Firstly, let’s begin by describing the distinction between replicate and duplicate. Afterward, we will address the most perplexing aspect: the meanings of reproduction and the differences between reproduction and replication.

Replicate vs. Duplicate

“Replicate” and “duplicate” are very similar in meaning and, in most cases, can be used interchangeably. However, “duplication” carries the nuance of copying, while “replication” is closer to recreating the same thing with a separate identity.

For example, when you make a copy of your license or obtain a second one after losing the original, you then have a duplicate of it in your hand. Similarly, when you read an argument in a book and republish it on social media, you are replicating the original content.

Here are a few examples of using “duplicate” (copy) in sentences:

– How do you duplicate a key? (Make a copy of your key)
– This application takes up too much space because it duplicates all the songs on my hard disk and stores them again in its directory.
– With the ever-increasing volume of data, duplicate detection is an essential feature in dataset management systems.

However, when you read a study and attempt to recreate it precisely in the same way, you are replicating it. It’s termed as replication because it maintains a separate and independent identity despite using the same methods and procedures. You are either confirming or refuting the original study with your replication study. Therefore, in this context, the research is being replicated and not duplicated.

Reproduce vs. Replicate | Examples from the IT & Software World

You can guess the difference if you are working in the information technology and network infrastructure field. There are hardware devices called replicators, whose function is to receive a data packet and deliver it elsewhere. The receiver collects packets in such a way that there’s virtually no way to spot the difference between the primary source and the replicator. Replicators, also available as software, are used for long-range data transmission.

Now let’s talk about reproduction. Again in the same context: software and information technology. You always hear from software developers that they are trying to reproduce a bug. What do they mean by that?

They mean that the user has faced a problem while working with the software. And although they don’t know the reason behind the problem, they play with the code and the interface, hoping that the problem will happen again and they can analyze and solve it.

So when we are talking about reproducing a bug, we are not going to make exactly the same problem again. There can be minor differences, and reproduction would still help detect the bug’s source.

Reproduction, in this sense, means the process of creating a very similar but not exact copy of something.

Having the above example in mind, now you understand why we say humans and animals can reproduce themselves. We don’t use the term replicate, as the children are not precise copies of their parents.

Let me restate the difference between replication and reproduction in other words:

In replication, outputs are exactly the same as the inputs but in reproduction, outputs are just similar to the inputs.

Here you can see a few examples of the word reproduce (process of building a similar copy) in different sentences:

• The concert will be reproduced on compact disc (It’s not the concert, but it’s a concert anyway)
• Let me reproduce that scene in my mind (It wouldn’t be the same scene, but very close to reality)
• Skin cells reproduce too quickly (Make new cells very similar to themselves)

Reproduction and Replication in Biology

Just imagine if every single cell was replicating itself or if all of the species were replicating themselves. There was not any kind of evolution on the planet: No change, No improvement, No growth, No chance for the emergence of the new species, No life!

As I have emphasized in the definition of the living entities:

But is the story as simple as it seems? Unfortunately not!

In the core of every living cell, RNAs just replicate themselves:

If there was not any minor error or mutation in replication at the lower levels, there was no chance for the emergence of reproduction in the higher levels. RNAs and cells are genderless. The concept of the gender just appears in the higher and more complex levels of the hierarchy as a trick to get more distance from replication and get closer to the reproduction.

Considering reproduction vs replication we can say every living entity has to find some way to get closer to the reproduction concept.

This is one of the reasons I am so optimistic about the social networks as a trick for making the cyberspace more alive. Comparing them with the printed content and even digital contents in the websites, social networks are more prone to human and technical errors in quotation and replication, which can move the digital world one step further from the simple mechanical replication process.

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In my approach to the living entities and the articles about living things and cellular organization and predictability and the living things, complexity was considered as the most important aspect of a living entity usually associated with existence of some kind of living spirit by primitive cultures.

My introduction to the concept of emergence and coining of the term inferiomorphism has some roots in my mindset based on the complexity of the systems.

So it might be a good time to bring up the complexity concept seriously and discuss it as clear as possible. I would start with the definition of a complex system and will elaborate on the technical terms in future articles.

