The Synergism Hypothesis: On the Concept of Synergy and It’s Role in the Evolution of Complex Systems



Generalizations derived from a juxtaposition of facts are not fruitful unless some conceptual, theoretical scheme guided the generalizations and, incidentally, the selection of facts…”

Anatol Rapoport

“Experiments unguided by an appropriate theoretical framework usually amount to little more than ‘watching the pot boil’… We need experiments to inform theory, but without theory all is lost.”

John H. Holland



It is one of the paradoxes of our age that as the tools of scientific research have grown ever more powerful — from positron emission tomography to electron microscopy, nuclear magnetic resonance and massively parallel computers — the phenomena we are able to investigate (and their causal dynamics) seem to grow ever more complex. The relentless reductionism of particle physics, polymer chemistry, molecular biology and neurobiology, among other disciplines, has not (so far) revealed the decisive “mechanisms” or underlying “laws” of the phenomenal world. Instead, the “microcosmos” (to borrow Lynn Margulis’s term) displays profound complexity, interactionism, interrelatedness and, not least, historical specificity.

It has been suggested that our era should be called “the age of complexity.” While this sobriquet (or epithet, depending on your values) may be appropriate, complexity is certainly not a newly discovered aspect of the natural world.1 The debate over “wholes” and “parts” (or holism and reductionism) can be traced at least to Periclean Athens and to the writings, especially, of Aristotle. Although scholars these days have a propensity for forgetting their forebears, over the course of this century there have been successive waves of holistic and reductionist theorizing — a sort of transgenerational dialectic — in which many of our most distinguished scientists have played a part. After reaching an apogee of sorts with the imposing theoretical edifice of the 19th century polymath Herbert Spencer (1892/1852, 1874-82), holistic theorizing was all but banished by the supporters of Darwin’s theory, and (later) of “Weismannism” and “mutation theory,” at the turn of this century. However, in the 1920s holism (especially the concept of “emergent evolution”) reappeared, thanks to the writings of C. Lloyd Morgan (1923), Jan Smuts (1926), and William Morton Wheeler (1927), and others. Following another haitus in the 1940s, holism recast as “systems theory” was revived again in the 1950s with the emergence of the systems sciences (see especially Ludwig von Bertalanffy, 1950, 1956, 1968; Kenneth Boulding 1956, 1977; H. Ross Ashby 1958; Anatol Rapoport 1968; Arthur Koestler and John R. Smythies 1969; Ervin Laszlo 1972; and James Grier Miller 1995[1978].) Nowadays, systems theory — which is partial to cybernetics and feedback models — seems to have been temporarily eclipsed by “complexity theory” — which is partial to chaos models and hypotheses of “self-organization.” (Stuart Kauffman, 1995, calls it “order for free.”) However, the two disciplines are really close kin.

What sets the present era apart is the fact that the scientific enterprise seems to be in the process of bridging the theoretical chasm between holism and reductionism; there seems to be a growing appreciation of the inextricable relationships between (and within) wholes and parts, and between various “levels” of organization, relationships which necessitate multi-leveled, multi-disciplinary, “interactional” analyses. (See Corning 1983; Kline 1995; also Polanyi 1968; Anderson 1972; Ghiselin 1981, 1997; Eldredge 1985; Buss 1987; Grene 1987; Maynard Smith and Szathmáry 1995; Miller 1995/1978.) Witness Francis Crick (1994), a Nobel Laureate (for the double helix) and a reformed arch-reductionist, who now embraces the phenomenon of emergence in his recent book on the nature of “consciousness” (see below). Indeed, the very terms “mechanism” and “laws” seem increasingly to be naive formulations in light of the enormously complex, dynamic processes that we can observe (and model) in ever more sophisticated ways. Consider just a few examples: quantum non-locality and quantum entanglement in physics; the highly conserved homeobox domain, consisting of some 60 amino acids, which plays a key role in morphogenesis; the awesome functional organization of the human immune system, which includes at least nine different subsystems; the elaborate cortical substrate of human vision, which involves many millions of neurons and at least 20 distinct visual areas; the intricate relationships and multi-leveled feedback processes associated with even a relatively simple ecosystem; the daunting interconnections between world population growth, technology, economic activity and vested political interests and rivalries, on the one hand, and the problems of environmental pollution, habitat destruction and resource depletion.

There have been many efforts in recent years to gain greater theoretical control over this overwhelming complexity. Best known, perhaps, are the non-linear dynamical systems models that are capable of exploiting the computing power of super-computers. (See Yates et al.,1987; Kauffman 1993, 1995; Holland 1992, 1995; TK and TK.) This has proven to be a fertile and productive enterprise, and we can at present barely glimpse its ultimate potential. For instance, computer scientist John H. Holland is involved in an ambitious attempt to model the evolution, aggregate behavior (emergence) and anticipation (purposiveness and cybernetic feedback processes) of what he characterizes as “complex adaptive systems.” (See also Chauvet, 1993.)



Here we will describe a complementary approach. It involves, in effect, a conceptual revisioning of the phenomenal world — a paradigm shift — which directs our attention to an underlying causal principle that is concerned with structural and functional relationships of various kinds and with the concrete consequences, or effects that they produce. Albert Einstein many years ago observed that “we should make things as simple as possible, but not simpler.” Theoretical simplifications, or generalizations, may serve to identify key features, common properties, or important relationships among various phenomena. Equally important, a concept which encompasses a broad range of phenomena may also serve as the anchor for a theoretical framework which, in turn, may catalyze specific hypotheses, predictions or tests.

One example is the concept of natural selection. Evolutionists often speak metaphorically about natural selection (as did Darwin himself) as if it were an active selecting agency, or a mechanism. But in fact natural selection is an “umbrella” category that refers to whatever functionally-significant factors (as distinct from, say, stochastic or teleological influences) are responsible in a given context for causing the differential survival and reproduction of genes, genic “interaction systems” (in Sewall Wright’s term), genomes, groups, populations and species. Genes are the units that are selected, but it is the functional consequences of the genes that (by and large) determine their ultimate fate. (The “classical” population genetics definition of natural selection as a change in gene frequencies in a population is — as Wimsatt, 1980, has pointed out — inadequate because it focusses on the informational and “bookkeeping” aspect of a larger, iterative functional process.)

Accordingly, as a theory of evolutionary change natural selection makes no global predictions about the overall course of evolution or the future of any given species, in contrast with various “orthogenetic” or law-like theories of evolution. Nevertheless, the concept leads to many situation-specific explanations, predictions and postdictions about the properties of various organisms, about the relationships among species (and between any species and its environment) and about the causes of various directional changes through time.

Another example of an “umbrella” term is the concept of hierarchies. The basic principle was well understood by Aristotle, and by the 19th century taxonomists and evolutionists, but the term itself apparently traces to the turn of this century (reviewed in Grene, 1987). Today the term is used in a variety of ways, with each usage having its own theoretical connotations. (See the discussions in Weiss 1971; Pattee 1973; and the references for multi-levelled organization cited above.) Thus, the postulate of a taxonomic hierarchy, which entails a classification of various species into more inclusive groupings (genera, families, orders, etc.), also implies that a given species has certain characteristics and evolutionary relationships in common with (or different from) other species, both extant and extinct. The physiologists, in contrast, associate the term hierarchy with organelles, cells, tissues, organs, etc., a scheme which implies a nested set of functional parts-wholes relationships. Likewise, to political scientists a hierarchy refers to structured relationships of power, rule or authority — to different “levels” of cybernetic (political) control. And when biologists Niles Eldredge and Stanley Salthe (1984) drew a distinction between “genealogical” and “ecological” hierarchies in nature, they were also implicitly making certain claims about the causal dynamics of the evolutionary process (see also Ghiselin 1981, 1997; Eldredge 1985; and Salthe 1985).

“Synergy” (from the Greek word synergos) is another such umbrella term. Although it is often overlooked, underrated, or misunderstood (or called by a different name), synergy is a ubiquitous and fundamentally important aspect of the natural world. (For an in-depth discussion, see Corning 1983; also 1995, 1996, 1997.) Synergy, broadly defined, refers to combined or “co-operative” effects — literally, the effects produced by things that “operate together” (parts, elements or individuals). The term is frequently associated with the slogan “the whole is greater than the sum of its parts” (which traces back to Aristotle in The Metaphysics) or “2+2=5”, but, as we shall see, this is actually a caricature, a narrow and perhaps even misleading definition of a multi-faceted concept. We prefer to say that the effects produced by wholes are different from what the parts can produce alone.

