Sunday, July 07, 2019

VUCA World - Wikipedia

VUCA stands for Volatility, Unpredictability, Complexity, and Ambiguity. All self evident.

  • V = Volatility. The nature and dynamics of change, and the nature and speed of change forces and change catalysts.
  • U = Uncertainty. The lack of predictability, the prospects for surprise, and the sense of awareness and understanding of issues and events.
  • C = Complexity. The multiplex of forces, the confounding of issues, no cause-and-effect chain and confusion that surrounds organization.
  • A = Ambiguity. The haziness of reality, the potential for misreads, and the mixed meanings of conditions; cause-and-effect confusion.

Saturday, July 06, 2019

Metaphors -- Jurgen Appelo

  • Figure of speech; Likeness or Analogy of something
  • Science metaphors -- Butterfly effect, Edge of Chaos, Survival of the Fittest, etc.
  • Metaphors in Management
    • Organisations as Machines
    • Organisations as Organisms
    • Organisations as Brains, Cultures, Political Systems, etc.
Organisations as Machines (These are all Machine Metaphor Thinking)
    • Machine images pervade management jargon
    • "Running a Company"
    • "Driving a Change"
    • ""Owner" of a Company (As if they are owners of a car, how can someone be "owners" of a social structures (where human beings are key)?
Dangers of Metaphors

Metaphors sometime lead to "Reminiscence Syndrome", which is jumping to conclusions (when going too far) because "Things Look the Same". [Jack Cowan]

Example - "Inventory as Waste" makes perfect sense in case of book publishing (lying in warehouse) or cars (lying in warehouse adding no value to anyone). However in case of "writing a book" (Jurgen was writing a book and had about 16 half finished chapters "lying in inventory", however it was still adding value as the ideas were continuously interacting with other topics in his brain). Representing them as "waste" in this case goes too far and therefore fail.

Metaphors fail much faster. Science likes mathematical models and they fail much later. 

Complexity science says we cannot have one strong model. This requires prediction and we cannot have it in complex systems. So we need plenty of weak models. We can therefore have Multiple Weak Models that can make as much sense as One Strong Model. And it is certainly better than No Models

However in the end all models, all metaphors fail.

Long Tail and Weak Ties in social networking

One single perspective is not enough to understand complexity. You will need multiple perspectives.

Friday, July 05, 2019

What is a Model - Based on Jurgen Appelo's Lecture

  • A model is a miniature representation of something.
  • It is a description or analogy that helps us to visualise something we cannot directly observe
  • A system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs.
There are two types of models

Confirmatory Models: Used for Prediction and Control -- cannot use them in complexity context

Exploratory Models: Insight & Understanding [Sense making].

There is only ONE criterion for a Model -- Does the Model help me Make Sense of the world? (Insight and understanding)

How detailed / rigorous should a model be?

Usefulness of a model depends on the complexity of the person's mind (analysing the problem) and the environment / situation (context) within which they find themselves.

Simple and Complicated Models

[Prince 2 Process :-) ]

Note: Models are never perfect. Usefulness is context-dependent. It depends on the people and environment. (My interpretation -- therefore it is very important to understand whether the Agile framework you are recommending (Scrum / Kanban / or another) really aligns with the product development / project you are working on).

Complexity Models

1. Stacey's Matrix
2. Dave Snowden's Cynefin

With respect to Agile, we try to frame the context of a given product development / organisation / or a System within Stacey's or the Cynefin Model.

Learning Models

Agility in IT Operating Model - KPMG

Courtesy - KPMG

Bloom's Taxonomy - Venderbelt University

Bloom's Taxonomy is a hierarchical ordering of Cognitive (mental skills), Affective (growth in feelings or emotional areas) and Psychomotor (manual or physical skills) skills of learning. 

Below we are looking at Cognitive skill hierarchy. 

Continuous Improvement Models over the Years

Tuesday, July 02, 2019

Complex Adaptive Systems -- MIT and Jurgen Appelo

Courtesy: MIT.Edu

Examples of Complex Adaptive Systems -- Economy, Organisations, Human Brain, Developing Embryos, Ant Colonies, etc.

