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PHILOSOPHY OF BIOLOGICAL SYSTEMATICS

Kirk Fitzhugh, [email protected]

Table of Contents

Introduction ......................................................................................................................... 2

The Goal of Science. The goal of Biological Systematics ....................................................... 10

Causal Relationships in Systematics .................................................................................... 52

The Nature of Why-Questions ............................................................................................. 63

The Three Forms of Inference: Deduction, Induction, Abduction ......................................... 83

The Uses of Deduction, Induction, and Abduction in Science .............................................. 110

Systematics Involves Abductive Inference .......................................................................... 155

Inferences of Systematics Hypotheses, i.e. Taxa .................................................................. 176

Some Implications for Phylogenetic Methods ................................................................. 228

The Requirement of Total Evidence .................................................................................... 336

Homology & Homogeny & Homoplasy ............................................................................... 403

Character Coding ............................................................................................................... 466

The Mechanics of Hypothesis Testing in Biological Systematics .......................................... 500

Implications for Nomenclature ........................................................................................... 627

Defining Biodiversity and Conservation .............................................................................. 683
mailto:[email protected]

THE PHILOSOPHY

OF

BIOLOGICAL SYSTEMATICS

Kirk Fitzhugh

[email protected]

Natural History Museum of Los Angeles County

This course differs from most courses on biological systematics in that the emphasiswill not be on instructing you on how to use the variety of methods available toresearchers. Instead, the emphasis will be on examining what is required to ensurethat systematics, as a field of science, has an overarching framework that isconsistent with all fields of scientific inquiry. It is from this framework that one canreadily decide which methods are scientifically acceptable.

The Philosophy of Biological Systematics

The contrast with a systematics course

A TYPICAL PHYLOGENETICS COURSE:

Phylogenetic theory

Characters and character coding

Tree building techniques

Tree statistics and tree support

Bayesian inference

Maximum Likelihood

Alignment

Molecular Dating Various tree building programs (e.g., MrBayes, POY, and TNT)

P Systematics courses usuallyfocus on how to use methods.

P The present course will focuson what is required to treatsystematics as a science.

P The goal is to give you the abilityto determine which methods arescientifically acceptable.

The outline of topics shown here formed the basis for a recent 'phylogenetics'course in Denmark. The topics exemplify how the present course differs from whatis typically covered in systematics courses.

If science is not to degenerate into amedley of ad hoc hypotheses, it mustbecome philosophical and must enterinto a thorough criticism of its ownfoundations.

Alfred North Whitehead (1925: 25),Science and the Modern World.

One of the interesting phenomena surrounding the practice of systematics for thepast 40 years is that distinct schools of thought have arisen regarding what it meansto infer systematics hypotheses and to evaluate them. For instance, with regard tophylogenetic [sic] inference, the two most recognized schools of thought are whatare said to be 'parsimony' and 'maximum likelihood.' Or, this dichotomy issometimes seen as a distinction between hypothetico-deductive and statisticalpoints of view. One of the hallmarks of these different opinions is that no criticalassessment of the formal inferential structure of systematics is ever considered,such that neither the concept of parsimony nor likelihood are correctly justified.This lack of critical examination then extends to the matter of how one tests,evaluates, determines support, etc., for systematics hypotheses.

As indicated in the quote shown here, in order to address the matter of how we areto assess whether or not one systematics hypothesis is better or worse than anotherrequires that we carefully examine the philosophical foundations of hypothesisinference and testing.


Course Outline Part 1

1. The goal of Science. The goal of biological systematics.

2. Causal relationships in systematics.

3. The nature of why-questions.

4. The three forms of inference: deduction, induction, abduction.

5. The uses of deduction, induction, and abduction in science.

This course is arranged in four parts. Part 1 has as its focus identifying the goals ofscientific inquiry and biological systematics, followed by some of the consequencesof those goals. The three recognized forms of reasoning used throughout thesciences are then described in detail.



1. Systematics involves abductive inference.

2. Inferences of systematics hypotheses, i.e. taxa.

3. Some implications for phylogenetic methods.

In Part 2, we will identify the type of reasoning used in biological systematics toinfer hypotheses. We will see that all taxa have the form of explanatory hypotheses,directed at giving us at least initial causal understanding of some of the characterswe observe among organisms. Significant implications are then identified for someof the methods commonly used in phylogenetic [sic] systematics.



1. The requirement of total evidence.

2. Homology & homogeny & homoplasy.

3. Character coding.

4. The mechanics of hypothesis testing in biological systematics.

Part 3 will address four different issues, all of which have received considerableattention in biological systmeatics, but also have been misrepresented.

We will examine the correct interpretation of the requirement of total evidence,which has significant implications for the common approach of inferringsystematics hypotheses from partitioned data, as well as attempts to comparecladograms inferred from different data sets.

Next we will examine the definition of the term homology (sensu Owen) in relationto E. Ray Lankester's (1870) suggested replacement of that term with the two terms,homogeny and homoplasy.

A general overview of character coding will then be presented as it relates to thenature of our observation statements, why-questions, and the goal of biologicalsystematics reasoning.

Finally, the nature of hypothesis testing will be carefully examined, showing thattraditional attempts to characterize testing in systematics have been incorrect. Theproper approach to testing systematics hypotheses will be examined.



1. Implications for nomenclature.

2. Defining biodiversity and conservation.

In the final part of the course, we first will examine the implications of theinferential framework for biological systematics on our nomenclatural practices.The main focus here will be on the 'Linnean' system and the PhyloCode, to showthat neither approach correctly takes into consideration the nature of our inferences.For any nomenclatural system to be successful, it must be consistent with the factthat biological systematics is about inferring explanatory hypotheses, referred to astaxa, and formal names must refer to those hypotheses, not just organisms.

The last talk will be an opportunity to tie our systematics practices, as presented inthis course, to new formal definitions of biodiversity and conservation.Interestingly, the outcome will be to show that the term biodiversity is largelyuseless and potentially deceptive.

Science depends on judgments of thebearing of evidence on theory.... Oneof the central aims of the philosophyof science is to give a principledaccount of those judgments andinferences connecting evidence totheory.

Peter Lipton (2001: 184, Inference to the bestexplanation). In: A Companion to the Philosophyof Science.

The quote shown here epitomizes what will be most fundamental throughout thiscourse. Our emphasis will be on recognizing the relations between evidence andbiological systematics hypotheses. As we will see, these relations occur in differentways, depending on what we mean by 'evidence,' as well as our objectives inmaintaining particular relations. It is by applying the principles of philosophy ofscience to biological systematics that we can clearly understand why these relationsexist between evidence and hypotheses, and recognize the forms evidence takeswith respect to hypotheses.








Let's start by looking at the goals of science and biological systematics.

characterweighting?

measures ofsupport?

total evidence?hypothesistesting?

What is the philosophicalbasis for choosing a method?

The Confusing Variety of Systematics Methods

What are species? What are taxa?

One of the greatest difficulties in biological systematics is that we have available avariety of methods. But, there is no clear consensus among systematists as to whichmethods to use. Similarly, there are fundamental questions regarding what we meanby terms like 'species' or 'taxon.'

The only real way to resolve these problems is to have a philosophical basis forchoosing among methods. That basis can only come from first ackowledging thegoal of doing science, and then applying that goal to systematics.

Basic Criteria for JudgingMethods in Biological Systematics

Recognize the goal of Science.

The goal of biological systematics should beconsistent with this goal.

Does a particular systematics method satisfy thegoal of Science?

Does a particular systematics method accuratelyrepresent our perceptions and why-questions?

To determine whether or not an approach to biological systematics is scientificallyappropriate, we must first acknowledge the goal of doing science, as well asunderstand that the goal of systematics must be consistent with that more generalgoal. We can then determine whether or not specific methods actually serve tofulfill both the goal of science as well as systematics. In related fashion, we have toensure that the methods we use do accurately represent our observation statementsand why-questions, since these are the issues to which the goal of science inquiry isdirected.

