Consider what would be needed to unify three disparate subjects: physics, biology and economics. This would appear theoretically possible since all of these subjects ultimately relate to physical objects and their interaction. Now consider how you would unify the following scientific explanations into a common physical language: in physics the conduct of electricity through copper wire, in biology the hibernation of Brown bears in winter, and in economics the relationship between interest rates and inflation.
We must answer this question in the light of Principles 1 and 2: firstly, that all matter is of equal status regardless of its size (although we find certain units of special explanatory value): secondly, that there are no factor(s) that clearly distinguishes science from non-science or pseudo-science. In other words we cannot claim that molecules are more real or fundamental than animals, or that economics is not a science.
Intuitively we might attribute the difficulty of translating these three disciplines into a unified physical theory to one or all of the following: scale, complexity, language.
Is there a reducing gradation of predictive power with increasing complexity – physics, biology, economics?
If the unit scales are generally more complex then do causal pathways and pattern also become more complex? Perhaps scale cannot be extrapolated across domains because the results are nonlinear – as we pass between domains quantitative change becomes qualitative change?
Our emphasis until recent times has been mostly on the analytic breaking up of things into components to see how they work. Part of this history has been the creation of literally hundreds of disciplines out of what was once the single study of biology. We are now passing through a phase of re-synthesis as biology merges at one extreme with the physical sciences and at the other extreme with the social sciences. One extreme is represented by the new insights of molecular biology, genomics and biotechnology while at the other we see the integration of ideas from sociology, anthropology, linguistics and especially new developments in psychology, moral philosophy.
If in causal terms, the whole can be completely explained in terms of its component interactions then the whole, having no causal agency, is referred to as an epiphenomenon.
The epiphenomena are then termed to be “nothing but” the outcome of the workings of the fundamental phenomena. In this way, for example, religion can be deemed to be “nothing but” an evolutionary adaptation, and beliefs can be considered “nothing but” the outcome of neurobiological processes. There is a tendency to avoid taking the epiphenomena as being important in its own right.
Social and behavioural systems, political science and sociology, can be explained in terms of neurochemistry, genes and brain structure. At the highest sociocultural level, explanations focus on the influence on behavior of where and how we live. Between these extremes there are behavioural, cognitive and social explanations.
Reality is a multi-layered unity. Another person as an aggregation of atoms, an open biochemical system in interaction with the environment, a specimen of homo sapiens, an object of beauty, someone whose needs deserve my respect and compassion,
But it is hard to avoid the conclusion that we either pass into a knowledge regress or deny that studying human behaviour is science.
In philosophy thought about emergence often turns on whether we can distinguish what you might call mere epistemological emergence from genuinely ontological emergence. Where epistemological emergence is in play, we grant that the low level facts do in fact determine everything at the upper level, even if, as it happens, we have no way of predicting the upper level from the lower, and even if our ways of comprehending the lower-level are shaped by what we know about the higher level. Think chaotic systems, traffic patterns, etc. Full-blown ontological emergence makes a much stronger claim. Facts at the lower-level do not fix the facts at the upper level. There are, then, on such a view, genuinely emergent phenomena. The trick has always been to explain how that could be and whether it even makes sense. Can one give an example of genuine ontological emergence?
Working with different domains of knowledge is like zooming in and out of regions of the world in space and time, seeing different patterns and regularities in nature as we do so. As we focus on one domain the laws, principles, and categories of the others become part of a blurred and much less relevant background. When working in the world of biology the world of physics is mostly irrelevant, not because it is unimportant but because it is taken for granted in our selective cognition.
It appears to be a function of our minds that we must apprehend the world through categories of scale and the greater the difference in scale the more difficult this becomes. It simply makes no sense to explain monetary and fiscal policy in physicochemical terms – though in theory this is not absurd since these matters are a consequence of interacting physical objects – but this would need an infinitely complex computer since our minds could not cope with the problems of scale and their difficulties in relation to vocabulary, categories, properties and relations.
When the microscope was discovered it was necessary to create a whole new language of terms as we observed structures in animal and plant cells. The same is true at the molecular scale. The new terms were needed to deal with a new scale of comprehension.