There are innumerable illustrations of synergy. One pointedly non-quantitative example has to do with pattern recognition, or what is referred to in psychology as gestalt phenomena (reviewed in Rock and Palmer 1990). The two-letter combinations PT, TP, RT, and TR, mean nothing to most of us (except perhaps to old salts of the World War Two era, who may remember the PT Boats). But if you add a vowel — either an “o” or an “a” — to each of these consonants, you will get the results shown in Table I below. The three-letter combinations in the table are now meaningful (at least to English-literate readers), although, interestingly enough, the combinations shown in column four are utilized only as acronyms, perhaps because they are more difficult to pronounce. (Some of these acronyms are “please turn over,” “Parent-Teacher’s Association,” and “Rapid Transit Authority.” No doubt there are others as well.)




Unless you happen to be a fan of cross-word puzzles, you may not recognize in the table the more obscure words “tor” (rocky promontory) and “ort” (food scrap). This illustrates an important point about the nature of synergistic phenomena. Synergy refers specifically to the structural or functional effects that are produced by various combinations of things. In this case, it refers to the effects that the words produce in the reader’s mind; it is not something that is inherent in the patterns themselves. Thus if a word evokes no mental image, it is at best an example of latent synergy.

There are obviously many different kinds of cooperative/synergistic effects. Some arise from linear or additive phenomena. Larger size, frequently the result of an aggregation of similar units, may provide a collective advantage. For instance, colonies of the predatory myxobacterium (Myxococcus xanthus) are jointly able to engulf much larger prey than any one or a few could do and, more important, are able collectively to secrete digestive enzymes in concentrations that would otherwise be dissipated in the surrounding medium (Bonner 1988; Shapiro 1988).

A variant of this type of synergy involves frequency or density-dependent phenomena. Brood parasitism in birds is a case in point. The effectiveness of this freeloading reproductive strategy depends upon, among other things, the availability of nest sites, the number of eggs laid by the “hosts” and the number of eggs laid by their parasites (Read and Harvey 1993). Density dependent effects are also involved in the well-known correlations between bacterial colony size and a bacterium’s ability to cause infections or resist drugs (e.g., Staphylococcus aureus).

So-called “emergent phenomena” are a particularly important class of synergistic effects. (We restrict the term “emergence” to the subset of synergistic effects in which new physical “wholes” are synthesized.) Thus, stainless steel is an alloy of steel (itself an alloy) together with nickel and chromium, a combination which exhibits rust- and tarnish-resistance and increased durability. Duralumin, which is a compound of aluminum, copper, manganese and magnesium, combines the light weight of aluminum and the strength of steel. And the so-called super alloys comprised of nickel, cobalt and other elements are favored for jet engines and spacecraft because they can resist very high temperatures, high pressures and oxidation.

The “division of labor” (or, very often, various combinations of labor), a phenomenon appreciated by Plato and further articulated by Adam Smith and the classical economists, represents another important category of synergy. To illustrate, one important component of the reproductive machinery in living systems involves a division of labor and coordinated efforts of three different kinds of RNA — Messenger RNA, Transfer RNA and Ribosomal RNA. Darnell et al., argue, in their textbook on molecular and cell biology, that “the development of three distinct functions of RNA was probably the molecular key to the origin of life” (1990: 88).

Among the many other examples of a division of labor found in nature, some of the most remarkable appear in very primitive life forms: bacterial colonies, eukaryotic protists, cellular slime molds, etc. One case in point involves Anabaena, a cyanobacterium which engages in both nitrogen fixation and photosynthesis, a dual capability that gives it a significant functional advantage. However, these two functional processes happen to be chemically incompatible; the oxygen produced by photosynthesis can inactivate the nitrogenase required for nitrogen-fixing. Anabaena has solved this problem by complexifying. When nitrogen is abundantly available in the environment, the cells are uniforn in character. However, when ambient nitrogen levels are low, specializaed heterocysts are developed which lack chlorophyll but which are able to synthesize nitrogenase. The heterocysts are then connected to the primary photosynthesizing cells by filaments. Thus, a compartmentalization and specialization of functions exists which benefits the “whole” (Shapiro 1988).

Synergy is also found in a variety of mutually enhancing or augmenting functional effects in nature. Hemoglobin, a tetrameric protein whose four monomers cooperatively bind oxygen, is one well-known example. (Indeed, there is an area of biochemistry called “cooperativity theory,” which is focussed on the study of the many kinds of synergistic phenomena that occur at the biochemical level– see Hill 1985.) Another example at the micro-level concerns the observed error rate in normal cellular DNA replication, which is remarkably low (about 10-10 to 10-8 per base pair) compared with the theoretical potential, given the ambient sources of decay, damage and copying errors of about 10-2. The explanation for this discrepancy is that it is the combined result of a complex set of mechanisms that “work together,” including proofreading by DNA polymerases, methylation-instructed mismatch correction, enzymatic systems that repair or bypass potentially lethal or mutagenic DNA damage, processes that neutralize or detoxify mutagenic molecules, the regulation of nucleotide precursor pools and, of course, the redundancy achieved by double-stranded genetic material (Haynes 1991).

There is also in nature a broad category of what might be called “bioeconomic” efficiencies that are derived from cooperative behaviors of various kinds, such as joint environmental conditioning, cost and/or risk sharing, information sharing, etc. Thus, emperor penguins (Aptenodytes forsteri) are able to buffer themselves against the intense Antarctic cold by huddling together during the winter months in dense, heat-sharing colonies numbering in the tens of thousands. Experiments have shown that, in so doing, the penguins are able to reduce their individual energy expenditures by 20-50 percent (Le Maho 1977). Similarly, honey bees, through joint heat production or fanning activities as the need arises, are able to maintain the “core” temperature of their hives within a narrow range (Gould and Gould 1995). And Partridge (1982) and his colleagues have shown that fish schools, which can include the active coordination of behaviors, may be highly adaptive for individual members. For instance, the evasive maneuvers utilized by dwarf-herring against predatory barracudas dramatically reduce the joint risk of being eaten.

Information sharing (wittingly or otherwise) may be highly synergistic. Social insects and communally nesting birds frequently share information about food patches. Many animals engage in alarm calling, which may alert nearby members of their own and other species. And many flocking birds and herd animals share in the tasks — and energy costs — associated with scanning for potential predators (King 1955; Wilson 1975; Caraco 1975; de Groot 1980).

Another preliminary point about the concept of synergy is that it is value-neutral. Over the years various writers have equated the term with mutualism or even altruism. However, this is not correct. Synergy refers to combined effects of all kinds. These effects may be considered eufunctional (positive synergy), dysfunctional (negative synergy), or even neutral, depending on the context. For instance, the mutation-enhancing effects of gamma rays and metallic salts in combination might be viewed positively if you were a geneticist who wanted to enhance mutation rates in a laboratory study. By the same token, the synergies achieved by pack-hunting social animals (in terms of, say, capture efficiency or the size of the prey) may be viewed positively from the point of view of the predators but rather negatively from the point of view of their victims.



As noted above, synergistic phenomena can be found in many different disciplines. In some disciplines, the term synergy is used extensively. (A literature search of a data base for the biological sciences for the five-year period from 1991 through 1995 produced some 3,400 “synerg” references, concentrated for the most part in such “hard sciences” as endocrinology, neurochemistry, pharmacology, etc.) Yet in other biological disciplines, paradoxically, the term synergy is used sparingly, if at all. Consider sociobiology, for instance, which is primarily concerned with cooperative behaviors among conspecifics in nature. A highly organized social species, such as honey bees, army ants, or naked mole rats, exhibit many different forms of social synergy — from joint thermoregulation to information sharing, cooperative foraging, mutual grooming, mutual defense, cooperative nest-building, etc. Nevertheless, these behaviors are typically characterized as “kin selection,” or “reciprocal altruism,” a “division of labor,” “emergence,” “mutualism” or simply “cooperation.”

Likewise, various formal models of cooperation in sociobiology also implicitly depend on synergy. For instance, in the well-known Tit-For-Tat model of Axelrod and Hamilton (1981), mutual cooperation is rewarded with 6 points, while “defection” by one player is rewarded with 5 and mutual defection yields only 2 points. And yet, the dependence of this and other “game theory” models on synergy is seldom acknowledged.

It is not possible to provide here a detailed, discipline-by-discipline analysis of the many different kinds of synergy, and the terminology that is used to describe it. As an alternative, a selected sample of the scientific disciplines is presented in Table II, along with representative examples and some associated terminology. This is followed by a brief description of each example, with special reference to its synergistic aspects.