I have modified the text to suit my needs, however it doesn't take away the core essence of the MIT Essay. 
  • Complexity results from inter relationships, interactions, and inter-connectivity among the constituent elements within a system, and between the system and its environment.
  • Many natural systems exhibit such complexity -- Brain, Immune system, Ecologies, societies, etc.)
  • Such systems are called Complex Adaptive Systems (CAS).

  • CAS are dynamic systems capable of Adapting, Evolving, and Changing with environment.
  • There is no separation between the system and its environment as the system always keep adapting to the changing environment.
  • The system closely linked with other related systems and together with the environment forms an ecosystem.
  • Change within such an ecosystem needs to be seen as co-evolution with all related systems rather than an adaptation to a separate and distinct environment.
  • CAS are real systems and are fundamentally unpredictable in their behaviour. Long term prediction and control are therefore believed to be not possible.  
Attributes of CAS
  • Distributed Control: There is no single centralised control mechanism that governs system behaviour. Although the inter-relationships between elements of the system produces coherence, the overall behaviour usually cannot be explained merely as the sum of individual parts.
  • Connectivity: Because all the entities within the system are inter-related, have an inter-action, and are inter-connected, a decision or action in one part of the system will have an influence in all other parts, but may be nor uniformly.
  • Co-evolution: Elements within the system will change based on their interactions with one another and the environment. The behaviour patterns are also seen to change with passage of time. The co-evolution patterns are captured in Fitness landscape, which is is nothing but an array of all possible survival strategies available to a system. The landscape is  a series of overlapping graphs with crests and troughs. Fitness landscape is the mathematical term for inspect & adapt and a state of continuous learning. 
  • Sensitive dependence on initial conditions: CAS are very sensitive to their dependence on their initial conditions. Small changes my have profound impact on overall behaviour, or a huge upset may not have an impact at all [Example Edward Lorentz's Weather Forecast experiments, Butterfly Effect]. 
  • Emergent Order: From interaction of individual agents arises some kind of global property or pattern that could not be predicted from understanding agents at individual level. E.g. Consciousness is an emergent property of constant interactions of neurons. Global properties arise from aggregate behaviour of individuals. And this arises from competition and cooperation among the agents. [This basically contravenes with the accepted 2nd law of thermodynamics that systems tend towards disorder. This has been shown to be not true, by Ilya Prigogine's seminal work on Dissipative Structures]. It is basically possible for order of "new survival strategies" to emerge from disorder through a process of spontaneous self organisation (If a system is in a state of spontaneous self organisation, it can go from a state of disorder to a state of order). Example, Starlings. 
  • Far from Equilibrium: If a system remains at equilibrium, it will die, however if you push it away from equilibrium, where they are allowed to explore their space of possibilities, they will create different structures and new patterns of relationships to thrive. Complex Adaptive Systems function best when they combine order and chaos in an appropriate measure. E.g. heartbeat is orderly and regular, however there is a subtle but apparently fundamental irregularity. 
  • State of Paradox: CAS ingrain dynamics combining both order and chaos. There is a bounded instability, or the edge of chaos -- this is the state of paradox where there is both stability and instability, competition and cooperation, order and disorder.
  • Fractal: CAS exhibits fractal symmetry.  
A Team is a Complex Adaptive System (CAS)

A Team (for e.g. Software Development Team) is a CAS since it consists of parts (people) that for a system (team) and the system shows complex behaviour while it keeps adapting to a changing environment. 

Management 3.0 - Jurgen Appelo

Monday, July 01, 2019

How to Change? - Daniel Gilbert

  • If you want to change, change your belief system (you won't change by simply adopting a new line of thinking; because you won't buy into it for long)
  • To change your belief system, seek "changing" experiences; because your beliefs always spring up in response to your experiences.

4 Conversational Models - Susan Scott

  1. Team conversations: engage individuals and teams in friction-less debates that interrogate reality and ignite dialogue around clarifying goals, solving problems, evaluating opportunities and designing strategies. 
  2. Coaching conversations: engage individuals and teams in conversations that increase clarity, improve understanding as part of change management. 
  3. Delegation conversations: clarify responsibilities and raise level of personal accountability, ensuring that each employee has a clear path for development, action plans are implemented, deadlines are met, goals achieved, and leaders are free to take on more complex responsibilities. 
  4. Confrontation conversations: engage individuals and teams in conversations which successfully resolve attitudinal, performance or behavioural issues, naming and addressing tough challenges, provoking learning, and enriching relationships.