Looking at life on Earth over its full time period of 3.5 billion years and on the scale of all life we might imagine a three-dimensional branching tree-like structure as different life forms differentiate along the branches up to the present day and there are many dead ends. To assist our perception we fix on particular categories or units of scale depending on utility and our interests. We recognise various aggregates of organisms with individual species as the ill-defined fundamental unit, these arranged into a progressively more inclusive units as genera, families etc. Within an organism like ourselves we select operational units like organs, tissues, cells, molecules and atoms. When thinking about evolution we choose the units gene, organism, population and this.
Are some aspects of biological science autonomous in that they do not benefit or utilise the knowledge of molecular biology?
We know a television is made up of tiny units called pixels and that these pixels can flash different colours in a predetermined and coordinated way that allows us to produce images of people and other objects. This representation of people and other objects by means of a pixel matrix is interpreted by our eyes and brains in the same way that we interpret the representations created by actual objects in the world. This metaphor illustrates several important aspects of our perception and cognition.
Firstly, the images that are so meaningful to us are made up of simple basic constituents, flashing pixels, that individually lack meaning.
Secondly, the activity of the pixels acting together is meaningful because the pixels have been organised to distribute colour across the TV screen in a highly coordinated way.
Thirdly, the meaningful images we see are interpreted by our eyes and brains as objects in the real world: they are categories created in our minds since the TV screen is just composed of pixels, not people and other objects.
Fourthly, the fact that TV screens (which are just a layer of flashing pixels) can create visual representations that are highly convincing to our eyes and brains makes us aware that our brains can add structure to the world that does not exist. It is the task of science to establish as close a correlation as possible between our perceptions and reality knowing that our minds can be deceived.
Problems with reduction:
The effects of molecular processes often depend on the context in which they occur. So one molecular kind can correspond to many kinds at a larger scale (one to many) while at the same time large-scale structures and processes can arise from different kinds of molecular processes, so that many molecular kinds can also correspond to a single larger-scale kind (many to one or multiple realization).
Structure and function relate to spatial and temporal (spatiotemporal) factors respectively. Each represent a mode or type of organisation important in reduction. This is why development is an important aspect here.
Scientific explanation often involves units from different scales of reference.
In a reductive explanation the intrinsic can be important (what is internal and what external), reduction favouring internal causality. Protein folding can have external causality. Temporal and intrinsic factors thus play a part in reduction as well as simply the relations of parts and wholes. Perhaps there are different kinds of reduction?
Perhaps we should move away from the idea of reduction towards science as best characterised as proceeding by unification as integration and synthesis rather than reduction. The theories and disciplinary approaches to be used depend on the nature of the problem being discussed.
Complexity – the unconscious collective behaviour of social insects as an emergent property. Complexity arises from dynamics not constitution? Chaos theory, for example, demonstrates how some systems are acutely sensitive to the minutest changes that can totally changes their behaviour. Such systems are widespread and difficult to analyse in a reductionist way. Such complex systems seemingly spontaneously generate remarkable patterns of behaviour in a holistic manner. Highly complex systems seem to contain vast amounts of information, ‘active information’ being a new arena for theorising.(see )
The problem is not whether explanations are reductionist or not, but whether the particular degree of reduction is sufficient to answer the question being posed.
The task of science is to describe, as accurately as possible, the structure and workings of the objects that exist outside the human brain. But to do that we must use the brain itself, an object that has been moulded and limited by its evolution. To describe the universe we must first understand as much as we can about the limitations of the tool we use to comprehend it.
As we pass from physics and chemistry to biology and sociology the cognitive units or categories of nature that we use as tools to do our science tend to increase in abstraction, complexity of material organisation, and causal intricacy. We sense a graded change in the character of the subject matter that is a difference in degree but not in kind, more a matter of trends. Physics and chemistry appear to proceed mostly by analysis while much of biology is about synthesis as it attempts to explain organisational complexity and the role of phenomena within functional systems, its teleonomic character tending to look to the future. The compositional or holistic concern with organisational factors and adaptive function.
