Revison Flashcards

1
Q

System

A

A whole composed of interacting parts.

System model: nodes+ connections

The parts (nodes) of a system can be objects, people, activities, etc. The interactions (connections) can be exchange of goods, information, influences, temporal sequences, etc.

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2
Q

System characteristics

A

A whole composed of interacting part:

  • Boundaries
  • Purpose
  • Emerging properties

Two complimentary system approaches:

  • Viable System Model
  • Soft Systems Methodology
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3
Q

Viable System Model

A

A system approach, which focuses on how to regulate and control activities so that an organisation can carry out its work, with particular attention to information flows.

But people’s models are implicit and to tackle them the differences need to be surfaced.

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4
Q

Soft system methodology

A

Focus is to surface and integrate different views of a system and it’s problems.

It is about how do we achieve a shared understanding of the situation.

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5
Q

Sketchnote

A

Rich visual notes created from a mic of handwriting, drawings, shapes, visual elements, etc.

Note taking (especially by writing) helps learning better.

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6
Q

Model

A

A simplified representation or description of a system or complex entity, especially one designed to facilitate calculations and predictions.

All models are incorrect except a few.

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7
Q

Systems thinking models

A

System thinking models as a way to see and think about the world. It is a set of modelling techniques.

  • Emphasises complexity
  • Holistic v reductionist
  • Emergent behaviour
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8
Q

Emphasises complexity

A
  • Multiple interacting parts

- Multi-cause explanations as opposed to single cause explanation (root cause)

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9
Q

Holism

A

The whole is more than some o it’s parts.

The theory that parts of a whole are in intimate interconnections, such that they cannot exist independently of the whole, it cannot be understood without reference to the whole, which is thus regarded as greater than the sum of its parts.

Eg: sociology (causality in explaining is too-down which is whole to the part).

Causality in explaining behaviour flows bottom-up (part to the whole).

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10
Q

Reductionism

A

The simple is the source of the complex.

Complex phenomena can be explained by looking at their constituent elements.

Explanations should be parsimonious (reduce to the simplest principles/entities possible).

Eg: economics (aggression)

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11
Q

Holism and emergence

A

The systems develop characteristics that are not present in its components.

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12
Q

Hierarchy

A

Generally:

A ranking or classification of things or people (eg: power, status, etc)

A ranking on the basis of authority relationship.

In systems:

As many systems are composed of subsystems which also have subsystems, this structure is nested. This is hierarchal in a sense that each system is nested in another higher level system. No sense of more importance.

Eg: digestive system, car, etc.

The hierarchical nature of many system gives them order and assists in dealing with complexity. Interactions within subsystems are more frequent or intense than between subsystems. Attention can be given to the subsystem of interest and the rest becomes environment. Eg: businesses (operations, finance, etc).

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13
Q

Emergent features of systems

A

Neglected interactions.

In hierarchal systems, attention needs to be paid to what is simplified away. Emergent phenomena are the result of the necked interactions.

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14
Q

Usefulness of a model

A

Problem/objective context: the broader situation in which a problem is placed.

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15
Q

Problem context

A

Few subsystems, with few structured interactions that are stable over time or vice versa.

Table (pluralists, coercive, unitary—participants) (simple, complex—systems) images

Participants context:

  • Unitary: similar belief, values, and interests.
  • Pluralist: interests are compatible but values and beliefs are different.
  • Coercive: diverging interests and limited ability to express different beliefs and values.
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16
Q

Systems thinking

A

Focus on tasks, processes, performance (efficiency)

Reality is objective
-goals are clear and the systems involved can be modelled objectively.

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17
Q

Systematic thinking

A

Focus is on people, perceptions, relationships.

Reality is subjective
-What the situation, problem, solution is need to be negotiated and objective models are unlikely used.

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18
Q

Why systems thinking is useful

A

Provide a broad range of techniques that cater to many problems as the environment is complex and uncertain and therefore Nono-causal explanations and silver bullets approaches are useless.

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19
Q

Viable systems model

A

Living systems:

-Viable systems
•able to continue living
•working successfully

-Open systems in interactions with the environment
•flows of information
•flows of energy
•flows of matter

-Constant adjustment rather than equilibrium

It is hierarchal as systems are nested.

Same structure across hierarchal levels (each subsystem will be viable and therefore needs to have all 5 subsystems).

The meta system (for planning and control) however must not be viable as a hierarchal meta system would mean a bureaucracy that takes a life of its own.

Three key elements:

  • Environment
  • Metasystem (for planning and control)
  • Operating units (for getting things done)
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20
Q

Feedback

A

In systems thinking feedback refers to a circuit or loop (feedback refers to feedback loop)

The output of activity/process A affects the activity process B and vice versa.

Feedback can be immediate or delayed, negative or positive.

Feedback loop may take place at the system level, with the output of the system feeding back to the system as input.

Complex systems contain a multitude of interacting positive and negative feedback loops. This makes the exact prediction of their behaviour impossible.

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21
Q

Positive feedback

A

The more of the input, the more of the output.

It accelerates the system and pushes it out of equilibrium.

