Essay

Emergence: The Connected Lives of Ants, Brains, Cities, and Software Emergence by Steven Johnson

Swam intelligence could also be the subtopic of this book. Emergence is all around us and we don’t really realize that we are an important part of it.

Steven Johnson begins with the amoeba-like slime mold cell. These cells coalesce with thousands of neighbours to create a creature of greater complexity and even intelligence in certain circumstances. In the year 2000, scientists from Japan could create conditions in which a slime mold found its way through a maze.

Henceforward the author gives a good overview what we can call emergent behav-iour: Alan Turing's work with complexity theory, Jane Jacob's study of the emer-gent properties of cities, Oliver Selfridge and John Holland's experiments at the nascence of artificial intelligence, and E.O. Wilson's study of ants and theories of sociobiology.

For me there is not only one concept that I like most. There are a few good ones so I want to start with the ant colonies which serves an excellent example of the suc-cess of emergence in nature. Every ant operates on a set of low level rules and feedback from its neighbours.

Therefore Johnson illustrates five fundamental principles for building bottom-up systems:

More is different:

A critical mass of ants is necessary for useful statistical averages to emerge. One or two ants bumping against each other is not a colony.

Ignorance is Useful:

Simplicity of the individual components (i.e.: the ants) is beneficial. There is no need for each ant to have imprinted a map of what is in the colony's best interests, and in fact such ideas would be a disadvantage to the colony as a whole.

Encourage random encounters: The author exemplifies how ants use the feedback from encountering the activities

of other ants to usefully modify their behaviour. Analogical in “The Death and Life of Great American Cities” by Jane Jacobs shows how humans in urban areas posi-tively effect the emergence of cities by their encounters in public areas.

Look for Patterns in the Signs: Ants follow trails of pheromones left by other ants. In the research field it is com-mon that you study a significant number of papers and emerge these thoughts to a

newer and bigger one. Pay Attention to Your Neighbours:

"Local information leads to global wisdom." When an ant notices a large number of his fellow ants are foraging, he will alter his behaviour to another activity. Likewise, in the development of a human embryo, individual cells are able to get information

from their neighbours that will guide them in their own formation, whether that be as skin cells, bone cells, muscle cells...

The next interesting concept is the WWW – World Wide Web. Johnson cites the

news aggregation website called Slashdot.org. The users have the possibility to control the quality of its content. The website was built in the late 90`s. Over 10 years later we have a few websites for example Reddit, Digg or Facebook which try

to build their own version of an intelligent self-organizing website. In these systems the users represent the ants. They can give positive or negative feedback, creates structured randomness, neighbour interactions and decentralized control. The de-

centralized feedback necessity for emergence is provided by for example user-ratings.

The most interesting thing is that the book was published in 2001. After that the

avalanche of social networking and social content was starting. Wikipedia, MySpace, del.icio.us, Second Life, Flickr, YouTube and twitter were built up of these ideas. All editors and individuals of Wikipedia are aware of information. Each

user just knows a part or a small part of the whole content. Teamwork will facili-tate the compilation of information and makes each participant smarter. Facebook for example combines social-networking with other aspects of the social

web. All users can share their feelings and product recommendations with others. So a disorganized entity organizes rapidly into bottom-up intelligence. Pay atten-tion to your neighbour!

Another concept of emergence theory is the field of genetic algorithms which gen-

erate solutions to optimization problems using techniques inspired by natural evo-lution, such as mutation, inheritance, selection and crossover. Instead of giving a line by line instruction the programmer creates the framework with a randomly generated initial state. During each successive generation, a proportion of the ex-

isting population is selected to breed a new generation. Individual solutions are se-lected through a fitness-based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected. The sorting experiment of

Danny Hill proves that this way of programming results in the emergence of the best algorithm.

In conclusion, this book was very likely to read. It gives my many new thoughts and interesting views about topics I already know, but don’t really particular recognize.

Emergence is more than a process or a concept. It is the combination of all influ-ences and relationships of each participant so the whole is greater than the sum of the parts.

Maybe the best way to create truly intelligent machines is to combine an organism with more than one smart segment and let them interact with each other.

Brain, Mind and Cognition – 2013

Emergence -

The Connected Lives of Ants, Brains,

Cities and Software

By Steven Johnson

Emergence, by Steven Johnson deals with the topic of emergence. For me with no experience in this field

it was a really nice book. Johnson used a very descriptive language with many examples.

But what is emergence? Emergence seems for me to be the intelligence of a cluster, developed by

‘dumb’ members (agents), but without a pacemaker. Emergence is a bottom-up approach of explaining

smart behavior, where single agents team up and build a big and smart whole. Even if no agent is aware

of the major plan.

I was really surprised in how different areas emergent behavior occurs. Johnson has examples from

cities, ants, brains and many more. Johnson has some nice, fresh and surprising points of view on many

subjects. One of the more surprising thoughts is about unconscious learning. A city learns, not only its

residents. It is like the human body learns to defend the pox.

From the first book we have read (On Intelligence by Jeff Hawkins), we already know some principles

(feedback, pattern matching …) explained in emergence. Even one example (about recognizing

handwriting) was the same. In the following, I will pick one of the new aspects as the one, most

interesting.

For me, the most interesting concept was the one about neighborhood.

According to Johnson, neighborhood is essential for emergence. A single entity can’t emerge. For

emergence a certain, critical, amount of neighboring objects are necessary. To evolve an intelligent from

micro ‘dumb’ behavior to macro smart behavior interaction is essential. So it is not only the existence of

neighbors, that leads to emergence, it is the interaction between neighbors. So the simple existence of a

mass of agents does not lead to emergence. That is why sidewalks are more important in a city than

highways. Johnson’s wrote “Random collisions should be encouraged to accelerate emergence”. Collision

delivers the essential feedback that is necessary to build an intelligent mass. So if you pay attention to

your neighbors, you may adapt certain characteristics. And they adapt from you, it’s a mutual

development. Or if you decide that is not the neighborhood you want to live in, you move and by that

you form that quarter too. (In this case I refer to the real neighborhood, not the abstract idea.)

This feedback loop among neighbors is extremely important. ‘Feedback is a way of transforming a

complex system into a complex adaptive system’, (page 139).

With feedback we adapt to our neighborhood. Adaption to our environment is a part of intelligent

behavior. If every agent adapts to his neighbors, they start to form a bigger entity. Not so smart agents

team up and emerge.

The principle of interaction in a certain neighborhood can be seen in ant colonies. A single ant is stupid.

But the ant is aware of what happens in his neighborhood. If many ants are following a certain trail that

leads to food, the single ant will follow the others. So easy emerge an intelligent pattern in ant life.

Cities are another good example. First there is maybe just a small accumulation of residences. But then

maybe a shopping mall opens. This attracts more residence, what even leads to more shops. A few years

later, you may see how different quarters have emerged. In the city are different quarters with different

characteristics. In one quarter just lives the upper class and in another a slum has developed. Both

happened, without anybody instructed the residents to do so.

Both examples show, that some kind of intelligent behavior shows up. Both examples have in common,

that the neighborhood plays an important role.