Broadly speaking, the vocabulary of science has two basicfunctions: first, to permit an adequate description of the thingsand events that are the objects of scientific investigation;second, to permit the establishment of general laws or theoriesby means of which particular events may be explained andpredicted and thus scientifically understood; for to understanda phenomenon scientifically is to show that it occurs inaccordance with general laws or theoretical principles.

Hempel (1965: 139, emphasis original), Aspects ofScientific Explanation

The Goal of Science: To Causally UnderstandWhat We Observe

An answer to the question of what is the goal of science was nicely described by thephilosopher of science, Carl G. Hempel. The goal can be identified as having twoparts: (1) describing the objects and events we encounter, and (2) presentingexplanations of those objects and events, for the purpose of ever-increasing ourunderstanding as well as having the ability to make predictions into the future.

Overall, the goal of science is to enable us to *causally understand* phenomena. Aswe will see throughout this course, this goal will be the highest priority forbiological systematics.


Scientific inquiry has two fundamental components:

Descriptive: Theoretical:

observations inferences of hypotheses and theories

Based on Hempel's characterization of science, we recognize science as having twobasic parts: 'descriptive' and 'theoretical.' The descriptive component refers to ourcommunicating observations, as observation statements. The theoretical refers toour applications of theories and hypotheses to those observation statements.



The descriptive and theoretical aspects of inquiry are interdependent objects and events cannot be described in the absence of theory, and thebasis for theories and hypotheses are the objects and events which are in

need of understanding.



But, it is well known that observation statements cannot be made in the absence oftheories, and theories and hypotheses have their origins in observations. So, thedescriptive and theoretical realms are clearly interdependent.


Understanding


explanation / prediction

It is the interplay between the descriptive and theoretical thatleads to scientific understanding.



As the principle goal of scientific inquiry is to acquire causal understanding, andfrom that understanding we have the ability to explain phenomena as well as makepredictions of future phenomena, it is the interplay between the descriptive andtheoretical that leads to the acquisition of understanding.

Scientific Understanding, Defined

A phenomenon P can be under-stood if a theory T of P exists thatis intelligible (and meets the usuallogical, methodological and empi-rical requirements).

de Regt & Dieks (Synthese 2005: 150)

We have been referring to 'understanding' in the previous diagrams, so it will beuseful to have a formal definition of the term. The definition shown here offers theview that to understand a phenomenon is to associate that phenomenon with sometheory.

...the cognitive achievement realiz-able by scientists through theirability to coordinate theoreticaland embodied knowledge that applyto a specific phenomenon.

Leonelli (2009: 197)

Scientific Understanding, Defined

Leonelli (2009) offers a similar perspective with regard to biological understanding.We apply not only our theories but also our previous established knowledge to aphenomenon to provide us understanding of the latter.

1904-2005

...biology can be divided into thestudy of proximate causes, the sub-ject of the physiological sciences(broadly conceived)

Mayr (1982: 67)

, and into thestudy of ultimate (evolutionary)causes, the subject matter ofnatural history....

Even within biology, there have been attempts to characterize the nature of theunderstanding we seek regarding organisms. An excellent and very usefulcharacterization of biological understanding was developed by evolutionarybiologist, Ernst Mayr.

Mayr suggested that biological inquiry seeks to acquire understanding that is causal,and that such causal understanding can be separated into 'proximate' and 'ultimate'causes. While Mayr distinguishes proximate and ultimate causes as related to'physiological sciences' and 'natural history,' we will need to be more precise.

1904-2005

...proximate causes govern theresponses of the individual (andhis organs) to immediate factorsof the environment while ultimatecauses are responsible for theevolution of the particular DNAcode of information with whichevery individual of every speciesis endowed. Mayr (1961: 1503)

Mayr originally published his idea of proximate and ultimate causes in biology in1962. What might be noticed is that proximate causes refer to those causes that onlyoccur within an organism during its lifetime. Ultimate causes, on the other hand,transcend lifetimes.

Beatty (1994: 334)

The proximate causes of anorganisms traits occur within thelifetime of the organism....

The ultimate causes occur prior to thelifetime of the organism, within theevolutionary history of the organismsspecies.

In his analysis of Mayr's proximate/ultimate distinction, Beatty (1994) offers a verygood characterization, shown here.

proximate

ontogenetic /functional

evolutionary

ultimate

Biological Understanding sensu Mayr

We can now begin to summarize Mayr's view of causal understanding in biologywith the more general goal of science we examined earlier. We can see thatproximate understanding refers to ontogenetic and functional aspects during thelifetime of an individual organism. Ultimate understanding refers to evolutionarycauses that can apply to groups of organisms over time.

descriptive biology(observation statements)

It is sometimes overlooked how essentiala component in the methodology ofevolutionary biology the underlyingdescriptive work is.

Mayr (1982: 70)

Goal of Science acquire ever-increasing understanding:

descriptive causal - proximate / ultimate predictive


proximate


evolutionary

ultimate

But in addition to proximate and ultimate understanding, Mayr was very clear in hiswritings on the subject that there is a third dimension to understanding, what hereferred to as 'descriptive biology.' Mayr was correct that in order to pursue eitherproximate or ultimate understanding, one must already have observations of effectsthat are in need of explanation. These effects are in the form of the properties,features, characters, etc., of organisms, that we communicate by way of ourobservation statements.

Notice that Mayr's descriptive, proximate, and ultimate understanding areconsistent with the goal of science presented earlier. To acquire ever-increasingunderstanding we see that it must be descriptive as well as causal, and alsopredictive. We seek descriptive understanding of what we perceive, as well asoffering possible past causes that explain what we observe in the present. And weattempt to apply our understanding into the future with predictions of effects due tocausal conditions that exist in the present.


descriptive biology(observation statements)

proximate


evolutionary

ultimate

To what extent is biological systematicssuccessful at acquiring ever-increasingunderstanding that is descriptive, proxi-mate, and especially ultimate?

Mayr (1982: 70)

It is sometimes overlooked how essentiala component in the methodology ofevolutionary biology the underlyingdescriptive work is.

An important part of this course will be to examine the extent to which descriptive,proximate, and ultimate understanding is acquired in biological systematics. Thesewill be issues that need to be addressed both in terms of knowing the nature of ourreasoning from observations to the variety of hypotheses used in systematics, aswell as the correct approach to testing any of those hypotheses. It is especially theact of testing that accomplishes the task of increasing our causal understanding,which is the most fundamental goal of scientific inquiry.

Astronomy

Chemistry

Physics

Geology

Biology

Psychology

SpecializedTechniques






SCIENCE: General Principles and Specialized Techniques

Principles ofScientific Method

While the goal in all fields of science is the acquisition of causal understanding, andthat must be regulated by our general rules and methods in science, and moregenerally by philosophy of science, each field of science must adopt its ownspecialized techniques for the purpose of acquiring that understanding.

The problem we will identify in biological systematics, however, is that thespecialized techniques are too often divorced from the more general principles ofscientific inquiry and philosophy. We will attempt in this course to correct thatproblem.

1. Rationality

Beliefs and actions should be rational, i.e. theyshould make sense. A rational belief or action isone based on all evidence that is relevant to theformation of that belief or action.

Four Fundamental Criteria Applied in Science

In order to correctly characterize the nature of biological systematics as a fieldwithin the broader realm of science, we need to recognize four fundamental criteriathat are applied throughout the sciences.

The first criterion is rationality.

External physical world ofobjects and events

Internal mental world ofperceptions and beliefs

correspondence


2. Truth

Truth is a property of statements. Thecorrespondence theory of truth is the mostcommon concept of truth applied in Science: truestatements correspond with reality. Facts aboutthe world determine the truth of statements.