Sure my definition, is not the most comprehensive or accurate one. But would be sufficient for a preliminary introduction. In future, we will study each of the following terms in more details.

So let me introduce a very simplified and basic definition of a complex system:

A complex system is a collection (or population) of many different entities with the specific properties and basic behaviors interacting with each other as well as the environment. When the population of the entities grows enough, there would be hard for the observer to predict the behavior of the whole system, even assuming that we know all the basic properties and behavioral rules of the entities. At this stage we have no pre-assumption about autonomy of the entities as we will see that the autonomy is a label for the complex systems beyond our full understanding.

Having the above-mentioned definition in mind, every society can be considered as a complex system made of humans.

Every human can be considered as a complex system made of organs.

Every organ can be considered as a complex system made of cells.

Every solid or liquid material can be considered as a complex system made of molecules and atoms.

In other words, with this approach, biology is nothing more than the behavioral study of a complex chemical system.

In the same way, psychology is nothing more than the behavioral study of a complex biological system.

And sociology is nothing more than the behavioral study of a complex psychological system.

The same framework can be applied to the entities with higher or lower levels of complexity. This is the reason that the complexity theory, accepting our ontological and epistemological limitations, can be considered as a theory of everything.

You can consider the complex systems somewhere in the middle of the chaotic systems and the mechanical ones. In a chaotic system, any small disturbance can lead to a major disruption and the system can not be considered as stable or even as an integrated entity. In a mechanical system, on the other hand, everything is predictable and response of the system to the external stimuli can be predicted with an acceptable precision.

Most of the words I used in the last sentences are inaccurate ones. So in the future articles we will try to define them more clearly. It may take some time, but it’s worth the effort.

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I have coined the term inferiomorphism by myself to emphasize on one of the greatest mistakes we’ve ever made.

When I was talking about the practical definition of emergence, I’ve noted that the reductionist approach to the world is absolutely against the emergence concept.

Here’s the simplest definition of the reductionist approach:

You can divide the whole into the parts and study them, without losing anything important.

Let me rephrase the above definition:

You can sum up the parts and make a whole new entity, without emerging any new characteristics or properties.

You may think of a brick wall as the first example. We put bricks together and build a wall. Are there any new characteristics emerged? You may say yes. Now we have a wall but we had nothing more than a pile of bricks before. Someone else may say we have nothing new. There is still a pile of bricks with a new structure.

Such a discussion will lead nowhere as it’s a debate over the meaning of the words.

But let’s think about another example which dates back to one century ago when Boltzmann and his colleagues were working on the atomic theory and statistical mechanics. The atomic theory and his mindset was too early for his time. So he depreciated himself through long and strong debates with the contemporary philosophers and the governing paradigm. He finally hanged himself as a result of a depression attack  on September 5, 1906. But now we know that his valuable studies can be a vivid example of the emergence concept.

Just imagine the pressure or temperature of a gas. Can you define the temperature or pressure for every single molecule? Sure not. We can’t imagine the concept of pressure and temperature for a single molecule. These characteristics just emerge by the interaction of a large group of molecules with each other.

Pressure and temperature have a fundamental difference with the other physical concepts like mass. As the total mass of a gas is the sum of the masses of its molecules. But pressure and temperature are not sums of the atom properties. Pressure and temperature emerge from the interactions of a large group of atoms and molecules.

From now on, I will frequently use the following terms: agent and superagent.

A superagent is a new entity emerged from the interaction of many similar agents with each other.

So, molecules are agents and the gas is a superagent. Organs are the agent and the humans are superagents. Humans are agents and the society is a superagent. All the firms and government and families are agents and the whole economic system is a superagent.

So the agent and the superagent concepts are completely relative. Every single agent can be a superagent for the lower levels of the hierarchy and vice versa.

The one who has the emergence mindset and a holistic approach to the world, would be able to look for the new characteristics of every superagent and characteristics of the agents in the lower levels of the hierarchy, independently.

Just think about your society and try to describe it in few sentences. Would you call it outgoing? or emotional? or rule conscious?

As you see all of the above-mentioned characteristics are based on our understanding of the humans as agents not the society as a superagent. These descriptions are more similar to the concept of mass, not the concept of temperature.