A nice thing about emergence and neighborhood is that we can easily comprehend this, by playing a

videogame. In the first moment this maybe sound strange, but there are computer games that have

emergent behavior implemented. SimCity is a popular one among them. In SimCity the player is in

control to build and run a city. If we have a city, suffering from a high crime rate, we may start at an edge

to build up a police station. Starting from this point, we can clean up the next neighboring districts. A

nonviolently quarter attracts more civil residents and chase off the criminals. And so, we can go step-by-

step through the whole city and clean it up.

In this example, we can see how important the edges are in emergence. Along the edges development

takes places. If in an ant colony not a single ant leaves the well-known paths, they would never find new

food sources. Just by exploring their neighborhood, they ensure their surviving, development and

emergence.

But it is not as simple as it seems. Just assemble a certain mass of agents and wait for emergence may

not work. Just a simple mass, without organization is nothing. We can see this in the internet. There is no

structure; therefore companies that can provide a service to find things are so extremely valuable.

Does Johnson’s book stimulate thoughts inside of you about what it

takes to build intelligent technical systems?

Johnson’s emergence stimulated a few thoughts about, what it takes to build an intelligent system inside

me. Wherever we try to implement some intelligence, we should try to write an evolving code.

I was really astonished by the outcome of the number-sorting example (page 170). Hillis wrote a self-

evolving code with the goal to sort numbers in as few steps as possible. After a few runs the self-

developing code, build a function which is only marginally worse than the all-time favorite.

Amazing that code-developed code can lead so fast to a goal. For me a bit scary is the part, that Hillis

could not detect a superior scheme, how the code works. The source code was his best way to describe,

what the Algorithm actually does.

All Hillis used were different starting code snippets (comparable to a gene pool) and a clearly defined

goal. With these two ingredients he started a simple repetitive evolution algorithm: Mix, mutate,

evaluate, and then repeat (page 171). These three steps lead to a very good code, adapted to the goal of

number sorting.

I think this algorithm of mix, mutate, evaluate and repetition could be even used for more complex

challenges. All we need is defined goal to find a correct rating and a ‘gene pool’ to start with.

In a simpler way this algorithm could be used to modify a general-purpose algorithm according to a

single purpose or a special environment. So could without a big effort specialists be developed.

I made the resolution to implement an own version of the described turtles simulation (page 169) and

play with the different parameters.

Brain, Mind and Cognition

Essay

Emergence

Steven Johnson

1 What do you think is the most interesting

thought or concept proposed by Johnson?

When I think back on the whole book, there is one phrase which keeps present in

my mind all way through: More is different. It is like the punchline of the book

which keeps bouncing in my head constantly. I have several reasons for that

insistency.

For one it is the hardest issue to understand for me in the field of emergence. I

have a hard time to get what is the point about it that makes the crucial

difference. Why is the scale, the extent, the amount of elementary units in an

emergent system such an important factor. Notably because in most cases there

can’t be argued some break even point or critical mass which constitutes the

border between the different behaviours. Unless there could be found some

separatrix which defines a border in behaviour, it is all just a matter of

scalability. Mostly one could scale things and regard large anthills as equal to

two halfsize anthills very very close to each other. This thought arises as well,

when I think about those ants that behave just locally. As the act and react just

in relation to their very next neighbours, how can it be that the overall size

matters. The radius of their perception is finite and it is indeed small. There is

no way an ant can have an idea about how big its colony is and thus behave

differently - given the ant is rather narrow in intelligence or perception and

behaves according to its neighbours.

My intuitive understanding would propose that More is not different, its just

scalable. Evidently I couldn’t be more wrong with that assumption. More is

1

indeed different. As a matter of fact scale turns out to be a key to emergent

behaviour. Actually this is the second reason why I account that idea to be that

interesting to me. It is a key requirement to have large amount of elements to

conduct emergence. One has to understand why this is before you can get proper

insight in selforganized complex systems. I cannot actually claim to have fully

understood this issue. But I can sense that there lies a immense importance on

it, although it seems quite marginal.

Before I have had read the book my view was that there could be enough for

something to succeed and there could be not enough. So for the case of an ant

colony and its succesful emergent behaviour I would have said either there are

surviving enough ants or there are not surviving enought ants to play their

inherent culture. If there is not enough ants you would need more. But more is

different.

Same applies to multicell organisms. In the book there is considered a growing

embryo. Each and every cell divides into two cells. They copy them self. All

beginning with only one cell. From one cell upto thousands and thousands of

cells that build up a child after some time. The first few cells just duplicate with

no further specification. At some point the cells begin to determine whether they

should become braincells, cells of a leg, heart or other body parts. How does each

and every cell know what to be. They inherit the same DNA. How do they know

which part to pick and develop. Ok, they can determine their situation by their

neighbours. An leg cell could somehow find out that its surround cells are leg

cells as well. Maybe it even can distinguish whether to be the left leg or right leg

according to its neighbours. But then I think back to the beginning and the

building of legs. How does the billionth cell know that it should become a

specialiced cell in the leg and not like the seventy-eigth cell just duplicate. When

and why do the cells start behaving that way. What is the reason that they know

to start emergence, or why does emergence begin at all? More is different.

Somehow it must be that reason. Once a certain size of the whole system is

achieved it makes sense to start building structures as organs. It wouldn’t work

to begin building eyes whene there are thirty-two cells in the embryo. But if

there are several thousands, the state for building eyes might be achieved. This is

why more must be different. Although I don’t get how every cell gets the

information of the overall amount of cells. There is some indicator for that. As

2

the ants recognize pheromone gradients or count foragers. But still it is the scale

which makes up the selforganized structures and decides when to start to emerge

certain structures.

2 Does Johnsons book stimulate thoughts

inside of you about what it takes to build

emergent technical systems?

As mentioned before the scale should be taken account of. Size would matter.

But if I had to think of and create something emergent in a technical way I

would consider most the thought of paying attention to neighbours. I guess this

rule how to respond to the environment and the conspecifics is essential for

artificial emergence. But surely I have no idea how design this rule. It would be

rather a try and error way of designing it. It would need to undergo an evolution

to find something modestly succesfull.

Another thought which the book brought up to me is unintuitive bottom-up

approach. Since we as humans are used to be concious and percept the world in

an ego perspective we tend to think more in a top down fashion. It is natural to

think of us and our actions to be controlled by us in our own tyranny. We choose

structures in our lives. We don’t let them conciously emerge. Which is why I find

it very difficult to find bottom-up approaches to meet a given specification. I

could think of creating something that emerges somewhat of structure or

behaviour. But never to design a system that emerges specially the one

behaviour which is desired. We are used to think more in top-down structures

and not in bottom-up ways. I think there is to be done a lot of research before

one can say more about bottom-up approaches. Though I can imagine there is

huge potential to this paradigm, it just needs to be explored richfully.

3

[1]

Essay on:

Emergence: The Connected Lives of Ants, Brains, Cities, and

Software

by Steven Johnson

In his book "Emergence" Steven Johnson introduced different types of self

organized systems. He shows how fascinating "intelligent" patterns can be

created by bottom-up systems with simple rules. Johnson lines up many

different fields where emergence is included, and I will pick out only the

most interesting ones.