The second criterion is truth. As noted in this slide, the 'correspondence theory' istypically used in the sciences, although there are about six theories of truthavailable. With regard to systematics, in which there has developed a popularculture of thinking in terms of 'true phylogenetic trees' as a basis for judgingmethods of cladogram inference, it should be apparent that truth cannot be assertedseparate from some empirical basis for the truth of statements.


3. Objectivity

The existence of objects and events apart fromhuman minds. Objective knowlege is concernedwith physical objects and events.

The third criterion is objectivity, which should be apparent.


4. Realism

The correspondence of human perceptions withthe external and independent (and possiblyunobservalbe) realities of physical objects andevents.

And finally there is the criterion of realism.

Common Sense

Common Sense: The assumption that physical reality exists.

The Foundations of Science

As we have already noted, the ultimate goal of science is to acquire ever-increasingcausal understanding of the phenomena (objects and events) we encounter. Tosuccessfully achieve that goal, we have to recognize the hierarchical structurewithin which science resides as part of human reasoning.

The most general rule we have is that of common sense. In other words, we operateunder the assumption that physical reality does exist - that all that we perceivearound us are not just hallucinations. Without this assumption, empirical inquiry ofany kind would not be possible.

Common Sense

Philosophy

Philosophy: The study of the way humans think and reason.Composed of four main branches:

logic, the study of reasoning

epistemology, the study of knowledge

metaphysics, the study of concepts and their relations

ethics, the study of moral evaluation


Within the realm of common sense, we have the field known as philosophy - thestudy of the way humans think and reason. And within philosophy there are fourbranches.

Common Sense

Philosophy

Philosophy of Science

Philosophy of Science: The study of the principles and methodsapplied in all fields of science.


The four branches of philosophy presented in the previous slide are often applied tothe subfield of philosophy, known as philosophy of science, which studies theprinciples and methods used throughout the sciences.

Common Sense

Philosophy


Scientific Methods

Scientific Methods: The processes of hypothesis and theoryformation, testing, and revision, for thepurpose of acquiring understanding ofphysical reality.


It is by way of philosophy of science that scientific methods are developed. It isthose methods that are intended to enable us to achieve the goal of scientificinquiry, i.e. causal understanding.

Common Sense

Philosophy


Scientific Methods

Scientific Specialties

Scientific Specialties: The fields of study that address specificaspects of physical reality, e.g., physics,chemistry, paleontology, systematics.


By way of particular scientific methods there are the applications of scientificspecialities.

Common Sense

Philosophy


Scientific Methods


Technology

Technology: The specialized techniques appliedin a specific field of study.


The applications of scientific methods within scientific specialities are often onlypossible because of technology, e.g. computers, microscopes, etc.

Common Sense

Philosophy


Technology

Scientific Methods



Scientific methods are constrained by the principles of philosophy, as wellas the philosophy of science. The problem in systematics is that methodsare too often developed and considered in isolation of philosophy.

Finally, it is important to notice that if we are going to critically evaluate ourscientific methods, then this must be done in the context of philosophy of science aswell as philosophy in general. Scientific methods cannot operate independent ofphilosophical principles. Unfortunately, this is exactly what has too often occurredin biological systematics. This course is intended to help correct that error.

What is the Goal of Biological Systematics?

C To explain shared similarities among a group of organisms.

Some common answers

We have seen that the goal of scientific inquiry is to not only describe the objectsand events we encounter (observation statements), but more importantly to causallyunderstand those phenomena. We now need to determine if the goal of biologicalsystematics is consistent with the goal of science.

When we ask the question, 'What is the goal of systematics?', there are at least threegeneral answers given. What you will notice is that most of these answers are notconsistent with the goal of science. And this is a serious problem.

One answer to the question we sometimes encounter is that systematics is intendedto explain shared similarities.

Parsimony [sic]

A genealogy is able to explainobserved points of similarity amongorganisms just when it can accountfor them as identical by virtue ofinheritance from a commonancestor.

Farris (1983: 18), The logical basis ofphylogenetic analysis

The idea of explaining shared similarities has been especially common in thecontext of cladograms. Unfortunately, this notion is not usually extended to otheraspects of systematics, as we will see later in this course.

The idea of explanation in systematics has been common in the cladistics literature,especially in connection with the principle of parsimony.

Likelihood [sic]

The concept of likelihood refers tosituations that typically arise innatural sciences in which givensome data D, a decision must bemade about an adequateexplanation of the data.

Schmidt & von Haeseler (2010: 181),Phylogenetic inference using maximumlikelihood methods.

But we also find claims that explanation is important when 'likelihood' methods areused. But as we will see later in this course, these claims of importance ofexplanation are too often poorly formulated and usually insufficient.



Some common answers

C To show the phylogeny / evolutionary history of a group oforganisms.

A much more vague reference to explanation being the goal of systematics comesfrom the popular view that we want to present 'phylogeny' or 'evolutionary history.'

Systematics is the study of organicdiversity as that diversity is relevantto some specified pattern ofevolutionary relationship thought toexist among the entities [sic]studied.

Wiley & Lieberman (2011: 8),Phylogenetics: Theory and Practice ofPhylogenetic Systematics

And as we see in this quote, even the explain-as-phylogeny point of view can betaken to a point of being uninformative.

A phylogenetic tree [cladogram]...is a graphic representation of thehistorical course of speciation.

Wiley & Lieberman (2011: 4),Phylogenetics: Theory and Practice ofPhylogenetic Systematics

And the explanatory nature of cladograms is often inconsistent.



C To discover natural, hierarchical order, then reflect that orderin classifications.

Some common answers


Rather than having a direct or indirect goal of explanation of systematics, there isthe still-popular school of thought that causality should be removed fromsystematics. In this instance, diagrams such as cladograms have no explanatoryinterpretation, but instead either summarize character distributions, or conveynebulous ideas such as 'natural order' or 'natural hierarchies.' The general phrasecommonly used to identify this less-than-scientific perspective is 'pattern cladistics.'

Systematics is primarily concernedwith problem solving. This mightseem an obvious statement, yet themajority of those interested insystematics and phylogenyapproach the subject as beingconcerned with inferences,reconstructions, or estimations....

Williams & Ebach (2008: 21)Williams & Ebach (2008: 21)

The general problem may bephrased as follows: What are theinterrelationships amongorganisms?

The pattern cladistic approach has serious problems in that it is inconsistent withthe goal of scientific inquiry.



C To discover natural, hierarchical order, then reflect that orderin classifications.

Some common answers


ARE ANY OF THESE GOALS CONSISTENT WITH THEOVERALL GOAL OF SCIENCE?

Based on what we have already seen regarding the goal of scientific inquiry, thecommonly identified goals of biological systematics are not sufficiently consistentwith the goal required of all sciences.


A Formal Definition of Biological Systematics

The actions of biological systematization. The goal of which isto obtain causal understanding of the properties or charactersof organisms exhibited at different stages of their life history orshared among some set of individuals.

The term taxonomy is unnecessary because it is a synonym ofsystematics.

To correctly and effectively identify the goal of biological systematics requires thatthis goal be fully consistent with the more general goal of scientific inquiry. Shownhere is a formal definition of biological systematics that not only places it squarelyin the realm of science, but also establishes the field as having the same goal as allfields of science: to acquire causal understanding of the features we observe oforganisms.

Notice that with this formal definition, we no longer need to make a distinctionbetween the terms 'systematics' and 'taxonomy.' While some might think thattaxonomy refers only to 'species descriptions,' as we will see during this course, allfacets of systematics have the same inferential framework, wherein all actions insystematics are directed at achieving the goal of causal understanding. Taxonomy isa term that should be regarded as a synonym of systematics. Systematics is the moreaccurate term to use.