Representative Example(s) Associated
Quantum Physics Quantum Coherence holism, ordering
Physics Chaotic Phenomena emergence, interactions, attractors, order
Self-organized Criticality interactions, holism
Phase Transitions cooperative effects, symmetry, breaking
Thermodynamics Dissipative Structures order/disorder, low entropy, negative entropy, order, emergence
Biophysics Hypercycles cooperation, interactions, coordination, order, emergence
Chemistry Molecular Macrostructures symmetry, collective stability, order
Biochemistry Supramolecules interaction, functional integration, coordination
Molecular Biology DNA complementarity, epistasis, heterosis
Developmental Biology Homeotic genes organization, coordination, cooperation
Neurobiology Neuronal Transmission cooperativity, threshold effects, emergence
Ecology and Behavioral Biology Coevolution interactions, mutualism, parasitism
Symbiosis mutualism, cooperation
Sociobiology mutualism, reciprocal altruism, emergence, cooperation, division of labor
Anthropology Cultural Evolution symbiosis, cooperation, division of labor


  • Quantum Coherence: Sometimes referred to as “Bose-Einstein condensation,” quantum coherence involves situations in which a large number of particles participate collectively in a quantum state; there is a wave function that is unentangled with its environment and behaves as if it were a single particle (Frölich 1970, 1975). Large scale versions of quantum coherence can be observed in superconductivity and superfluidity. In any case, collective quantum effects are synergistic in nature.
  • Chaotic Phenomena: “Chaos” involves unpredictable but deterministic interactions among phenomena that may result in coherent, collective states of order or disorder that are synergistic. Laminar flow and turbulence in fluid dynamics are examples. The so-called “strange attractors” or “dynamical attractors” that are highlighted in chaos models represent states whose stability properties arise from the interactions among the variables (Crutchfield 1986; Ditto and Pecora 1993).
  • Self-Organized Criticality: A process in which large composite systems that are involved in dynamic interactions may self-evolve to a critical state in which any further change can result in a global transformation (Bak and Chen 1991). Avalanches are an example, and it has been discovered that such phenomena are the result of the global properties of the whole.
  • Phase Transitions: Physicist Herman Haken (1973, 1977, 1983) and various colleagues have spent more than 20 years developing a science of cooperative phenomena called “synergetics”, the objective of which is the elucidation of a set of general principles of cooperativity and cooperative effects. Phase transitions, which involve a collective change of state (e.g., laser beams, water turning into ice crystals or to steam at various critical temperatures, or the loss of magnetism in iron crystals at 7740C.) have been among the many important areas of synergetic analyses.
  • Dissipative Structures: Ilya Prigogine (1978) and his co-workers have built upon Erwin Schödinger’s idea that there is a class of systems in nature that can defy the Second Law of Thermodynamics by being “open”, or energy-processing in nature. They feed on throughputs of energy to sustain order, or “negative entropy,” and can thereby remain in a sustained state of thermodynamic disequilibrium. Dissipative structures, according to Prigogine, may also be self-organizing. They may arise spontaneously and then spontaneously evolve toward greater complexity. This may occur when an open system is driven so far from an equilibrium condition that non-linear discontinuities, or threshold instabilities may occur that will transform the system in the direction of greater complexity and more structural stability. In any event, the behavior of dissipative structures, and the transitions between system states, are holistic phenomena; they involve cooperative effects. Indeed, an often overlooked point about thermodynamic processes is that “negative entropy”, or a highly ordered energy state, also entails synergy — a concentration of energy such that it is capable, collectively, of doing work.
  • Hypercycles: A major hypothesis about prebiotic evolution, the hypercycle model envisions a cyclical, mutually-reinforcing catalytic process among interrelated RNA-like precursors. As Manfred Eigen and Peter Schuster (1977, 1979) note, the build-up of functionally-proficient translation machinery in evolution required the integration of several different replicative components. This integration, they maintain, could only be achieved by a “mechanism” such as a hypercycle, which they characterize as a “cooperative” process. (Stuart Kauffman, 1993, has proposed an alternative model based on the idea of a collective phase transition involving connected sequences of biochemical transformations. He likens the process to one in which a set of pegs distributed on a floor are gradually connected by strings until, in due course, they reach a critical point where they combine to form a net — a synergistic effect. Indeed, Kauffman characterizes the origins of life as an “emergent property” of complex systems of polymer catalysts; life, he says, has an “innate holism.”)
  • Molecular Macrostructures: The macro-level material world exhibits many kinds of synergy. Indeed, Mendeleev’s periodic table is a monument to the extraordinarily diverse ways in which the basic building-blocks of nature can be combined to produce emergent new phenomena with a great variety of synergistic physical properties and effects. Likewise, the multifarious chemical compounds that are found in nature (or synthesized in our laboratories and factories) are products of the covalent, ionic and coordinate bonds that “glue” atoms together. Some are so commonplace that we take them for granted: water, table salt, ammonia. Others are more exotic. Buckminster Fullerenes (Carbon 60 and several variants) provide a particularly apt example, because these recently synthesized molecules of pure carbon are named for the well-known engineer who invented geodesic domes (and incidentally promoted the concept of synergy). Nicknamed the “Bucky Ball,” C60 was given its moniker because it derives its extraordinary stability from its physical resemblance to a geodesic dome, or a soccer ball. And it is the geometry of the whole that gives the Bucky Ball its distinctive collective properties (Curl and Smalley 1988).
  • Supramolecular Chemistry: The chemistry of molecular assemblies and intermolecular interactions is a fast-developing, interdisciplinary enterprise. It is focussed on the processes by which substrates bind to enzymes, how drugs find their targets, how coordinated actions occur among molecular assemblies, how signals propagate between cells, etc. Among the remarkable features of this research domain is the fact that “information” plays a key role, both in the processes of polymolecular self-assembly and in the intermolecular interactions, with results that are systemic in nature (Lehn 1993).
  • DNA: In the age of biotechnology and recombinant DNA, the renowned three-letter acronym for deoxyribonucleic acid is a household word. Most school children now learn about the double helix and the four nucleotide “letters” which make up the genetic “code.” What is often glossed over is the fact that the properties of DNA are highly cooperative in nature. There is, first of all, the double-stranded, antiparallel “backbone” in which phosphate groups alternate with a sugar, deoxyribose, to form covalently linked chains, a structure which hangs together because its atoms share pairs of electrons. Also, the four nucleotide “bases” — each a complex synthesis of carbon, nitrogen, hydrogen, and (except for adenine) oxygen — can only perform their vital informational function because of their very specific complementarities: Adenine only pairs with thymine and guanine with cytosine. And it is the order in which the bases are arrayed in various three-letter “codons” (like the words in Table I above) that gives DNA its capacity for constructing amino acids. Furthermore, the functional capabilities of DNA depend on the highly coordinated transcriptional role played by three distinct forms of RNA (a division of labor), as noted above. Finally, the construction of a living organism requires a complex, multi-layered fabrication process. An estimated 2.5 billion base pairs are required to define the 50-100,000 genes in the human genome. (Even a simple virus like the much-studied SV40 in monkeys has 5,243 base pairs and five genes.) The genes function cooperatively to construct the 20 different amino acids which, in turn, are used to build several thousand different proteins. And, if there is even a single alteration in the gene sequence that codes for, say, hemoglobin, the result may be sickle-cell anemia, or worse.
  • *The Homeobox: It has long been appreciated that genes generally do not act alone in producing the phenotypes of the next generation. Many years ago, the geneticist Theodosius Dobzhansky (1937) demonstrated that, even in Drosophila (fruit flies), factors on all of the chromosomes (Drosophila have four) may contribute even to a simple trait like the size of the testes, and many experiments since then have confirmed the cooperative nature of the genome’s functional organization. A particularly dramatic example is the homeobox domain, which was discovered in the 1980s (Gehring 1985; Maynard Smith and Szathmáry 1995). The homeobox refers a distinctive DNA segment, consisting of some 60 amino acids, that are found in all of the so-called homeotic genes and, remarkably, have been conserved over many millions of years of evolution in organisms ranging from fruit flies to humans. (There are about 10 homeobox segments in Drosophila and at least 40 in humans, arranged in four complexes on different chromosomes.) The homeotic genes apparently utilize the homeobox complex to determine the basic body plan of a given organism and serve as key regulators of embryonic development; they establish the body’s architecture and tell the developing embryo what kinds of structures to make in each location. It is, in effect, a microscopic example of a combination of labor that is oriented to the production of a combined, emergent result (De Robertis, et al., 1990). More recently, the human genome mapping projects have greatly expanded our appreciation for the synergies that are involved in morphogenesis. The process of constructing a human brain, for instance, involves some 3,195 distinctive genes, the precise functions of which are rapidly being decoded (Goodfellow 1995; Little 1995).