The most interesting idea in the book is the simplicity that can lead to a

higher order just by many agents following a given rule set. Of course, not

all rules lead to a useful behavior, but choose the right ones and you will

see patterns emerging. Very interesting is the fact, that the number of

agents in such a bottom-up system has a very strong influence on the

emerging patterns.

The most important rules that lead to a sensible behavior can be seen on

ants:

1. More is different

2. Ignorance is useful

3. Encourage random encounters

4. Look for patterns

5. Pay attention to your neighbors

The first rule just says, that there has to be a minimum number for a order

to emerge and that this order also changes significant with the number.

[2]

For me most difficult to accept is the second rule: Ignorance is useful.

In our everyday life we try to know and control as much as possible, but for

emergence this is unnecessary. Even worse, if one ant for example would

try to give commands to the others, this would lead to a total chaos. Only

because every ant looks at its nearest environment the whole system

works. There is no control over the system, it's only depending on the right

interactions between its components.

Due to every single ant only knowing a small part of what the whole group

is doing, its decisions can't be precise. It's much more a random thing,

where in total the error cancels out for enough agents making their own

decisions.

Of course the ants have to have some data to know what they should do.

So every ant leaves a pheromone trail. The other ants can than look for

patterns in the trails they encounter and decide whether they change their

own behavior or not.

The last rule is very similar, the ant recognizes what the other ants it meets

are doing and tries to find a pattern in this interactions to adjust again its

behavior.

All this rules lead to a group behavior, where always enough ants are doing

a special task without anyone ordering them to do so. It even leads to

specified places such as a cemetery or a garbage dump. I think the simple

rules emerging to a high order can be best seen on the ants Johnson

describes, because the simplicity of the group member is quite obvious.

Thus the emergence to intelligent behavior from below is easiest to see and

belief with the ants in comparison to the other examples Johnson explains.

One other example is the development of neighborhoods with similar

interests of its inhabitants, like related shops settling down in one street of

the city without any city planner telling them to do so.

[3]

But of course we can also use or simulate emergence for our own interests.

Johnson is very fascinated by the simulation of slime mold cells, forming to

bigger clusters on the monitor, just like they do in reality under special

constraints. Each cell moves randomly until it runs into the trace of another

slime mold cell, which it then follows into the right direction. Many cells

following this simple rule clinch together and form a higher order pattern.

More interesting in my opinion is the use of emergence in the ways of

evolution. Being able to "grow" a program, that does the right calculations

just by starting with very simple commands and then letting the program

evolve by mutating and merging the best fits is a challenging thought, even

when using parallel computing for the task.

Johnson points out, that also the behavior of bigger human groups has

emerged out of quite simple abilities. From being able to predict one

persons thoughts it is just a emergence to interacting with a whole group of

people.

But we need to see the other people to do this type of interaction. We look

at their faces, their gestures etc. to get a clue of what they are thinking. In

internet communities this system doesn't work anymore. So there has to be

another control system that sorted out the topics not interesting for most of

the users. But this would be too much work for a single person or group.

Controlling a system with too many participants gets impossible, unless you

get every user to do his part of the work, by rating the others. This leads to

a bottom-up system, that works quite good for the majority.

Johnson's examples show, that it shouldn't be necessary to plan and

control every step that might arise in the development of an intelligent

system, to build a working system. Far more important is to find the right

simple rules to create a system from the bottom-up, that does its job

autonomously.

Rakesh Jha

What do you think is the most interesting thought proposed by Steven Johnson?

The most important and most interesting thing that the author espouses in the book Emergence is

the idea of Emergence as a way of solving complex problems and as a basis of creating self-realising

systems. He shows how it is possible using bottom up approach to create self-realising systems. The

belief that there has to be a central monitor or pacemaker for creating intelligent machines has been

challenged. The idea that small units, which work under simple rules, can perform complex tasks

through interactions between themselves, which would otherwise seem impossible for each of them

separately, seems rebellious to the general intuition.

Slime mold cells which are simple single celled organism exhibit this emergent behaviour. In

unfavourable weather they live as separate single celled bodies but when weather becomes

favourable and plenty of food is available the individual cells start coming together to form a large

common organism. They achieve this even though they have simple structure and no special

controlling cells are there to control the coming together of the individual cells. This is quite

confounding and the secrets behind this can help us understand ourselves better – how billions of

cells that constitute us are able to work coherently and how it endows us with our intelligence? It is

one of the primary motivations which make the author come up with his idea of Emergence.

Emergence can be defined as the movement from low level rules to higher level sophistication. This

can be seen from the patterns in how a slime mold cells come together, how our neurons make our

brain and how city neighbourhoods are formed. It is natural for us to look for pacemakers when we

see repeated shapes and structures emerge out of apparent chaos as it provides the simplest

explanation. Turing’s work on morphogenesis marked the beginning of shapes and patterns which

form the basis for emergence which is used in lot of fields nowadays like in the recommendations

that we receive on websites like Amazon and EBay.

With the help of Gordon’s ant farm the author explains about emergent behaviour. In an ant colony

we can see there are different functional segregations like harvesters, workers and queen. This

despite the fact all those ants are otherwise alike and clearly there is no top down command and

control system and yet they manage to form these colonies where they work in different groups

towards a common purpose. It is really amazing and mind boggling and one explanation for this is

emergence. All, these ants individually have no idea about their bigger world. The queen ant is not an

authority figure and she is fed and cared for by worker ants. Worker ants are not picked by the queen

and neither does the queen decide the tasks of the worker ants. Similarly, she does not create the

group of harvester ants or assign its responsibilities or provide commands to it and yet all the ants

seem to work in tandem. The explanation that the author provides is each of these ants produce

pheromone trails and each of them can also sense the trails left by the other ants. Using these trails,

the interactions between the neighbouring ants produce a self-organising behaviour. Since, there are

lot of ants in a colony these interactions generate a common pattern and the anomalous behaviours

of individuals tend to cancel out. Other social insects and animals behaviours can also be seen be

explained on similar lines. On similar lines the author has explained how we humans form the city

neighbourhoods where no individual has idea of the entire city at a time, instead he has idea of only

his surroundings and through his interactions with his surroundings he shapes the neighbourhoods.

Of course the interactions of humans with his surroundings are different because of his intelligence

but yet at a higher level it is through his interactions that the neighbourhood shapes up.

Rakesh Jha

The five pre-requisites, according to the author, for deriving macro-intelligence from local knowledge

or for emergent systems are – more is different, ignorance is useful, encourage random encounters,

look for pattern in the signs and pay attention to your neighbours. Another interesting thing is that

the macro-behaviour keeps evolving with time. This is again demonstrated with the analogy of ants’

colony and it is also true for city neighbourhoods formed by humans. Using the same theory he is

able to explain how cells in our bodies differentiate when all of them have the same set of genes. An

interesting thing to note here is that consciousness is not a pre-requisite for learning through

emergent behaviour and therefore we can say even cities learn and evolve just like our body’s

immune system learns to fight infections when it is exposed to pathogens.