Any of a set of classes of hypotheses used in biological syste-matics for the purpose of explaining particular characters ofobserved organisms.

A Formal Definition of Taxon

With the formal definition of biological systematics accurately manifesting the goalof scientific inquiry, it is crucial that our reference to a taxon also be consistent withthat goal. Clearly, as the goal of systematics is to present explanatory hypothesesthat give us an opportunity to understand the occurrences of features amongorganisms, then taxa can only be regarded as synonymous with those hypotheses.

Of course, this will have profound consequences, because too often taxa are thoughtof as being either individuals or things that exist in time and space, much likeorganisms. As we will see throughout this course, taxa can only be regarded asexplanatory hypotheses, not as things or individuals. Indeed, our use of the termtaxon or taxa is entirely unnecessary. It would be more appropriate to simply referto hypotheses.

...the semaphoront [character bearer]corresponds to the individual in acertain, theoretically infinitely small,time span of its life, during which it canbe considered unchangeable.

W. Hennig (1966: 65)

The definition of taxon presented in the previous slide refers to individualorganisms and the characters we observe. In his book, "Phylogenetic Systematics"(1966), Willi Hennig correctly stressed that we make observations of individuals atparticular moments in time during their entire ontogeny or life history. Hennigsuggested that the appropriate term for the individuals we observe should be'semaphoront.'

...it follows that we should not regard theorganism or the individual (not to speakof the species) as the ultimate element ofthe biological system. Rather it should bethe organism or the individual at a parti-cular point of time, or even better, duringa certain, theoretically infinitely small,period of its life. We will call this elementof all biological systematics... thecharacter-bearing semaphoront.

W. Hennig (1966: 6, emphasis original)

Hennig's use of the term semaphoront to indicate our observations of organisms atspecific times during their life history is especially significant because it betteremphasizes that the fundamental units in biological systematics are individualorganisms. Indeed, notice that Hennig understands that species are not thefundamental units in systematics.

Obtain causal understanding of the properties or charactersof organisms exhibited at different stages of their life historyor shared among some set of individuals.


Some Consequences:

Biological systematics involves the non-deductive inference ofexplanatory hypotheses and, where possible, their subsequenttesting.

The goal of biological systematics is to move toward causalunderstanding of what we observe, not merely to obtaincladograms, trees, or to reconstruct phylogeny.

Cladograms are not things in themselves, but are very limitedexplanatory hypotheses of observed properties of individualsamong different taxa.

With a formal definition of biological systematics that is consistent with the goal ofscientific inquiry, there are several significant implications.

The first is that as systematics is about the inferences of explanatory hypotheses, wewill be clearly identifying the type of inference involved, as well as acknowledgingthat the testing of those hypotheses is very different from what has traditionallybeen presented by systematists.

Second, since the goal of systematics is consistent with the goal of science, i.e. toacquire causal understanding, our goal is *never* to just get trees, cladograms, orreconstruct phylogeny [sic].

And finally, we will see in this course that cladograms are *very vague*explanatory accounts. Indeed, they are so poor as explanations that they offer usvery little to serve as vehicles for the goal of doing systematics, much less science.

Present(the realm of Observation)

Past Future

Cause

Effect

predictionEffect

ExperimentalSciences

abduction

CausalHypothesis

HistoricalSciences

The Two Realms of Science

Biological systematics is part of the historicalsciences, where observations in the present areused to infer explanatory hypotheses about pastevents to account for those observations.

For our purposes in biological systematics, we can think of science as having twobroad, operational realms: historical and experimental. The historical sciencesinclude such fields as systematics, evolutionary biology (in part), paleontology,archaeology. The experimental sciences include physics, chemistry, geology (inpart). There is, of course, a lot of overlap between these.

The main distinction between the historical and experimental sciences is that thehistorical sciences focus on effects that exist in the present, and our goal is todevelop explanatory hypotheses of possible past causal events that can account forthose oberved effects. The experimental sciences, on the other hand, begin withknown causal, or experimental, conditions in the present, to see if predicted effectsoccur in the future.

Another way to think about this distinction is that the historical sciences are mainlyconcerned with the inferences and testing of explanatory hypotheses, whereas theexperimental sciences are mainly concerned with the testing of theories. But, becautious about this distinction, since there always are exceptions.








We can now take an initial look at the nature of the relationships that are referred toin biological systematics. Since the goal of systematics is to present us with causalunderstanding of the features of organisms, then the nature of the relationshipsthroughout systematics must be causal in form.

RELATIONSHIPS &BIOLOGICAL SYSTEMATICS

When we speak of relationships in systematics,we mean causal relationships. The basic unitto which these causal relationships refer is indivi-dual organisms. Taxa as explanatory hypothe-ses indicate particular causal relationshipsamong groups of organisms.

To start with our examination of these issues, we need to understand what we meanwhen we speak of 'relationships' in biological systematics. We use the termrelationship on a regular basis, but, the word is often not clearly understood when itis used in systematics.

We need to first recognize that when we speak of relationships, we are speaking ofcausal relations. For example, we say we are related to our parents, we are relatedto our sisters or brothers, we are related to our grand parents. In every instance, therelations we are talking about are causal relations, because it is that type ofrelationship that gives one understanding. And, as we will see in the remainder ofthis course, the units to which those causal relationships refer are individualorganisms.

Then, we can specifically look at the way in which we infer each of the types ofcausal relationships, as explanatory hypotheses, that are used in biologicalsystematics. And again, it is causal relationships that we are interested in, because itis those types of relations that best serve the overall goal of scientific inquiry.

Hennig, W. 1966.Phylogenetic Systematics

(1913-1976)

One of the best examinations of the nature of causal relationships in biologicalsystematics can be found in Willi Hennig's (1966) book, "PhylogeneticSystematics."

Hennig, W. 1966. Phylogenetic Systematics

Classes of Relationships

1. ontogenetic

1

2. cyclomorphic

2

3. sexual dimorphic

3

4. tokogenetic

45. polymorphic

5

6. specific

6

7. phylogenetic

7

Each of these classesof relationships refer tothe different classes ofexplanatory hypotheseswe call taxa.

Shown here is Hennig's (1966) well known figure 6, which we often see reproduced in other works on the principlesof biological systematics. It is in this figure that Hennig identifies the fundamental classes of relationships used insystematics.

But too often, what is not recognized is that Hennig pointed out that all of these relationships deal with individualorganisms. He discussed in great detail seven classes of relationships involving organisms, all of which are shown inhis diagram.

Ontogenetic relationships. Where we speak of an individual at a particular point in it's life history.

Cyclomorphic relationships. Where there are seasonal phenotypic differences among individuals of differentgenerations.

Sexual dimorphic relationships. The phenotypic differences between males and females.

Tokogenetic relationships. Parents producing offsrping as a result of reproductive events (tokogeny).

Polymorphic relationships. Different phenotypes expressed among individuals in a population.

Specific relationships. Refers to species hypotheses, accounting for features among a group of organisms that arereproductively isolated from other groups.

Phylogenetic relationships. The most general type of relationship in systematics, accounting for shared featuresamong organisms to which different species hypotheses refer, as well as strictly asexual or strictly self-fertilizinghermphroditic organisms. But as will be noted later in the course, because of what classes of causal events areentailed by phylogenetic hypotheses, such hypotheses are actually not applicable to obligate asexual or self-fertilizinghermaphroditic organisms.

7 Classes of Causal Relationships

1

2

3

4

5

6

7

Descriptive Biology(observation statements)

1. ontogenetic Proximate

1

2. cyclomorphic

3. sexual dimorphic

4. tokogenetic

5. polymorphic

6. specific (species)

7. phylogenetic

Ultimate

2

3

4

5

6

7

Using Hennig's (1966) figure 6, we can clearly identify the three broad classes ofcausal understanding recognized by Ersnt Mayr, that were referred to earlier.