  • Neuronal transmission: The human brain and nervous system is characterized by a complex and as yet only partially understood division (and synthesis) of labor among numerous functionally specialized areas/regions that are interconnected by an estimated 100 billion neurons. Many aspects of the brain’smodus operandi (particularly the “binding” process by which the activities of the various parts are integrated into the “whole” that constitutes our conscious experience) still elude us. However, what we do know affirms that the workings of the brain are synergistic. We know that the neurons are in constant communication with one another via a network of staggering complexity. An individual neuron may have anywhere from 500 to 20,000 synaptic connections with other neurons, and at any given moment millions of neurons are firing in concert in a highly cooperative process. Even the transmission of a signal (an electro-chemical impulse) by a single neuron turns out to be synergistic. In brief, the “firing” process involves what Francis Crick (1994), characterizes as a “complex dynamic sum” of both excitatory and inhibitory inputs from all of the other neurons with which the neuron is in contact via the “synapses” (or junctions) between its own dendrites and the axonal endings, or “knobs” of neighboring neurons. The way in which signals bridge the synaptic cleft, the gap between neuronal junctions, is also highly cooperative. Even the mechanics of the transmission process within each neuron is, in Crick’s words, a “chemical miracle.” It is not at all like electricity flowing through a wire but an intricate process of chemical (ionic) balance shifts in electrical potentials, a dynamic which is facilitated by an elaborate system of molecular “gates” and metabolic “pumps.” As Crick concludes: “A neuron, then, is tantalizingly simple…It is only when we try to figure out exactly how it responds…that we are overwhelmed by the inherent complexity of its behavior…All this shows, if nothing else, that we cannot just consider one neuron at a time. It is the combined effect of many neurons that we have to consider” (1994:103-104).
  • Coevolution: A term coined by biologists Paul Ehrlich and Peter Raven (1964) and subsequently developed by a number of other biologists (Thompson 1982; Futuyma and Slatkin 1983; Nitecki 1983), coevolution refers to the aspect of the evolutionary process that is driven by the interactions among species. Some are mutually beneficial; some are commensalistic (with benefits to one or more species without apparent detriment to others); and some are competitive, parasitic or predatory. Broadly defined, coevolution could encompass a major part of the total evolutionary process. But the consensus seems to be that the term should only be applied to situations in which one species becomes a “selection pressure” for another species in such a way that it precipitates step-wise directional changes over a number of generations in two (or more) species via reciprocal causation — a sort of “arms race” or, conversely, a process of progressive accommodation and mutual facilitation. The paradigm-defining example, provided by Ehrlich and Raven, is butterflies and the plants on which they feed. Over the course of time, many plants have evolved chemical compounds that are apparently without physiological functions (alkaloids, quinones, glycosides, flavonoids, etc.,), yet they seem to be repugnant to otherwise predatory butterflies. The butterflies, in turn, appear to have diversified their diets (and their habitats) and to have evolved appropriate new digestive and concealment adaptations. In more extreme cases, coevolution may resemble what biologist Leigh Van Valen characterized as a Red Queen’s race (from Lewis Carroll’sThrough the Looking Glass), where interacting species must run as fast as they can just to stay in place (see also Vermeij 1987). But, in any case, coevolutionary processes are relational and synergistic.
  • Symbiosis: The term “symbiosis” is generally used by biologists to connote the “living together” of “dissimilar” organisms — sometimes for their mutual benefit and sometimes not. The classic example of a mutualistic symbiosis is lichen, a generic label for the roughly 20,000 different species of partnerships between some 300 genera of fungi and various species of cyanobacteria and green algae. Although many lichen partners can apparently live independently, in combination they enjoy significant functional advantages (synergies). (Empirical support can be found in a recent quantitative analysis by Raven, 1992.) Fungi have gripping and water-retention capabilities that can be especially advantageous in a relatively harsh or barren environment, while the cyanobacteria and green algae bring photosynthesizing capabilities to the partnership. The symbionts also commonly combine forces to produce a thallus. Some lichen even reproduce together (asexually) via symbiotic diaspores. Although symbiosis is often equated with mutualism, it also includes many examples of parasitism — relationships which may or may not be deleterious (negative synergy) for one of the partners, including many cases in which the functional consequences vary with the circumstances. For example, the so-called VAM (vesicular-arbuscular mycorrhizal) fungi that are models of mutualism with many species of plants do in fact enhance plant growth in low phosphorous soils, but in high phosphorous soils or in low light conditions (when photosynthetic activity is reduced), they may become parasitic and reduce plant growth (Bethlenfalvay et al., 1983; Daft and El-Giahmi 1978). Ten years ago, symbiosis was still considered by many biologists to be a minor theme in evolution. However, a number of subsequent developments have elevated symbiosis to a place at the head table. Not only is the endosymbiotic origin of eukaryotic cells — a major turning point in evolution — now widely accepted, but there is a recognition that mutualistic and commensalistic associations (not to mention parasitism) are widespread throughout the living world. For instance, there is growing evidence of mutualism (as well as competition and parasitism) between many species of plants (Margulis 1993; Hunter and Aarssen 1988). Perhaps most remarkable, we are even discovering a vast new domain of mutualistic and parasitic interactions at the micro level, among bacteria, viruses, plasmids, etc., (Sonea and Panisset 1983; Weinberg 1985; Margulis 1993; Margulis and Sagan 1995; also, see below).
  • Sociobiology: As defined by Edward O. Wilson (1975), sociobiology is concerned with behavioral relationships among members of the same species, ranging from parent-offspring interactions to elephant seal “harems” and the tightly integrated division of labor in a number of social insect species. One of the most impressive examples of the latter is Eciton burchelli, a species of army ants found in Central and South America. E. Burchelli form highly organized colonies of about 500,000 members, with four morphologically distinct castes (in addition to the queen) that divide up the responsibilities for colony defense, foraging, transport, nest-making, and care of the brood. The so-called “submajors” (or porters), for instance, team up to carry sometimes very large prey which, if split up into pieces, would be more than each individual ant could carry alone. E. Burchelli’s highly cooordinated foraging system is legendary. In a single day, a raiding party of up to 200,000 individuals, marching in a dense phalanx, may cover as much as 200 meters and reap some 30,000 prey items, many of which must be transported back to the nest. (Army ants are actually top carnivores; so far as is known, nobody preys on them, so formidable are their combined defensive capabilities.) One of the most remarkable features of E. Burchelli’s adaptive strategy is the fact that the workers form nests out of their own interlinked bodies and are able to maintain an internal nest temperature within ± 10C. Moreover, during their 15-day nomadic periods, the colony moves its nest daily (to provide sufficient food for its growing brood), a process which involves a highly complex, coordinated maneuver. As ecologist Nigel Franks notes, army ant colonies also display flexible, problem-solving behaviors as an emergent property of the colony; their actions are not centrally-controlled (Franks 1989, 1991).
  • Cultural Evolution: The Igorot provide an example of how even a “primitive” human economy may depend on an intricate network of ecological, technological, social and political components. When they were studied in the 1970s by anthropologist Charles B. Drucker (1978), the Igorot occupied a mountainous area of Luzon, in the Philippine Islands, where for centuries they had practiced irrigated rice cultivation within an awe-inspiring system of earthwork terraces, dams and canals that were laboriously carved with simple tools out of the precipitous mountainsides. One key to the Igorot’s subsistence mode was the remarkable, sustained fecundity achieved by the constant replenishment of soil nutrients, especially nitrogen. This depended on the presence in the rice ponds of nitrogen-fixing cyanobacteria that maintained a symbiotic relationship with the rice plants. Thus, over a period of several centuries, the Igorot were able to grow almost enough staple food on a single hectare to feed a family of five. Yet this is only part of the story. The Igorot’s cultural adaptation also depended on a cooperative set of social arrangements. Whereas the ancestral Igorot had lived in small family groups and practiced a form of shifting, small-scale (slash-and-burn) plant cultivation, the successful adoption of the rice terrace mode of production required the coalescence of an integrated social organization. Sustained cooperative efforts became necessary, first to build the rice-terrace system and then to utilize, maintain and expand it over time, for without constant weeding and repairs the system would soon deteriorate. In effect, the productivity of each individual family plot depended on the productivity of the “whole”. Accordingly, the Igorot had to invent a set of social and political structures and processes (and cultural norms) to coordinate in a disciplined manner the activities of the many previously isolated family groups. (A more recent study of the Balinese water temple system and its relationship to ecological and subsistence patterns, utilizing a dynamical systems model too complex to detail here, suggests the possibility that cultural practices with emergent, collective effects may also arise from self-organizing processes (Lansing and Kremer 1993).