This is the mistake I would like to call inferiomorphism. Thinking about the superagent with the concepts which are just applicable to the agents.

You can see the result of this misleading mindset in various fields such as religion, science, technology, and philosophy of the digital age.

In the future articles, we will think about the approaches which try to go beyond this limiting mindset. The most complicated challenge would be trying to understand the superagent when we ourselves are the agents.

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Scientists observe the world. Develop theories based on their observations. Validate their theories using existing data and finally, use these theories to predict the future.

The science without the power of prediction is history at best and story at worst. Just suppose someone tells you that all the waters on the earth were boiled at 100 C till now, but we know nothing about the future!

In this sense, something like statistics would not be part of the science as it describes what happened and predicts the future just with the ceteris paribus assumption. Such an assumption will not hold true for most of the complex systems.

Statistics can be considered as a tool helping scientists to compare theories with each other to see which one had a better prediction power. That’s all.

Actually most of the spiritual and metaphysical descriptions of the world are descriptions of the past rather than predictions of the future. They are able to vividly and firmly describe the reason behind every past event but have almost no power in predicting what will happen in the future.

As one the most complicated species ever existed on the planet, seems that our next aim shall be developing a mindset primed for predicting the future instead of merely describing the past.

Sure most of us are interested in future. The long time existence of fortunetellers from Oracle of the Delphi up to today’s palmists and phrenologists proves that there has been kind of inclination toward predicting the future. But it’s a great difference between the people with the predictive mindset and the ones with descriptive mindset who believe they are able to predict the future.

One of the best business cases would be market researchers. Most of them just provide us with the description of the current preferences and behaviors of the customers based on some statistical reports and convince us that these reports are the most reliable roadmap to the future. Sure if nothing radical happens, this kind of analysis can be valid. But why we need such an analysis if there’s nothing radical going to happen?

Sure there’s not a clear dividing line between descriptive models and predictive ones, but it’s not so hard to compare them from this aspect. The predictability power of the models also depends on the subject of the study. Sure physics can claim a more power of predictability in comparison to the psychology. As the later on studies a more complex subject.

In general I believe that for thousands of years, most of the models proposed by humans for the complex systems such as the universe, the nature, the weather, the society and themselves, has been descriptive in some sense and the future belongs to the models which are aiming at the prediction of the behaviors of these complex systems.

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The concept of randomness seems obvious at first glance. Random and Randomness are so popular in our daily talks which we never think about their exact definition.

Let me start with a simple question. Which of the following series of the numbers can be called random:

1  0  0  1  0  0  1  0  0  1  0  0 1  0  0  1  0  0  1  0  0  1  0  0 1  0  0  1  0  0  1  0  0  1  0  0

1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1

1  0  1  1  1  1  0  1  1  0  1  0  0  0  0  1  0  1  1  1  0  1  1  0  0  0  1  0  1  0  0  0  1  1  0

Most of us consider the third-row as a random series of bits. The second-row doesn’t seem a random serie and the first-row is something in the middle. Not as random as the third-row and not as ordered as the second row. But sure it has a pattern inside. It’s just a ‘1  0  0’ repeated 12 times.

Now let me tell you that all these 3 rows are the result of consecutive tossing a coin. Anyone familiar with the basics of probability knows that if you toss a coin for 36 times all of the above series have exactly the same chance of appearance. Actually the chance is 0.000000000014551915. So they all can be called a random series of numbers somehow.

In theory, it’s understandable but we all feel that there’s a difference between these 3 series. The first and the second series have some pattern, but the third one seems absolutely patternless.

Here we can define two different concepts of randomness: Product Randomness and Process Randomness. As far as I know, this distinction between the product randomness and the process randomness was done by Gregory Chaitin. Although he didn’t use the same names, but sure he distinguishes the two different approaches to the randomness.

So let’s make a brief definition of the both terms:

Product Randomness is an attribute of a series of events with no visible pattern.  So any patternless series of events can be called as a sample of product randomness.

Process Randomness is an attribute of a series of events resulting from a process with two or more products which all have the same probability of happening.

