Philosophy postures in systematics: Which is the best approach to do science?

The diversity of viewpoints and positions around all study topics is very important in the science world. This variety thoughts allows to evaluate different perspectives of the same problem using different methodologies, and generate a great framework (Pavese and De Bièvre, 2015). Nevertheless, science must go hand in hand with objectivity (Hanna, 2004), therefore we must ask ourselves, which is the best approach to carry out scientific research?

Firstly, we must located on a radical idea: Falsification (Popper, 2002a). For this Popperian vision we only know what we don’t know, thus, being literals, it’s impossible discover absolutes trues, and we can only corroborate and falsify hypotheses (Popper, 2002b). My viewpoint about Popper’s falsification in science is: we must use it only as a reminder of why we shouldn't take for granted the current theories, thus, the search of knowledge remains standing. However, beyond this last idea, I don’t think Popper’s thought has a direct applicability nowadays.

Now, we can understand that the impulse of science is to understand more and more things, but, how do we trust what we know? The knowledge search through science needs statistics, and frequentists with their classical interpretation of probability based on finite observations of experimental events, have contributed greatly (Bayarri and Berger, 2004). Nevertheless, this vision can generate problems like wrong interpretations or the impossibility of  assign probabilities to unrepeatable events (Bayarri and Berger, 2004; Box and Tiao, 1992). Moreover, Bayesian vision doesn’t have these problems being a better option because probabilities are based on a prior  knowledge, it means, there is always uncertainly because we never know all facts but we can assign a value of how much knowledge have about results (Briggs, 1999; Schoot et al., 2014). 

For this reason, I must emphasize that we can’t know everything but we can know a lot, therefore, methods based on the Bayesian philosophy, give us, in my opinion, the best approximation to the scientific truth, especially in systematics, where the evolutionary history is a large set of inferences.

References

- Bayarri, M. J. and Berger, J. O. (2004). The interplay of Bayesian and frequestist analysis. Statistical Science, 19(1), 58-80.

- Briggs, A.H. (1999). A Bayesian approach to stochastic cost‐effectiveness analysis. Health Econ., 18, 257-261. doi:10.1002/(SICI)1099-1050(199905)8:3<257::AID-HEC427>3.0.CO;2-E

- Box, G. E. and Tiao, G. C. (1992). Bayesian inference in statistical analysis. New York: John Wiley & Sons.

- Hanna, J. (2004). The Scope and Limits of Scientific Objectivity. Philosophy of Science, 71(3), 339-361. doi:10.1086/421537

- Pavese, F. and De Bièvre, P. (2015). Fostering diversity of thought in measurement science. In F. Pavese, W. Bremser, A. Chunovkina, N. Fischer and A. Forbes (Ed.), Advanced Mathematical and Computational Tools in Metrology and Testing X (pp. 1–8). Singapore: World Scientific

- Popper, K. (2002a). The logic of scientific discovery. London, England: Routledge. doi:https://doi.org/10.4324/9780203994627

- Popper, K. (2002b). Conjectures and refutations: The growth of scientific knowledge. London, England: Routledge. doi:https://doi.org/10.4324/9780203538074

- Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J. and Aken, M. A. (2014). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Dev, 85, 842-860. doi:10.1111/cdev.12169

Philosophy and Systematics

When we think the role of philosophy in systematics, we can talk about Popperian falsification and corroboration, frequentism and Bayesian philosophy different philosophical currents, bases for phylogenetic methods.  However, if we want to know the relations between groups of organism, which one is the better current to do phylogeny? 

Popperian falsification and corroboration never search the best classification hypothesis, it looks hypothesis with the highest degree of corroboration (Bock, 1973); in other way frequentism has a statistical approach, assess the expected frequencies of good and bad results of a repeated number of measures (Sober, 2008; VanderPlas, 2014). As frequestism, Bayesian philosophy based on statistics, but using a priori and a posteriori probabilities (degrees of knowledge) that let us include ranks of certainty about statements (Stevens, 2006).

Some authors highlight the influence of that Popperian falsification in systematics and others defend its use as the appropriate approach to the search of phylogeny (Farris, 2012), the same with frequentism (Sober, 2008). However, Bayesian is the most reliable way to do phylogeny, because estimate the best probability of classification hypothesis (posterior) and evaluating prior probabilities, giving a background knowledge to the topology (Ronquist, et al. 20014) and in my point of view allows a broader approach.