Author pops up the question whether web is learning as well, by connecting individual minds all

across the world. Web by itself is highly disorganised space and there is no way a site knows which

site links to it and the inverse is also true. So, the interaction and feedback is missing. However, with

the coming of search engines and software like Alexa which try to arrange the sites and find interlinks

between them and study user traffic pattern, it is slowly evolving as an emergent system which is at a

still higher level than the city neighbourhoods. Feedback is also an important part of realising smarter

systems. It helps in self-learning and self-organisation as displayed by the websites of Slashdot and

EBay. It would enable smarter TV which can receive feeds from millions of channels and present

programs according to your taste. Feedback allows the system to be adaptive by self-learning and

self-organisation. Through a similar biofeedback mechanism our body is able to maintain

homeostasis. It is the rules of feedback which determines how the system is going to self-organise

itself and by tinkering with these rules we can change the way the system works. Resnick’s slime

mold simulation has been used by the author to demonstrate the emergent behaviour. The

formation of mold and its organisations in this simulation is only directly controlled from the margins.

At the start of the program no one knows where the mold is going to form but the rules under which

it is to form are well defined and when these conditions are met the molds appear on the screen.

The idea of emergence is used to an extent in the gaming industry for game development. It has also

been used to generate emergent software using genetic algorithm, where smaller piece of code form

part of the code gene pool. These undergo mutations across generations to emerge with the

solutions. Of course here also the parameters defining the rules of mutations and selection of

mutants are predefined and so it is not exactly as random as it in the Darwinian Theory but it is more

pragmatic since there is no certainty if it works completely randomly that it will ever arrive at the

solution in a reasonable timeframe. For a fully emergent system it would be impossible to predict the

higher level behaviour in advance.

Our ability of mind reading is another example of emergent behaviour. We have it in our genes but

none of us are born ready with this ability and yet we learn it as we grow and this has been

demonstrated by the experiments of Simon Baron-Cohen, Alan Leslie and Uta Frith on three and four

year old kids. Even our ability of self-awareness comes from the ability to read status of other minds.

Through several analogies, some of which has been touched upon in the above discussion, the

author tries to portray his idea of Emergence. Emergence as he says is about how patterns and order

is obtained at a macro-level from seemingly utter simplicity or randomness. The beauty is there is no

central control and yet somehow through their interaction the micro-components project a more

sophisticated behaviour at macro-level.

Rakesh Jha

Does Johnson’s book stimulate thought within you about what it takes to build intelligent technical

systems?

Johnson’s book definitely comes up with a nice theory which could explain how we derive our

intelligence and awareness but he does not explicitly define intelligence anywhere in his book. The

bottom up approach of emergence behaviour has been used to explain the behaviour of ants,

moulds and humans as well. The self-awareness of humans and the way they come together to form

city neighbourhoods and how these neighbourhoods seem to have their own intelligence has been

explained. Whether he provides us with the information about what it takes to build intelligent

systems depends largely on what we view as intelligent systems.

If we talk of intelligence at human level which can learn and respond in an open ended manner then I

think the information falls way short of requirements. Of course basic idea of emergence can explain

this but we do not have any idea about the rules and parameters under which it will operate and

moreover we cannot build a fully emergent system whose higher level output we can predict in

advance.

However, we can build systems which would have only limited intelligence with no open ended

learning capability as has already been displayed in the development of games using the concepts of

emergent systems. Games like SimCity utilise the idea of emergence, of course with certain

limitations, to create a game where one can design and give shape to entire cities and the city seems

to self-organise itself. The actions seem life like but all those are coded into the rules of the game by

the programmer. From the example of the game we can see that sometimes it makes more sense to

develop solutions whose higher level outcomes are more predictable because that keeps the interest

of the user in mind and can target a specific set of user. In the absence of any such predictability and

time limitations no one would be willing to use it. Again it would be questionable, if the system is

fully Emergent, whether it can be qualified as intelligent if we cannot produce a predictable action –

what if it comes out with a bogus solution?

As has been described by the author the idea of emergent systems can be used in the area of e-

commerce to gather feedback from the users and accordingly tune the offerings to the user.

Similarly, the TV programming can be customised. Internet forums and discussion boards also can be

made smarter that can automatically block spammers and remove degrading posts.

Thus we see it can help us achieve a certain degree of intelligence in the system but to achieve an

open ended human like intelligence using the idea of Emergence as explained in the book seems far-

fetched and also undesirable since there would be no way to know in advance how the system is

going to perform.

In my opinion the author falls short of providing ideas about making intelligent systems if we mean

human like intelligence but definitely the idea of emergence, as explained by him, is usable for

creating less intelligent systems like in artificial intelligence.

Essay

On Emergence by Steven Johnson

What do you think is the most interesting thought or concept proposed by Johnson?

Clearly the most interesting concept of the book is emergence. Emergence is everywhere! I

think that is the most stunning fact. It is not only a theoretical concept or philosophy, but

can be found in all day life, nature, physics, … and already finds its application in software.

For me the representative examples of emergence are an ant colony or a bee hive, a

snowflake, cities and brains. And as technical application software and increasingly the

internet.

As emergence is a very wide expression, I would like to define an emergent system with the

expressions that I consider most important. Emergent systems are bottom up, decentralized

systems with local rules formed by not necessarily intelligent, heavily interlinked agents

leading to higher order (unforeseeable) patterns.

For an explanation let us look at the example of an ant colony. The ant colony is not driven

by a leader or “the queen”. Every ant in the nest works on its own. Neither of the ants is in

particular intelligent, not even the eye lying ant that is often called the queen. Every ant

follows a duty based on the interaction with neighboring ants. Depending how many for

example harvester or maiden duty ants one ant meets it changes its behavior. If it

encounters 100 harvester ants und only one maiden ant it may become a maiden ant itself,

as it figures that there are too few of them. Out of this local reasoning a higher order

functioning ant colony arises, with ant highways to the food sources (marked by pheromone

trails) and an anthill structured into functional areas. And the most astonishing thing is: the

single ant is oblivious to that highly ordered system due to its local perception.

The same works for cities: single households or businesses form a neighborhood.

Likeminded or complementary businesses aggregate at one place. In history a butchers

street, a backers street or a trade street formed. Today a business area or an industrial area

evolves. We humans are conscious to those patterns, whereas in the forming process we are

also often oblivious like the ants. It just happens and is not done on purpose (unless as

sometimes, when it is dictated by a city planner).

But how does that happen? It happens because there is some advantage for the participants.

The customer knows where to go to do his shopping. As more customers come do to the

shopping more stores come to provide goods. More customers come and so forth. This is

called a positive feedback loop.

There already comes in one of the most important features of emergent systems: feedback.

The emergent systems evolve out of nothing, because a positive feedback loop is closed. If it

is not interrupted, the structure grows and grows. For homeostasis a negative feedback

loops has to exist. As in the case of the ants, if there are too many harvester ants, some

harvesters may change to maiden duty as they only encountered harvester ants beforehand.

