Complexity science is the study of comprehending phenomena as a whole and looks for universal patterns that exist between them. This differs from classical science which conducts studies on phenomena closely with the purpose of explaining events and things.
The following synopsis is a description of methods in which disciplines which may seem not to be related with each other can be combined to reveal previously concealed relations between them.
The complexity comes up from common association and conducting research on this is able to assist in solving difficult problems.
If you did not put much thought into it, you would probably concur with the notion that that traffic jams and financial markets are complex things.
However, if you take the time to contemplate over it for a longer period you’ll find out that complexity is pretty difficult to define. As a matter of fact, even the scientific community faces challenges while attempting to define the term.
Nonetheless, the writer has created a definition of complexity science as the study of phenomena which emerge from a collection of interacting objects. An everyday suitable real-life example of this could be a crowd because it comes up from a group of people who are interacting.
As an ever-occurring figure in life, complexity is exhibited in our day-to-day experience when things or humans compete for resources such as space, wealth or more importantly food.
Take for instance a group of financial traders who when they’re trying to make a dale, compete for buyers and customers, or when drivers are stuck in heavy traffic they compete for space on the road. Moreover, even a cancer tumor can be viewed as a “war” where cancer cells and normal cells fight for space.
Competition could at times go askew and lead to conflicts which result in market crashes.
The emerging complexity dictates a problem that complexity science can help bring a solution to. This is the benefit of complexity science: utilizing ideas from different sciences such as biology, sociology, and ecology to solve problems by making up otherwise hidden connections between complex systems.
Once we unravel universal patterns in a complex system in one field of science, we are able to accelerate our comprehension of complex systems in other areas. This enables us to find a solution to the challenges that are derived from them.
Therefore, complexity science still being in its growing phases does not hinder its range of potential application to different fields. As a matter of fact, its application areas are vast, which could make it extremely important in our day-to-day lives.
The behavior of a complex system is spontaneously altered through the response of data it receives.
The phenomena that come from complex systems make it a very interesting subject. These phenomena, which includes market crashes or traffic jams, can simply happen without the influence of control or coordination. In fact, the gathering of items, or humans, is actually self-disposed. This makes the phenomena seem to pop up as if they did so by a spell.
Changes within the complex systems can be inspired by the complex system itself. This means the complex system changes from its own actions, which ranges from reasonably sporadic to quite extremely random.
Sticking to the traffic jam example, we can see that traffic jams come up at a particular time and place and then gradually but spontaneously disappear. For a large number of cases, there is no obvious cause for their appearance or disappearance.
So what makes complex systems sway to and fro between ordered and disordered behavior?
The answer to this is the memory or feedback which affects the actions of the objects within a complex system.
In this situation, by feedback, we are referring to an event in the past which affects something in the present. Or something occurring at one place which then affects what is happening at another.
For instance, if someone is used to driving home on route A for the previous few nights (a past event) and the traffic was horribly bad every night, they could choose to drive along route B tonight instead (a change of mind in the present informed by the past events).
The reason why complex systems are intricate is owed to feedback. Feedback has the ability to create order and disorder. Considering that this data is abstract, and drivers or financial traders consistently get information about their own and others’ behavior, traffic jams and market crashes can come up without any clear reason.
The complex system hence appears “alive” due to the intricate relationship between the objects, or agents, within the particular system.
Although they may seem similar from the first sight, complexity and chaos are not identical.
Oftentimes we encounter the words “complexity” and “chaos” jointly, which could give off the misleading idea that they are basically the same.
However, this is not true. Chaos, as a matter of fact, can be an outcome of complexity in that it is a certain example of a complex system’s output.
When we talk about a complex system’s output we are referring to a number that is made up by a collection of objects. For instance, in finance, the output could be the price or value of a stock in a market at any particular moment.
So what is the position of chaos in all of this? Its position lies in that chaos happens once the system’s output varies so widely that it seems to be random.
As a matter of fact, the unstable market values we observe in the news could well be chaotic – even though they need not be.
Despite showing chaotic behavior, complex systems also have the capability to display periodic or even static behavior. Therefore, we can conclude that complexity does not implicitly mean chaos. Chaos can be defined as complicated and not complex because even systematic rules can create chaos.
A good example would be an office worker whose daily task it is to shelve and organize files by constantly applying a complicated mathematical rule. Once the number of files and shelves increases, so does it become even increasingly challenging to deduce the rule.
Therefore, for the office workers who are not aware of the rule, the whole shelving system appears chaotic.
A complex system, nonetheless, is more intricate than the behavior created by applying a mathematical rule or formula repeatedly. The interaction that takes place within the complex system is what really makes it complicated. This, coupled with the method in which it shifts between different behaviors caused by feedback.