Semaphoronts ontogenetic hypotheses(e.g., larva, juvenile, adult)

Individuals the objects we perceive

Kingdom

Phylum

Class phylogenetic hypotheses

Order

Family

Genus

Species specific hypotheses

Subspecies intraspecific hypotheses

Families, demes, populations tokogenetic hypotheses

Individuals the objects we perceiveIndividuals the objects we perceiveDescriptive explanations

(observation statements)

Proximate explanations

Ultimate explanations

And here are the distributions of classes of hypotheses shown in the previous slide,in a different arrangement.

Semaphoronts ontogenetic hypotheses(e.g., larva, juvenile, adult)

Individuals the objects we perceive

Kingdom

Phylum

Class phylogenetic hypotheses

Order

Family

Genus

Species specific hypotheses

Subspecies intraspecific hypotheses

Families, demes, populations tokogenetic hypotheses

Individuals the objects we perceiveIndividuals the objects we perceive (observation statements)

All taxa/hypotheses in biological systematicsare inferred by way of abduction

As we saw earlier with the definition of the term 'taxon,' all taxa are explanatoryhypotheses. All of the different classes of hypotheses-as-taxa shown here, indicatedby the red arrows, are the products of a type of reasoning known as 'abduction,'which we will examine in depth later in the course. And abduction, or abductiveinference will form the foundation for the remainder of our examination ofsystematics in this course.

individuallarva, juvenile, adult

Ontogenetic, Specific, Phylogenetic

Semaphoront:an explanatory hypothesis of ontoge-netic relationships, derived from onto-genetic theories applied to a particularorganism. The hypothesis accountsfor features of an organism at aparticular age relative to features atanother age, by way of ontogeny.

Examples of Causal Relationships in Systematics

We can briefly look at three of the most common classes of relationships referred toin biological systematics, and discussed by Hennig (1966): semaphoront, specific(species) relationships, and phylogenetic relationships.

A semaphoront is an individual at a specific point in time during its life history. Inother words, it is a hypothesis that gives us an explanatory account relative to theontogenetic history of the individual.

individual

speciesa-us

Species:an explanatory hypothesis ofspecific relationships derived fromtheories of character origin/fixationduring tokogeny, applied to a setof semaphorants. A lineage,accounting for features of a groupof semaphoronts relative to differ-ent features in other semapho-ronts (in other species).



A species is an explanatory hypothesis that refers to specific relationships.

individual

speciesa-us

species b-us species c-us

b-us c-us

a-us

phylogeneticrelationships

Supraspecific Taxon:an explanatory hypothesis ofphylogentic relationships, deriv-ed from tokogenetic, evolution-ary, and population splittingtheories, applied to particularsemaphoronts. Accounting, byway of phylogeny, for the samefeatures shared by semapho-ronts among two or morespecies relative to differentfeatures in semaphoronts ofother species.



And a phylogenetic hypothesis refers to phylogenetic relationships. Notice that it ismore accurate to refer to such relationships as hypotheses as opposed to 'taxa.'

Phylogenetic systematics sensu Hennig (1966) only provides causalunderstanding of the properties of groups of organisms to which two ormore species hypotheses also refer. Explanatory hypotheses of ontogeny,tokogeny, species, etc., represent other levels at which causal under-standing can also be achieved, but by using theories different from thoseapplied in phylogeny.

Not All of Systematics is Phylogenetic

As we will see, distinguishing the different classes of explanatoryhypotheses used in systematics is fundamental to identifying theappropriate levels at which our why-questions should be asked andanswered.

Historically, there has been confusion regarding what is meant by the phrase'phylogenetic systematics.' Yet, when one carefully reads Hennig's (1966) book, it isclear that he understood biological ('hologenetic') systematics to refer to the varietyof explanatory hypotheses, with phylogenetic systematics only referring to one ofthose classes of relationships.

As we will see in this course, as the goal of systematics is the same as the goal in allfields of science, to acquire causal understanding, to achieve such understandingcomes from different classes of hypotheses used to answer our different why-questions.








All of understanding in science begins with observations and our questionsassociated with those observations in need of being explained. The type ofquestions most commonly asked in systematics are known as why-questions. It isour why-questions that form the basis for all aspects of biological systematics.

The scientist is not a personwho gives the right answers,he's the one who asks theright questions.

Claude Levi-Strauss (1964)Le Cru et le Cuit

Unfortunately, when we speak of science, we almost always neglect to consider thequestions we are actually asking, for which we seek hypotheses and theories to giveus answers.

...a logic in which the answersare attended to and the questionsneglected is a false logic.

R.G. Collingwood (1938: 31),An Autobiography

Like any endeavor, science is one we perform to achieve particular goals. And aswith any action carried out among a group of people, science has its socialcomponent, such that scientific procedures tend to become standardized to the pointwhere we stop examining the bases for what we do, and we just go through themotions.

Biological systematics suffers from a perspective where practitioners are seekinganswers, yet they either don't know the questions they are asking, or they are askinginappropriate questions. This neglect is what has allowed for the rapid developmentof systematics methods and computer algorithms that offer contradictoryapproaches, and with no jusification based on the goal of using those methodsaccording to the why-questions we should be asking.

Why Ask Why!!!!Questions?

To explain the phenomena in theworld of our experience, to answerthe question why? rather than onlythe question what?, is one of theforemost objectives of all rationalinquiry;

From: Hempel & Oppenheim (1948: 135), The logic of explanation.Philosophy of Science 15: 135-175.

Look to the goal of scientific inquiry

and especially [for science]...to go beyond a mere description ofits subject matter by providing anexplanation of the phenomena itinvestigates.

By this time it is probably obvious why we ask why-questions -- because we seekcausal understanding of the phenomena we encounter. The quote shown here, byCarl Hempel, exemplifies the reason we ask why-questions, and the fact that suchquestions are a fundamental part of the goal of scientific inquiry.

If the goal of biological systematics is to providecausal understanding of the properties of organisms,then we must first recognize the nature of our why-questions, to which evolutionary theories andsystematics hypotheses provide answers.

The Foundation for All of Systematics

The Nature of Our Why-Questions

We now need to examine the specific properties of why-questions, without whichany treatment of biological systematics would be incomplete. As we will seethroughout much of this course, our why-questions are fundamental components.

Why-Questions

Why P?

Example: Why do these specimens havelateral body wall extensionscalled appendages?

How we usually ask them

It is essential to know the formal structure of the why-questions we ask. We usuallythink of why-questions as simply having the form, "Why P?", or "Why is it the casethat x is P?" This form is, however, incomplete and thus does not fully represent thebasis for such questions.

Why P in contrast to X?

PX

Example: Why do these specimens have lateral body wallextensions (= appendages) in contrast to otherspecimens with convex body walls?

Why-Questions

The proper form: Contrastive questions

The correct form of why-questions is that they are 'contrastive.' In other words, weask questions that contrast the surprising or unexpected condition in need of beingexplained with the expected condition(s) that has already been explained.

In the case of systematics-based observations, our contrastive why-questions are ofthe form shown here.

Three parts: why

Why P in contrast to X?

PX


Why-Questions

There are three components to why-questions. First is that such questions areprefaced with 'why.'

fact

+ fact(s) + foilThree parts: why

foilWhy P in contrast to X?

PX


Why-Questions

The other two components of why-questions are known as 'fact' and 'foil.' The 'fact'is what is in need of being explained, in contrast to the 'foil.'

contrast class

fact foilWhy P in contrast to X?

PX


Why-Questions

Three parts: why + fact(s) + foil

+ fact(s) + foilThree parts: why

The 'fact' and 'foil' together comprise the 'contrast class' of a contrastive why-question.


Question: Why P in contrast to X?

Presupposition: it is true that P is the case.

