<p>I'd say maximum parsimony approaches assume genetic similarity is unlikely to occur by convergence, but likely a result of divergence. If this assumptions holds, finding the tree configuration that minimizes the number of unlikely events makes sense.</p>

CC BY-SA 4.0
2019-04-24T13:06:55.330Z
2019-04-24T13:06:55.330Z
Pallie
<p>Parsimony was a nice idea and developed the algorithms and tree methods towards its successors, maximum likelihood and Bayesian. The fundamental assumption of mutational independence at each alignment position was founded via parsimony. Just the recursion algorithm involved in  reading a tree isn't trivial coding. Also Walter Fitch (Fitch Marigold method) was a nice guy.</p>

<p>Weighted parsimony has performed well in simulation studies to prevent 'long branch attraction' artefacts. The key issue that was never resolved was how to objectively weight an MP analysis. It could be resurrected however, because machine learning ML could independently provide the weights in specific contexts. That a lot of "coulds" though.</p>

CC BY-SA 4.0
2019-04-24T14:54:44.720Z
2019-04-24T14:54:44.720Z
M__
<blockquote>
  <p><em>The principle of parsimony states that the simplest explanation consistent with a data set should be chosen over more complex explanations...</em> (Stewart 1993)</p>
</blockquote>

<p>Sometimes independent events in different lineages cause similarities in molecular sequences (or morphology or behavior or whatever characteristics one happens to be studying). These events are not true evidence of recently shared ancestry, but it is easy to mistake them as such evidence.</p>

<p>The principle of parsimony asserts that we will be better off assuming the simplest case--that is, that identical characters <strong><em>are</em></strong> evidence of recent shared ancestry, since the simple explanation should be true more frequently than alternative explanations. There are cases for which the maximum parsimony model works very well, and there are cases for which maximum likelihood, distance matrix, Bayesian, or other models perform better.</p>

<hr>

<p>Stewart CB (1993) The powers and pitfalls of parsimony. <em>Nature</em> <strong>361</strong>, 603-607, <a href="https://doi.org/10.1038/361603a0" rel="nofollow noreferrer">doi:10.1038/361603a0</a>.</p>

CC BY-SA 4.0
2019-04-24T14:56:32.590Z
Alot happened in phylogeny since 1993
M__
CC BY-SA 4.0
2019-04-24T14:57:29.357Z
And yet the principle of parsimony remains the same, no?
Daniel Standage
CC BY-SA 4.0
2019-04-24T15:00:33.733Z
Not entirely sure what you mean, but parsimony is still parsimony and the fundamental idea of assessing a tree as an aggregate of every individual alignment position remains the same.
M__
CC BY-SA 4.0
2019-04-24T15:01:32.913Z
The OP asks why it even works at all, and my answer is that in some cases the simplest assumption is sufficient to infer the correct relationships. I don't see anything there that is compromised by the impressive advances in phylogeny inference that have occurred since.
Daniel Standage
CC BY-SA 4.0
2019-04-24T15:14:31.800Z
IMO had parsimony focused on protein trees - that might have succeeded. It gets a bit stuck with DNA. A key problem was they (parsimony exponents) didn't defend their technique rigorously, but I suspect you've never come up against a parsimony reviewer. Well they defended their technique with opinion rather than analytical fact.
M__
CC BY-SA 4.0
2019-04-24T15:21:16.457Z
I think you're mistaking me for a proponent of parsimony as opposed to someone trying to answer a question. :-) Don't worry, I was just as alarmed (and amused) several years ago when *Cladistics* announced it would only accept parsimony trees!
Daniel Standage
CC BY-SA 4.0
2019-04-24T14:47:22.523Z
2019-04-24T14:47:22.523Z
Daniel Standage
<p>Beside it (maximum parsimony) being compuationally cheap what other good arguments are there for it? Is there any model behind this principle ? Why would one expect this principle to provide right phylogeny in any situation at all ?</p>

CC BY-SA 4.0
2019-04-24T18:25:33.803Z
You cross-posted this on [Bio](https://biology.stackexchange.com/questions/83949/what-is-the-best-argument-for-maximum-parsimony-method-in-phylogenetic-tree-cons) - cross-posting is discouraged across the SE network.
AliceD
CC BY-SA 4.0
2019-04-25T03:20:00.680Z
Noted. I didn't now that.
Ahmed Abdullah
<phylogenetics><phylogeny>
What is the best argument for maximum parsimony method in phylogenetic tree construction?
# What is the best argument for maximum parsimony method in phylogenetic tree construction?

