When we play we are participating in a game. What defines a game is a set of rules that define outcomes. A game can involve any number of agents or systems. When we talk about games we usually refer to crafted artifacts made for artistic, entertainment or educational purpose. These artifacts involves and organizes art forms. Any art that involves decision-making and rules could be called a game.
In essence games are simulations. It is: “Let’s define the rules, and play by the rules and see what happens.” So when asking, what can we learned from games, it’s the same as asking “what can we learn from simulation and thought experiments.” and I believe the answer is that we only learn from simulation and thought experiments. (If you are a skeptic to this argument, you can imagine that the reality you perceive (your world view) is by no doubt an experience simulated by your physical brain which is what enables you do good and bad decision in the world which you learn from.)
What we can potentially learn from games is only limited by what games we are able to create and communicate and involve others in. (I believe the limits are not theoretical but practical. A problem of the limits of what you can imagine and communicate.)
But in the end I suppose games is just the same type of simulation as the one created by smokes and mirrors.
In what circumstances is it practical to play video games for learning?
To answer this I’ll convey what I think is the unique position video games have compared to most other means of learning:
In video games you learned through a focused simulation in a practical field. You don’t read about the problems, you meet them. It’s the difference from reading about juggling and actually juggling.
In video games you can control where you are in the problem space you are learning. You can slow down the difficult parts. You can skip the parts that are unimportant to you. You can pause, you can save an interesting state you want to return to at a later point. You can quickly put yourself in exactly the situation and scenario where you need to be.
Externalized Rules and correction checking
The game can be made to guide you, it can check your errors, it can adapt to your needs. It lets you hold in your head only what is essential for the problem, and leave the rest to be presented to you rather than simulated in your mind.
Faster than human
A game can load the appearance/simulation of an experience faster and with more detail than most people could imagine the same experience.
Possibility to experience with limited physical consequences
It allows you to experience the things you can not afford to experience in the real world. You can explore without being judged, without anyone watching. Without any physical effect at all on the external world. You can explore even the immoral and the consequences of what would be gravely dangerous to do in the real world.
Artificial Intelligence as a term points to vastly different concepts.
In game development it can mean arbitrary decision making. (Aka using if statement in software.)
Or it can simply mean to convey the appearance or illusion of intelligence from a non-intelligent system. This concept has a much higher emphasis on “Articial”, than “intelligence”.
More broadly the term Artificial Intelligence can mean modelling aspects of logic. In which case it should be called Logic Modelling. Much of the confusion about Artificial Intelligence comes from the inability to distinguish intelligence from logic models (aka software). The difference lies in scope in the context which such system can provide meaningful results.
The closest meaningful concept to to the nonsense term Artificial Intelligence is Procedural Intelligence. Procedural Intelligence denotes two things: 1) That it is a process and 2) That it discerns the meaning in a pattern.
A more limited form of Procedural intelligence is Machine Intelligence. This term denotes a Procedural Intelligence constructed through mechanical means. This focus on “machine” is often an overly broad concept for how the term is commonly used. In almost all cases the term Machine Intelligence actually refers to Computer Intelligence.
Within the aspect of Intelligence lies many sub-aspects. One of which is learning, which has become very trendy with the big tech companies in the form of Machine Learning (actually meaning Computer Learning). The point is usually that a system can be formed through trial and error, to generate meaning intelligent decision after being exposed to a lot of examples.
Learning as a concept is also too vague of a concept here to be particularly meaningful. I want to see a shift the cultural focus from Machine Intelligence to Generated Computer Intelligence. Generated, meaning the Procedural Intelligence Model is generated; Whether it is from “learning” or other means.
Learning is the memorization of relationships between things in a context, enabling us to simulate things in our minds to generate predictable behavior.
When we learn, we enable ourselves to not only observe, but imagine how something works in our minds. We imagine and play with how different scenarios unfold through a model in our heads. These mental models are our minds best guesses about how things work based on our previous experience and observations of how things worked in the past.
There are two fundamental ways to learn things:
Analytical / Dissectional
What does it contain?
What is it made of?
What is its sub parts?
Systemic / Relational
What is it connected to?
How is it affected by other things?
How does it affect other things?
How is it different from other things?
I’ve talked about Black boxes briefly in an earlier post. But if you are unfamiliar I’ll describe it briefly: A Black box is a mental model about something with only systemic / relational information. What makes the box “Black” is that it’s content is not observed.
The brain is a pattern matching machine. When we are repeatedly presented with a pattern we starts to see what relates and what doesn’t. This is what, in psychology terms, is called association and dissociation.
There is a rather common heuristic used within the field of psychology that brain cells “that fire together wire together.” The meaning of that statement is that, as learners, we put meaning onto things that occur together in the same context or time. The classical psychological experiments by Pavlov showed how stimuli that is presented together “associates”. What this means is that meaning itself is transferred to the things that something is presented together with. The most famous example is Pavlov’s bell, that for his dogs got so associated with food that the dogs started to drool simply from hearing the sound of the bell.
Dissociation is created when we observe that something no longer happens within a context. It is when we learn that we thought was true really isn’t or that maybe it is more nuanced than it first seemed.
Dissociation is also when we choose not to acknowledge things. It is the brains way of filtering out the information that is not relevant in the current context. When we are really focused on something our minds keeps track of and associates things within a specific context, but tries to ignore and dissociate everything else. Dissociation is the brain’s way of creating relevancy.
Information is more commonly formatted for the writer and not for the reader. What might seem logical and educational from the writers perspective, usually either lacks the right contexts to be useful for the reader or repeats information for the reader that they already know.
If it is too wordy it gets boring, but if it contains conclusions without it being evident how, the information is hard to follow.
Making something informative is a constant balance between introducing new information, and presenting the new information in relation to information that the reader already knows.
When asked why, you have to choose a story and a context.
Why did I write this?
Because I’m frustrated about this word.
Because I want people to read my blog and understand me better?
Because neurons in my brain fired in a way that made my fingers dance over my keyboard which caused this blog post to be written.
Because I was motivated to do so.
Because it filled my mind and I want to get it out in a clear and understandable way for myself.
Because I want people to stop using it in unhelpful ways.
Because I want to communicate better and I want people to understand each other.
The answer is not one. All of the statements might be valid, and yet when we ask why we usually want a definite answer. I can’t give you all reasons without creating an infinite list of these causalities. And to give you a truly accurate answer I must explain all the history of what happened in this world since the start of time and existence itself.
When we ask why we really say: “Tell a convincing story. What made it happen?”
“Give me a satisfying answer”
So any answer to a why question will always be:
a constructed story
a simplification of reality
based on belief
Why would I use why then?
It can be used to make someone reflect and formalize their own reasoning.
It can be used to obtain indications of someone’s perspective.
A Black Box is a system or process where all you see is:
Input – The things that affects the black box.
Output – The things that the black box produces or affects.
This implies two things:
That you sometimes don’t know what happen within a system, even though you know what effects it produces.
That you sometimes don’t need to know how something works on the inside to interact with it on a meaningful level.
Whether used with intention or not, a black box symbolizes and represents hidden knowledge and the unmeasured parts of systems. Everything that is unmeasured or uncertain but happens anyways can thus be thought of as a Black Box.