Serendip is an independent site partnering with faculty at multiple colleges and universities around the world. Happy exploring!

Emergence, Week 7

Paul Grobstein's picture

Welcome to the on-line forum associated with the Biology 361 = Computer Science 361 at Bryn Mawr College. Its a way to keep conversations going between course meetings, and to do so in a way that makes our conversations available to other who may in turn have interesting thoughts to contribute to them. Leave whatever thoughts in progress you think might be useful to others, see what other people are thinking, and add thoughts that that in turn generates in you.

As always, you can leave whatever thoughts occurred to you this week. But if you need something to get you started ...

Reactons to From Complexity to Emergence and Beyond ?  What challenges does it suggest for the remainder of the course?  What new models might help with those challenges?

ssv's picture

Thoughts on From Complexity to Emergence & Beyond

In addition to the comments above, I do think that the addition of weak and strong emergence to our discussions is a very helpful way to distinguish between deterministic and non-deterministic systems. 

Adding to Evan's excitement about the new big program to write, I think it will be interesting to observe how all of our modules that do simple interactions will yield complex behavior.  It's quite a "size shift" as to what we have been writing so far, but relatively speaking all that we're linking together in this new big program is small with perhaps a more complex outcome than we've seen before.

Something I'd really like to explore more/talk about is how we can make it "think".

jguillen's picture


I agree that Netlogo is a useful tool for exploring emergence because it allows us to create a world and a bunch of agents that can do interesting things, but I still think that using models to explore the capabilities that we listed will get increasingly challenging the more we move down the following list of what agents need to do:

  • move, with some degree of randomness
  • observe, interact with world
  • learn from interactions with world
  • get it less wrong - induction
  • create internal models of the world
  • think - deduction, abduction
  • evolve, from inanimate to animate to story tellers?

I guess we will just have to start out simple and go from there, switching things and playing with the codes. AS far as the reading for this week, I enjoyed reading “From Complexity to Emergence and Beyond” again because it sort of highlighted a lot of the questions that we have been discussing during the semester such as the underlying principles of emergence as well as different examples of this phenomenon. I think that the distinction between strong and weak emergence made a lot more sense now that I have a greater understanding of emergence deterministic vs. non-deterministic systems.

We talked about evolution and in our last class and I agree that while randomness is a driving force of evolution, evolution is a driving force for the complexity and diversity that is present in nature. I found an interesting paper about the top questions of emergence (, and one of the questions that is discussed in the paper is about the relation between emergence and evolution. Are there any processes similar or related to ‘emergence” in evolution? The paper mentions that sudden jumps in complexity due to evolution are often related to fitness barriers. I know that in our last class we briefly talked about barriers and how that can affect how an entity observes and interacts with the world and this is something that we observed very early on in the course using Langton’s Ant model. According to the paper, there are at least three difference ways to cope with fitness barriers:

1)      to wait for a catastrophe, until the barrier is reduced through catastrophic events

2)      to bypass through exaptation (a process in which a feature acquires a function that was not acquired through natural selection): explore a different direction and make a sudden side lap

3)      to tunnel right through the barrier by burrowing complexity

The paper did not mention more on evolution and emergence…I’m interested in what else we can say about emergence and evolution…especially after we continue exploring models using netlogo…



Sahitya P.'s picture

Thoughts on From Complexity to Emergence and Beyond

I think I have a better understanding of the distinction between weak versus strong emergence discussed in From Complexity to Emergence and Beyond  because of our discussions on randomness. I understood  weak emergence to refer to deterministic emergence  and strong emergence to refer to non-deterministic emergence .   I think that “strong emergence” as defined in the article makes sense because all phenomena can not be reduced down to “properties and rules” since emergent processes produce phenomena where the whole is not necessarity the sum of the parts and in addition to the parts interating/influencing each other and the whole, the whole may also influence the parts which results in a complex net of interactions.  But I do think that some systems display underlying properties and rules and  I am not sure I understand the inquirer problem brought up.  Are “properties and rules” just constructions of our brains? I think that while we recognize properties in systems, wouldn’t that organization be present even if we didn’t give significance to it?

EMR's picture

Modeling the Universe

I'm excited about trying to work our models as far as possible down the list of capabilities we considered in class.  It feels to me like an evolutionary process, although not purely so.  The way I program tends to be iterative in an evolutionary way- I try something a lot of different ways and then select the one most effective for my purpose.  I then often incorporate this paradigm into my later programs, as it has shown its 'adaptiveness.'    It seems the best way to make an attempt at modeling complexity is go at it in this way, the same way the compleity arose in the first place (arguably).  However, the selective forces used in making our models tend to be directed or driven in a particular direction, unlike true evolution. 

Following the evolutionary paradigm, I think we will have the most success if we start with simple pieces of code and then gradually stitch them together with more bits of code in a hierarchical way, rather than trying to cram everything into one long procedure.  As our models grow more complex, we may also find it useful to start visualizing  them with flow charts of call trees.  I hope that we will be surprised at how far we can get if we each go in our own directions but periodically shareour thoughts with each other.

kdilliplan's picture

Blog difficulties

       ACK, I tried to post my entry and I got logged out, and apparently it got posted twice.  I couldn't figure out how to delete the second copy, so I just changed it.  Sorry!   

kdilliplan's picture

Thoughts About Models and SNAILS!

            After reading “From Complexity to Emergence and Beyond”, I thought a lot about how we approach problems when working in Netlogo.  As suggested in the paper, we could do one of any number of things.  We could begin with a program, observe what it does, and then look at the code and learn more about the rules and properties that make it do what it does, which essentially is looking at the whole and using its parts to be able to understand and predict it.  We did this when we looked at Langton’s Ant.  Alternately, we could begin with the code and try and predict what the program will do.  We did this with the Game of Life and Cellular Automata.

            We’ve done the same things when building our own models as well.  We’ve begun by entering code first and observing what it does later.  We do this especially when we’re learning new code.  We sometimes begin with a specific behavior in mind, and from there we try and come up with a code to make that behavior happen.  We do this both with code we already know and to try out new code.

            So far, both approaches have been instrumental for me, both in thinking about the models we’ve looked at and in building my own.  I am not yet advanced enough in either the theory or practice of computer modeling to be able to decide which approach to use if I’m working on my own.  In other classes on other subjects I’ve found it useful to be able to choose an efficient way to approach problems right off the bat.  This tends to save time and energy.  It is my goal to be able to do that for this course as well, and then be able to continue to apply it.


In other news, I was at the Delaware Museum of Natural History over break, being a geek, and I noticed something really awesome.  There was a large display of shells from Cone Snails, a particularly venomous genus of mollusk.  One of the species, the Textile Cone, has a very familiar pattern on its shell:

Textile Snail Textile Snails


How crazy is that!?  Mollusks add new shell on the end of the old shell as they grow, so could it be possible that these organisms have some sort of genetic version of a Cellular Automata rule that controls the color of the new shell based on the previously secreted shell?