(Asked by: username_0 on 2019-04-24)

Beside it (maximum parsimony) being compuationally cheap what other good arguments are there for it? Is there any model behind this principle ? Why would one expect this principle to provide right phylogeny in any situation at all ?

**Tags:** phylogenetics, phylogeny
> **Comments:**
> **username_1**: You cross-posted this on [Bio](https://biology.stackexchange.com/questions/83949/what-is-the-best-argument-for-maximum-parsimony-method-in-phylogenetic-tree-cons) - cross-posting is discouraged across the SE network.  
> **username_0**: Noted. I didn't now that.  

---

## 3 answers

### Answer by username_4 on 2019-04-24. Score: 2 | Accepted answer

> *The principle of parsimony states that the simplest explanation consistent with a data set should be chosen over more complex explanations...* (Stewart 1993)

Sometimes independent events in different lineages cause similarities in molecular sequences (or morphology or behavior or whatever characteristics one happens to be studying). These events are not true evidence of recently shared ancestry, but it is easy to mistake them as such evidence.

The principle of parsimony asserts that we will be better off assuming the simplest case--that is, that identical characters ***are*** evidence of recent shared ancestry, since the simple explanation should be true more frequently than alternative explanations. There are cases for which the maximum parsimony model works very well, and there are cases for which maximum likelihood, distance matrix, Bayesian, or other models perform better.

---

Stewart CB (1993) The powers and pitfalls of parsimony. *Nature* **361**, 603-607, [doi:10.1038/361603a0](https://doi.org/10.1038/361603a0).

> **Comments:**
> **username_3**: Alot happened in phylogeny since 1993  
> **username_4**: And yet the principle of parsimony remains the same, no?  
> **username_3**: Not entirely sure what you mean, but parsimony is still parsimony and the fundamental idea of assessing a tree as an aggregate of every individual alignment position remains the same.  
> **username_4**: The OP asks why it even works at all, and my answer is that in some cases the simplest assumption is sufficient to infer the correct relationships. I don't see anything there that is compromised by the impressive advances in phylogeny inference that have occurred since.  
> **username_3**: IMO had parsimony focused on protein trees - that might have succeeded. It gets a bit stuck with DNA. A key problem was they (parsimony exponents) didn't defend their technique rigorously, but I suspect you've never come up against a parsimony reviewer. Well they defended their technique with opinion rather than analytical fact.  
> **username_4**: I think you're mistaking me for a proponent of parsimony as opposed to someone trying to answer a question. :-) Don't worry, I was just as alarmed (and amused) several years ago when *Cladistics* announced it would only accept parsimony trees!  

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### Answer by username_2 on 2019-04-24. Score: 2

I'd say maximum parsimony approaches assume genetic similarity is unlikely to occur by convergence, but likely a result of divergence. If this assumptions holds, finding the tree configuration that minimizes the number of unlikely events makes sense.


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### Answer by username_3 on 2019-04-24. Score: 1

Parsimony was a nice idea and developed the algorithms and tree methods towards its successors, maximum likelihood and Bayesian. The fundamental assumption of mutational independence at each alignment position was founded via parsimony. Just the recursion algorithm involved in reading a tree isn't trivial coding. Also Walter Fitch (Fitch Marigold method) was a nice guy.

Weighted parsimony has performed well in simulation studies to prevent 'long branch attraction' artefacts. The key issue that was never resolved was how to objectively weight an MP analysis. It could be resurrected however, because machine learning ML could independently provide the weights in specific contexts. That a lot of "coulds" though.


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