Mathews effect (the rich get richer and the poorer get poorer)

22
Q

Negative feedback

A

The more of the input, the less of the output.

Pushes the system back towards equilibrium or the desired level and balances it it.

Eg: body temperature (decreases so mechanism activated which is shivering and then the temp increases).

The Kanban system, which is the centrepiece of Just-in-Time and the lean production systems. An inventor control that moves inventory control from a push to a pull configuration.

(Cupcakes and oven example) empty tray is the feedback signal that regulates activity of the processing machine (oven). By controlling the number of trays, one controls the Work In Progress Inventory.

Push models are based on forecast demand and pull on actual demand.

23
Q

How does a human body maintain its viability

A

Stafford Beer puts emphasis on the role of the nervous system in ensuring viability of the human body as it interacts with the environment.

In this way, the human body can be seen as composed of 5 subsystems, which regulate the feedback loops with the environment and within the body.

System 1: The Cortex—policy, ultimate authority, identity, ground rules.

System 2: Input from senses—outside info, sustainability, toward planning, strategy.

System 3: Base Brain—overview, internal regulation, optimisation, day to day management.

System 4: Nervous system—regulation, conflict resolution, stability.

System 5: Muscles, organs—primary activities, parts that actually do something.

24
Q

Human body viability systems applied to organisation

A

System 5: Policy—identity, vision, strategy.

System 4: Intelligence (external eye)—interaction with the changing environment, innovation, planning.

System 3: Management and monitoring (internal eye)—resource allocation, day to day problem solving, optimisation, overview.

System 2: Coordination—lateral communication, standards, etc.

System 1: Operations—primary activities.

25
Q

Work In Progress (WIP)

A

Expensive

Acts as a buffer absorbing uncertainty—guarantying that work can still be carried out in case of machine breakdown or defective pieces produced.

Reducing WIP requires relatable production (less defects or breakdowns) as otherwise production of the end product stops.

Kanban and Lean production drive a higher quality and cheaper production system.

26
Q

How does a viable system relate to its environment

A

It must somehow deal with its complexity.

Complexity can be measured by using variety as it’s proxy.

Variety is the number of possible states of a system or its environment.

Eg. Variety of a switch is 2 and of single digit numbers is 10 (0 to 9)

27
Q

Ashby law of requisite variety

A

The variety of the controller must match the variety of the system being controlled.

Only variety can absorb variety.

However, variety of the environment is typically much higher than the variety of the system.

28
Q

Strategies to deal with variety

A
  • Attenuate external variety
  • Amplify internal variety

Eg: system 4

  • Attenuates the variety of the future environment.
  • Amplifies the variety of the organisations—enabling it to deal with the future.

Standardisation is a key variety attenuation strategy.

Copying with a highly uncertainty technology space with high variety (variety amplification) (Eg: google—ads, cloud, android, hardware, infrastructure)

29
Q

Uses of VSM

A
  • Diagnoses of current organisations

- Design of new organisation as viable systems

30
Q

Applying the viable system model

A

1) Identify the system boundaries and purpose
2) Identify the system ‘in-focus’ the object of analysis
3) Identify one system above and one below
4) Identify System 1 for the system in focus and then work up to Identify the other systems
5) Check how reality matches what the various systems and their connections should be
- connections amongst systems
- matching varieties
- relationship with the environment

31
Q

Typical problems (applying VSM)

A
  • Not all primary activities are sufficiently recognised as they are not treated as implementation units with their own management.
  • Different recursion levels are defined incorrectly with inappropriate connections and or activities at the wrong level
  • Irrelevant structures
  • The metasystem attempts to become viable
  • S2 (Coordination) is too weak
  • S4 (intelligence) is too weak (s5 relies on s3 too much)
  • S3 interferes too much in s1 (micromanagement)
  • S5 does not create a sufficient identity (system is unsure of the purpose)
32
Q

VSM pros

A
  • Comprehensive tool that is scalable
  • Allows to consider a system in context through recursion/hierarchal structure
  • Recognises the deep links with the environment
  • Recognises the crucial role of information flows and of command and control aspects
  • Diagnoses and design
33
Q

VSM cons

A

-Too simple while too complex
-Disregards the nature of organisations as social and political systems
•motivation
•political action, conflict, and diverging interests
-It’s proponents argue that it emphasis maximum possible decentralisation while its detractors argue that all thinking is done above and it assumes a ‘father knows best’ approach.
-It does not deal well with changing environments.

34
Q

Order

Soft systems methodology

A

Perceptual patterns

Eg: developing a narrative to impose order on the events in the response (hurricane Katrina)

We project order onto reality in order to be able to act.

Reality is constructed by imposing interpretations on it does not mean that reality can’t tick back. Construction of reality is synonymous with anything goes.

35
Q

Structure of interpretation

A
  • Assumptions
  • Values
  • Beliefs
  • Knowledge (theoretical and experiential)

^About ourselves, the world, the situation we are in and our position in it.

36
Q

Social construction

A

Consensus (shared) reality emerges from shared experiences and interpretation of reality.