As the sampler above suggests, a broad definition of synergy as cooperative, or combined effects of all kinds casts a very wide net over the phenomenal world. Indeed, synergy travels under many different aliases. Among other things, it encompasses emergent phenomena, functional and structural complementarities, the division of labor, threshold effects, phase transitions, symbioses, symmetries, coevolution, heterosis, interactions, cooperation, epistasis, systemic effects, even “dynamical attractors.” Can such a broad definition be useful? We believe the answer is emphatically yes. Cooperative interactions are everywhere in nature, to be sure, but the particular focus of a synergy orientation is the subset of all imaginable interactions that have combined functional effects (positive or negative) for those aspects of the material world that we wish to understand more fully. Synergy shifts our theoretical focus from mechanisms, objects or discrete bounded entities to the relationships among things, and, more important, to the functional effects that these relationships produce. Synergistic causation is configurational; synergistic effects are always co-determined.

A point that was made earlier should also be stressed at this point. Synergy is real. Its effects are measurable or quantifiable: e.g., economies of scale, increased efficiencies, reduced costs, higher yields, lower mortality rates, a larger number of viable offspring, etc. More subtle measuring rods include enhanced stability properties, greater stress tolerance, increased fidelity in reproduction, the melding of functional complementarities to achieve new properties, and so forth. A frequently-invoked example of nutritional synergy can perhaps be used to illustrate: One-half cup of beans provides the nutritional equivalent (in terms of usable protein) of two ounces of steak, while three cups of whole wheat flour provide the equivalent of five ounces of steak. Eaten at separate times, the two food items contribute the equivalent of seven ounces of steak. But because of the complementarity of their amino acids, if the two substances are consumed together they provide the equivalent of nine ounces of steak, or 33 percent more useable protein. Here is a case where, literally, the whole is greater than the sum of its parts.

Another illustration can be derived from rowing, a sport which seems to be popular with biologists. In his near-legendary book The Selfish Gene, Richard Dawkins (1989/1976) concedes that genes are not really free and independent agents. “They collaborate and interact in inextricably complex ways…Building a leg is a multi-gene cooperative enterprise” (p.37). To underscore the point, Dawkins employs a rowing metaphor. “One oarsman on his own cannot win the Oxford and Cambridge boat race. He needs eight colleagues…Rowing the boat is a cooperative venture” (p.38). Furthermore, Dawkins notes: “One of the qualities of a good oarsman is teamwork, the ability to fit in and cooperate with the rest of a crew” (p.39). (The obvious inconsistencies between these statements by Dawkins and his ruling selfish gene metaphor are treated at length in Corning 1996, 1997.) In a similar vein, Maynard Smith and Szathmáry (1995) use as a metaphor specifically for synergy the image of two men in a rowboat, each with one oar. If only one oarsman is rowing, the boat will go in circles.

We can add a quantitative example to these nautical metaphors: A world class “varsity eight” (plus coxswain) can cover 2000 meters over the water in about 5.5 minutes. However, a single sculler can at best row the same distance in about 7 minutes. The difference is synergy, and if rowing faster were a matter of survival (and it may very well have been at various times in our history as a species), the cooperators would be the fittest. 2


Many years ago, the chemist Michael Polanyi (1968) proposed a definitive resolution to the long-standing debate over reductionist versus holistic approaches to scientific explanation. The natural world consists of a hierarchy of “levels,” Polanyi wrote in “Life’s Irreducible Structure,” which can be identified empirically in relation to distinct “boundary conditions” that impose more or less inclusive constraints on the laws of nature. Each level works under principles that are irreducible to the principles governing “lower levels.” Thus, the “laws” governing the properties of DNA are not reducible to the laws of physics and chemistry. Nor are the principles governing morphogenesis reducible to those that govern nucleic acids. Equally important, the principles that control higher levels may serve to restrict, order and “harness” lower levels. To use one of Polanyi’s examples, the grammatical rules that govern the structure of various human languages utilize but also subsume the principles of phonetics. Accordingly, any hierarchically-organized phenomenon may embody several distinct sets of level-specific principles.

In another classic article entitled “More is Different,” physicist Philip Anderson (1972) subsequently reinforced and expanded on Polanyi’s argument. “The reductionist hypothesis does not by any means imply a ‘constructionist’ one: The ability to reduce everything to simple fundamental laws does not imply the ability to start from those laws and reconstruct the universe…The constructionist hypothesis breaks down when confronted with the twin difficulties of scale and complexity…At each level of complexity entirely new properties appear… Psychology is not applied biology, nor is biology applied chemistry.” Anderson used the now familiar term “broken symmetry” to characterize such qualitative shifts. Examples cited by Anderson included complex organic molecules, superconductivity and crystal lattices. “We can see how the whole becomes not merely more but very different from the sum of its parts,” Anderson concluded. (Needless to say, these arguments have been reiterated many times since the early 1970s.)

While these two landmark articles helped to legitimize the development of such umbrella disciplines as theoretical ecology, hierarchy theory, chaos theory, dynamical systems theory and complexity theory, neither one squarely addressed the question: what precisely are the relationships between levels (and disciplines)? What are the causal factors in the phenomenal world that are responsible for producing the phase transitions and dynamical attractors that are simulated in our mathematical models? In other words, what are the functional relationships between parts and wholes? The frequent use by scientists of such descriptive terms as “emergence,” “interdependency,” “interactions,” “positive cooperativity,” “co-determination,” and even “synergy” represent at least a tacit acknowledgment that the various levels of organization in nature are connected to one another. The term “emergence” is especially popular among die-hard reductionists, because it implies that wholes are merely the epiphenomenal effects of laws and causal processes that can be fully illuminated at lower levels. What is less frequently acknowledged — and sometimes even denied — is the fact that various levels may be interdependent; wholes and parts may interact, and coevolve, in complex ways. Indeed, the very concept of hierarchical “levels” may sometimes become an obstacle to understanding. (For more extended discussions of these points, see Wimsatt 1974; Kline 1995).

A synergy perspective suggests a paradigm that explicitly focusses on both wholes and parts, and on the interactions that occur among the parts, between parts and wholes and between wholes at various “levels” of interaction and causation. It might be called “a science of relationships,” as distinct from a science of “mechanisms” or “laws”. Thus, the phenomenon of consciousness may well be an emergent/synergistic product of a vastly complex set of interactions within the machinery of the brain and between the brain and the environment. However, as the late psychobiologist Roger Sperry argued repeatedly (eg., 1969, 1991), “downward” causation is also important in evolutionary processes (see also Campbell 1974). Synergistic wholes are also units of causation in the phenomenal world — and in evolutionary change. Sperry was fond of using the metaphor of a wheel rolling downhill; all of its spokes, and its atoms and molecules, are bound to go along for the ride.

Of course, the relationship between parts and wholes is often more complicated than that. Parts may constrain the wholes in various ways. The mobility of a particular animal species, for instance, is strictly limited by the capabilites of its locomotive machinery, which in turn has been shaped by a complex nexus of influences ranging from the principles of physics and thermodynamics to the particular evolutionary history of its lineage. An organism’s parts may also establish functional priorities for the whole (eg., its pressing survival and reproductive needs). Conversely, “higher levels” in the biological hierarchy may set priorities for lower levels. Thus, social insects, we now appreciate, respond in complex ways to the cues and signals that arise in their social environments — at the “superorganism” level of organization (in Herbert Spencer’s original formulation) and even beyond. (Indeed, many of these higher-level cues may be combined properties of the whole.) Thus, while the proximate mechanisms of behavioral control in social insects may be distributed, the organizational and functional principles are nevertheless superordinate and holistic.

In short, there is both upward and downward causation in nature, and very often a synthesis of the two. Moreover, wholes of various kinds may become interdependent “units” of selection and evolution, just as, conversely, the evolution of various parts may be shaped by the functional requisites of the whole. An example of the latter are the army ant submajors referred to above; their large size and long legs are morphological adaptations that reflect their role in the E. Burchelli division of labor. In like manner, symbionts (say mitochondria and their primitive protist hosts) may coevolve adaptations that serve the functional needs of the partnership as an emergent “unit” of selection (an obvious example is the synchronization of reproductive efforts).

Accordingly, a “synergy paradigm” implies a multi-leveled, interactional research focus, one which gives equal weight to both reductionist and holistic perspectives and invites both intra and inter-level analyses and explanatory models. (A number of philosophers of science have discoursed on this issue in recent years. In particular, see Wimsatt 1974, and Bechtel 1986.) One implication is that increased collaboration among the traditionally autonomous and often competing disciplines may be imperative for longer term scientific progress.