So considering my question at the start of this article, all three rows can be called random if we consider the process definition of randomness, but only the third one can be called random if we consider the product definition of randomness.

Gregory Chaitin has an amazing definition of product randomness: Any series of numbers can be considered as random if there is no shorter way to communicate them with someone else (or with a computer) than copying the whole series of numbers itself.

So in his point of view the second row is not a random row as you can describe it for a computer in this way:

Print ‘1’ for 36 times

Even the first raw is not random because you can write:

Print ‘1  0  0’ for 12 times

But the third one can be considered random as there’s no way to compress it to a shorter message.

But what if our computer understands other commands too? Let’s suppose that our computer understands DecToBin command for converting decimal numbers to binaries.

then all of the three rows are not random anymore (or they are at the equal randomness level):

First row: DecToBin(39268272420)

Second row: DecToBin(68719476735)

Third row: DecToBin(25451802950)

Here Chaitin has a very simple answer: as far you can make it shorter, just do it. when it’s not absolutely possible then you have a random series in hand!

So this is an understandable yet non-measurable definition of product randomness. Anyway, he is a mathematician and for the mathematicians the most important concern is to prove that there exists an answer. To know the actual answer is not the first priority.

But in the real world we need an exactly quantifiable definition. The whole existing world is a series of events. So considering it as a sample of process randomness means that this nice pattern-full world has the same value as any other pattern-less world which could happen.  On the other hand considering the whole world as a result of a process randomness has its own implications and complications.

The most serious challenge in understanding the world is limitation of our brain as it is hardwired to look for pattern and meaning even in pattern-less and meaningless things.

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After you play too much with the spring something extraordinary happens. spring moves away from you with a worm-like movement. The spring pushes himself forward by stretching and contracting its body and hides somewhere behind a book.

Seems it was not a real spring. It was a spring-like living creature! This is a simplified statement of my idea behind the concept of predictability and connection of this concept with being alive.

Taking a look at the literature on mind and free will, you see the most examples and metaphors about predictability  revolves around predictability of physical movements. Same holds true for mine. But soon we will extend it to the other aspects of predictability.

When an object becomes absolutely predictable for us, it’s not considered as a living entity anymore. Physical movements of water are predictable. So it’s not considered a living thing. Movements of a fish are not predictable. So it’s kind of living entity!

The idea of predictability of behavior in living things is easy to grasp yet hard to define. As Dale states four decades ago:

An important feature of the response made to stimuli by living things is that in general the magnitude of the response has no obvious relation to the intensity of the stimulus.

Taking a look at the general literature, seems that without being seriously criticized, we can consider unpredictability and randomness as similar or same concepts. As Patrick Suppes states in his book called Probabilistic Metaphysics:

Phenomena that we cannot predict must be judged random.

In our daily talks, such a definition seems a bit radical. We are not able to predict stock market movement, however, no-one would be happy if you call the market movement as a random walk. Malkiel‘s book Random Walk Down Wall Street, caught the attention of the great stock market players and individual investors, but investors are still asking expensive consultants to help them with selecting an above average portfolio based on past performance of them.

The same story holds true for destiny of a marriage or result of a surgery and many other life incidents.

But taking a second look at the randomness definition shows that we have missed the context of the statement.

Are we trying to predict a single behavior or the pattern of behavior over time? If we flip a coin for 1000 times would be easy to predict that it will land head-side-up for about 500 times. But can you have any prediction with the same certainty if I have just one chance to toss the coin? Sure not.

Therefore, we must clearly define our scope of the study and boundary of the system before making any judgment about predictability.

So, let me state an oversimplified definition of unpredictability at this stage:

An entity can be considered unpredictable if giving the same history of states and unlimited time to experiment, still be impossible to predict the response to the same stimulus  within a predefined tolerance.

This is the place where the real story begins. There are some obvious and many hidden assumptions in the above definition. Not only about scope and the boundary, but also about observer and stimuli. Back to Wittgenstein’s ruler metaphor mentioned before, here’s the challenge. We are unreliable rulers trying to measure the table. But let’s try to do so.

I have continued this topic with a brief look at the concept of randomness.

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