System Analysis and Sustainable Development

System Analysis and Sustainable Development

• The best course of action requires considering objectives, alternatives, and ranking methods

• When addressing a problem, define: -Objectives: The desired outcomes -Alternatives: Ways to achieve objectives -Consequences: Results of adopting alternatives -Criteria: Standards for ranking alternatives -Models: Representations of the problem's factors and their relationships for predicting consequences

• Iteration and feedback are essential in component activities

• Communication with decision-makers helps set goals and constraints, fostering a positive attitude

• A framework for analysis includes: -Formulating the problem -Identifying, designing, and screening alternatives -Forecasting future context -Building and using models -Comparing and ranking alternatives

Climate Change Discourse

• Climate change is a physical, cultural, and psychological phenomenon
• Climate change's true meaning lacks consensus

Climate Change Frameworks

• These shape how climate change is perceived: -Edenic Myth: Nostalgia for a simpler time, protecting a fragile natural world -Apocalyptic Myth: Centres on fear, disaster, and a call to action -Promethean Myth: Controlling climate without wisdom or humility -Themisian Myth: Focuses on justice, equity, and correcting wrongs

• The core idea views climate change as cultural and psychological, needing reflection on social, political, and personal goals -Climate change influences politics and business -Calls for long-term, diverse responses other than viewing it as a battle

Problem Analysis

• A problem in science is the gap between the current and goal states
• Reality not matching an ideal situation can be defined as a problem

Importance of Problem Definition

• Defining a problem aids its resolution
• Disagreements on whether a problem exists are common
• The problem is viewed differently depending on the person
• Problem understanding varies

Issues in Defining Problems

• Not everyone agrees on a problem's existence
• Problems are not always clearly defined

Cynefin Framework

• Useful in problem solving
• Clear: The problem is solved with a known best practice
• Complicated: Cause and effect are known but require expertise
• Complex: Cause and effect emerge over time, unknown unknowns
• Chaotic: Cause and effect are unknowable and constantly shifting
• Disorder: Unclear which of the above contexts is predominant

Methods of Problem Analysis

• Focus is on identifying and structuring causes and effects
• requires Agreement on the problem
• Also, to get a clear delineation of the problem
• and to find root causes, not just symptoms

Five Whys

• Aims to find root causes by asking "why" five times
• Requires accurate statements, honesty, and determination
• Criticism: Assumes one root cause, may stop at symptoms, lacks facilitation, and has low repeat rate

Fishbone Diagram

• Identifies and classifies cause-and-effect relationships in categories
• Criticism: Can be complex with irrelevant causes, making solutions seem unattainable

Problem Tree Analysis

• Maps main problems with causes and effects
• Conducted by stakeholders
• Criticism: Lacks magnitude, hierarchy, and interconnections

Causal Diagram

• Represents causal relationships between variables
• Uses nodes connected by arrows
• Makes use of Loops

System Dynamic Modelling

• There is no correct problem analysis, it is a starting point

Models of Reality

• Models are simplified representations
• Models are always less than reality,
• Models are always wrong to some extent
• Models never exactly match reality
• Models are useful depending on the goal

Types of Models

• Two types exist
• Stochastic Models: -Make probability-based generalised statements from a random sample -Likelihood or chance is represented -Uncertainty is handled through probability distributions -Linear regression included in models
• Deterministic Models: -Use predetermined variables -Used in specific situations -Seeing system evolution over time (no sampling required) -System dynamic models included

System Dynamic Modelling Details

• SD Modelling understands dynamic behavior in systems over time
• Makes use of these tools: stock flows, feedback loops, table functions and time delays
• Characterized by a complex causal character
• Employs analytical and forecast methods using both qualitative and quantitative approaches and known parameters

System Dynamic Modelling Properties

• Dynamic behaviors based on changes and feedback loops
• Causal relationships
• Probability distributions
• Describes a System
• Implies Feedback
• Includes a Causal diagram

System Dynamic Modelling Process

• Steps include: -Identify the problem -Develop a dynamic hypothesis explaining its cause -Design a computer simulation model -Test the model -Develop and test alternatives -Implement the solution

Complex Systems

• These: -Have multiple components -Components interact via linear/non-linear relationships and interdependencies -Components may show emergent behavior

Conceptualization

• A mental model explains how things work
• It can be graphically represented in our understanding of the world
• Useful in Communication within complex systems

Feedback Loops

• System outputs are fed back as inputs, either reinforcing or counteracting initial changes -Reinforcing loops (+) cause growth -Balancing loops (-) stabilize

The Causal (Loop) Diagram

• Used to diagram to capture the system, can also show feedback loops
• Representation tool for the feedback structure systems

Qualitative vs. Quantitative

• Qualitative analysis: causal loop diagrams represent the problem structure
• Quantitative analysis: uses stock and flow diagrams

Stock and Flow

• Main variables in System Dynamics Modelling
• Past value determines current value in Variables -Stock is the system's actual state or behavior
• stock value changes in Variables -Flows depending on decisions and actions

Definitions in SDM

• Stock: System's actual state, level, or inventory
• Flow: Variables altering stock values
• Auxiliary: Supporting variable without direct stock impact
• Source: External origin for a flow
• Sink: Destination outside the system

Key Principles of Stock and Flow

• Stocks persist when time stops
• Flows disappear
• Stock properties: memory, influencing flow timing, potentially disconnecting/ decoupling flows
• Flows: change stocks, depend on decisions or actions

VENSIM Grammar and Model Quality

• Reliability: Model reasonably replicates reality
• Validate the Model

Sensitivity Analysis

• Methods: -Change variable initial values -Change constant parameter values -Change the causal structure