If they encounter too few harvester ants they may change back again. As such the

percentage of ants on every duty is regulated by feedback.

The keyword feedback brings me back to the book “On intelligence”. The human brain is

actually nothing else than a complex emergent system. The neurons are heavily interlinked.

Learning or remembering only works out, because of this thick interlinking. The more

interlinks the more probable is a feedback loop. So if a neuron fires and its output returns

through a feedback loop, the signal is reinforced. A pattern has been learnt or recognized.

Yet, it is only local interaction or local rules every neuron acts on, as every neuron itself only

fires upon the input of its neighboring cells. Still, the sum of all the neuronal interaction

forms what we call intelligence.

Therefore the human intelligence is also somehow not that much different from the

intelligence formed by ants, cities or as we will see later software. They all have together

that they are able to detect structures or patterns and store them. In the case of ants we

may call it swarm intelligence and in cities maybe neighborhoods. Therefore opposing to “On

intelligence” I would say, that intelligence is also possible outside the neocortex, even if it

looks a little bit different and might not have a consciousness like our brain does.

Does Johnson’s book stimulate thoughts inside of you about what it takes to build

intelligent technical systems?

I must admit, that Johnsons’ book has actually stimulated more thoughts how to be

realistically able to build intelligent systems than both the other books together did. Maybe

that is, because I get the feeling, that we are already surrounded by intelligent emergent

systems and started using that effect on purpose.

Right now I think emergent systems are the most realistic form of intelligence to be

implemented technically as there are already functioning implementations thereof. The

probably oldest human made emergent structures are cities (though back then the theory of

emergence had not yet been known). Nowadays the most used application for emergence is

probably software. Programmers deliberately create software with emerges out of a sea of

software pieces. One example are genetic algorithms. A functioning software bundle evolves

out of many mini-programs without the programmer having to program every step. This kind

of programming goes away from the older principle, where in fact every step had to be

dictated onto the computer. The big advantage of the genetic algorithms is, that they

develop to pretty good, maybe not optimal, but functioning software. And the programmer

does neither have to think up every step, nor do any testing or debugging. Again work gets

more efficient through the emergent structure, as the computer does the work instead of

the humans.

The most inspiring applications for emergent software are for me the rating pages on the

internet. Amazon and Ebay have taken the first step in finding out the users’ interests and

using information given by the users for rating their goods based on emergent software. The

information is created by the many user interactions, ratings and likes on their homepages.

This idea could be taken much further yet.

Just think of the possibility described in the book, that media and advertisement gets

completely personalized. Up to now it has been possible to record TV shows on hard disc

and watch the movies whenever you want. Of course commercials would be skipped either

automatically by the record program or by fast-forward when watching. But how would it

look like, if we were able to “order” which show or movie we would like to watch right now

and therefore get our own, personal TV program. The book says that this would be possible

if the point of “convergence” is hit, when data transfer is fast enough to stream whole

movies in high quality. I say, we have already reached that point, at least in cities with their

fast glass fiber lines.

So where will this end up? Only pay TV in the future, as free TV does not pay off due to lack

of commercial? Well, the other possibility could be free TV with personalized commercials.

The user obliges to watch commercials in their streamed movies and in turn he can watch it

for free. The custom tailoring is done not unlike the ratings and user likings on Amazon.

Emergent software can learn the users’ interests and needs. Consequently, it can supply a

personalized TV program with personalized commercials. A win-win situation. You only

watch what you like including more interesting or even useful commercials. You get your TV

program for free and the advertiser got perfect product placement with lower cost, as it is

only distributed to people to whom a certain ad concerns.

Another area where emergent systems could find their way into real world applications

comes in through the travelling salesman problem. Until recently this problem has been

tried to solve by thinking up always better algorithms. But why not let an emergent

algorithm do the work to find the way? This has been tried and figured out to work well. It

results in maybe not the best solution but better than many solutions before and with much

less development effort. If we generalize the travelling salesman, we come to much bigger

problems: internet routing or the energy flow in the power supply; actually everything that

can be represented by a network graph with nodes and (weighted) branches. We could

solve all these unnerving problems with one simple algorithm: emergence. Set out a couple

of agents and let them explore the field and they will soon be back with a pretty good

solution.

To conclude I can say, that “Emergence” was a very good, inspiring and sometimes also

entertaining lecture. It opened me a new view on a concept that can be applied onto almost

every system in the world. Almost every bottom up and not hierarchically organized system

at least.

Vishwarath Tomar

Renaissance Volume 2 ft. Emergence

Our main theme of discussion this semester namely brain, mind and cognition has taken us on a

myriad journey starting with the proposed (what I internally refer to as) ‘Hawkinian’ theory with all

its invariant representations and cortical hierarchies, right through to the convoluted and often

overwhelming Shapirian analysis of the said theme, this time with embodiment, conceptualization,

replacement and constitution. In essence, the flow till now had been— intelligence seated in brain

and then— intelligence embodied, combined in brain and body. What was needed now was another

theory that would push this intelligence out of the confines of an organic body and into space, a

theory that would truly enable intelligence to break the barriers of conventional wisdom that have

commited the inexpiable act of tangentializing it to something. And that’s exactly where this book

comes in, helping intelligence to achieve exactly what logical extension would have implied.

However having said this, it must be noted that the book mentions its main ingredient as self-

organization and not intelligence but for someone with my background— given the books that I

have read earlier, it is not too hard to see the described connection.

I strongly detest the idea of starting the first chapter of a book with an experiment with slime mold

aggregation. But this according to the author Steven Johnson is the best way to build up a theory of

emergent systems. Basically the idea is that without the need of pacemakers (i.e. slimy counterpart

of present day heads of state) or actually without any external impetus, slime molds begin to

aggregate in the common interest of the whole community as though echoing the motto “ united

we stand, divided we fall ”. The question now is that if not pacemakers then who orchestrates their

congregation. The answer to this question is the stepping stone into the realm of emergence and

self-organization. The fact is that these molds along with other systems like ant colonies and city

neighborhoods are bottom up systems, not top down. The individual agents showcase through their

actions which themselves are truly primitive, a behaviour that speaks for itself by having a

characteristic of its own. An appending question that can be asked to distinguish such systems from

any baseless congregations can be met with the answer pointing out the feature of adaptability in

the mentioned systems. And this is what intrigues us as humans, that how seemingly simple

constituents carrying out local actions, give rise to a collective intelligence.