Water, perhaps the most studied of all substances, provides an illustration from the physical sciences. Despite our considerable knowledge of the remarkable substance which covers 70 percent of the earth’s surface and which comprises about 65 percent of our bodies by weight, there are still properties and aspects of its behavior that we do not understand. The basic atomic properties of water have been understood for almost two centuries, thanks to John Dalton. We also know a great deal about the chemistry, statics, dynamics and thermodynamics of water, which is subject to numerous macro-level physical principles (as Polanyi pointed out). We understand, for instance, how the constituent atoms of hydrogen and oxygen are linked together by their covalent bonds. We know that quantum theory is required to explain some of the remarkable energetic properties of water. Additional principles of chemistry are needed to account for the state changes that produce water from its constituent gases and, under appropriate conditions, the changes that can reverse the process. Still other principles are required to account for the macroscopic properties of water as a liquid medium: its compressibility, surface tension, cohesion, adhesion, and capillarity. Thermodynamic principles are needed to understand the dynamics of temperature changes in water. Static principles relating to density and specific gravity must be invoked to account for the buoyancy of rowboats and varsity eights. Hydraulics are needed to understand how water reacts to a force exerted upon it. Dynamics, and Newton’s laws, are relevant for understanding the tidal action of water in large bodies, while hydrodynamics is required to explain the behavior of water flowing through a pipe, or in a river bed. Here Bernoulli’s principle also becomes relevant.

And yet, despite all of this knowledge, we still do not know exactly how water molecules “network” with other water molecules — a key to understanding how water can be so fluid and yet have such an anomalously large capacity for absorbing heat and holding other substances in suspension (Amato 1992). Significantly, progress in studying various kinds of intermolecular interactions in water is being made via interdisciplinary efforts. In a recent report on molecular clusters in water, Colson and Dunning (1994) conclude: “This work also illustrates the synergism that has developed between experimental and theoretical studies in modern chemical physics.”

By the same token, at the most inclusive geophysical level, the problem of understanding the role of water in world climate patterns presents a formidable research challenge that has necessitated multi-leveled, multi-disciplinary modelling efforts. Larry Goldberg (1994), a philosopher of science, has studied this research domain intensively. The complexity of the problem arises from the interdependence of various component subsystems — the atmosphere (troposphere and stratosphere), the oceans and other large water bodies, the so-called cryosphere (continental ice sheets), the lithosphere (the earth’s crust and upper mantle) and the biosphere (the activities of the earth’s biota). Each of these “subsystems”, which cut across the subject matter of at least half a dozen different disciplines, presents a complex set of modelling problems in its own right. Yet they also interact in different ways depending on the particular spatial location and time-frame. Consider, for example, the impacts on the oceans from fluctuations in solar output over various scales: days (r-Mode oscillations), years (quasi-biennial oscillations), tens of years (11- and 22-year solar magnetic cycles) and hundreds of years (Maunder-minimum type cycles), not to mention such regularities as the time of day and seasonal cycles, and variables such as cloud cover and cloud density. Thus, as Goldberg notes, at any given location, date and time of day, the level of solar radiation being absorbed by the oceans depends upon an extraordinarily complex set of interacting (synergistic) causal factors. These synergies demand multi-disciplinary analyses.3

Similar multi-disciplinary challenges confront the life sciences. A particularly striking example at the micro level is a recent study reported by Wang and his co-workers (1993) showing that the movement, shape and polarity of individual cells in a multi-cellular cluster depends on close cooperation among proteins outside the cell in the extracellular matrix (ECM), proteins that are found on the surface of the cell (cell adhesion molecules, or integrins) and proteins inside the cell (the cytoskeleton). Not only are there close interconnections between these system “components” but they are organized according to the so-called “tensegrity” (or tensional integrity) architecture that underlies Buckminster Fuller’s geodesic domes. Furthermore, this parts/whole/environment interaction has been illuminated by the melding of two separate disciplines — cyto-mechanics and detailed biochemistry (Heidemann 1993).

The same sort of challenge applies to the macro-biotic level, where there have been various efforts by theoretical ecologists in recent years to address parts-wholes interactions and the complex feedbacks that exist among various ecosystem “levels”. Particularly notable is the hierarchical network approach developed by ecologist Claudia Pahl-Wostl (1993, 1995), which utilizes information theory and patterns of interlevel feedbacks in an effort to capture the spatiotemporal dynamics of an ecosystem. Pahl-Wostl (1993) concludes: “These order parameters arise from the interactions among the components of the systems through processes of self-organization. Along this line of reasoning the dichotomy between top-down and bottom-up control converges to a mutual and inseparable dependence on both factors. Neither a purely reductionist approach nor a merely holistic perspective is sufficient to encompass the intrinsic nature of the system’s behavior.”

A further implication of the “synergy paradigm” is that the phenomenon of synergy is more than simply the end-point, or outcome of the processes that drive the phenomenal world. Synergy is also an important source of causation in the ongoing evolutionary process. Indeed, a synergy focus directs our attention to one of the major wellsprings of creativity in evolution. The novelist and polymath Arthur Koestler observed that “true novelty occurs when things are put together for the first time that had been separate” (Koestler and Smythies 1969). A number of examples were cited above: the emergent properties of chemical compounds; the mitochondria that provide eukaryotic cells with specialized metabolic capabilities; the functional complementarity of the lichen partnerships; the exotic compounds that comprise super alloys and composite materials. 4


In The Synergism Hypothesis (Corning 1983), it was proposed that synergistic phenomena of various kinds have played a key causal role in the evolution of cooperation generally and the evolution of complex systems in particular; it was argued that a common functional principle has been associated with the various steps in this important directional trend. The reasoning behind this hypothesis can be briefly summarized.

First, it is necessary to return to the problem of defining natural selection — a much-debated subject and an issue that may seem tiresome to those who are already familiar with the debate. And yet, misunderstandings persist. Evolutionists often speak metaphorically about natural selection (as did Darwin himself) as if it were an active selecting agency, or mechanism. Thus, George Gaylord Simpson (1967:219) asserted that “The mechanism of adaptation is natural selection….[It] usually operates in favor of maintained or increased adaptation to a given way of life.” Similarly, Ernst Mayr (1976:365) informed us that “Natural selection does its best to favor the production of programs guaranteeing behavior that increases fitness.” In his discipline-defining volume Sociobiology (1975:67), E.O. Wilson assured us that “natural selection is the agent that molds virtually all of the characters of species.” More recently, Wilson (1987) provided a more ecologically-oriented definition of natural selection as “all the events that cause differential survival and reproduction.” Nor does it clarify matters when Dawkins (1989[1976]:v) characterizes living organisms as “robot vehicles blindly programmed to preserve the selfish molecules known as genes,” which implies that genes are the locus of evolutionary causation. (See also the contribution by Endler in Keywords in Evolutionary Biology, 1992.)

The problem is that natural selection is not a mechanism. Natural selection does not do anything; nothing is ever actively “selected” (although sexual selection and artificial selection are special cases). Nor can the sources of causation be localized either within an organism or externally in its environment. In fact, the term natural selection identifies an aspect of an ongoing dynamic process. It is an umbrella concept that refers to whatever functionally-significant factors are responsible in a given context for causing differential survival and reproduction. Properly conceptualized, these “factors” are always interactional and relational; they are defined by both the organism(s) and their environment(s)..

This crucially important point can be illustrated with a textbook example of evolutionary change — “industrial melanism.” Until the Industrial Revolution, a “cryptic” (light-colored) species of the peppered moth (Biston betularia) predominated in the English countryside over a darker “melanic” form (Biston carbonaria). The wing coloration of B. betularia provided camouflage from avian predators as the moths rested on the trunks of lichen-encrusted trees, an advantage that was not shared by the darker form. But as soot blackened the tree trunks in areas near growing industrial cities, in due course the relative frequency of the two forms was reversed; the birds began to prey more heavily on the now more visible cryptic species (Kettlewell 1973).

The question is, where in this example was natural selection “located?” The short answer is that natural selection encompasses the entire configuration of factors that combined to influence differential survival and reproduction. In this case, an alteration in the relationship between the coloration of the trees and the wing pigmentation of the moths, as a consequence of industrial pollution, was an important proximate factor. But this factor was important only because of the inflexible resting behavior of the moths and the feeding habits and perceptual abilities of the birds. Had the moths been subject only to insect-eating bats that use “sonar” rather than a visual detection system to catch insects on the wing, the change in background coloration would not have been significant. Nor would it have been significant had there not been genetically based patterns of wing coloration in the two forms that were available for “selection” in the two forms. (Later studies concerning the additional influence of air pollution can be left out of the discussion for our purpose.)