Three parts: why + facts-as-presuppositions + foil

Why-Questions

Another important condition is that we assume the truth of the observationstatement(s) that comprise the 'fact.' These facts are then said to be presuppositions.

Question: Why P in contrast to X?

fact foil

Criterion for sensibility

...we evaluate the sensibility of a why questionby considering whether the fact and foil can beviewed as [alternative] culminating outcomes ofsome single type of natural causal process.

Barnes, E. 1994. Why P rather than Q? The curiosities of fact and foil.Philosophical Studies 73: 3553.

Why-Questions

The choice of foil for why-questions is not arbitrary. Instead, correctly choosing afoil requires that fact and foil are alternative effects from a single type of causalprocess. The following examples exemplify this requirement.

Question: Why did the match not ignitein contrast to igniting?

fact foil

Common causal process: frictional surface


Why-Questions

In this example we have the why-question, "Why did the match not ignite incontrast to igniting?" Notice that fact and foil trace back to the common causalprocess of rubbing a match along a frictional or rough surface. The why-questionseeks an explanation for why the match did not ignite given that under theconditions we would have expected it to ignite. The question has proper formregarding an appropriate foil for the fact.

Incorrect Question: Why did the match not ignitein contrast to breaking?

fact foil

frictional surface thumb pressure

Separate causal processes


Why-Questions

Here is a why-question of incorrect form. Notice that the fact and foil would traceback to separate and different causal processes. Explaining why the match did notignite cannot be contrasted with why the match broke. The two conditions refer toentirely different causal processes.

Complete Why-Questions

Question: Why are these matches burned, in contrast to unburned?

fact foil

Common cause versus separate causes

There is an additional issue that we need to consider with regard to why-questions.This is an issue that is of importance in systematics because we observe sharedfeatures or characters among groups of organisms. When we observe multipleeffects that have the appearance of being correlated, we have to decide how toexplain such correlations.




Common cause explanation Separate cause explanation

In the example shown here, the correlation of finding a group of burned matchesrequires that we decide whether to answer the why-question, "Why are thesematches burned, in contrast to unburned?", by either a common cause explanationor by way of separate cause explanations.




How to decide?

background knowledge

Making a decision to provide a common cause explanation or separate causeexplanations requires that one take into consideration their background knowledgeregarding such effects. What is important to recognize is that offereng separatecause answers will be based on a set of questions that will be different from whatwill be required for a common cause answer.


Q1 Q2 Q3

Separate causes

A1 A2 A3

In the case of treating the observation of the burned matches as explainable by wayof separate causes, we would treat each effect (i.e. burned match) as leading toseparate why-questions and separate respective answers.


Q

Common cause

A

For a common cause explanation, we regard the correlation would be far lesssurprising if explained by a single, commmon cause. Hence the single questionshown earlier, "Why are these matches burned, in contrast to unburned?"


All Questions Have a Contrastive Form

The contrastive nature of why-questions, plusthe reasoning used to answer to thosequestions, provide the strongest criteria forcritically evaluating the methods and proceduresused in systematics.

Any critical appraisal of biological systematics must stand on two issues. The firstbeing the form of contrastive why-questions, as we have just seen. The second isthat the proper form of our why-questions then lead to inferences of answers tothose questions.








Now that we have identified that the goals of science and biological systematics areboth to acquire causal understanding of the phenomena we encounter, we next needto carefully examine the types of reasoning used in the sciences to achieve our goal.

As we will see later in the course, these types of reasoning will play critical roles inattempting to correctly characterize the tasks of systematics.

The Fundamentals of Inference

Inference:

The act of reasoning from a statement(premise) or statements (premises), to aconclusion or set of conclusions.

This section of the course will focus on identifying the types of reasoning, known asinference, we use every day as well as in the sciences.

Two Types of Inference HaveTraditionally Been Recognized

Deduction:

Inferences in which a conclusion drawn from a set of(true) premises cannot contradict those premises, andtherefore must also be true.

All humans are mortal

Kirk is human

Kirk is mortal

Traditionally, when people speak of logic as the study of reasoning, they only makea distinction between two types of reasoning: deductive and inductive. Let's firstlook at this distinction, before more accurately segregating reasoning.

In this example of deduction, notice that the premises, 'All humans are mortal' and'Kirk is human,' is separated from the conclusion, 'Kirk is mortal,' by a single line.


Induction:

Inferences in which similarities are identified betweenobserved objects or events of a given class, andhypothetically extended to unobserved objects orfuture events of that class.

Kirk is human

Kirk is mortal

All humans are mortal

In the case of an induction (or any non-deductive inference), the premises areseparated from the conclusion, or conclusions, by a double line.


Deduction:

Inferences in which a conclusion drawn from a set of(true) premises cannot contradict those premises, andtherefore must also be true.

Induction:

Inferences in which similarities are identified betweenobserved objects or events of a given class, andhypothetically extended to unobserved objects orfuture events of that class.

hypothesish

datad

deduction

(1) Deduction: predictions of potential test consequencesderived from the hypothesis to be tested.

Deduction & Induction

The Popular View of Their Relations

induction

h

(2) Induction: performing the test; observations of testconsequences, providing either confirming/corroborating or disconfirming/falsifyingevidence.

deduction induction

h

d

This diagram illustrates how people often speak of the relations between deductionand induction in science. Starting with a hypothesis or theory, inferred by way ofinduction, one uses deduction to predict potential test evidence, then induction isused in the process of testing. The view is that there are cycles of deduction andinduction in a continual process of evaluating theories and hypotheses.

Given Hypothesis Expected Data

Inferred Hypothesis Actual Data

deduction

induction

H.G. Gauch, Jr. (2003: 160), Scientific Method in Practice


Deduction is reasoning from what is in the mind to whatis in the world.

Induction is reasoning from what is in the world to what isin the mind.


This is another, common view of the relation between deduction and induction inscience.

The premises and conclusion(s) of an inference containstatements that can be categorized as three possible forms:

The Structure of Inferences

Rule: a law, empirical generalization, or theory, often statinga relation between cause and effect;

Case: a statement about a thing(s), or event(s), in the form ofcausal or initial conditions;

Result: a statement of a consequence or effect that is relatedto the Case.

The Basic Components

For our purposes of examining the nature of reasoning that exists throughoutbiological systematics, we need to make more precise distinctions between thetypes of reasoning used in science. To compare and contrast the different types ofreasoning, we will use a set of statements that can be used as either premises orconclusions. These statements are referred to as Rule, Case, and Result.

By identifying premises or conclusions as Rule, Case, and Reult, we will find thatin addition to deduction and induction (sensu stricto), we will also have torecognize a third type of non-deductive reasoning, called abduction.

Rule: All marbles in this bag [M] are red [P].

S = subjectP = predicateM = middle term

end terms

Deduction

A Simple Example

In this example of deduction, as well as in following examples, the components ineach of the statements comprising the premises and conclusions are identified assubject, predicate, or 'middle term.' The subject and predicate are sometimesreferred to as 'end terms' since in a deductive arrangement they are present in thepremises and conclusion. The 'middle term,' which functions as a predicate, thenjoins together the end terms in the conclusion.



end terms

Case: This marble [S] is from this bag [M & P].

Deduction

A Simple Example

Notice that 'this bag' functions as both the middle term and predicate for the Case.



end terms


Result: This marble [S] is red [P].

Deduction

A Simple Example

The predicate 'red' in the Rule, and the subject 'marble' in th Case are broughttogether in the Result. The middle term, 'this bag,' is only referred to in thepremises. In deduction, the middle term serves to bring together the end terms inthe conclusion.





end terms

Deduction

A Simple Example

TRUE

TRUE

TRUE

Because of the form required of the premises in deduction, if the premises are true,then the conclusion must also be true. In other words, the conclusion is certain.

