"Centralized vs Decentralized"
These two terms explicitly describe "where" an activity takes place.
I think sometimes teams might implicitly choose to use these two terms as euphemisms for "performed in a standardized manner by experts" versus "performed with high variability by a crowd".
Such an implicit meaning is not necessarily fair because there are both pros and cons to each approach.
Expert processing is expensive with limited availability but more likely to be consistent.
Crowd processing is harder to control (activity and outcome) but ultimately quite cost efficient if it works.
I know sometimes our team is troubled by the “variability” in distributed activity. (e.g. Do project owners get their software listed in the software list, do change owners complete risk scores etc.)
And I know sometimes we are tempted to switch models and “centralize” to address these issues. But time and money are short for our Quality Improvement team and centralization and the transformation to centralization is expensive.
While that may be an appropriate solution, it isn’t necessarily arrived at after a careful assessment of all of the alternatives.
For example, there are ways to leverage the “bandwidth” of the crowd without necessarily sacrificing quality of results. i.e. While not controlling activity we may find appropriate means to positively affect outcome, whether through support or appropriate feedback loops, or gates etc.
It makes me think that I want to invest in my own learning about how to influence the outcomes, rather than necessarily taking over the work involved in the activity…
Here is an interesting excerpt on 3 kinds of “crowd wisdom” found in “disorganized decisions”
Types of crowd wisdom
Surowiecki breaks down the advantages he sees in disorganized decisions into three main types, which he classifies as
Thinking and information Processing
Market judgment, which he argues can be much faster, more reliable, and less subject to political forces than the deliberations of experts or expert committees.
Coordination of behavior includes optimizing the utilization of a popular bar and not colliding in moving traffic flows. The book is replete with examples from experimental economics, but this section relies more on naturally occurring experiments such as pedestrians optimizing the pavement flow or the extent of crowding in popular restaurants. He examines how common understanding within a culture allows remarkably accurate judgments about specific reactions of other members of the culture.
How groups of people can form networks of trust without a central system controlling their behavior or directly enforcing their compliance. This section is especially pro free market.