Summary of all material required for the midterm of Network Organizations. The summary includes:
• All course slides
• Menczer et al. (2020) - A First Course in Network Science: H1, H2, and H4
• Vennix (1999) - Group model‐building tackling messy problems
• Sterman (2001) - System Dyn...
Slides
Organisations are collaborations, target orientated (organisations are a means to an end), can be
formal or informal and are relatively permanent.
We-intentions, external en internal perspective
Organisations use resources including (information) technologies
Within organisations there are coordination and task distribution mechanisms (hence agent-
roles)
A systems perspective on organisations:
A system is a set of elements ('components’) with relationships between them.
A system has relationships to its environment.
Depending on our perspective we choose what is in or out the system
We can perceive/describe a system from an internal or external perspective.
Systems:
Systems have a structure defined by their components/elements and composition;
Systems show behaviour, requiring input, processing and output of resources, including energy,
data etc.;
Systems have interconnectivity, i.e. different system parts have functional and structural
relationships to other parts.
Systems can be attributed with functions
Network organisations pro’s and cons:
Economy of scale
Specialisation & (knowledge) productivity
Resilience and flexibility
Vulnerability due to interconnectivity
Levels of network organisations:
Micro level; model to understand the function(ing) of one specific model node (one
organisation)
Meso level; model a subsystem, i.e. the nodes and edges forming a particular interconnected set
of nodes forming the subsystem of interest
Macro level; model the system of interest in order to study the emerging effects on society
level.
Two ways of modelling:
- Knowledge-driven: search for theories and evidence, supported by empirical data
- Data-driven: search for data and inductively find relations
What is a model?
A model (M) for a system (S) and an experiment (E) is anything to which E can be applied in order to
answer questions about S.”
By definition, a model can be qualified as a system, which allows to cut out smaller pieces to
generate a new model (implying a hierarchy of models). We have to choose the level of abstraction!
A model has limitations:
- It has a domain of validity
- It only represents part of the original system
- Its output will not exactly match that of the original system (i.e. It has a limited accuracy)
, Validity: does the concept really measure the concept?
- Construct validity (does one measure what one thinks to measure)
- Face validity (is the set-up of the research reasonable)
- Concurrent validity (measures the test against a benchmark test and high correlation indicates
that the test has strong criterion validity)
- Predictive Criterion validity (the extent to which a score on a scale or test predicts scores on
some criterion measure)
- Convergent validity (measures of constructs that theoretically should be related to each other
are, in fact, observed to be related to each other )
- Internal validity (causal relationships)
- External validity (sometimes called population validity: how well can the sample used be
extrapolated to a population as a whole)
- Ecological validity (the extent to which the findings can be generalized to real-life settings)
What is System Dynamics?
System dynamics (SD) is an approach to understanding the nonlinear behaviour of complex systems
over time using stocks, flows, internal feedback loops, table functions and time delays.
System Dynamics is a computer-aided approach to policy analysis and design. It applies to dynamic
problems arising in complex social, managerial, economic, or ecological systems–literally any
dynamic systems characterized by interdependence, mutual interaction, information feedback, and
circular causality.
We build a model step by step:
1. Decide on the scope (What Experiment do we aim for)
2. Create the initial Qualitative Model and validate
3. Expand the model Quantitative Model
4. What interventions (policies) do we want to test
5. Build the network model (SD simulation)
6. Run the experiment(s) and validate the results
7. Use simulation to predict the effects of policies
Limitations
- With scope come limitations (what is the domain of validity?)
- It only represents part of the original system
- Its output will not exactly match that of the original system (i.e. It has a limited accuracy)
Collecting quantitative data for SD models:
- Look for existing research
- In case you have other ‘literature’ check the ‘facts’
- Conduct your own empirical study and analyse your data
Policy making:
- Usually addresses complex problems
- Firstly we develop a policy field theory
- Such a policy field theory describes the relevant phenomenon from two perspectives:
o Causal
o Normative
- Then we decide on possible actions
- This is called the policy effect theory
- The policy effect theory describes:
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