<p>Yes, <strong>you can think of biological entities as algorithms, but that doesn't give you any explanatory power.</strong></p>

<p>Unfortunately, most people have little to no understanding of algorithms, and how little actually constraint is imposed on something when you say it is "algorithmic". In particular, <strong>there is no restrictions on algorithms that require them to be deterministic.</strong> When theoretical computer scientists talk about pseudorandom number generators, they are talking at <a href="https://cogsci.stackexchange.com/a/4807/29">a much higher level of rigour</a> than any biologist, psychologist, or neurologist ever uses. From their point of view, unless the biological entities you are thinking about are trying to defeat each other's cryptosystems, <strong>all random or stochastic events in biology can be viewed as pseudorandom with absolutely no consequence for biological theory</strong>. In fact, Keegan's answer that we can think of stochastic events as deterministic events with a mechanism that is outside of our model scope would be equivalent to the worry of pseudorandom versus true-random numbers.</p>

<p>Even if you subscribe to the (<a href="https://biology.stackexchange.com/a/2663/500">silly</a>) Penrose-Hameroff stance that quantum effects in microtubles have a macroscopic effect at the level of consciousness that leads to "true randomness" then that still wouldn't place you outside of algorithmic thinking. You would just have to think in terms of quantum algorithms, which is more difficult mathematically, but a well studied field of computer science. Finally, cstheory extends the notion of algorithms beyond the typical confines of randomness as studied by statistics and most scientists, and looks at <a href="http://egtheory.wordpress.com/2013/09/13/worst-case/" rel="nofollow noreferrer">rigorous models of non-determinism</a>. </p>

<p>In fact, there are people (for example, I advocate <a href="http://egtheory.wordpress.com/2013/06/26/algorithmic-philosophy/" rel="nofollow noreferrer">algorithmic philosophy</a> as an extension of standard analytic philosophy) that believe that any communicable theory is inherently algorithmic and ammendable to analysis of cstheorists. Hence, not thinking of something as algorithmic would be impossible.</p>

<p>That being said, is that useful? In general, no. <strong>Algorithms are incredibly difficult to study.</strong> In the case of the computing machines, we make it easier by separating the algorithm underlying the physics of the hardware from an abstraction that we call the software. This often leads non-cstheorists (as you can see in <a href="https://cogsci.stackexchange.com/a/5237/29">Keagan's answer</a>) to thinking that the idea of algorithms applies only at this "software abstraction" level. Of course, this is not the case. However, this distinction can be a useful metaphor at times, and much of cognitive science is based on the idea that the mind is software that is at least in principle capable of being implemented by different hardware. This is the philosophical stance of <a href="http://plato.stanford.edu/entries/multiple-realizability/" rel="nofollow noreferrer">multiple realizability</a>.</p>

<p>Neuroscientists hold this stance much less often, and - as suggested in Keagan's answer - view the mind and brain as non-seperable and best analyzed at the same level. This does not make the process non-algorithmic, it just takes away any handles we have at easily isolating properties of this algorithm.</p>

<p>If we continue this idea further, and just consider an algorithmic black box then we return to the ideas of <a href="http://plato.stanford.edu/entries/behaviorism/" rel="nofollow noreferrer">behaviorism</a> that are now considered passe in the cognitive sciences, but still popular in many parts of biology. In this setting, the black-box algorithm is a correct but completely useless notion. We have <a href="http://en.wikipedia.org/wiki/Post&#39;s_theorem" rel="nofollow noreferrer">mathematical theorems</a> that tell us that if we try to treat algorithms at this level of abstraction then we can never (in general) conclude anything. In this case, we have to be more restrictive than just "algorithmic" or "computable". This can get us to fun cstheory questions like "<a href="https://cstheory.stackexchange.com/q/5379/1037">What is the complexity class most closely associated with what the human mind can accomplish quickly?</a>"</p>

CC BY-SA 3.0
2013-12-30T18:12:09.330Z
I've edited my answer to make my ontology clearer so your first reference to my post is inconsistent now.  My issue with life being reduced to *just* algorithms is more because I favor the dynamical systems view of continuous evolution of states (and that how humans break such processes into algorithmic steps is a more about human ontology than nature).  My ontology views life, inevitably, as a very complicated classical physics problem.  Algorithms would be an emergent property of living things (and other physical processes) as opposed to what life is reduced to.
Keegan Keplinger
CC BY-SA 3.0
2013-12-30T21:33:24.887Z
Also, an interesting note about multiple realizability.  What I call 'degeneracy' in my answer is the observable analog of multiple relizability, but pertaining to function.  If you believe mind is a functional result (as is typical of monism and physicalism) then degeneracy *is* multiple realizability.
Keegan Keplinger
CC BY-SA 3.0
2013-12-30T00:38:40.843Z
2013-12-30T00:38:40.843Z
2017-04-13T12:56:16.467Z
Artem Kaznatcheev
<p>No.  Algorithmic behavior can be observed in organisms, but an organism cannot be reduced to just algorithms because the information processing is tightly coupled to the physical hardware, which evolves continuously and (largely) deterministically.  That is, nature doesn't break behaviors into discrete algorithmic steps.  Similar to how humans break up the color spectrum into colors, we break up these continuous processes into steps.  In some cases, this time discretization is physically meaningful, but not always.  So algorithms are necessary, but not sufficient.  And certainly one can draw on information theory and consider the algorithmic aspects of life to help understand it.</p>

