Understanding complexity and Why Agile works only if done right
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The document discusses the principles of complex adaptive systems (CAS) and how they relate to agile project management. It emphasizes the idea that system behavior is influenced by the distributed nature of control, the interdependencies among components, and the unpredictability of outcomes based on initial conditions. Additionally, it highlights the importance of self-organization and co-evolution in achieving effective outcomes within such systems.
Understanding complexity and Why Agile works only if done right
- 1. And why Agile works… only if done right.. Hrishikesh Karekar @hrishikarekar
- 3. From the book: Getting to Maybe: How the World Is Changed, Frances Westley, Brenda Zimmerman, Michael Patton
- 7. Distributed Control There is no single centralized control mechanism that governs system behaviour. Although the interrelationships between elements of the system produce coherence, the overall behaviour usually cannot be explained merely as the sum of individual parts While we may want to believe that there is "control" in large software projects, in reality there is none.There are complex relationships at play between the buyer and seller, and also between the different vendors that make up the project. Not just that, inside each organization that has a stake, there are inter relationships that affect how that organization reacts and responds to certain situations and players. The system behaviour is governed by all of these inter relationships and is clearly distributed control. No one players truly calls the shots.
- 8. Connectivity Complexity results from the inter- relationship, inter-action and inter- connectivity of the elements within a system and between a system and its environment. This implies that a decision or action by one part within a system will influence all other related parts but not in any uniform manner What happens when one of the players decides to go for a system upgrade. Some players may not be impacted directly, some will be directly and some eventually get an impact because one of the systems they depended on got it, not the original system that started the upgrade cycle in first place. Such cascading effects of actions and decisions are common place and its almost impossible to predict the connection till something actually hits you. In most cases, you would fully comprehend only in retrospect.
- 9. Co-evolution With co-evolution, elements in a system can change based on their interactions with one another and with the environment. Additionally, patterns of behaviour can change over time Elements in the system are observing one another and learning and adapting constantly. • Take a scenario. A few scrum teams decide to go explore Continuous integration. • They have initial success and the project ecosystem values this highly. • This reinforces their behavior and also sets a positive trend for others to follow. • In a similar scenario what will happen if the project ecosystem does not value this highly. ? • They don't put it down, but they don't respect it too much.
- 10. Sensitive Dependence on Initial Conditions CAS are sensitive due to their dependence on initial conditions. Changes in the input characteristics or rules are not correlated in a linear fashion with outcomes. Small changes can have a surprisingly profound impact on overall behavior, or vice-versa, a huge upset to the system may not affect it.... Real systems, especially living organisms, are fundamentally unpredictable in their behaviour. Long-term prediction and control are therefore believed to not be possible in complex systems
- 11. Emergent Order Complexity in complex adaptive systems refers to the potential for emergent behaviour in complex and unpredictable phenomena... There is constant action and reaction to what other agents are doing, thus nothing in the environment is essentially fixed... From the interaction of the individual agents arises some kind of global property or pattern, something that could not have been predicted from understanding each particular agent... Any coherent behavior in a system arises from competition and cooperation among the agents themselves.... For many years, the second law of thermodynamics - that systems tend toward disorder - has generally been accepted. Ilya Prigogine's work on “dissipative structures” in 1977 showed that this was not true for all systems. Some systems tend towards order not disorder and this is one of the big discoveries in the science of complexity.... Order can result from non-linear feedback interactions between agents where each agent goes about his own business... it appears that self-organization is an inherent property of CAS
- 12. Far from Equilibrium / State of Paradox In 1989, Nicolis and Prigogine showed that when a physical or chemical system is pushed away from equilibrium, it could survive and thrive. If the system remains at equilibrium, it will die. The “far from equilibrium” phenomenon illustrates how systems that are forced to explore their space of possibilities will create different structures and new patterns of relationships.... it can be said that complex adaptive systems function best when they combine order and chaos in an appropriate measure
- 13. How do we operate in such a world?
- 14. Ant Colony Here is how ants work: • Travel randomly in search for food. • Take a piece of food and head straight back to the nest. On the way back to the nest lay down an odour trail. • Notify nest mates of the discovered food encouraging them to leave the nest. These newly recruited ants will follow the odour trail directly to the food source. In their turn, each ant will reinforce the odour trail until the food is gone.
- 15. Bird Flocks • Birds flocks are beautiful. • You may think that the movement gets orchestrated by one savvy bird. But this is not the case. • A bird flock is guided by three simple principles (every decent bird knows them): • Separation: steer to avoid stumbling upon local flockmates. • Alignment: steer towards the average heading of local flockmates. • Cohesion: steer to move towards the average position of local flockmates.
- 19. Why it does not work.. Most times?
Editor's Notes
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