Johnson in part one of the book goes about as a myth busting crusader, first tackling the myth of

the pacemaker. He mentions the city of Manchester that for most part of the pre 1900 era was

essentially without any representational city council that would have been responsible for planning

and developing the city. Instead the city that was one of the workhorses of the industrial revolution

in the steam powered 18th and 19th centuries, had to the outside world developed arbitrarily

without structure due to the great influx of all kinds of people overwhelming the city. It took the

careful observation of one Friedrich Engels to find real order among all the chaos the city was

notorious for. Even without direct control of a pacemaker of any sort or any legal deliberation, the

working class districts were ingeniously separated from their more upscale counterparts. This had

happened due to simple interactions between Manchester’s residents generating a kind of pattern

Vishwarath Tomar

in city districts that was fed back to them thus amplifying the whole process and transforming into

forms that were to last a lifetime. To further his claim he states through the words of scientist

Warren Weaver that we have simple systems involving a few variables and we know how to tackle

them, we also have systems of statistical complexity and we possess the know how to tackle these

as well, but at least till the late 1950’s the third system comprising disorganized complexity and

lying in between the earlier two systems was only beginning to be understood and that this would

be the road to redemption when busting the myth of the pacemaker. Naturally then, the examples

of how Jacobs’ helped engender the thinking of cities as self-organizing clusters, how Selfridge’s

‘demons’ learned to identify letters (reminded me of the cortical hierarchies) and how Holland’s

genetic algorithm internally decided upon the best code combination, all serve to support his claim.

To put it in a nutshell, there are more paradigm shifts in history brought about by the whole of

humanity echoing the phase changes subtly rather than the single genius moments that change our

perception of the world.

After giving a glimpse into the history of emergence the author in the next section discusses

emergence in current times. The first aspect dealt with is neighbor interaction. In an ant colony ants

switch between tasks like foraging and nest building by paying attention to their immediate

neighbors. There is no bird’s eye view; everything goes on at the street level. Drawing a parallel, we

can also say the same about cells. Although they all have DNA information to develop into any

specialized cell, they develop only into the kind of cell that fits well into the surroundings i.e. by

communicating with other cells nearby. And what better example can one hope for to prove that

this leads to a globally coherent system, than our own existence. Similarly it can be seen that

neighbor interaction is an essential ingredient in the design of a game such as ‘Sim City’ or in the

creation of clusters of businesses in a city—micromotives combining to form macrobehaviour. For

the same reason it is pressed that the threat to sidewalks from freeways and gated communities

should be mitigated as a means to promote interactions and create global order. However to truly

appreciate this global order, to retrieve the emergent intelligence it possesses— it is essential to

analyze it at the scale at which it unfolds. And that can be a problem since the lives of the

constituents of this global order unfolds at a different scale. If emergent intelligence is to be made

readily available to the constituents, where is it stored and how can it be retrieved?

Pattern recognition offers a way to overcome this apparent disability. Emergent systems maintain

their patterns for a lifetime. The body’s immune system comprises of antibodies that annihilate

malevolent viruses and die along with them, the new ones that are generated simply follow the

pattern of swarm logic to know what to do when the next bout of viruses come around. Ditto can

be said of cities that have existed for many centuries and have kept their shapes. Only here these

emergent intelligence patterns have been likened to tradition. Cities are not built with the explicit

aim of functioning as libraries; their latent aim is to provide an interface for information storage and

retrieval. This latency has in fact been replaced by a manifesto in the digital age as the World Wide

Web tries to replace cities as information centers. But the problem with the web is that though it is

self-organizing, it is not adaptive; it still needs search engines. For the web to be truly adaptive it

Vishwarath Tomar

needs to pre-compute and to do that it needs to be self-aware; not like a human, more like the

immune system of that human. A point that matches the aspirations of Jeff Hawkins.

A typical web page points to many others but none of them maintain a directory of who’s pointing

back. So if they are to be self-aware they need to have feedback; we don’t need to go far to find

proof for this, we can derive inspiration from the neural feedback loops, another aspect Jeff would

be in line with. But caution must not be thrown to the winds, not all men are equal, similarly a form

of feedback is more equal than others and should be guarded against. Positive feedback has the

self-amplifying property of blowing stability out of a system. Negative feedback on the other hand

instead of amplifying the system, regulates it; reaching equilibrium in unpredictable conditions and

transforming complex systems into complex adaptive systems. Also its platform agnosticism

enables it to work well in all kinds of situations. But as it goes, too much of everything is bad and

too much control takes out the serendipity and surprise, makes the system monotonous and

boring. The solution is to have a mix of both kinds of feedback systems— medium and message

remains same but by adjusting the feedbacks one can tailor content according to preference.

What happens when control finally descends down to the masses? What happens when there is no

one dolling out orders? What happens when hierarchy ceases to exist? One example can

singlehandedly convince even the hardest of cynics that this scheme really does work (I am one of

the less easily convinced readers and it worked like a charm on me). Hillis’ attempt at sorting

numbers in the shortest number of steps is a landmark attempt in demonstrating the powers of

self-organization. Here instead of a developer doing all the programming from the top, trying to

make the program fullproof, the program develops itself into a potent piece of software following

simple rules of local action and natural selection. So much so that the term ‘developer’ would seem

a bit ill-suited to the programmer who may be likened to an initiator of some sort, like a gardener or

a baker. The age of the control artist it seems has finally arrived.

The final part of the book tries to speculate how a distributed future where personalization would

be as integral a part of our lives as our daily ablutions, look like. But the roots to this personalization

are to be found in our own mind reading capabilities. It is normal to believe that we are expert mind

readers, what’s against natural deduction is the fact that our own self-awareness projects from

reaching out into the mind of others. This thus inserts the missing link in our natural progression,

we first peered into the minds of others to see patterns, then the city allowed us to see patterns

when we could no longer have interactions with everyone, then when the no. of people exploded—

the web came into existence using pattern recognition software and feedback tools to continue this

trend. What’s even more interesting is that the web with all its recognition and feedback tools

developing a sort of theory of our mind, promotes a way to perpetuate the trend by enclosing it in a

circle. Unfortunately Johnson’s vision of the future that he begins to describe is part reality

nowadays. However some aspects like neural net like organizational structures in business to

improve efficiency and incorporation of self-organization in politics to limit centralization of power

have implications that are still to be tested. Nevertheless, the momentum is undoubtedly with us.

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Brain, Mind and CognitionEssay on ”Emergence”

1. What do you think is the most interesting thought/concept proposedby Johnson?

”Emergence” is a very interesting book written by Johnson, which uses the phe-

nomenon of ant colonies as opening to introduce some ideas about building a macro

intelligent system derived from local knowledge adaptability. Johnson mentioned

that the following principles are the lesson that the ants gave us:

• More is different

• Ignorance is useful

• Encourage random encounters

• Look for patterns in the signs

• Pay attention to your neighbours

Johnson thought, that the most important one is: pay attention to your neighbours,

which means, local information can lead to global wisdom.

As we have known, individual ants are not intelligent and only capable of perform-

ing simple actions. However, an ant colony expresses a complex collective behaviour

providing intelligent solutions to problems such as carrying large items, forming

bridges and finding the shortest routes from the nest to a food source. The pri-

mary mechanism is the interaction between neighboring ants in the field. And this

densely interconnected behaviour correlates directly the feedback loops. Thus, in

my opinion, the most interesting concept in this book is: ”all decentralized systems

rely extensively on feedback, for both growth and self-regulation”.

Feedback loops can happen in the human brain, the contemporary media sphere,

the control theory, etc. There are two kinds of feedback mentioned in this book,

positive and negative one.