<p>Randomness and non-random (i.e. deterministic) are human modelling terms based on mathematics (time evolution of states).  It's more accurate to ask whether deterministic models or stochastic models best fit a particular phenomena.  That being said, there are lots of things that appear as stochastic processes in biological systems, but there's always the possibility that a very complex deterministic processes underlie them.  Most phenomena probably include some combination of deterministic and random processes.  And sometimes, no matter how stochastic the microstates are, the macrostate is robust (this is called 'degeneracy').  For instance, a given channel in the membrane of a neuron is stochastic with respect to inputs, but the population of channels will reliably lead to spiking for a given input current.</p>

CC BY-SA 3.0
2013-12-30T00:03:48.810Z
Although I agree with your comment on randomness vs. non-randomness in the context of biology, keep in mind that in general your comment is rather controversial. In fact, a lot of people working on foundations of quantum mechanics would disagree, as would many philosophers.
Artem Kaznatcheev
CC BY-SA 3.0
2013-12-29T20:54:53.417Z
2013-12-30T18:13:23.587Z
2013-12-30T18:13:23.587Z
Keegan Keplinger
<p><a href="http://fr.wikipedia.org/wiki/John_von_Neumann" rel="nofollow">John Von Neuman</a> believed that all organisms can be though of as information-processing systems. He built on the work of <a href="http://en.wikipedia.org/wiki/Alan_Turing" rel="nofollow">Alan Turing</a> (algorithmic) to create simulations of biological entities. This philosophy lead to the use of <a href="http://en.wikipedia.org/wiki/Cellular_automaton" rel="nofollow">cellular automaton</a>. This kind of analysis allowed to gain many insights in biology.</p>

<p>Does it make sense to you to reduce the behavior of an organism to the output of an algorithm? </p>

<p>There are several things that are not feasible in algorithmic. <a href="http://en.wikipedia.org/wiki/Randomness" rel="nofollow">Randomness</a> is one of these. Does it seem fair to you to consider that we actually produce <a href="http://en.wikipedia.org/wiki/Pseudorandomness" rel="nofollow">pseudorandomness</a>, a function with a chaotic behavior that cycle and might end up repeating itself?</p>

<p>The questions in short:</p>

<ul>
<li>Can an organism be reduced to as being an algorithm as found in computers?</li>
<li>Can an organism produce randomness (random choice, random behavior) or only pseudo-randomness?</li>
</ul>

<theoretical-neuroscience><behaviorism><philosophy-of-mind>
Can a biological entity be thought as a simple algorithm? Case-study with the concept of randomness
Remi.b
# Can a biological entity be thought as a simple algorithm? Case-study with the concept of randomness

(Asked by: username_0 on 2013-12-29)

[John Von Neuman](http://fr.wikipedia.org/wiki/John_von_Neumann) believed that all organisms can be though of as information-processing systems. He built on the work of [Alan Turing](http://en.wikipedia.org/wiki/Alan_Turing) (algorithmic) to create simulations of biological entities. This philosophy lead to the use of [cellular automaton](http://en.wikipedia.org/wiki/Cellular_automaton). This kind of analysis allowed to gain many insights in biology.

Does it make sense to you to reduce the behavior of an organism to the output of an algorithm?

There are several things that are not feasible in algorithmic. [Randomness](http://en.wikipedia.org/wiki/Randomness) is one of these. Does it seem fair to you to consider that we actually produce [pseudorandomness](http://en.wikipedia.org/wiki/Pseudorandomness), a function with a chaotic behavior that cycle and might end up repeating itself?

The questions in short:

* Can an organism be reduced to as being an algorithm as found in computers?
* Can an organism produce randomness (random choice, random behavior) or only pseudo-randomness?

**Tags:** theoretical-neuroscience, behaviorism, philosophy-of-mind


---

## 2 answers

### Answer by username_1 on 2013-12-30. Score: 4

Yes, **you can think of biological entities as algorithms, but that doesn't give you any explanatory power.**

Unfortunately, most people have little to no understanding of algorithms, and how little actually constraint is imposed on something when you say it is "algorithmic". In particular, **there is no restrictions on algorithms that require them to be deterministic.** When theoretical computer scientists talk about pseudorandom number generators, they are talking at [a much higher level of rigour](https://cogsci.stackexchange.com/a/4807/29) than any biologist, psychologist, or neurologist ever uses. From their point of view, unless the biological entities you are thinking about are trying to defeat each other's cryptosystems, **all random or stochastic events in biology can be viewed as pseudorandom with absolutely no consequence for biological theory**. In fact, Keegan's answer that we can think of stochastic events as deterministic events with a mechanism that is outside of our model scope would be equivalent to the worry of pseudorandom versus true-random numbers.