The sharing and consensus are temporary and partial as our experiences are never identical and our position (purpose, relationships) in the situation are different.

It is more a social construction of the understanding of reality than reality itself.

Our position is the situation= our context

37
Q

Pluralism

A

Acknowledging that there are multiple, equally valid narratives.

38
Q

What is the source of problems in an organisation

A

In a unitary context

  • Lack of information
  • Wrong incentives
  • No best practices

In a pluralistic context

  • Lack of acknowledgment of others points of views
  • Lack of consensus
39
Q

Methodology

A

A body of rules, procedures and practices to engage in an enquiry.

40
Q

Soft system methodology

A

1) Enter the problem situation
-Get familiar with background.
•Interview (what is the main task of this organisation? What is the most important thing this organisation should do? What are the main problems? Etc)
•Observation (what do people discuss about? What does the language of their discussion say? Etc)

2) Express the problem situation
•Narratives
•Rich picture (the richness and relationships between different takes on the problem situation expressed in a compact visual form. Expresses the situation not the problem, no causation, an art)
•Identify key themes and issues

3) Formulate root definitions for the relevant systems
•Identify relevant systems (science system, manufacturing system, pharmaceutical trial system, etc)
•provide a root definition for the most important system using CATWOE

4) Build conceptual models of the systems in the root definition
- what should the system do? What is described in the root definition? Considerations oh how the system is currently structured should not enter at this point in the process.

5) Compare a conceptual model with the situation
- how is the process currently carried out? Identify issues in comparison with current situation

6) Define changes that are desirable and feasible
-discuss items that are problematic and that can be flagged as recommendation
•informal discussion
•workshops with stakeholders
•white paper

  • define actions or changes
  • the selection of what is to be discussed always requires judgement about the context in which you operate
    7) Take action
41
Q

Root definition

A

A concise statement outlining what the system does (immediate goal), how it does it (means) and why it does it (long term purpose)

Developing root definitions is an iterative process.

42
Q

CATWOE

A
  • C (client/customers/user/beneficiary/victim)
  • A (actors)
  • T (transformation)
  • W (worldview)
  • O (owner)
  • E (environment)
43
Q

Accidents

A

They are emergent properties of complex systems.

When care is take to design a safe system, accidents can still be caused by the (unanticipated) interaction of small trivial failures (job interview/group presentation eg)

44
Q

Reason’s Swiss Cheese Model of accident causation

A

The Swiss cheese disks (a system’s defended against failure) rotate, and the position and dimension of holes change.

Includes;

  • physical infrastructure (breakdowns, etc)
  • systems and processes
  • people
  • organisational and environmental conditions
45
Q

Root cause

A

It is the contributing factor you are working on when the money or the time (for the accident investigation) runs out

46
Q

Normal Accident Theory (NAT) (Perrow 1984)

A

System accident involve the unanticipated interaction of multiple failures.

Two dimensions define the propensity of a system to experience failure (accident): complex and coupling

47
Q

Tightly coupled

A

Small changes can produce large effects.

The system reacts very quickly— faster than operators can react.

Making sense of what is happening is a collective endeavour based on communication and problem solving activities of several people, which takes time.

48
Q

Interactively complex

A

Changes in one part of the system affect multiple parts.

Not all interactions are expected.

Interactions may not always be visible and immediately comprehensible (like in technically complex systems in which the information about the state of the components and processes is indirect and inferential (through computers,etc)).

This makes it possible that operator intervention can make hinge worst as the true nature of the cause may not be understood well.

49
Q

Normal

A

It means that the system’s characteristics make it inherently vulnerable to such accidents.

Normal accidents in a particular system may be common or are— so normal does not refer to high frequency.

50
Q

Redundancy

A

It is a classical technical approach to increase reliability.

Redundant subsystems provide a backup that will work in the event that the primary method fails.

It is also some unexpected consequence in normal accidents systems.

Cons:

  • It only works well if the redundant systems are independent.
  • It increases interactive complexity and opacity of the system to operators. This instead increases risks instead of reducing them.
  • It also encourages risk taking.

Eg: deep water horizon

Bottomline: physical and social processes contribute to connect supposedly independent systems.

51
Q

Levels of analysis

A

-Management of individual systems:
Do all you can to prevent accidents in the system under your responsibility, in the knowledge that they are emergent properties of complex systems.

The ability to cope with accidents is as critical as prevention.

-Policy/society level (NAT):
We can limit the number of accidents but they are intrinsic properties of complex technical systems and our ability to organise to prevent them is inadequate even with the best design and behaviour.

52
Q

Person vs system approach to accidents

A

-The person approach focuses on human errors (unsafe acts committed by people at the sharp end such as doctors, operators, etc).
They are slips, mistakes or procedural violations.

-The system approach recognised that:
•errors cluster around specific situations rather than specific people
•errors are consequences rather than causes
•it focuses on the conditions at system, process, organisational level that make active failure more likely.

The person approach tries to limit the number of errors.

The system approach tries to make the system more reliable and able to cope with inevitable errors and contain them.

Human errors are difficult to predict.

The characteristics of the environment that make errors likely are often evident before. (Swiss cheese model).