Reduction is complicated by our metaphysics – the way we assume the natural world is structured – the nature of reality. Science is now providing us with an improved picture of this reality.
The confusing aspects of language include metaphor, anaphor and polysemy.
We can now combine the principles and findings of this section as follows:
Reduction is only useful and appropriate when it improves our understanding. In considering the relationship between wholes and parts each particular case must be examined on its own merit. We regard scientific categories as important because we believe they map the natural world as best we can ultimately providing us with compelling explanations that help us manage the world.
Science uses categories that map the natural world as accurately as possible but some of these may be mental categories with no instantiation in the physical world and others may be relational in character. The scientific need for explanation, like the philosophical requirement for rational justification or causal origin, leads to an explanatory regress seeking more ‘fundamental’ answers. However, a satisfactory answer does not depend on the size of the unit but the plausibility, effectiveness or utility of the answer (Principle 1). Hierarchical language applied to biological organisation implies value and is best replaced with the language of scale. The greater difference in size of scale units used by different domains, the greater the difficulties of reduction – communication and translation. Provided scientific units are credible then the scale we use for explanation is simply a matter of utility.
We analyse a problem to obtain a broader understanding, a better synthesis. Science progresses by a process of both analysis and synthesis with emphasis alternating between the two in a kind of dialectic.
With decreasing levels of complexity compensatory activity or ‘self-regulation’ decreases in likelihood.
Reductionism, the translation of ideas from one domain of knowledge into those of another
Scientifically credible units of matter have no precedence over one-another based on size alone
The idea of something being more ‘fundamental’ probably derives from our tendency to explain by a process of analysis, by breaking down into smaller parts. It is also probably related to internalised hierarchical thinking in terms of ‘levels’ to which we unconsciously apply value
Science examines matter at various scales which correspond loosely to disciplines as domains of knowledge, language and theory
Though there are clear links between domains of knowledge, each domain seeks optimal explanatory results using its own language, principles and procedures. Linking or even uniting (reducing one domain to another) may have benefits but presently appears to pose insurmountable difficulties.
If we regard science as the matching of our mental categories to the reality of the external world then there can be no ‘fundamental’ science and also no clear distinction between what is science and what is not. There will simply be better and worse explanations of the world of matter and energy. For a whole variety of reasons it is evident that astronomy is more scientific than astrology.
Are some scientific explanations ‘better’ than others?
Is physics more ‘fundamental’ than biology?
Does the physical world exist in ‘levels’ of organisation?
Do new properties emerge as things get more complex or are the ‘fundamental’ properties always the same – is the whole greater than the sum of the parts?
What is reductionism and why is it often treated as an error in thinking?
We draw scales of convenience which we believe reflect reality. Why should gene selectionism not reduce further to physics and chemistry?
And scientific categories, we believe, relate closely to objects in the external world. Even so, the scientific information considered valuable to the modern world would be inconsequential for a native living in the New Guinea jungle, much more important is whether it is edible or poisonous. And for an ecologist the actual species in a particular environment may not matter – more important is their role within an ecosystem, say, whether the organism is a predator or herbivore.
We can imagine scientists investigating nature as a watchmaker investigates a watch: if we want to know how the watch works then we examine the parts, how they fit together, and how together they operate effectively. By a process of analysis we then see how the parts interact to produce an operational whole. In terms of our classification of categories this is a sum or additive category.
Reductionism reflects a particular perspective on causality: supervening (more inclusive) phenomena that are completely explained by smaller scale (less inclusive) phenomena can be termed epiphenomena. It is often assumed that epiphenomena have no causal effect on the phenomena that explain it.
Only statements can be deduced, not properties: properties require a theory.
Though we do not know why the laws of physics are as they are
A part of the teleonomic view of the world in which there are many paths to the same end. The development of cells in embryology is determined by their environment.
Species exist because they perform their functions (Aristotle).
