How is Contingency in the Sciences Dealt With?

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BillTre

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Complex situations are commonly generated in biology by dependent actions occurring only after (or contingent upon) a previous occurrence that "set-up" or made its occurrence possible.
The stringing together through time, of sets of these series of occurrences (including molecular scale events), can result in much complexity. This can result in problems of classification and determining causes.

Examples:
The wild and crazy biological complexity of different forms we can currently observe are generated through both its long term evolutionary history (that has generated the complexity of different species we see today), as well as through a series of shorter term events that shape the individual forms of each organism (during its developmental (or medical) history).
This makes biology (and other similar sciences) different from much of physics, where situations being addressed are usually framed in "simpler" situations.
However, it seems to me that similar contingencies underlie the complexity of geology (what is the history of those sedimentary rocks, for example) and perhaps cosmology. There are probably examples in other sciences I am not thinking of.

Questions:

  • Is history the best common term for all this?
  • What terms from computer science (contingent, dependent?) are used for similar situations? Perhaps CS has some well developed way of addressing these situations that I am not aware of.
  • Is there some branch of physics that deals with this general kind of chained (through time) phenomena?
 

fresh_42

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Markov chains, chaos theory, and network science come to mind. But in physics as well as in computer science, many simplifications are made to investigate certain special dependencies. Too complex systems hide the view on what shall be considered. In any case all sciences try to get down to a causality, and I assume even biology. And many cause and effect events form chains and networks. A comparable subject could be climate, since it is determined by many partly random events and variables, too, and also follows some few fundamental principles. I do not want to distract the thread towards climate models, but I believe we have a similar degree of complexity in these two cases, including the difficulty to make predictions. You won't use a computer algorithm of similar complexity in order to solve a problem, neither would you track down thermodynamic processes onto the molecular level. Those fields need simplifications.

I occasionally search for algebras used in biology as examples for applied mathematics. But I regularly end up with Mendel's peas - more or less. So there is a huge gap from there to the complexity of certain evolutionary results.
 

Klystron

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[snip!]
Questions:
  • Is history the best common term for all this?
  • What terms from computer science (contingent, dependent?) are used for similar situations? Perhaps CS has some well developed way of addressing these situations that I am not aware of. [snip]
Approaching the problem from the other end, computer science teaches several simplification methods and procedures to make complex problems tractable to simple solutions. In the first non-programming language CS course we studied the modular hypotheses often applied via step-wise refinement of a complex problem. Classic examples include "Go to the Grocery Store".

History or Setting:
Problem: Prof. B. invites people to tea but discovers no milk in the refrigerator.
Solution: Prof. B. goes to the grocery store for milk.

At the top-most (outer) level the problem seems solved; but what is the actual solution.
Detailed solution: Prof. B. drives their car to the grocery store, buys milk and returns with the milk.

As we refine the steps in this solution we can define basic modules such as "Drive the Car".
  • find the car keys.
  • walk to the car.
  • enter the car and start the engine.
  • navigate to destination.
This basic module can be used for any solution that requires driving. Further refinement adds specificity:
  • Locate wallet, check for money to buy milk.
  • use (call) "drive the car" destination = "grocery store".
  • buy milk from grocery store.
  • "drive car" destination = "Prof.B.'s home"
I presume you know these methods but notice how refinement introduces detail while reducing complexity by using common modules. One method to study history includes ignoring details and skimming across time to a documented culture, then digging deeply into that historical setting.


 

Ygggdrasil

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Questions:
  • Is history the best common term for all this?
  • What terms from computer science (contingent, dependent?) are used for similar situations? Perhaps CS has some well developed way of addressing these situations that I am not aware of.
  • Is there some branch of physics that deals with this general kind of chained (through time) phenomena?
In terms of evolutionary biology, I've seen the term "historical contingency" be applied to these phenomena. For example:

Another useful term for this phenomenon I have seen from social science is the concept of path dependence.

These concepts have been discussed much in the context of exploring the extent to which evolutionary change can be predicted. Here's a good discussion of the topic with regards to evolution:
The evolutionary biologist Stephen Jay Gould once dreamed about replaying the tape of life in order to identify whether evolution is more subject to deterministic or contingent forces. Greater influence of determinism would mean that outcomes are more repeatable and less subject to variations of history. Contingency, on the other hand, suggests that outcomes are contingent on specific events, making them less repeatable. Blount et al. review the numerous studies that have been done since Gould put forward this question, both experimental and observational, and find that many patterns of adaptation are convergent. Nevertheless, there is still much variation with regard to the mechanisms and forms that converge.
 

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