intro’d to Deborah here (while looking for self-organizing ness):
ted 2003 – The emergent genius of ant colonies
i like to think about how org’s work.. how the simple parts interact to create the behavior of the whole org
systems like ants: web, brain, cells, …
about 10 000 species of ants.. live in colonies .. 1 or 2 queens.. most walking around sterile female workers.. no central control…
how behavior changes as colonies get older…
3 min – every year on same day.. each colony sends out virgin unmated queen. and male. and they mate.. after that males die.. the newly mated queen … fly off.. drop wings.. live for 15-20 yrs continuing to lay eggs from that original mating..
change in years.. 10-12 000 ants at age 5… stays that age for 15 ish years till queen dies.. cycles again..
colony size changes as colony age.. what i think about – task allocation.. how does it change what it’s doing.. ie: adjusting as conditions change.. w/nobody telling anybody what to do .. how do they adjust…
4 categories: foraging/patrollers/nest maintenance workers/midden workers (put territorial chemical in garbage) – only about 25% of colony.. and oldest ants
ants you see walking around don’t do much eating
curiously looks as though about 1/2 ants in colony are doing nothing.. bible.. look to the ant oh sluggard… if something happened might all come out if needed.. but rarely come out… what function to a reserve of ants.. doing nothing..
never nothing going on…
9 min – don’t see very well – mostly work by smell… even if queen did have intelligence to send chemical messages to tell others what to do.. no way could make it in time to see shifts.. so .. she’s not directing behavior of colony
what’s relationship between ants doing diff tasks.. i was thinking .. each ant dedicated to one task from birth.. so i experimented.. found… diff tasks are interdependent and ants switch tasks.. they go off what’s needed to be done…
13 min – older colonies.. more stable… not any older.. not due to experience of older/wiser ants… (because only live a year)… only change is colony size…
so what kinds of decision or rules.. since no ant can access global situation.. that would change as colony gets larger… what i’ve found.. is antennae interaction.. it’s the rate at which it meets other ants.. that tells them what to do..
how can an ant tell i’m a forager… and if i start to meet higher number of nest maintenance.. i switch.. this layer of grease on outside of ant… longer they stay outside… so task specific order.. used in brief antennal contacts…
17 min – this system is messy/variable/noisy.. in particular.. 1\ experience of each ant can’t be very predictable.. rate at which it comes back depends on what happens to it.. 2\ ants ability to access this pattern must be very crude.. because ant can’t do counting..
it’s not that this haphazard pattern of interactions produces a factory with precisions.. in fact .. if watch them.. end up wanting to help them… it’s not that perfection arises.. but works pretty well.. been around a long time.. something they’re doing is clearly successful enough…
you’d expect interactions to be closely connected to colony size… when ant in large/colony can’t use same rule.. as colony develops produces diff behavior..
ted 2014 – What ants teach us about the brain, cancer and the Internet
i study ants.. using interactions diff in diff environments.. could learn from them about .. brains, data networks, cancer… they all have no central control…
in ant colony.. no one in charge.. rather.. very simple interactions.. ants use smell..
mechanism simple enough ness
doesn’t matter which ant it meets.. and no major complicated signal/message.. all that matters to the ant is the rate at which it meets other ants.. this constantly shifting network that produces the behavior of the colony…
our brain works in the same way .. but what’s great about ants is you can see the whole network as it happens
using interactions diff to meet diff challenges.. ie: operation costs, find/collect resources…
the system works to stay stopped unless something is happening…
above video – on doing nothing ness
why some colonies forage less than others.. thinking of ants as neurons.. whether there might be small diff’s and how many interactions each ant needs to go out and forage…
the brain – maybe some individuals/conditions that electrical neurons need more stimulus to fire..
reproductive success.. colony lives about 28 years , ie: colony 154 is a great grandmother.. offspring colonies resemble parent colonies in their decisions.. even though ants never meet.. so can’t be learning from parent colony… so look at genetics..
colonies that conserve water.. ie: stay in when really hot outside.. do better… i thought colony 154 was a loser because they stayed in and did nothing.. but not so..
finding out what’s actually working best
internet uses algo to generate flow of data… we call this analogy.. the anternet… data doesn’t leave unless signal that there’s enough bandwidth to flow… ants using algo so similar to one we recently invented… and ants have had 130 mill years to evolve.. and thinking other colonies will have great insight we haven’t thought of yet
where ants abundant and diverse… where it’s go.. unless something negative happens..
they use their interactions to measure density
cancer – ants never poison own colonies… many diff kinds of cancer… some will spread… where must be getting resources they need… if resources are clustered likely to use interactions for recruitment.. so if we figure out how.. like ants.. we could set traps…
the gordon lab:
Gordon studies ant colony behavior and ecology, with a particular focus on red harvester ants. She focuses on the developing behavior of colonies, even as individual ants change functions within their own lifetimes.