Concepts provide the meaning that language expresses and they comprise the blocks of information on which reason can work. If we regard categories as concepts (units of thought or mental representations) then they can be of two kinds, either universals (types) which are general categories like ‘chair’ and ‘tree’, or particulars (tokens) like my chair or the oak tree outside my window. Though categories may sometimes be clearly defined as having necessary and sufficient conditions, most simply share a family resemblance – a set of characteristics that overlap with those of other categories.
We can also regard universals as sets and particulars as sums and, for simplicity mind-objects are called types and physical objects are called tokens.
Sets are abstract, consisting of objects that are ‘members’ of that set even when their members are physical objects. So, for example, English Oak and Chinese Elm are members of the set ‘tree’. Sums consist of parts rather than members: so a leg is a part of our body and a body can be physically moved. A forest is a collection of trees, so it is a sum not a set. The distinction between a sum and set may not always be crystal-clear but it helps to be aware of the idea – that is, it helps to be wary of the use of abstract and concrete nouns.
Categories can also consist of properties or relations. Plants share the physical property of being photosynthetic (they instantiate photosynthesis). When dealing with properties it is useful to distinguish intrinsic properties (inner properties that are independent of external influences) and extrinsic (relational) properties that do. Categories like this are easiest to understand when the properties are intrinsic but when relations between properties are important then we get the language of parts and wholes.
Scientific properties like specific colours, weights, densities, and temperatures or the ability to photosynthesise, are regarded as contingent (they are tokens that instantiate the types colour, weight and process) factors that are part of the scientific world of empirical investigation (what might be called a naturalistic ontology).
The particular kind of category that we use will depend on the particular situation and mixtures are possible. We must be aware of difficulties relating to precision and clarity of our categories. The word ‘goldfish’ can refers to a specific physical object or token, the word ‘society’ refers to something that is physically undefined – like a distinction between abstract and concrete nouns.
Principle 5 – Science uses categories (names, explanations, definitions, theories etc.) that reflect as accurately as possible the natural world: these categories consist of either sets (universals), sums (particulars) or properties. Properties may be either intrinsic (internal) or extrinsic (relational).
Principle 6 – Sets (universals), being abstract, can add complexity to the analysis of whole and parts
There is an expectation that biology should produce universal laws like those of physics but as biology is only concerned with living organisms this is an unreasonable expectation.
We must ask what could possibly be the point of converting the language of one into another: it is not only unnecessary but also unimaginably complex.
‘Predispositions’ and ‘propensities’ are proximate mechanisms.
Cognitive focus and cognitive illusion
We have all experienced visual illusions where a stick in water appears to bend and how when we focus on some images they seem to be one think one moment and another the next, but never both at the same time. In a similar way we struggle with cognitive illusions that create cognitive dissonance: something cannot be simultaneously similar and different, a whole and a part … it must be either one or the other. And yet we know that an ant is a whole individual while at the same time being part of a colony.
Our brains organise knowledge by classifying it into categories or. The method we use to classify or organise knowledge can influence the way we perceive the world and our ability to discover and create new knowledge.
Our scientific map of reality, like all our cognition, removes unwanted noise, acting like a camera lens by filtering out inconsequential information as we ‘zoom in’ and ‘zoom out’ of different regions of categorisation. What we must ask ourselves as scientists is the extent to which the categories we create are accurate representations of objects in the external world, the extent to which the groupings we create are accurate representations of objects in the external world, and the extent to which the way we rank these categories and groups is an accurate representations of what is going on in the external world.
We can immediately comment on these questions. Categories are tricky: the dog in front of me seems a real and concrete object in the world but the general category ‘dog’ is abstract, rather like the non-existent category ‘unicorn’. Groupings are similar although perhaps not so clear: ‘primate’ seems OK, and ‘London trams’ alright but I’m not so sure about ‘institutions’ and ‘society’ , they seem more fuzzy categories. Both categories and groups, we might say, need scientific investigation – we need sound evidence for their existence. Ranking itself is different. The external world does not rank its contents, that is what we do. All we can do is try and determine as accurately as possible what there is in the world, what exists. When we draw up a biological classification we are ranking organisms according to their similarities and differences which we assume has something to do with the way they evolved, with the nature of their existence in the external world. Ranking plants according to edibility is clearly more subjective.