 

References

Bock, W. J. (1973). Philosophical foundations of classical evolutionary classification. Systematic Zoology. Vol. 22 (4), 375–392.

Farris, J. (2012). Popper: not Bayes or Rieppel. Cladistics. 

Ronquist, F. Huelsenbeck, J. P. Britton, T. (2004). Bayesian supertrees. In Bininda-Emonds, O. R. P. Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life. Amsterdam, Netherlands. Kluwer.

Sober, E. (2008). Evidence and Evolution: The logic behind the science. New York, United States of America. Cambridge University Press.

Stevens, M. (2006). The Bayesian Approach to the Philosophy of Science. In Borchert, D. (Ed.), The Encyclopedia of Philosophy. Michigan, United States of America. Thompson Gale.

VanderPlas, J. (2014). Frequentism and Bayesianism: A Practical Introduction, Part III. Pythonic Perambulations. Visible in: http://jakevdp.github.io/blog/2014/06/12/frequentism-and-bayesianism-3-confidence-credibility/

 

Me and the comparative biology

Systematics as a discipline, plays an important role in comparative biology, it allows us to understand species phylogenetic history, taking into account their evolutionary processes and patterns¹. There is a universe of philosophical ideas discussed in the systematic literature, where even the reasoning process from data, to the final tree hypothesis can have a direct impact on our phylogenetic analyzes^1-2. Hence, having a philosophical position should be the beginning to our study², how we know what we know and how we decided to address that knowledge has been debated along history³. 

knowledge understood as the data obtained from the evidence, made me choose an induction approach, where multiple instances observed of a phenomenon will lead us to a more general view, in spite of deductivism, that falls into a syllogism². The contemporary systematics employs different forms of logic in combined forms nowadays^2,4. Therefore, the method that best fits my scientific inference is a Bayesian approach, the analyzes can rarely be sure of a result, but they can be very safe. Its inference differs from traditional statistical inference in preserving uncertainty^5-7.

First, in order to carry out the respective phylogenetic analyzes, it’s necessary to define the research question of interest. The conceptual framework needs to be clarified and subsequently, generate a quantifiable hypothesis^6-7, the probability of how possible it’s depends on all previous information available. Observation alone can not give a posterior probability, we need to have a prior probability as well^3,6-7. Also, it’s necessary the establishment of interest parameters given our current data and models. The choice of a prior is based on how much information we believe we have prior to the data, and how accurate we believe that information to be^6-7. Therefore, the posterior distribution reflects our updated knowledge, balancing prior knowledge with observed data⁷. 

The use of Bayesian statistics allows us, to update knowledge instead of testing a null hypothesis over and over again. If the evidence changes, knowledge will also change or be more reliable^3,6,8-9, thus that’s why I prefer it.

References

1. Funk, V. & Brooks, D. Phylogenetic systematics as the basis of comparative biology. (Smithsonian Institution Press, 1993). 

2. Wiley, E. & Lieberman, B. Phylogenetics: Theory and Practice of Phylogenetic Systematics. (John Wiley & Sons, 2011).

3. Sober, E. Evidence and Evolution: The Logic Behind the Science. 3-107 (Cambridge University Press, 2008).

4. Hume, D. 1748. An Enquiry concerning Human Understanding, T. Cadell, London.

5. Glickman, M. & Dyk, D. Basic Bayesian Methods. Topics in Biostatistics 319-338 (2007). doi:10.1007/978-1-59745-530-5_16

6. Van de Schoot, R. et al. A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development 85, 842-860 (2013).

7. Smith, A., Skene, A., Shaw, J., Naylor, J. & Dransfield, M. The implementation of the bayesian paradigm. Communications in Statistics - Theory and Methods 14, 1079- 1102 (1985).

