(sorry Allie, I meant to rewrite them, but I'm too lazy and I will just copy/paste them)
On the call: Giulio, Daijiang, Tim, Allie, Leonardo
to do today: 1) what will be our take home message? 2) who is our audience? 3) how will we write the manuscript?
On complexity
'complexity' can be many different things at the same time, representing many different facets
some facets of complexity easy to compute/formalize, some are proxies of complexity (are correlated with it)
what are the different meanings of complexity that folks talk about? can classify the different definitions/clarify the semantics and then define the meanings mathematically
there's an implicit hierarchy of complexity metrics: 1) number of species 2) connectance 3) some metric of the different types of interactions in the community (beyond food web) 4) topological, structural, dynamical metrics
ecologists see a lot of species and assume the web is complex - is that true? could be that most species are interacting weakly - is that web then complex?
but, on the other hand, weak/rare interactions not the same as having no influence, wrt information
unpredictability as a metric of complexity? (unpredictability as not knowing initial conditions/structure of the network), though random processes are also unpredictable.
assuming we know the "true" network - then how do we quantify complexity: is there a universal scale of complexity? or is it always relative to a "random" or "null model" network?
are all communities/food webs complex? is it a binary?
why is complexity relevant to ecologists? not because it is an indicator of stability, but maybe through its relationship to evolution/learning
could a metric of complexity incorporate the evolutionary past of the network? in other words, are food webs with a shorter history less complex? or are they more complex? for example, a pair of interacting species that just recently diverged might have a less predictable (more complex?) interaction than a pair that diverged long ago. nontrivial to introduce these ideas into a metric of complexity
exploring the comparison between observed & randomized networks: for some datasets, can’t randomly generate the same properties by chance. does that mean it is complex? (allie note: is there a connection to kolmogorov complexity here?)
complexity as informational complexity: a complex system is somewhere between complete regularity and complete randomness
Audience
we all are in some way unhappy with the way ecologists talk about complexity, so the target audience would be community ecologists likely to use network theory
perhaps write an opinion paper with some numerical experiments to back up/illustrate ideas
how to reach out to different communities of community ecologists (beyond food web types)
(sorry Allie, I meant to rewrite them, but I'm too lazy and I will just copy/paste them)
On the call: Giulio, Daijiang, Tim, Allie, Leonardo
On complexity
'complexity' can be many different things at the same time, representing many different facets
some facets of complexity easy to compute/formalize, some are proxies of complexity (are correlated with it)
what are the different meanings of complexity that folks talk about? can classify the different definitions/clarify the semantics and then define the meanings mathematically
there's an implicit hierarchy of complexity metrics: 1) number of species 2) connectance 3) some metric of the different types of interactions in the community (beyond food web) 4) topological, structural, dynamical metrics
ecologists see a lot of species and assume the web is complex - is that true? could be that most species are interacting weakly - is that web then complex?
but, on the other hand, weak/rare interactions not the same as having no influence, wrt information
unpredictability as a metric of complexity? (unpredictability as not knowing initial conditions/structure of the network), though random processes are also unpredictable.
assuming we know the "true" network - then how do we quantify complexity: is there a universal scale of complexity? or is it always relative to a "random" or "null model" network?
are all communities/food webs complex? is it a binary?
why is complexity relevant to ecologists? not because it is an indicator of stability, but maybe through its relationship to evolution/learning
could a metric of complexity incorporate the evolutionary past of the network? in other words, are food webs with a shorter history less complex? or are they more complex? for example, a pair of interacting species that just recently diverged might have a less predictable (more complex?) interaction than a pair that diverged long ago. nontrivial to introduce these ideas into a metric of complexity
exploring the comparison between observed & randomized networks: for some datasets, can’t randomly generate the same properties by chance. does that mean it is complex? (allie note: is there a connection to kolmogorov complexity here?)
complexity as informational complexity: a complex system is somewhere between complete regularity and complete randomness
Audience
we all are in some way unhappy with the way ecologists talk about complexity, so the target audience would be community ecologists likely to use network theory
perhaps write an opinion paper with some numerical experiments to back up/illustrate ideas
how to reach out to different communities of community ecologists (beyond food web types)