present for a system to carry out its purpose optimally
- The order in which the parts are arranged affects the performance of a system
- Systems attempt to maintain stability through feedback (eg. body temperature)
Key attributes:
1. Elements
2. (Inter) connections
3. Purpose
& Important to define a boundary
Putting Systems into broader context:
“The Iceberg”
Introduction to Systems Thinking - D. Kim
Multi-levels of perspective (iceberg/pyramid, top to bottom), like an iceberg, we only see the tip!:
Events: day-to-day occurences
Patterns: Accumulated memories of events –> trends!
Systemic structures: the ways in which the parts of a system are organized.
–> redesigning things at the systemic level that offers us far more leverage to shape our future than sim-ply reacting to events does.
Also useful:
Mental Models: beliefs and assumptions or systemic structure generators
Vision: what we want for the future, the guiding force that determines what mental models we hold as important when pursuing our goals.
What do Systems do?
Introduction to Systems Thinking - D. Kim
–> Causal loop diagrams & behavior over time graphs = ST tools
Feedback loops:
- How do the consequences of my actions feed back to affect the system?
- How we describe actions affects the actions we take i.e. understanding interrelationships, feedback loops…
–>Reinforcing processes:
Arise from positive feedback, successive changes add to the previous changes and keep the change going in the same direction.
Growth & collapse, rising or falling time graph, virtuous or vicious circles
eg. popularity of music
–> Balancing processes:
They resist change in one direction by producing change in the opposite direction, which negates the previous effects. There is always an inherent goal in a balancing process, and what “drives” a balancing loop is the gap between the goal (the desired level) and the actual level. Balancing processes always try to bring conditions into some state of equilibrium. They are everywhere, keep the status quo, more discrete than RFs, negative feedback, oscillating time graph; stabilizing (correcting for change) or stagnating (growing ever slower to an asymptote), return to equilibrium state
eg. body temperature
Systems thinking as a complement for analytical thinking…
Introduction to Systems Thinking - D. Kim
- Understanding how systems work—and how we play a role in them—lets us function more effectively and proactively within them.
- The more we understand systemic behavior, the more we can anticipate that behavior and work with systems (rather than being controlled by them) to shape the quality of our lives.
- uncovering hidden assumptions
Systems thinking - delays
Introduction to Systems Thinking - D. Kim
Delays:
- physical (eg. transport)
- transactional (eg. phone call)
- informational (eg. miscommunication)
- perceptual (eg. no shift in perception because of deeply engrained assumptions)
–> they can make a system’s behavior unpredictable and confound our efforts to produce the results
–> The amount of delay has a significant impact on whether it will be a virtuous or vicious cycle.
–> Such is the nature of complex systems and the world of circular feedback loops: Once a loop gets going, it’s hard to tell what is driving what. So important to have indicators & measures of delays.