Even if you subscribe to the ([silly](https://biology.stackexchange.com/a/2663/500)) Penrose-Hameroff stance that quantum effects in microtubles have a macroscopic effect at the level of consciousness that leads to "true randomness" then that still wouldn't place you outside of algorithmic thinking. You would just have to think in terms of quantum algorithms, which is more difficult mathematically, but a well studied field of computer science. Finally, cstheory extends the notion of algorithms beyond the typical confines of randomness as studied by statistics and most scientists, and looks at [rigorous models of non-determinism](http://egtheory.wordpress.com/2013/09/13/worst-case/).

In fact, there are people (for example, I advocate [algorithmic philosophy](http://egtheory.wordpress.com/2013/06/26/algorithmic-philosophy/) as an extension of standard analytic philosophy) that believe that any communicable theory is inherently algorithmic and ammendable to analysis of cstheorists. Hence, not thinking of something as algorithmic would be impossible.

That being said, is that useful? In general, no. **Algorithms are incredibly difficult to study.** In the case of the computing machines, we make it easier by separating the algorithm underlying the physics of the hardware from an abstraction that we call the software. This often leads non-cstheorists (as you can see in [Keagan's answer](https://cogsci.stackexchange.com/a/5237/29)) to thinking that the idea of algorithms applies only at this "software abstraction" level. Of course, this is not the case. However, this distinction can be a useful metaphor at times, and much of cognitive science is based on the idea that the mind is software that is at least in principle capable of being implemented by different hardware. This is the philosophical stance of [multiple realizability](http://plato.stanford.edu/entries/multiple-realizability/).

Neuroscientists hold this stance much less often, and - as suggested in Keagan's answer - view the mind and brain as non-seperable and best analyzed at the same level. This does not make the process non-algorithmic, it just takes away any handles we have at easily isolating properties of this algorithm.

If we continue this idea further, and just consider an algorithmic black box then we return to the ideas of [behaviorism](http://plato.stanford.edu/entries/behaviorism/) that are now considered passe in the cognitive sciences, but still popular in many parts of biology. In this setting, the black-box algorithm is a correct but completely useless notion. We have [mathematical theorems](http://en.wikipedia.org/wiki/Post's_theorem) that tell us that if we try to treat algorithms at this level of abstraction then we can never (in general) conclude anything. In this case, we have to be more restrictive than just "algorithmic" or "computable". This can get us to fun cstheory questions like "[What is the complexity class most closely associated with what the human mind can accomplish quickly?](https://cstheory.stackexchange.com/q/5379/1037)"

> **Comments:**
> **username_2**: I've edited my answer to make my ontology clearer so your first reference to my post is inconsistent now.  My issue with life being reduced to *just* algorithms is more because I favor the dynamical systems view of continuous evolution of states (and that how humans break such processes into algorithmic steps is a more about human ontology than nature).  My ontology views life, inevitably, as a very complicated classical physics problem.  Algorithms would be an emergent property of living things (and other physical processes) as opposed to what life is reduced to.  
> **username_2**: Also, an interesting note about multiple realizability.  What I call 'degeneracy' in my answer is the observable analog of multiple relizability, but pertaining to function.  If you believe mind is a functional result (as is typical of monism and physicalism) then degeneracy *is* multiple realizability.  

---

### Answer by username_2 on 2013-12-29. Score: 2

No. Algorithmic behavior can be observed in organisms, but an organism cannot be reduced to just algorithms because the information processing is tightly coupled to the physical hardware, which evolves continuously and (largely) deterministically. That is, nature doesn't break behaviors into discrete algorithmic steps. Similar to how humans break up the color spectrum into colors, we break up these continuous processes into steps. In some cases, this time discretization is physically meaningful, but not always. So algorithms are necessary, but not sufficient. And certainly one can draw on information theory and consider the algorithmic aspects of life to help understand it.

Randomness and non-random (i.e. deterministic) are human modelling terms based on mathematics (time evolution of states). It's more accurate to ask whether deterministic models or stochastic models best fit a particular phenomena. That being said, there are lots of things that appear as stochastic processes in biological systems, but there's always the possibility that a very complex deterministic processes underlie them. Most phenomena probably include some combination of deterministic and random processes. And sometimes, no matter how stochastic the microstates are, the macrostate is robust (this is called 'degeneracy'). For instance, a given channel in the membrane of a neuron is stochastic with respect to inputs, but the population of channels will reliably lead to spiking for a given input current.

> **Comments:**
> **username_1**: Although I agree with your comment on randomness vs. non-randomness in the context of biology, keep in mind that in general your comment is rather controversial. In fact, a lot of people working on foundations of quantum mechanics would disagree, as would many philosophers.  

---