P

M S

(a)

PMS

completeinclusion

(b)

Rule: The marbles in this bag [M] are red [P].



Deduction

Deduction has a structure wherein the 'middle term' [M] serves to bring together thesubject [S] and predicate [P] in the conclusion. This relationship is illustrated herein (a), where the solid lines indicate relations stated in the premises, and the dashedline denotes the relation provided by the conclusion. The Euler diagram in (b)provides another representation of these relations, where deduction is characterizedby 'complete inclusion:' the subject [S] is a subset of the middle term [M], and thelatter is a subset of the predicate [P].

Induction

A Simple Example

Case: These marbles [S] are from this bag [M & P].

Result: These marbles [S] are red [P].

With induction, the premises are comprised of the Case and Result. Notice that thesubject [S] is present in both premises.


Induction

A Simple Example



From the premises is concluded the Rule. You might notice that the premises state alimted set of observations, from which a general statement is inferred. In fact, theexample looks very similar to a statistical inference, proceeding from observationsof a sample to a conclusion about the population from which the sample was taken.As we will see later in the course, induction is the principle mode of reasoning usedin statistics. And, since statistics is about testing statistical hypotheses, we will findthat induction is the approach taken for testing in general.




Induction

A Simple Example

TRUE

TRUE

TRUE / FALSE

In contrast to deduction, where true premises always guarantee a true conclusion,an inductive conclusion from true premises cannot guarantee a true conclusion. Theconclusion is not certain; it is only probable, as determined by the premises. Noticethat the conclusion thus makes a claim that goes beyond what is offered by thepremises.

P

M S

(a)

Case: This marble [S] is from thisbag [M & P].


Rule: The marbles in this bag [M] arered [P].

partialinclusion

PSM

(b)

Induction

As shown in (a), induction differs from deduction in bringing together the predicate[P] and middle term [M] in the conclusion by the presence of the subject [S] in bothpremises. The Euler diagram (b) shows induction to be a matter of 'partialinclusion.'

A Third Type of Inference is Often Recognized

Abduction:

Reasoning from observed effects in the present(consequents) to a conclusion(s) of possible cause(or causes) in the past (antecedent).

Abduction is also the form of inference used todevelop our observation statements. As a result,abductive inference is the most common typeof reasoning we use on a daily basis.

In addition to deduction and induction, there is a third type of non-deductiveinference that is often recognized, called abduction. Abduction is a form ofreasoning we use on a daily basis to infer from observed effects to a possible causeor causes.

Charles Sanders Peirce(1839-1914)

Abduction

[A] hypothesis cannot be admitted,even as a hypothesis, unless it besupposed that it would account for thefacts or some of them. The form ofinference, therefore, is this:

The surprising fact, C, is observed;

But if A were true, C would be amatter of course,

Hence, there is reason to suspectthat A is true.

A Third Type of Inference is Often Recognized

While abduction was recognized by Aristotle, it was not until the 19th century thatthe importance of this type of reasoning was recognized. The most prominentproponent to study the relations of abduction to deduction and induction wasCharles Sanders Peirce (pronounced 'Purse'). But, it was not until the second half ofthe 20th century that philosophers and scientists started to take seriously theimportance of abduction.


Abduction

A Simple Example

In abduction, the major premise is the Rule.



Abduction

A Simple Example

The minor premise is the Result. Notice that the predicate, 'red,' appears in bothpremises.




Abduction

A Simple Example

What you should notice is that the Rule, as a theory, is applied to the Result, wherethe Result can be regarded as an effect. The conclusion, Case, then has the qualityof an explanatory account. In this example, we explain why 'this marble' is redbecause it came from 'this bag' of red marbles.



Case: This marble [S] is from this bag [M].

Abduction

A Simple Example

TRUE

TRUE

TRUE / FALSE

As with any non-deductive inference, the true premises of an abduction do notguarantee the truth of the conclusion.

P

M S

P

M S

Rule: The marbles in this bag [M]are red [P].


Case: This marble [S] is from this bag [M].

exclusion

Abduction

(a) (b)

The structure of abductive inference is the conjunction of some theory or law-likestatement (Rule) and observed effects (Result) to conclude a possible cause (Case).Abduction is sometimes referred to as 'reverse deduction' in that the Case (cause) isconcluded from the Rule (theory) and Result (effect), rather than the Result beingconcluded from the Rule and Case as in deduction.

As a result (a), it is the presence of the predicate (P) in both premises whichsuggests the relation between the subject (S) and middle term (M) in theconclusion. Unlike deduction, which shows 'inclusion,' and induction, which shows'partial inclusion,' abduction is characterized by 'exclusion' (b).

Induction & Abduction

Ampliative: conclusion can imply thingsnot stated in premises.

Not necessarily truth preserving: truthof conclusion not guaranteed.

Support for conclusion by premises canvary in strength.

Requirement of total evidence must beconsidered.

Deduction

Not ampliative: conclusion cannot gobeyond what is stated in premises.

Truth preserving: conclusion is true ifpremises are true.

Degree of support for conclusionirrelevant - conclusion is either true orfalse.

Requirement of total evidence issatisfied automatically.

Relations Between Non-Deductiveand Deductive Inference

There are some fundamental distinctions we need to be aware of between deductiveand non-deductive (induction & abduction) reasoning. As we will see, thesecharacteristics are significantly important when examining the types of reasoningused in biological systematics.

AMPLIATIVE REASONING

Requirements

C Non-monotonic: allow a certain conclusion to be defeatedby inclusion of additional information inpremises.

C Cut-off Point Problem: show that generalizations fromobservations are justified.

C Vertical Extrapolation: support conclusions that make referenceto entities not referred to in premises.

C Eliminative Dimension: allow multiple conclusions consistent withpremises.

Since non-deductive reasoning is ampliative (see previous slide), there are fourcharacteristics that need to be recognized.

C Non-monotonic: allow a certain conclusion to be defeatedby inclusion of additional information inpremises.

C Cut-off Point Problem: show that generalizations from observationsare justified.

C Vertical Extrapolation: support conclusions that make reference toentities not referred to in premises.

C Eliminative Dimension: allow multiple conclusions consistent withpremises.

Induction

Induction / Abduction



AMPLIATIVE REASONING

Requirements

Most of these characteristics apply to both induction and abduction.








We are now in a position to examine the specific ways in which deduction,induction, and abduction are used in our processes of scientific inquiry.

Inferences in Science

hypothesish

datad

deduction

(1) Deduction: predictions of potential test consequencesderived from the hypothesis to be tested.


induction

h

(2) Induction: performing the test; observations of testconsequences, providing either confirming/corroborating or disconfirming/falsifyingevidence.

deduction induction

h

d


Recall that earlier we noted that people often speak of the relations of deductionand induction in science, where science is only seen as cycles of deduction andinduction in a continual process of inferring and evaluating theories/hypotheses.

But in fact, abduction is a fundamental component that we need to take intoconsideration as completely separate from induction (sensu stricto).

Operational Relations BetweenTypes of Inference in Science

deduction

abduction

induction

Inferences ofHypotheses & Theories

Inferences of Tests

Conducting Tests:Hypothesis Acceptance or Rejection

The actual relations between abduction, deduction, and induction are summarizedhere. Abduction involves our reasoning process for inferring hypotheses andtheories. Deduction is used to derive potential consequences from our hypothesesand theories that might serve as test evidence when the act of testing occurs.Induction is the process of testing that leads to our concluding that a theory orhypothesis is confirmed or disconfirmed.

Fact

Hypothesis

Theory

What do these terms mean?

In order to clearly understand the different types of reasoning we use in science,including biological systematics, we need to first understand the meanings of threewords that are commonly used, but too often misunderstood.

The facts

Facts are objectsand events. Theconditions of truthor falsity do notapply to facts.