Positive feedback is a process in which the effects of a small disturbance on a sys-

tem include an increase in the magnitude of the perturbation, which tends to cause

system instability. For instance, the phenomenon of positive feedbacks occur by

ant colonies. As we know, an isolated ant moves randomly, however, when it finds

a pheromone trail, there is a high probability that this ant will decide to follow

the trail. In the meanwhile, it will lay more pheromone on it, if the information is

2

confirmed. Because of the reinforced trail, other ants in the proximities will have

greater probability to start following it and thereby laying more pheromone. And

this process is a good example for positive feedback loop system, because the higher

the intensity of the pheromone over a trail, the higher the probability of an ant start

following it.

And the second kind is the negative feedback and the most automated control sys-

tems rely extensively on it. Negative feedback is a way of reaching a balance point

of the system, even in a changing environment. It can compare the current state of a

system to the desired one, and pushing the system in a direction that minimized the

difference between the two states. The thermostat is a classic example mentioned

in the book, which solves the problem of controlling the temperature of the air in a

room. I am familiar with the negative feedback because I had some classes about

control theory in my undergraduate program. However, I am still surprising that

negative feedback comes in many shapes and sizes, that I never thought about. Like

”ballistic missiles, circuit board, neurons or blood vessels”, even our body is using a

complicated network of feedback mechanisms to keep itself stable in the dynamically

changing situations.

Even in the real life, we also use negative feedback to adjust our behaviour based

on the potential information that we have listened to speech patterns and observed

facial nuances from other people, in order to keep them comfortable. And there

is different levels of this skill. Normally, we can always know that our friends are

happy or not by looking at them. What’s more, some experts can differentiate the

lies from some strangers based on their micro-expression, which only shows up for a

few millisecond. And this is the so called ”self-regulatory social skills”. And those

words, body language, facial expressions are the feedbacks, which play an important

role in the face-to-face world.

Although the phenomenon, that negative feedback has been widely used on many

different area, really surprised me, but the most interesting thought from Johnson,

that inspired me, is the mix of negative and positive feedback.

The web Slashdot.org is an example, which successfully used a mix of negative and

positive feedback. Because of the growing number of daily visitors, Malda made ev-

eryone a potential moderator, instead of only twenty-five of them before. He pushed

the site toward the state without any single individual being in control, and from

a certain angle, keep the system stable. The Slashdot-style new media, which used

the rating system, took a big step, because they have found the appropriate rules

for them.

The rules can be considered as a mix of positive and negative feedback pushing

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the system toward a particular state. The hard part is to find the right place and

ratio to use them. If the ants use negative feedback to lay the pheromone, the prob-

ability, that other ants follow the trail, is always the same, and ant colonies will be

only capable of performing simple actions, just like an individual ant. Likewise, if

we use the positive feedback to control the temperature of a room, will only cause

a warm room even warmer.

2. Does Johnson’s book stimulate thoughts inside of you about whatit takes to build intelligent technical systems?

Yes, Johnson’s book really stimulate thoughts inside of me.

The feedback system in our brain is also introduced in the book ”On Intelligence”

written by Hawkins. He mentioned, that human brain has actually more feedbacks

than inputs. And before each prediction, our brain is able to use the previous results

as feedback to adjust the current or next prediction.

After I read this, it was the first time I have realized, that feedback is an important

part for the intelligent technical system and I need to adjust the result by using the

negative feedback to minimized the difference between the actual and designed state

and keep the system stable.

However, when I finished reading the book ”Emergence”, I have some new un-

derstanding of the feedback system. In order to build a self-regulation system, it is

important to mix the positive and negative feedback and push the system toward

each designed state. For instance, we can design several states and each time when

we found a ”food source” and the information is confirmed, the system can lay some

”pheromone”, in order to be pushed to the next state. In the meanwhile, the system

can use negative feedback to stay in or near the desired state. Thus, how to design

each state and right conditions for the next one, is the real art.

Johnson also gave us some suggestions to build the emergent systems, like ”fig-

ure out the specific rules of the system at hand and start thinking of ways to wire

it so that the feedback routines promote the values we want promoted”.

However, a true emergent system contains not only the feedback, but also struc-

tured randomness, neighbour interactions and decentralized control. It seems like

there is still a long way to go, until we can build the truly intelligent system. But

I have faith, because there are so many scientists working on this area. Each of us

is much more intelligent than an individual ant, so why can’t we build our truly

wisdom scientist colonies?

1

Essay on “Emergence” by Steven Johnson

Self-organization is one of the ideas that struck me at the very first time I read about it. This idea

constitutes a basis for understanding, or at least a methodology to understand different

phenomena in different scales, without leaving the domain of natural sciences. It also helps natural

sciences to keep their status as natural sciences, as it prevents them trying to attribute “intention”

to physical phenomena we observe in different scales, by showing that the macroscopic behavior

that looks like intentional to us is indeed a result of its behavior in a smaller scale.

The enormous organizational scheme I see around has always surprised me. The biology classes

that I took led me to question the language that is used there. “Intention” was everywhere.

Leukocytes seek and destroy the possible enemies of the body, some plants bend towards light,

some others use flowers or fruits to breed; cell membranes control the incoming and outgoing

materials according to the needs of the cell, lysosomes digest old organelles to keep the cell

functional, etc. How come they act so purposeful? Do they know what they are doing?

The documentaries that I watched about different animals also showed me very interesting

organizations and customs in animal societies, very different “intentions” among them. The very

different social systems of primates or lions and how we can find a meaning in their behavior

amazes me always. For instance, a male lion kills all offspring of his male opponent, if he wins the

fight against his opponent and becomes the only male of the lion group. If we try to understand this

situation in terms of the evolutionary aspects, there is an explanation to it: The offspring of the

strong one survive. But, the reason that the winner of the fight kills the ex-male of the group is

different: Female lions do want to mate when they have no children. That is to say, the motivations

of the male lions and the evolution are completely different. Lions’ drive to sexual act creates in

result a system where the best offspring survive. And we can follow the other way: Evolution

chooses the most powerful ones and the weaker ones are eliminated during this selective process.

Another instance of self-organization is to be found in a smaller scale: The emergence of life from

molecules. Somehow some molecules came together and a chain of reactions that preserved and

replicated the structure and the dynamics of this mixture occurred. This newly created pattern is a

mere result of the basic interactions between the elements, but the pattern that emerged is more

than its constituents. The interactions between the basic elements caused a higher order structure

that has effects on its basic elements as a result. This loop is one of the key features of a self-

2

organizing system. Human body and the cells it contains may be a good example at this point. The

main constituent of the human body is cells. This organization of cells as a body has then serious

effects on its constituents. The body has an amazing structure that nurtures the cells and creates a

convenient environment for them. If that wasn’t the case, i.e. if the organization of the cells did not

offer a sustainable life for the cells, the organization would have fade out or just didn’t come into

being. That is to say, that evolution is at work also in the smaller scales: Only the fittest

organizations survive. And fit to what? To its environment.