Actor Analysis

• Improves problem analysis quality
• Creates alternative strategies
• Provides insight for problem-solving
• Maps conflicts or potential coalitions
• Improves legitimation and inclusiveness
• Improve recognition and acceptance of policy options

Stakeholder Engagement

• Stakeholders are involved due to interest or stake in decisions

About Actors

• Actors, people or groups, influence decisions
• A actor is a social entity and is interdependent
• Recognizing actor needs and objectives is key

Considerations when analysing actors

• Playing field of actors is understood by their values, perspectives, and future ability to raise resistance
• Social entities not yet in coalition are taken into account too

Analysis levels

• Arena Level: Decision-making context, can be informal networks or formal institutions
• Actors Level: How actors are involved, their perceptions, values, and available resources

Actors Identifications

• Methods to identify actors: -Interest -Institution -Reputation -Social participation -Opinion leadership -Demographic -System analysis/group model building

Primary and Secondary Actors

• Primary (direct) vs secondary (indirect) relationships
• Internal (beneficiaries) vs external stakeholders
• Consider Supporting vs opposing actors
• Recognize key vs marginalized actors

Implementing Actor Analysis

• Do this using multiple methods

Conducting Actor Analysis

• Follow these steps: -Formulate a problem and decision arena -Identify all key actors -Map the formal institutional landscape -Determine actor characteristics (interests, objectives, and resources) -Summarize interdependencies -Determine problem formulation consequences

Multi-Criteria-Analysis

• Used by policy analysts

Impact Assessment

• Making the relevant decisions

Decision Theories

• Includes:
• Normative theories dictate how it should be done
• Positive/descriptive theories model how decisions are actually made
• Prescriptive/intervening analytical decision making guide the best decision makers

Decision types

• Single-Criterion Decision Analysis is on a single variable
• Multi-Criteria Decision Analysis orders all decision alternatives from best to worst best
• SMART: Simple Multi-Attribute Ranking Technique
  • Involves: -Identifying decision makers -Identifying purpose/objective -Identifying options/alternatives -Identifying relevant attributes/criteria -Assessing the performance of alternatives -Assigning weights to attributes -Aggregating the value of all attributes -Ordering/ranking the results for the appropriate decision

MKBA

• Not KBA; it deals with society's prosperity rather than environmental impact -In context of the Netherlands as a whole -Uses Monetarisation -Compared with "now" variant -relies on utility theory, ignoring ethical theory implications

Project Categories for MKBA

• Consists of projects with national significance (required)
• Has regional/local significance (simplified MKBA format)
• A project to make (recommended)

MKBA Elements

• Important components: -Understanding the eight steps -Effect typology -Steps 4/7 of projects -Calculating Net Present Value

• MKBA considers financial and social factors such as environmental pollution, health damage, and quality of life

• PM items are hard to quantify

• General effect types

MKBA-Led typologies

• Consider effects on Dutch or foreign parties
• Price formation on markets or market effects
• The national well-being
• Direct or indirect
• Who are the potential winners or losers (MKBA less useful here)

Effect Determination

• Standardise by Monetisation
• Make use of market pricing
• Value additional valuation methods and apply them.
• In reality, the use of real world KPIs also

Methods to effect effects

• Behavioural models
• Business cases
• Empirical information over-effect relations
• Experiments
• KPIs
• Quality effect
• 7 Steps of this MKBA step

Time value of money (TVM)

• Opportunity: you can't use capital without having it for any given amount of time

• Risk: a capital estimate for the future is always uncertain on it, therefore needs to be taken into consideration

• Inflation: the capital is worth less in the future than at present, due to inflation taking its toll

• Discount Rate: costs and expenses are devalued for consideration later down the line

• NPV: Net Present Value

• The assumption here is the time value of cost risk

Project management

• Is used in conjunction with certain assumptions

Down-scaling

• You have the final costs and figures, you then scale the model to represent your project accurately

When to apply

• The concept being delivered isn't a new one
• Only when a one off payment isn't sufficient
• When different parties are involved

A project management approach should be adopted, where you ask,

• How should we do it
• Why should we it
• What are the implications of making this choice
• and the question; what is the impact of all choices

Key benefits for a business

• time saving,
• reduced capital spend,
• improving working efficiency

And finally, meet all standards imposed

• The project management team focuses around all of the various above factors
• 80% is good enough
• The planning stage is critical, and its key members are essential also
• • Create trust in those around you
• • Demonstrate all steps that you are doing
• • Show how the project can be improved with your expertise

Key benefits in planning and applying;

• Initiate plans at all stages
• Record all progress for analysis later
• Plan and share as much data and information as possible, to build trust etc
• Monitor the time and quality output of your projects
• You can close your projects and analyse all data for your next one

Break down the method requirements

• This will allow you to improve the project and allow you to plan for a smooth execution
• The parts have to come together and make one single deliverable.

1. Write out the intended activities
2. Check the start and end times are correct
3. Use the chain of logic to help aid
4. Analyse at all points

• This involves, listing out how all of the activities are intended to run, and their effect

The intended activities must be visible to the chain of process

• Use parallel activities if needed
• And lastly work to ensure you progress

Risk analysis

• During the model building process, there may be:
• Limitations imposed,
• Scarcity or time, which also translates to money too.

Key methods of reducing project risk

• All stages of the model are visible
• All costs and spend are made explicit to those around
• Show data showing the best spend and effect on KPI is made

The difference between risk and certainty shown

• An uncertainty can't be quantified
• A quantifiable risk has an affect, that can be mitigated and has a known action to take

How to mitigate risk

• By applying a test paragraph to tests
• By naming the document so its clear
• You can make sure that you follow the plan, but be aware that there will be issues throughout the whole period