Fact

A 'fact' is nothing more than an object or event that exists, whether we perceive it ornot. It is important not to confuse the observation statement, 'This is a glass of icewater,' with the facts you perceive. The facts exist independent of you. Yourobservation statement is a conclusion from your inference (abduction!) used toexplain the facts.

Also, keep in mind that the conditions of truth or falsity cannot be applied to facts.Facts simply are! What can be true or false are your statements regarding thosefacts.

...a fact is either the being of a thing in a givenstate, or an event occurring in a thing.

Constructs do not qualify as facts since theyare not objects that can be in a certain state, letalone undergo changes of state.... Similarly,there are no 'scientific facts': only a procedureto attain knowledge can be scientific (or not),not the object of our investigation. Accordingly,scientists neither 'collect' facts nor do theycome up with or, worse, 'construct' facts, butadvance hypotheses and theories referring toor representing facts.

Mahner & Bunge (1997: 34), Foundations ofBiophilosophy

Fact

The quote shown here is an excellent definition of 'fact,' and corrects a long-standing misconception that we have 'scientific' facts as opposed to 'non-scientific'facts.

Inference of a Theory

Now that we know what facts are, we need to understand the meaning of the term'theory' and how they are inferred.

theories are spatio-temporallyunrestricted.

An explanatory concept(s),stating cause-effect relations,that we can apply to oursense perceptions, to give usunderstanding.

Theory

theories are not limited to therealm of Science.

What is important to notice in this definition is that a theory is a spatio-temporallyunrestricted concept. In other words, a theory can be applied to the past, present,and future. It does not refer to a specific instance. And, as you will recall that thegoal of science is to increase our causal understanding, theories are thefundamentally important conceptual tools that allow us to pursue thatunderstanding, because theories enable us to infer explanatory hypotheses.

Abductive Inference as theMechanism for Theory Formation

observed effects in need of being explained

background knowledge (theories, laws, etc.)

tentative theory of cause-effect relations(adapted from an analogous theory)

explanatory hypothesis

The inference of a theory is by way of abduction, and often as a matter of analogy.One takes a previously established theory, and uses it as an analogy for a newtheory, where that analogous application serves to explain some set of surprising orunexpected phenomena.

Observations:

There are differentially shared traits among these observed organisms.

variation / inheritance / differential survival and reproduction

Background knowledge:

Based on what is known of the actions of artificial selection, in con-junction with the above background knowledge, maybe an analogoussystem of cause and effect relations exists in nature:

Tentative theory:

Abductive Inference as the Mechanismfor Theory Formation

Natural selection - organisms with traits that enhance survival andreproduction will leave offspring with those traits .

Variation arose in an ancestral population, subsequent to which thetraits in question allowed for enhanced survival and reproduction.

Hypothesis:

A classic example of the combined use of analogy and abductive inference can befound in the development of Charles Darwin's (1859) theory of natural selection.

Inference of a Hypothesis

An explanation of someset of facts, giving us atleast initial understandingof what we perceive.

hypotheses are not limited to therealm of Science.

Hypothesis

hypotheses are spatio-temporallyrestricted.

Notice that unlike a theory, which does not refer to specific instances, a hypothesisdoes present a narrow set of conditions for a particular time and location. In thecontext of science, the most useful way to characterize hypotheses is as explanatoryaccounts, the purpose of which is to provide us with causal understanding of anobserved effect or set of effects.

Abductive Inference of a Hypothesis

observed effects in need of being explained

theory (cause-effect relations)

explanatory hypothesis

background knowledge

The schematic example shown here illustrates the most basic components of theabductive inference of a hypothesis. The premises comprise at least one theory thatis applied to the effect(s) we wish to explain, from which we conclude anexplanatory hypothesis that suggests that the effect(s) is/are the product ofparticular past causal events that are consistent with the theory.

The inferential process of critical-ly and empirically assessing theability of theories and hypothesesto give us understanding.

TESTING: a definition

Now that we have examined the basics of inference, including the inferences ofhypotheses and theories by way of abduction, we can briefly look at the process oftesting. We will address testing in greater detail later in the course, as it applies tothe testing of biological systematics hypotheses.


Past Future

Cause

Effect

predictionEffect

ExperimentalSciences

abductionExplanatoryHypothesis

HistoricalSciences

The Two Realms of Science

Hypothesis testing Theory testing

Recall the distinction we made earlier between 'historical' and 'experimental'sciences. This will serve to illustrate the difference between the testing ofhypotheses and theories.


Known Cause(experiment)

EXPERIMENTAL

Future

Effect (potentiallyobservable)prediction

(deduction via theory)

HISTORICAL

KnownEffect

Past

Unknown Cause(not observable) explanation

Testing: Experimental vs.Historical Sciences


While the focus of this course will be on explanatory hypotheses in the historicalsciences, most discussions about testing use examples from the experimentalsciences. There are some important differences between these fields regarding thenature of testing, that need to be mentioned. What is of principle interest in theexperimental sciences is testing by way of controlled experiments.

A theory is tested by providing controlled (e.g. experimental) causal conditions inthe present. In other words, the causal conditions are known to us. It is then a matterof observing whether or not a predicted effect occurs. What you will notice is thatboth cause and effect can be observed. We have the opportunity to know both.

But, in the case of the historical sciences, what we know in the present are effectsthat are in need of being explained. The difficulty is that the cause that explainsobserved effects occurred in the past so no longer exists in the present. As a result,the cause is often unknown and unobservable.


Known Cause(experiment)

EXPERIMENTAL

Future

Effect (potentiallyobservable)prediction

(deduction via theory)

HISTORICAL

KnownEffect

Past

explanation

ExplanatoryHypothesis


Testing: Experimental vs.Historical Sciences

Thus, we infer an explanatory hypothesis to account for the observed effects. It isthis hypothesis that we then want to test. But, in comparison to the experimentalsciences, where the relations between cause and effect can both be known, the factthat a past causal event is usually not known can make it very difficult to testexplanatory hypotheses since the relevant effects needed for a test might not beavailable. We will see in this course that this limitation certainly applies to thetesting of many biological systematics hypotheses.

KnownEffect


Past

abduction

ExplanatoryHypothesis

Specific CausalCondition(s)

test that shouldbe performed

deduction

Future

testing of hypothesis byobservations of effects

induction

Testing Explanatory Hypotheses

We can now summarize the relations between the abductive inference of anexplanatory hypothesis and the subsequent testing of that hypothesis.

It is from effects observed in the present that we infer by way of abduction anexplanatory hypothesis. From the specific causal conditions stated in thathypothesis we deduce effects that should be observed that are only possible becausethe specified causal conditions that occurred in the past would allow for thoseeffects. The deduction of such effects provides the basis for the tests that need to beperformed.

The act of testing the hypothesis is, however, a matter of induction, where thehypothesis is either accepted or rejected on the basis of searching for the specifiedtest evidence. Since no test can guarantee the truth of a hypothesis, and adisconfirmed hypothesis simply leaves us with alternative hypotheses to consider,testing is always inductive.

Observed Effects

Why...?

background knowledge+ causal theory

Abduction:causal theory + observed effects

Hypotheses

I. ABDUCTION

The Inference of Hypotheses

Let's now look a very simple summary of the relations between abduction,deduction, and induction. In this slide, the abductive inference of hypotheses ispresented.

AdditionalEffects

Hypotheses

The Inference of New Hypotheses:additional abductive inferences are required when new effects are observed

II. ABDUCTION

Very often, subsequent to inferring a hypothesis (or hypotheses), we encounteradditional effects or observations that also need to be explained in the same manneras the previous effects.

AdditionalEffects

New Set ofObserved Effects(old + new)

Hypotheses

The Inference of New Hypotheses:additional abductive inferences are required when new effects are observed

II. ABDUCTION

Why...?

The result is that these additional effects/o

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