This is the last article of the Free Will Series and the post that closes a period of regular writing on this blog.
This post focuses on the idea of simulation in sciences and in Artificial Intelligence. Previous posts dealt with the notion of simulation in fiction, how it has been portrayed and the relationship between simulation and recognition.
Simulations in Science
The scientific method: discovery through simulation
Science creates/discovers knowledge through a process called the scientific method. While it might not be obvious, the scientific method is essentially a simulation of a real process under controlled conditions. When you carry out an experimental test, you are trying to model an aspect of our physical world and the assumption is that, if the simulation is accurate enough, we can use the simulation to make inferences about the way our world works.
Apart from this, the idea of simulation lurks somewhere else: in statistics.
Random sampling: using trees to simulate forests
Scientists often cannot get large amount of subjects in their studies (for reasons of complexity or because it is not feasible) so they resort to lower amounts of subjects chosen randomly (in the social sciences, it is an opportunity sampling for ethical reasons) and rely on the idea of random sampling to make their models accurate. The idea behind random sampling is that a random sample tends to be more accurate of the population than a non-random sample. So a random sample is taken as a sort of micro-model of the population and by applying a test on the sample you assume that the test on the sample is statistically equivalent to running a test on the whole population. In other words, in a way, testing a sample simulates running a test on the population.
Simulation in Artificial Intelligence
A.I. is the field of simulation par excellence since one of its core aims is the simulation of human intelligence. The field is divided in two sub-fields: one where the simulation of the modus operandi of human intelligence is the main aim and another one where achieving the results of human intelligence is the ultimate goal. In the former sub-field, we find things like cognitive modelling/architectures and the Human Brain Project. In the latter sub-field, we find things like machine learning. In both cases, there is a simulation, whether it is a simulation of cognitive mechanisms or the simulation of skills performed by humans like recognising a flower in a picture.
Artificial Intelligence has been placed inside a broader sub-field called Artificial Life.
Simulation in Artificial Life
Just like A.I. is a field that simulates human intelligence, Artificial Life or A.L. simulates the properties of carbon-based life forms. And it is also divided in two research paths: life as it is (or the simulation of biological mechanisms) and life as it could be (or the creation of systems that simulate the general properties commonly associated with carbon-based life forms like metabolism, evolution, self-reproduction, etc). Just like in A.I. most of the advances are in the sub-field where systems perform human skills like visual recognition, progress in the field of A.L. mostly revolves around systems that perform stuff that we consider life-like. So in the “life as it is” sub-field, we have things like the OpenWorm project and the computational biology field. While in the “life as it could be” field, we have things like the robotics field, Tom Ray’s Tierra and other life simulators. In both cases, there is a simulation, whether it is a simulation of the mechanisms of carbon-based living systems or the simulation of traits possessed by life forms like group behaviour and the ability to change one’s behaviour in response to the surrounding environment.
Simulation is a central idea both in science in general and Artificial Life in particular. There is something fascinating about the idea of mimicking something. We find truths through simulation and science suggests that a lot of what we perceive and feel (including our sense of visual perception and free will) is a sort of simulation made by our brains. This seems to tell us that simulation is very important to us. Yet simulation itself is not a very clear notion. What is sameness? What is change?