Accordingly, one cannot properly speak of “mechanisms” or fix on a particular “selection pressure” in explaining the causes of evolutionary change via natural selection. One must focus on the interactions that occur within an organism and between the organism and its environment(s), inclusive of other organisms; natural selection is about adaptively significant changes in organism-environment relationships. But this begs the question: What factors are responsible for bringing about changes in organism-environment relationships? The answer, of course, is many things. It could be a functionally-significant mutation, a chromosomal transposition, a change in the physical environment, a change in one species that affects another species, or it could be a change in behavior that results in a new organism-environment relationship. In fact, a whole sequence of changes may ripple through a complex pattern of relationships. For instance, a climate change might alter the ecology, which might induce a behavioral shift to a new habitat, which might encourage an alteration in nutritional habits, which might precipitate changes in the interactions among different species, resulting ultimately in the differential survival and reproduction of alternative morphological characters and the genes that support them. (An excellent in vivo illustration of this causal dynamic can be found in the longitudinal research program in the Galapagos Islands among “Darwin’s finches.” See Grant and Grant 1979, 1989, 1993; Weiner 1994).

To underscore this rather more subtle conceptualization of natural selection than the short-hand characterizations that are often found in the literature, we will provide one more example. English land snails (Cepaea nemoralis) are subject to predation from thrushes, which have developed the clever habit of capturing the snails and then breaking open their shells with stones. Accordingly, a behavioral innovation (including tool use) in one species became a cause of natural selection in another species. However, two additional factors, one genetic and the other “ecological,” have also influenced the course of natural selection in C. nemoralis. It happens that these snails are polymorphic for shell banding patterns, which provide varying degrees of camouflage. The result is that the more “cryptic” genotypes have been less intensively preyed upon than those that are more visible. However, at the ecological level the pattern of predation by thrushes (and the frequencies of the different snail genotypes) varies greatly because the thrush populations, being themselves subject to predators, display a marked preference for well-sheltered localities. So, paradoxically, the snails are generally much less subject to predation in more open areas (see Clarke 1975).

The cardinal point in these examples is this: It is the functional (bioeconomic) effects or consequences of various organism-environment pattern-changes, insofar as they may impact on differential survival, that constitute the “causes” of natural selection. Another way of putting it is that causation in evolution runs backwards from our conventional view of things; in evolution, functional effects are causes. To use Ernst Mayr’s (1965) well-known distinction, it is the “proximate” functional effects which result from any change in the organism-environment relationship that are the causes of the “ultimate” (transgenerational) selective changes in the genotype, and the gene pool of a species. (It should be noted in passing that this dynamic is analogous to E.L. Thorndike’s famous Law of Effect in psychology, which forms the backbone of the “Behaviorist” learning paradigm.)

This is where synergistic phenomena fit into the picture. Cooperative interactions in nature that produce positive functional consequences, however they may arise, can become “units” of selection that differentially favor the survival and reproduction of the “parts” (and their genes). In other words, it is the proximate advantages (the payoffs) associated with various synergistic interactions (in relation to the particular organism’s needs) that constitute the underlying cause of the evolution of cooperative relationships, and complex organization, in nature. To put it baldly, functional synergy is the ultimate cause of cooperation (and complexity) in living systems, not the other way around. (As an aside, it it similar to the way in which market forces are said to work in human economies; if a “widget” sells, more widgets are likely to be produced for sale. If not, the widget will soon go extinct.)

This “bioeconomic” theory of cooperation/complexification in evolution is particularly relevant to symbiosis and sociobiology. The hypothesis is that the synergies which may result from cooperative behaviors are the very cause of their systematic evolution over time, via their impacts on differential survival and reproduction. Moreover, many of these evolutionary changes originate — and are initially adopted — at the behavioral level (as illustrated above with respect to the thrushes that prey on English land snails). As Ernst Mayr (1960) long ago observed, behavioral innovations are often the “pacemakers” of evolutionary change. In C.H. Waddington’s words: “It is the animal’s behavior which to a considerable extent determines the nature of the environment to which it will submit itself and the character of the selective forces with which it will consent to wrestle” (1975:170).

The idea that behavioral innovations might be a significant cause of evolutionary change can be traced back to Lamarck. Darwin also alluded to the idea in The Origin. At the turn of the century, a movement among evolutionists of that day known as “Organic Selection Theory” was developed in an effort to highlight the creative role of behavior in evolutionary change. (It was subsequently buried by “Weismannism” and, much later, was resurrected, downgraded and renamed the “Baldwin Effect” by George Gaylord Simpson and other exponents of what was then called “the modern synthesis.”) Waddington himself developed a variation on this theme in the 1950s, which he dubbed “genetic assimilation.” And Ernst Mayr has repeatedly argued the case for behavior as a cause of evolution in his various writings (reviewed in Corning 1983; also see Plotkin 1988; Bateson 1988; Bateson et al., 1993; TK and TK cf., Skinner 1981).


Over the past decade or so there has been a growing appreciation for the role of synergy in the natural world. Some explicit applications of the synergy concept include Kondrashov’s (1982, 1988) hypothesis regarding the basis of sexual reproduction, which relies on synergistic linkages between deleterious mutations. Similarly, Maynard Smith and Szathmáry’s (1993) theory of the origin of chromosomes postulates a synergistic relationship among primordial genes. Szathmáry (1993) also utilizes the concept in a model derived from metabolic control theory which suggests that, under some conditions, two mutations affecting a metabolic pathway could act synergistically. Rosenberg (1991) postulates a necessary role for “synergistic selection” in the evolution of warning coloration (aposmatism) in marine gastropods. Synergy occurs when a potential predator has multiple “distasteful” encounters with the same morph, which enhances the joint selective value for each bearer. (See the further discussion of this issue in the contributions by Guilford and Cuthill 1991, and Tuomi and Augner 1993.) Hurst (1990) suggests that parasite diversity in a given cell or organism may be more burdensome than a similar quantity of uniform types, because various synergistic interactions among different parasites may enhance their mutual effects. Hurst proposes that diploidy, multicellularity and anisogamy may all be anti-parasite mechanisms; they might serve to reduce parasite diversity.

Leo Buss (1987) utilizes the concept of synergy (or what he calls “synergisms”) in a broader theoretical context, as an explanatory principle in connection with the evolution of metazoa and “higher units” of selection. Though he never explicitly defines the term, his usage is idiosyncratic; he equates synergy with positive, or mutually beneficial relationships between lower and higher levels of organization, or wholes and parts, as contrasted with “conflicts” between levels. “The organization of any unit will come to reflect those synergisms between selection at the higher and the lower levels which permit the new unit to exploit new environments and those mechanisms which act to limit subsequent conflicts between the two units” (1987:viii).

Synergy has also been deployed in some recent sociobiological studies. Santillán-Doherty and his colleagues (1991), in a study of stump-tailed macaques (Macaca arctoides), found non-linear synergistic effects among three variables — kinship, sex and rank — in shaping the interactions among the animals in their study population. Packer and Ruttan (1988) also explicitly recognized the role of synergy in cooperative hunting. They observed that, when individual hunting success is already high, there is little to be gained by cooperating. Cooperation depends on synergy — an increase in the average individual feeding efficiency through joint efforts. “An increase in hunting success with group size therefore indicates synergism from cooperation, whereas a decrease indicates some form of interference [negative synergy?]” (1988:183). Some other examples of synergy in the literature include the following:

Nest construction in the social wasp (Polybia occidentalis) is a complex activity requiring the coordination of various tasks. To study the bioeconomics, Robert Jeanne (1986), conducted a comparative study of small versus large colonies, as well as the nest construction technique used by social wasps versus the less efficient method of solitary wasps. Jeanne found that small colonies required almost twice as many worker-minutes to complete the same amount of construction (due mainly to materials handling inefficiencies that larger colonies could minimize). In addition, he was able to determine that social wasps could collect and process a given amount of nest material with 2.6 times fewer foraging trips than were required by solitary wasps (with the added advantage that the social foragers were able to reduce their exposure to predators in the field). In other words, the synergies here were measurable.
Marzluff and Heinrich (1991) tested the hypothesis that immature common ravens (Corvus corax) form social groups (in contrast with breeding adults that are territorial) in order to gain access to defended carcasses. They found that groups ranging from 9-29 immature birds were significantly more likely to overcome adult carcass defenders and were able to feed at higher rates than were smaller groups or solitary individuals. The group benefits resulted from a combination of reductions in the neophobia of the foragers and the reduced aggression of adult defenders as group sizes increased.
In a comparative study of reproduction during a single breeding season among southern sea lions (Otaria byronia), Campagna, et al., (1992) observed that only 1 of 143 pups born to gregarious, group-living females died before the end of the season, as compared to a 60% mortality among the 57 pups born to solitary mating pairs. Pups in colonies were protected from harassment and infanticide by subordinate males and were far less likely to become separated from their mothers and die of starvation.