So, all we have is some kind of elements in different scales that organize and give rise to patterns in

higher scales. Then, the existence of a pattern is the proof of its constituent elements’ success in

preserving their existence. This is the point, where we see an “intention” in all the process. Here we

leave the physical laws that explain the interactions between the elements behind and observe

something at a different level than these “dead” physical laws, something “meaningful” and “alive”.

The “meaning” that arises as a result of self-organization is what appears to us as being

“emergent”. It looks like for me that this “meaning” is the relation between the self-organizing

system and its environment, i.e. where it distinguishes itself from. So, meaning is a result of the

evolutionary process of self-organization. As the time goes by, different environments in different

scales and different self-organizing systems that survive in these environments arise. At this point I

think that Danny Hillis’ solution to programming problems is a good imitation of what the Universe

does: Tries all possibilities that could solve the problem. The difference between the Universe and

the genetic algorithms proposed by Hillis is that it’s the Universe that also creates the problem, that

is to say that it tries to solve the problems that it itself created. For instance, it is the Universe that

created Earth and proposed the question of life and found the solution by trying the combinations

of molecules that gave rise to cells and from cells to animals/plants and so forth.

Trying all the possibilities to solve a problem that concerns a system, puts randomness in a central

position. As in the example of ants, trying other possibilities benefits the system a lot. This

randomness comes from the environment of the self-organizing system we have. In this fashion,

the system can get some more information about its environment and adapt better to it. Stopping

my ruminations here, I would like to shortly sum up my thoughts about all this emergence/self-

organization subject: Evolution is the most fundamental property of the Universe. By putting its

elements in interaction, the Universe creates different environments and self-organizing structures

3

that try to survive in these environments. This experience that occurred between environments and

structures in them is what we call “meaning”.

This book stimulated many thoughts in me, obviously. The examples from different scales (ants,

cities, computer science, media, etc.) reinforced my thoughts about self-organization as a

ubiquitous property of the Universe. Among the others, two discussions were great importance to

me.

First, the result of the sharing of CNN news feed with local affiliates was a very good example of a

“system event”. The media system changes its organization and this change is not a smooth one: It

is discontinuous, or emergent. The smooth changes in the system that do not change the

organization qualitatively remain unnoticed. But, after the system is forced to change its

organization scheme to adapt to its ever-changing environment, something remarkable occurs. This

startles everyone and is therefore called to be “emergent”.

Second, the sentence “To be self-aware means recognizing the limits of selfhood.” (p. 200) deserves

mentioning. This idea of differentiation between the self and the environment is what I find the

most exciting in the book. This is precisely what self-organizing systems do: They draw a boundary

between their environment and themselves. And if something can recognize its environment as

being outside, then it can recognize its own identity as separate from its environment, i.e. self-

awareness emerges. This may be a good explanatory touch to the problem of self-consciousness. Of

course, then self-consciousness would be a ubiquitous property of the Universe, and evidently

humans wouldn’t then be the only self-conscious entity in the Universe.

The parts on the video games and the Web were in my opinion much longer than required for being

informative. I would have preferred to read more about the instances of self-organization in nature

than the details of the video game industry. Nevertheless, I learned something from those pages.

Now comes my thoughts as an engineer: Emergence is an idea that is completely contradictory to

the engineering education. Bottom-up designs may give unforeseen results at the end and these

results may not always be desired. For that reason, I would approach carefully to the problems that

involve serious risks. Bottom-up designs can be very helpful though, if we keep in mind that they

may give unwanted or misleading results.

Essay:  

„Emergence: The Connected Lives of Ants, Brains, Cities and Software“

„ Emergence is what happens when an interconnected system of

relatively simple elements self-organize to form more intelligent, more

adaptive higher-level behavior.”

- Steven Johnson

I have found the book to be very interesting, especially the first part

where the author talks about emergence, the movement from low-level

rules to higher-level sophistication. The book is an interesting survey of

the phenomenon of bottom-up, disorganized organization that we see in

multiple areas of life, anywhere from the molecular level to society as a

system to computer gaming. Even if the part in which he talks about the

internet is out-dated it was still funny to see how he saw the internet

twelve years ago.

Johnson’s concept of emergence and the way in which he explained his

vision by showing example from different spectrums: animals, slime

mold, organization of cities or programming, seemed very strange at the

beginning. I see the concept of „self-organizing“ applied to this

different categories as very interesting, mostly because at first I couldn’t

see the connection between them. By reading the book I have started to

build the connections and understood what they all had in common: self-

organization and building new things.

I think something very important we, as people, can learn is to not

always think about what we have to do and if we are allowed to do it or

not. In order to be irreplaceable one must always be different.

For that one should be able to think outside the box, be more creative, be

daring and implement new things.

If we always follow the same pattern and never think out of bounds we

will not be able to adapt more quickly to the specific and changing needs

of the environment. Remaining to the term of the book I could say:

Sometimes we should allow our lives to be structured as non-authoritan

bottom up systems.

Johnson gave some good principles (inspired by the ants) in order to

model self-organizing systems:

- More is different: In order to observe the global behaviour you

have to observe the whole system.

- Ignorance is useful: If you miss some data samples out of a big

sudie it won’t make such a big difference

- Look for patterns in the signs: Ants respond the the frequency of

ant encounters and the gradient of pheromone trails, not to

messages from individual ants.

- Pay attention to your neighbours: Ants rely on the signals sent to

them by their fellowers

We mostly expect disorganization from organisms which are constructed

without pacemakers or leading components, but instead we find patterns

and working systems.

The book has shown that a system doesn’t always need a leading figure in

order to function. Even if in the ants example there is a queen, she

doesn’t activly interfere with the work of the system. I also want to put

the accent of „always“ because having rules about specific things proved

to be good, as we can see if we look around us: clean cities, civilized

traffic, etc.

Another interesting example is the book was the one about the silk

weavers of Florence. They have been located at the same location for

decade because they know from their ancestors that this was the best

place where to have their businnes.

I think that an important factor, which we should pay attention to, when

building intelligent systems, is implementing a feedback system. It

proved to be a right approach. Emergent systems are based on local

information and the amount of feedback over a particular period of time.

Using a reaction which reduces the condition responsible for the first

action, inducting the reaction, proved to be a right way to handle and

regulate systems (e.g. negative feedback in air-conditioning). It reduces

the need for repeating the first action which in turn reduces the reaction,

leading to a fixed point.

Another thing I learnd from the book is that we sould rethink our

approach about complex programming tasks in a more hands-on way and

not always think about a complicated solution. I have said before that

sometimes it is good to try to analyze a situation from another perspective

(e.g. Hawkins idea to understand brains before computing intelligent

systems). In his case it turned out to be a right approach, even if at the

beginning scientist didn’t agree with him.

I can say that I liked Johnsons book: the examples he gave were rnice and

easy to understand. The written style he used was also more appleaing to

me that Shapiros’. Not using (for me) unintelligible expressions and

following a clear target played an important role for the way in which I

perceived the book and like „On Intelligence“, it also awakened in me a

lot of thaughts and questions. Human intelligence is not the only one

existing and of course it can always be improved. We can always learn

from other organisms, even if they are more privmitive then we are, just

by stopping and watching what the nature build around us.