Complexity – move to emergence
(Scale) We understand all objects within a context. which depends on their relationship to other objects. Some wholes, like billiard balls on a table or sugar crystals in a sugar lump, we can understand fully by examining the properties of the individual parts in a process of analysis. With a complex whole like the human body we can only understand the parts by seeing how they are related to one-another in relation to the function of the whole and this process we call synthesis.
We can imagine a continuum of groups whose parts have varying degrees of connectivity. Increasing complexity is generally associated with other factors: an increasing number of elements, an increasing degree of connectivity, often into a network, where it is relationships that define the system, not the components themselves, there is also usually an increase in diversity of the parts. Adaptive complex systems like organisms are also capable of self-regulation (teleonomy). Analytic systems have simple and predictable linear causal relationships where input and output are eequal while complex systems have complicated and non-linear and often unpredictable causation that is not amenable to modelling.
The ‘possibility space’ allows us to think about random and complex situations without thinking about causes and effects. It assumes that each time a situation of that kind arises, the set of possible outcomes is the same and the probabilities are also the same. So, considering the likelihood of life on other planets would entail a sample space (the set of all possible outcomes), the set of events, and the assignment of probabilities to events.
Are the laws of physics (which may be strict or probabilistic) descriptive or prescriptive: that is, do they simply describe the way things are, or do they actually exert an influence on things?
The discovery of laws was long regarded as central to science and from a theistic perspective this made sense – natural laws were God’s laws as part of his divine plan for in the universe.
In everyday parlance we say that the laws of nature ‘determine outcomes’, that they ‘govern behaviour’ and so on. Taken literally this suggests that laws are rules that are in some way prior to activity, that they exert an extraneous influence or constraining force on things, they are something outside the system or circumstance itself, like a physical barrier, a programmer, or system of governance.
For many scientists this is unacceptable: laws do not exist in some transcendental realm acting on matter in the world. Law-language simply describes the way the world is, the decree-like lawfulness implied in language is metaphor and best treated as such.
Nevertheless, laws do explain or account for why the world is as it is, while descriptions simply state facts. Uniformities in classes of objects and activities can be described and given mathematical expression and this is critical to the predictive power of science. So how do we account for laws? Well, we simply replace the idea of law with that of succinct descriptions of patterns or regular behaviour with strength in their simplicity and generality.
One characteristic of the diverse range of scientific generalities (laws) is that they exhibit varying specificity: there is a trade-off between simplicity and generality.
One descriptive account of a scientific law is given by the late Australian-American David Lewis. Consider the set of all truths and select some of these as axioms, thus permitting the construction of a deductive system, the logical consequences of which become its theorems. These deductive systems compete with one another along (at least) two dimensions: the simplicity of the axioms, and the strength or information content of the system as a whole. We prefer to be well-informed but to achieve this we sacrifice simplicity. So, for example, a system comprising the entire set of truths about the world would be maximally strong, but also maximally complex. Conversely, a generality like ‘events occur’ is uninformative. So, what we need is the most useful balance between the two, and that, perhaps, is what the ‘laws’ of nature do. This is not a precise formula but a suggestion or heuristic for a way of thinking about scientific laws as we look for the simplest generalizations possible from which we can draw the most information. Thus there are laws covering a wide degree of resilience scattered among the various scientific disciplines. On this view the collection of particular facts about the world are the laws of nature because the laws are condensed descriptions of those facts.
Laws can also be regarded as above but with the best propositions within particular vocabularies so we could have different laws for different vocabularies. An economist wouldn’t be interested (at least not qua economist) in deductive systems that talked about quarks and leptons: her language would be along the lines of inflation and interest rates. The best system for this coarser-grained vocabulary will give us the laws of economics, distinct from the laws of physics.
On this descriptive account laws are part of our map, not the laws themselves which are just convenient ways of abbreviating reality. Because regularities assist the organization of knowledge and depend on facts about us. Nature does not make these regularities laws, we do.
Mereological reductionism is the claim that the stuff in the universe is built of things described by fundamental physics, even though physicists may be unsure of these. But nomic reductionism holds that the fundamental laws of physics are the only really existant laws, and that laws in other disciplines just convenient abbreviations necessitated by our computational limitations.