Gordon’s fieldwork includes a long-term study of ant colonies in Arizona. She is the author of numerous articles and papers as well as the book Ants at Work for the general public, and she was profiled in the New York Times Magazinein 1999.
from ant network page:
then got a bit deeper while reading Kevin Carson‘s regulated state
fantastic quote, re-watching it myself now :) Reminds me of a quote I pulled from this aeon article: twitter.com/ultimape/statu…
(dec 2016) – The queen does not rule… The ant colony has often served as a metaphor for human order and hierarchy. But real ant society is radical to its core
Historically, many have found the idea of division of labour a compelling and powerful model. .. But it’s not natural. A vision of human society ordered and improved by division of labour has permeated and distorted our understanding of nature.
science of people ness
The implication was that the workers in an ant colony, all sisters or half-sisters, are divided into naturally fixed groups, and genetically programmed to perform a particular task. .. each ant’s role is unalterable destiny,
we know now that ants do not perform as specialised factory workers. Instead ants switch tasks……Yet in an ant colony, no one is in charge or tells another what to do.
Like many natural systems without central control, ant societies are in fact organised not by division of labour but by a distributed process, in which an ant’s social role is a response to interactions with other ants.
to respond to the changing world.
This social coordination occurs without any individual ant making any assessment of what needs to be done.
rather.. indigenous us
However appealing it might be to imagine ant colonies organised by division of labour, the evidence tells us they are not.
the collective process of task allocation in ant colonies is based on networks of simple interactions.
Each encounter, in the form of a brief antennal contact, has no meaning to the ant, but in the aggregate, the rate of encounters determines how many ants are currently foraging.
simple mech to facil whimsy
The system that ant colonies use to organise their work is a distributed process.
Primarily, in a distributed process, there is never central control, while in division of labour there might be.
For division of labour, specialisation can lead to better work. By contrast, in a distributed process, the fact that individuals are interchangeable makes the whole system more robust and more resilient.
the term ‘distributed process’ originated in computer science…. no single unit,…knows what all the others are doing and tells them what to do. … Distributed processes often operate in parallel rather than in series. …In a parallel process, different steps can be done at the same time.
Ants can show how distributed processes might allow us to adjust to a changing environment
as temp placebo.. getting us back to listening..
although certain large regions of the brain seem to be involved in particular tasks, at the level of neurons it looks like division of labour is not the rule.
We say that disease, intelligence, psychosis, athletic ability and so on are ‘genetic’, as if inside a person’s cells there were little switches labelled ‘cancer’ or ‘paranoia’ or ‘endurance’. In fact stress, sunlight, exercise and similar influences can change which genes are turned off and on. Biologists are learning that what genes do depends as much on what is happening outside as well as inside the cell.
To envisage how an ant’s task of the moment arises from a pulsing network of brief, meaningless interactions might compel us instead to ponder what really accounts for why each of us has a particular job.
a story about people grokking what matters
The alternative, that a person is a shifting flux of impressions and feelings, lacking a defined core, is difficult to grasp.
The most fundamental appeal to the idea of division of labour is, perhaps, that it provides a reassuring sense of control.
reassuring..? or deadening..?
Reality is less soothing but much more interesting. A distributed process can be messy and not fully predictable, yet can provide greater resilience and robustness.