8. Gelman, A. Simulation of a Statistics Blogosphere - Statistical Modeling, Causal Inference, and Social Science. (2010). At https://andrewgelman.com/2010/03/22/simulation_of_a/ 

9. Cameron, D.Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics). (Addison-Wesley Professional, 2015). 

Bayesian's philosophy on Systematics

First, I’ll talk about the ideas and how we obtain them. Idea is defined as "an understanding, though, or picture in your mind" by Cambridge Dictionary (1), so I considered an idea as something that we obtained from previous knowledge but, how we get this knowledge? For that let's talk about Tolksdorf (2), he takes two different views and confronts them, the first is the knowledge as things that you can do, that suggest that theories are no-knowledge; the second defines knowledge as a cycle where you know that you really know. The author proposes knowledge as a thing that helps us to change from "beliefs" to "performance". So, in my opinion, the knowledge is based on experience, and theories are real-knowledge when are based on real data, that look more like the prospect of Plato (3), when he use epistemic analysis to differentiate between real stage with different opinion and which one is true; and look too like a kind of inductivism but explained by Hurley (4) where theories resulting from induction have a probability to be true or not.

The knowledge its taken as evidence for some theories but, how we test that theories or hypothesis? Well, to test hypothesis has to be based on personal postured, here I will talk about Bayesianism because it's my way to think. Maybe could be contradictory think to inductivism and Bayesian at the same time (5), but I referred to inductivism as the source of knowledge and Bayesian is the methodology to test a hypothesis based on prior knowledge (6).

So, how Bayesian works? Bayesian approach is "personal choice statistic" (7), firstly we have the empirical data and then we chose a prior, secondly evaluate how prior model your data and then calculate the posterior probability, that says how "good" or "bad" is your hypothesis for your data. A prior is a distribution's prospect for your data, here is the "personal choice", there are so many articles that develop this theme of choice prior (8) and the most are based on prior information and parameters assumption, but it's not my objective talk about this theme.

Given the above definitions of knowledge and Bayesian approach, therefore it’s time to discuss Systematics and Bayesian phylogenetics. Systematics is the discipline of biology that compares biological progress between animal, plants and all life's forms (9). Phylogenetic analysis studies the relationship between the organism and its principal aim is to find the common history and this could derive in a classification of species or clades -taxonomy- (10). Finally, Bayesian phylogenetics takes other statistics such as likelihood to estimate their prior tree and compare with data -evince of common history- (11), it seems to me a good way to estimate the true relationship between taxa. Furthermore, there's another conflict in phylogenetics relationship, how find the true phylogeny? That is impossible because the relationship could change even when we add or change data or evaluate different species (12). Finally, we just have probabilities, because we can't model all variables of evolution, and we won't ever know is that the real truth, this is the reason that I use the Bayesian approach to evaluate a phylogenetic hypothesis.

Reference

(1) Idea Meaning in the Cambridge English Dictionary. (n.d.). Retrieved September, 2018, from https://dictionary.cambridge.org/dictionary/english/idea

(2) Hetherington, S. (2011). Chapter One Knowledge, Ability, and Manifestation. In Conceptions of Knowledge (Vol. 4). Berlin, Germany: Technische Universität Berlin Institut für Philosophie.

(3) Plato. Book I, 344c. Plato Republic. Indianapolis: Hackett.

(4) Hurley, P. (2014).Chapter One: Basic Concepts. in A concise introduction to logic (Vol. 7). Nelson Education. pp 33-39

(5) Dorling, J., & Miller, D. (1981). Bayesian Personalism, Falsificationism, and the Problem of Induction. Proceedings of the Aristotelian Society, Supplementary Volumes, 55, 109-141.

(6) Hawthorne, J. (1993). Bayesian induction is eliminative induction. Philosophical Topics, 21(1), 99-138.

(7) Fienberg, S. E. (2006). When did Bayesian inference become" Bayesian"?. Bayesian analysis, 6-20.

(8) Yang, Z. (2008). Empirical evaluation of a prior for Bayesian phylogenetic inference. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 363(1512), 4031-4039.

(9) Kitching, I. J., Forey, P. L., Williams, D., & Humphries, C. (1998). Chapter One: Introduction. in Cladistics: the theory and practice of parsimony analysis (No. 11). Oxford University Press, USA.

(10) Futuyama, D. (2005) Chapter Two: Phylogeny. in Evolutionary Biology (3 ed.). New York: W.H. Freeman.

(11) Huelsenbeck, J. P., Rannala, B. and Masly, P. () An Introduction to Bayesian Inference of Phylogeny. Rochester: Department of Biology, University of Rochester.

(12) Nascimento, F. F., dos Reis, M., & Yang, Z. (2017). A biologist’s guide to Bayesian phylogenetic analysis. Nature ecology & evolution, 1(10), 1446.