Thus, it is a true conclusion that chaos does not necessarily imply complexity. And neither does complexity imply chaos. So the two cannot be identical.
Gatherings of people tend to behave identically, despite a crowd being complex.
A crowd or a group of people can be identified as a complex occurrence. This is because a crowd comes from a gathering of humans, and humans usually have intricate preferences, actions, and ideas. However, it can be seen that the various and different ways in which we are complicated as single individuals might not be so vital when it comes to group situations.
Interestingly, when we are in a crowd that is sufficiently large, our individual distinctions actually cancel each other out.
If we make an attempt to explain the intricate life of somebody such as Winston Churchill, it could take a very long time. This is despite the fact that a randomly selected group of similarly famous and successful people would probably behave identically to a group of the rest of us.
A good example would be reality TV programs such as Big Brother and Celebrity Big Brother. Both these programs display groups with characteristic human group dynamics in spite of the “individuality” of every single member. As a matter of fact, one might expect this to be even more apparent in the celebrity edition of the show.
This example can go a long way in helping to explain how group behavior – in financial markets, in the middle of a traffic jam or at war – can be extraordinarily identical. This is despite geographical, language or cultural differences.
Moreover, opposing personality types behaviors gravitate to nullify each other in groups.
Take the following situations as an example. Picture yourself on a Friday night wanting to go for a drink at a specific bar. You would only go to the bar if you can get a seat, otherwise, you would prefer to stay at home. Should you go or not?
The truth is a majority of us face the same problem. Not necessarily with going to the bar but the situation as a whole. What happens in this specific ‘bar case’ is that people will make their decision to either go or not go based on their particular individual history of success in getting a place to sit.
To further break-down this situation, the bar-goers can be separated into two groups: those who think the same scenario – getting a seat or not – will happen again and those who think that the opposite will happen. Therefore, when we look at both groups, we see that their actions eventually will cancel out each other.
The same can be applied in the commercial market. The number of buyers who choose to make a purchase at any given moment will nullify the sellers who opt to make a sale. Therefore, the groups of traders in a single market tend to behave identically to groups of traders in another.
Comprehending the complexity of network behavior has the potential to be a life-saver.
As humans, we are social animals who constantly create personal relationships and form alliances and affiliations.
To explain it in a simple manner, we make networks with each other. Within our very own created networks, we have the knowledge of who is connected to whom, and hence we know who are interacting with one another and what their interactions are.
We are encompassed by a variety of networks on a day-to-day basis.
For example, there are transportation, information, social and voting networks. What specifically defines a network is a group of nodes that are interconnected by various links. When considering a social network, for instance, we can observe that individual people make up a set of nodes and their interactions form the link between them.
A good example of complex systems is networks. In networks, feedback is a vital piece of complexity. This feedback could be in the form of a memory or information from various points within the network.
Therefore, we can see that the role that a network plays is a central one in giving back information from one side of the population to another. And in feeding this information back from one part to another, they create complexity.
Moreover, social networks are very much equal to a collection of competing and interacting objects. This makes social networks obvious illustrations of complex systems.
Further studies and research are being done on networks due to the fact that studying the behavior within networks can actually save lives.
Complex systems in biology also show the behavior of a network. For example, nature makes use of networks to provide life-giving nutrients, like the pumping of blood and nutrients through networks of veins and arteries in the human body.
A lot of aid can be offered to medical practitioners by having a good comprehension of the nutrient supply network.
For example, in the diagnosis and treatment of cancer tumors, and in treating disorders such as an AVM (arteriovenous malformation), an understanding of the supply network of nutrients would be quite helpful. This is because AVM is a disorder where the brain suffers from a nutritional deficit due to shortcuts in vessel networks.
Furthermore, a specifically important network involves the transference of viruses. In this particular situation, it is just as vital to comprehend the biology of the virus as it is to comprehend how it moves within a network of people since this can end the growth and escalation of the virus.
Using complexity science is a better method to explain the actions of the commercial market than the normal prediction model.
The traders, or “individual objects” – in any particular market – are each trying to forecast the price changes for the purpose of decision making. Either for the buying or selling of stocks, products or services.
However, there is one notable difficulty. This is the fact that commercial markets are intricate dynamic systems that are consistently being altered in ways that evade most market specialists.
This owes to the fact that the standard prediction model for commercial markets contains errors. The standard prediction model utilized by the majority of the commerce world to foresee market trends and behaviors presumes that the price fluctuates similarly to the toss of a coin – prices increase or decrease with a probability of p=0.5.
But this is not the exact situation since commercial markets are complex systems and thus cannot be adequately explained by anything other than a theory of complex systems.