Nomic reductionism appeals through the apparent redundancy of non-fundamental laws. Macroscopic systems are entirely built out of parts whose behavior is determined by the laws of physics the laws of other systems are therefore superfluous. This argument relies on the prescriptive conception of laws: it assumes that real laws do things, they physically influence matter and energy. Thisseems to be overdetermination but if we regard laws as descriptive all we have are different best systems, geared towards vocabularies at different scales and therefore different regularities described in different condensed ways. There is nothing problematic with having different ways to compress information about a system. We need not claim one methods of condensation as more real than another.
Accepting the descriptive conception of laws severs the ontological divide between the fundamental and non-fundamental laws, privileging the laws of physics is the result of a confused metaphysical picture.
However, even if we accept that laws of physics don’t possess a different ontological status, we can still believe that they have a prized position in the explanatory hierarchy. This leads to explanatory reductionism, the view that explanations couched in the vocabulary of fundamental physics are always better because fundamental physics provides us with more accurate models than the non-fundamental sciences. Also, even if one denies that the laws of physics themselves are pushing matter around, one can still believe that all the actual pushing and pulling there is, all the causal action, is described by the laws of physics, and that the non-fundamental laws do not describe genuine causal relations. We could call this kind of view causal reductionism.
Unfortunately for the reductionist, explanatory and causal reductionism don’t fare much better than nomic reductionism. Stay tuned for the reasons why!
We can imagine a continuum of groups whose parts have varying degrees of connectivity. Increasing complexity is generally associated with other factors: an increasing number of elements, an increasing degree of connectivity, often into a network, where it is relationships that define the system, not the components themselves, there is also usually an increase in diversity of the parts. Adaptive complex systems like organisms are also capable of self-regulation (teleonomy). Analytic systems have simple and predictable linear causal relationships where input and output are equal while complex systems have chaotic, fractal, complicated. non-linear, non-predictable and often unpredictable causation with fuzzy logic that is not amenable to modelling. Small initial conditions can have massive consequences.
A heart has little meaning except in relation to the body of which it is a part. Hydrogen and oxygen combined as water are different and unpredictable from the individual atoms.
Emergence generally entails the language of scale but causation may be helpful as ‘higher’ levels of behaviour arise from ‘lower’ level causes. Emergence is conveniently illustrated through the world of computers. Computer hardware enables but does not control – it is software, as information, that makes a computer work – software tells the hardware what to do. The information software carries is abstract, not physical, but it has causal agency, it sets the constraints for ‘lower-level’ action whose goals can be achieved in many ways (multiple realization).
Epiphenomena are by-products of things, not the things themselves: brain and mind; brain and consciousness.
We understand objects within a context which depends on their relationship to other objects. Some wholes, like billiard balls on a table or sugar crystals in a sugar lump, we can understand fully by examining the properties of the individual parts in a process of analysis. With a complex whole like the human body we can only understand the parts by seeing how they are related to one-another in relation to the function of the whole and this process we call synthesis.
Generalisations in biology are often not strict with various exceptions, it is often not uniquely biological since organism for example follow the rules of hydraulics and aerodynamics and its generalisations rarely have the law-like character of physical laws. Biology accepts the generalisations of physics and then proceeds within its own domain.
Classical reductionism – either the laws or generalities of biology, psychology, and social science are the deductive consequence of laws of physics or they are not true.
Multiple realization – if genes have many effects on many phenotypic characteristics and phenotypic characteristics are affected by many genes then. The relationship between genetic and phenotypic facts is many/many and therefore cannot be a deductive consequence. There is a complex two-way relationship between the genome and its molecular environment.
Paley was the watchmaker.
Synthesis & analysis
We can see in analysis and synthesis several opposing ideas. Analysis is breaking down, synthesis is building up. Analysis looking down or while synthesis is looking up. In a literary sense analysis is associated with Classicism that ‘looks back’ to old traditions and certainties and universal characteristics and conservatism while synthesis like Romanticism ‘looks forward’ for novelty, creativity, experiment and imagination, maybe even the great intellectual traditions of permanence of Being and the change of Becoming.