Division of labour is a human innovation, drawing on our ability to learn and improve by practice, and to trade goods and services. The growing recognition that natural processes work differently from our symphonies and armies will allow us to see the natural world more clearly.
article in aeon feb 2018
John Hagel (@jhagel) tweeted at 6:36 AM – 16 Feb 2018 :
Almost everything that happens in life is the result of a network. Making, or breaking, local links is the way to change. Deborah Gordon reports on the insights generated from studying networks of ants https://t.co/pZB65D4Kjn (http://twitter.com/jhagel/status/964493762524168192?s=17)
We live enmeshed in networks. The internet, a society, a body, an ant colony, a tumour: they are all networks of interactions, among people, ants or cells – aggregates of nodes or locations linked by some relation. The power of networks is in their local connections. All networks grow, shrink, merge or split, link by link. How they function and change depends on what forms, or disrupts, the connections between nodes. The internet dominates our lives, not because it is huge, but because each of us can make so many local links. Its size is the result, not the cause, of its impact on our communication.
local by necessity, because an ant cannot detect anything very far away.
The shifting network of brief interactions transforms a group of ants, each unable to assess any global purpose, into the *orderly chaos that is ant-colony behaviour.
*via 2 convos
These local mistakes, taking a path that is not reinforced, are what make the whole network resilient
They adjust their search paths to density. When there are many ants searching, each ant turns around a lot, almost at random. If there are only a few ants, they walk in straighter lines.
These swarm methods can be simpler, cheaper and more robust to failure than a system using central control. ..if it breaks down, all function is lost. By contrast, local interactions have redundancy; if one doesn’t work, another might
All organisms operate in some kind of ecological network; no living entity operates independently.
For example, cancer, like everything else that cells do, progresses in response to local interactions. Cancer cells are the descendants of healthy ones, and they can thrive and proliferate because they still speak the local language of their ancestors. These conversations allow them to find comfortable neighbourhoods in which to metastasise, summon a blood supply, disarm their immune-system cousins, and turn off the instructions from other cells that would stop them from reproducing. Interrupting these conversations would obstruct the growth of the cancer.
Relations among cancer cells account for many of the failures of chemotherapy. Tumours contain many different forms of cancer cells, each derived from a different evolutionary lineage. Even when chemotherapy wipes out detectable signs of a tumour, cancer cells can still remain. Continued application of the poisonous treatment favours the evolution of ever more resistant cells. These resistant cells, no longer competing for resources with their late, more sensitive neighbours, can reproduce rapidly, and there might be no drugs available to kill them.
Individuals in a population differ from each other in susceptibility. Using the same resources links them to each other by competition – what one individual eats is a loss for another. A pesticide or antibiotic assault kills all but the most resistant individuals, selecting for resistance over susceptibility. The resistant ones suddenly have the resources to grow rapidly in number.
Integrated pest-management and ‘adaptive chemotherapy’, for example, both work to kill only some, not all, of the sensitive pests or cancer cells. They leave the rest to help suppress the resistant ones. These new forms of control take into account the local interactions that regulate networks of pests or cells.
Networks come in different shapes, depending on the arrangement of links. The familiar tree-shaped network, for example, is a hierarchy…The tree and the fully connected network sit at extreme ends of a spectrum of possible network shapes
(on fb expo’ing middle school girls.. most linked= most popular).. The greater the betweenness centrality of any node, the more power over the network it exerts. In political terms, if you want to rule, set things up so that others cannot act without your permission, ie so that your betweenness centrality is very high and well-enforced. But such power can be undermined by the local; for example, climate change cannot be dismissed when everyone realises it is what they see outside the window (Land Talk), and disparate realities begin to blend when their adherents engage in face-to-face conversation.
Ants do very well without any identity; nothing distinguishes the hubs from the outsiders. Betweenness centrality is not important for them: an ant’s interaction rate depends only on whether it happens to run into others, and ants don’t seem to care which other ant they meet
Highly connected networks trade resiliency for security. More connections mean easier repair, but also more vulnerability.
How a network changes depends not just on its shape, but on how quickly and how often the local connections shift. Neural networks, for example, form links slowly, a fact that profoundly influences human society. Neurons must grow to find others, and the growth of neurons produces the synapses that carry signals. If synapses in neural networks are not used, they get pruned, disappearing from the axon. Pruning happens much more rapidly in young mammals than in old ones; networks in the brains of babies are in rapid flux. In this way, the time course of change in neural networks shapes another network, that of the family, which is based on the obligation to protect babies while their brains are forming, until they can protect themselves.
Networks in nature show how, for the networks that we engineer and those that tie us to each other, the pattern of links at the local scale sets the options for stability and transformation. Almost everything that happens in life is the result of a network. Making, or breaking, local links is the way to change.