Hence, even though the standard finance theory might be effective over a short period, it is more than likely to break down at some point. An adequate example is when wild fluctuations occur in the market because of crowd behavior.
In fact, a foolproof prediction model for financial markets does not exist. Even in the presence of a “perfect” prediction model, it would eventually stop being perfect due to the amount of feedback in financial markets.
Simply put, we would end up distorting the market by using this “perfect” prediction model to inform our next trade.
Markets are neither predictable nor unpredictable at all times – this is what we can deduce from complexity science. Just as all complex systems do, commercial markets sway in between order and disorder.
This informs us that markets have patterns where they are non-random and predictable, and periods where they are random and thus unpredictable. Therefore, it would be wise not to depend on prediction models that everyone is utilizing and, on the contrary, accept that commercial markets are often totally unpredictable.
Dating is complex, but complexity science informs us that we can still find our perfect partner.
Finding the right partner for yourself can be a difficult challenge.
First of all, our other half needs to actually be alive at the same time that we are and, secondly, the timing has to be perfect – meaning we might want to with someone who was not the right one for us in our past but would be now and the opportunity is missed. As we can clearly see, dating is complex!
Moreover, there’s another obstacle that we must triumph over in our search for the ideal mate. This is the fact that we are not the only ones looking.
Because many of us are simultaneously searching for that special someone for ourselves, it puts us in competition with others for something.
Dating is a complexity since each one of us constitutes part of a group of decision-making “objects” competing for a limited resource, in this case, a significant other. But do not be hopeless just yet. Even though a lot of us make up lists of what we want in a romantic partner, complexity science displays that there is still hope in finding a mate.
This is because of increasing personal sophistication. That is the number of partner preferences, does not lead to an increase in single people.
Two complexity scientists, Richard Ecob and David Smith, tried to tackle relationship questions while utilizing the perspective of a complex system. They made use of computer simulations to see how we act as we interact in our social networks as we search for the right partner.
A listing of personal preferences was handed to each person. This included individual preferences such as “likes jazz, likes spicy food, doesn’t like museums,” to determine the compatibility of possible partners.
Outcomes from the research displayed personal sophistication had little significance on the ratio of singles to non-singles in large populations. This goes to show that it is not impossible to meet your perfect mate even though your preferences may be getting more and more refined.
Complexity science can assist us in comprehending wars despite them being complex.
As humans, we have the potential of forming complex systems that are violent, equally as we have the ability to form complex systems that are simply as regular as the ones previously examined.
An evident example of this can be war. War can be described as a complex system because various groups of people are simultaneously fighting for some kind of benefit, usually an exhaustible resource, such as land, or political, social and economic power.
When more than one side is fighting in the same war, the fight becomes even more complicated. The result of this is increasing asymmetry in warfare.
Take for instance the conflict in Colombia which involves the army, guerilla groups, and paramilitary groups. These individual groups could decide on maintaining their individuality or ally with another force which makes this type of war particularly intricate.
But if we apply complex system analysis we can better understand the war.
Take for instance the groupings of people that we have seen who are probable to behaving identically to one another. This shows that there is also the existence of these kinds of behavioral patterns in war.
Research teams in complexity science from the University of London and the University of Bogota in Colombia recently carried out an examination of attack and casualty information on several contemporary wars, not excluding those in Iraq and Colombia.
What they found out was that the patterns of the wars in Iraq and Colombia which looked unrelated were actually the same at the moment. Moreover, the everyday attacks in Iraq are happening in a more orderly manner than we would anticipate for a random war.
The implications of these outcomes are that the way in which wars happen has less to do with geography or ideology and far more to do with how human groups interact.
Comprehension of these group dynamics and unraveling of universal patterns in war can equip us with the aspiration of resolving them.
Simply Complexity: A Clear Guide to Complexity Theory by Neil Johnson Book Review
Despite complexity science still being in its growing phases, it has several applications in the real world which allow it to gain the possibility of becoming such an important and influential science. Challenges faced in society and the real world, such as traffic jams, market crashes, pandemic viruses, and even warfare, can be comprehended better and potentially resolved by insights from the complexity theory.
In addition to this, we ought to abstain from utilizing the same commercial market model as everybody. This owes to the fact that if everyone utilizes just one model, it will result in major feedback in the market and actually reduce the value of the stock rather than assisting you in your money-making ventures.
And when it comes to dating and searching for the perfect mate, do not be worried about being too choosy with your preferences. As a matter of fact, boldly specify a long list of your personal preferences. As shown by complexity science, personal sophistication has little to no effect on the ratio of singles to non-singles in a society. Therefore, you can still find your ideal partner no matter how choosy you are!