Order & constraint
The ancients wondered why there was order in the world rather than randomicity and chaos. Today we can point out that this order comes about by means of constraints on possible outcomes. Not everything is possible. The most obvious constraints on activity in the universe are a consequence of physical constants, what we call ‘physical laws’. This means, for example, that given the initial conditions of the universe the possible outcomes are already limited. The best science at present indicates a heat death. Though the universe is mindless, owing to the constraining action of physical constants, at any given time it has potential that will become actualized in a more or less predictable way. That is, there are ‘ends’ to inexorable physical processes and in this sense these processes are teleological. When life emerged the nature of teleology would undergo a radical change. Some organisms would survive and others perish. Though morality is supplied by the human mind, the reasons for organismal survival exist in nature. Situations become ‘good for’ and ‘bad for’; there is rudimentary normativity and functional design. There is also the passage of historical information from one generation of organism to the next, the historical organism-environment interaction being ‘represented’ as information contained in genes or gene-like chemicals.
Emergence as new biological order is more a consequence of the constraining boundary conditions than the exisatence of biological laws or law-like behaviour.
The fallacies of composition and division
The fallacy of composition arises when it is inferred that something is true of the whole from the fact that it is true of some (or even all) its individual parts. For example: ‘fermions and bosons are not living, therefore nothing made of fermions and bosons is living’ this is akin to an assertion of emergence – that the whole may have properties or qualities not apparent in the parts. This is in contradistinction the fallacy of division when it is inferred that something true for the whole must also be true of its parts. For example: ‘my living brain exhibits consciousness therefore its constituent atoms display consciousness’.
To explain something is to subsume it under a law.
The adaptations of living organisms are treated as ‘forward-looking’.
This imposition of order (function) by natural selection, the process of adaptation, is a holistic feature that has been called ‘downward causation’ (the whole may alter its parts) and it cannot be improved by reductionist explanation.(Campbell, 1974).
Hierarchy implies that levels are ontologically distinct but they may be only epistemologically so. We must communicate in a linear way but the ideas being communicated may not be related in a linear way.
Memes are informational not physical.
We do not have to comprhend reasons we just do them. Though our mind understand neurons do not. Though an ants nest is a highly integrated and purposeful unit the individual antss do not understand this. Meaning can emerge from non-meaning.
Computers have taught us about the importance of software and system behaviour rather than physics and chemistry.
Abstract notions can be causal – habits, words, shapes, songs, techniques, learning by watching, long-division: none of these is in the genome.
So, to help understand the many issues at stake here we can imagine a continuum in the structure of ‘wholes’ ranging from those where there is minimum interaction between the parts (aggregations) to those in which there is a highly integrated interdependence of the parts – many variables and complex causation (systems). As an example of the former we might think of a rock made out of grains of sand and of the latter a living organism. But there are many different kinds of wholes, so consider the following: a car engine, an ant colony, a flock of birds, a shoal of fish, 4 as a sum of 2 and 2, a symphony as organised sound, economic patterns that are a consequence of mass markets.
The situation is complicated by the nature of the whole which might be: a mechanism or process, a behaviour (movement of flocks of birds or shoals of fish), a property, a system with parts in a state of dynamic interdependence, a concrete object.
In very general terms it seems that physics and chemistry tend to take a reductionist or atomistic approach to their disciplines while biology and the social sciences adopt a more holistic or organismic stance. This raises a major metaphysical question about the nature of reality. What is the structure of the physical world? Is the best scientific representation of the world expressed through the relations of fundamental particles acting under the influence of physical constants, or is it better represented in some other way?
Macro-causation supervenes on or is determined by micro-emergence (strong emergence)
(The humanistic view of human nature (Roger Scrution). Perhaps it is impossible to ‘reduce’ the language and ideas of the human realm to that of science which cannot capture the sense of self and other, I and though. So, for example, music, colour, and art can be subjected to meticulous scientific scrutiny but still lack a dimension of uniquely human understanding.)