A number of other theorists have implicitly recognized synergistic effects in their studies and analyses without using the term explicitly. Thus, Page and Robinson (1991) refer to “non-additive inter-individual effects” in relation to possible genetic influences on the division of labor in honey bees. Bell (1985) focusses on the non-additive functional efficiencies that arise with specialization and a division of labor in Volvocales and, in his analysis, invokes the reasoning of Adam Smith. Hoogland (1981) stresses that there is a strong relationship between group-size in prairie dog colonies and both functional improvements in the detection of predators and decreased individual scanning activity — efficiencies that are synergistic.

There are also many quantitative, cost-benefit studies (in addition to those mentioned above) that tacitly support the synergism hypotheses. To cite a few examples: In birds, Ligon and Ligon (1982) analyzed the communal nesting and extensive helping behaviors among green woodhoopoes (Phoeniculus purpureus), both among closely related and unrelated birds. They found that this behavior pattern markedly increased the woodhoopoes’ likelihood of survival and reproductive success in an East African environment characterized by a severe shortage of suitable nest sites. A similar pattern was identified by Clarke (1989) in the bell miner (Manorina melanophrys). (But note also the more strongly kin-oriented pattern observed in other woodhoopoe populations by Du Plessis, 1993.) Parker et al., (1994) used DNA fingerprinting to document that food sharing in feeding aggregations of common ravens (Corvus corax) in the forests of western Maine was not primarily kin-directed. Møller (1987) analyzed various trade-offs (costs and benefits) of colonial nesting in swallows (Hirundo rustica) and concluded that the costs and benefits of coloniality varied markedly with such factors as group size, the frequency of predation, exposure to parasites, etc.

In the same vein, Mumme and his co-workers (1988) were able to conduct a comparative cost-benefit analysis of a 15-year data set comparing joint-nesting and solitary acorn woodpeckers (Melanerpes formicivorous). The data indicated that communally-nesting females experienced a fitness trade off: lower average annual reproduction in exchange for higher year-to-year survival rates. In a later study of the Florida scrub jay (Aphelocoma c. coerulescens), Mumme (1992) showed that the presence of non-breeding helpers in experimental groups correlated with lower predation and higher nestling survival rates than was the case with control groups that were denied helpers. And Haig et al., (1994), utilizing a DNA analysis with 224 red-cockaded woodpeckers (Picoides borealis), found that helping behaviors involved a variety of related and unrelated birds and that there was no direct benefit to the helpers from “extra matings.” (Brown, 1987, in a book-length synthesis on communal breeding and helping behaviors in birds, provided additional evidence, although he also observed that, as a rule, unrelated helpers do not seem to work as hard as close kin.)

Recent discoveries that many insect colonies consist of multiple queens or multiple patrilines have presented a challenge to the long-standing inclusive fitness explanation for social insects (a thesis that can be traced all the way back to Darwin). For instance, Queller et al., (1988) observed that swarm-founding neotropical wasp colonies (Parachartergus colobopterus) may have multiple queens, sometimes numbering in the hundreds, and yet the level of relatedness and inbreeding is low. Similarly, Strassmann et al., (1994) compared allozyme polymorphisms in “incipient” social wasps of the subfamily Stenogastrinae and estimated that the average relatedness among colony members in one of the best studied species (Liostenogaster flavolineata) was .22, the lowest so far reported for any primitively eusocial insect. And Scott (1994) found that, in the burying beetle (Nicrophorus tomentosus), competition with flies (as well as conspecific groups) promotes communal breeding among unrelated males and females. As Breed (1988) points out, genetic models predict that reduced relatedness among colony members should have a divisive, if not fatal, effect on colony functioning. Nevertheless, eusocial species do exist and are obviously successful — if less than perfectly integrated.

Furthermore, Sherman et al., (1988) hypothesized that genetic diversity within social hymenoptera may have a previously unrecognized group-level advantage as a buffer against parasites and pathogens. This hypothesis was subsequently supported in a study of the bumblebee (Bombus terrestris) (Shykoff and Schmid-Hempel 1991). In addition, a series of reports by Robinson and Page (1988), Page et al., (1989) and Page and Robinson (1991), have supported the hypothesis that the genetic differences observed within honey bee colonies (Apis mellifera) can be correlated with performance differences among workers with respect to the division of labor and to ecological variations. In other words, the genetic composition of the colony may reflect “downward” causation in relation to colony-level functional (bioeconomic) needs; natural selection in this domain may operate on the parameters of the colony as a dynamic system — see below. Supporting evidence for this hypothesis was also found in both honey bees (Apis mellifera) and dwarf honey bees (Apis florea) by Oldroyd and his co-workers (1992a,b, 1994). (See also Woyciechowski 1990.) Likewise, Rissing et al., (1989) discovered in a field study of the colonial leaf-cutting ant (Acromyrmex versicolor) that co-foundresses were unrelated and yet the colonies exhibited specialization without apparent conflict. These researchers concluded that intense between-colony competition and brood raiding provided a group-level selection pressure in favor of such behaviors. (See also the analysis by Mesterton-Gibbons and Dugatkin, 1992.)

In social carnivores, Packer and Pusey (1982) observed that breeding coalitions of African male lions included non-relatives much more commonly than kin selection theory would predict. And Scheel and Packer (1990) found a similar pattern in the hunting and cub-guarding behaviors of female lions. In primates, Moore (1984) reviewed and reanalyzed the earlier studies of Goodall, Teleki, McGrew and others on meat sharing in chimpanzees, a pattern whose potential costs and benefits turned out to be surprisingly complex and were not unambiguously associated with inclusive fitness. Stanford (1992) studied allomothering in capped langurs (Presbytis pileata) and found that it could best be interpreted as a low-cost behavior that benefits both related and non-related recipients. And, in the evening bat (Nycticeius humeralis), Wilkinson (1992) documented an extensive pattern of communal nursing of pups that was not preferentially directed to kin.

Synergy is also implicit in Egbert Leigh’s several discussions of how “groups” are able to contain or override individual advantages for the “good of the group” (Leigh 1971, 1977, 1983, 1991). Leigh argues, in a nutshell, that if the potential payoffs (synergies) for each of the participants are high enough, this may provide a sufficient incentive for them to impose “government” in the “common interest.” Some examples cited by Leigh include: selection for “honest” meiosis and the elimination of segregation distorters from diploid genomes; the purging by honey bee workers of eggs laid by other workers rather than the queen (whose offspring represent the products of multiple matings and are genetically more closely related to the workers); the “self-regulating” division of labor and activity cycles in honey bee hives; the generally harmonious cooperative relationships between eukarytic cells and their endosymbiotic organelles; the suppression by leafcutter ants (Atta) of reproductive activity in their symbiotic fungi, except when colonizing a new nest; anisogamy in eukaryotes and the transmission of organelles and other cytoplasmic factors exclusively via the maternal line.


A particularly important development in support of the synergism hypothesis during the course of the past decade has been the re-discovery of “symbiogenesis” as a major cause of evolutionary change and complexification. The origin of this hypothesis traces back to an obscure school of nineteenth and early twentieth-century Russian botanists, most notably A.S. Famintsyn (1907a,b, 1918) and K.S. Merezhkovsky (1909, 1920). In fact, it was Merzhkovsky (1920) who coined the term “symbiogenesis”, which he defined as “the origin of organisms through combination and unification of two or many beings, entering into symbiosis” (p.65). These and others of the Russian school correctly inferred that the chloroplasts in eukarytic cells are endosymbionts and they correctly recognized the symbiotic character of lichens, but their hypothesis was presented as an alternative to Darwin’s theory and was generally ignored or rejected in the West. However, in the 1920s and 1930s, another Russian theorist, B.M. Kozo-Polyansky (1924, 1932) recognized the compatibility between Darwinism and the symbiogenesis hypothesis. As Kozo-Polyansky observed: “The theory of symbiogenesis is a theory of selection relying on the phenomenon of symbiosis” (1932:25). However, Kozo-Polyansky’s works were also not known or appreciated in the West; they were published only in Russian and had the misfortune to appear at the height of the Stalinist era. (We are indebted to Liya Khakhina of the Russian Academy of Sciences for her efforts to bring this work to our attention, and for her translations of key passages. See Khakhina 1979; 1992; also Margulis and McMenamin 1993.)

Meanwhile, the American biologist Ivan Wallin (1927) independently advanced a similar hypothesis. He made the “rather startling proposal” (as he candidly acknowledged) that bacteria might be “the fundamental causative factor” in the origin of species (1927:8). Claiming that mitochondria could be grown independently of their host cells (a dubious proposition), his theory was widely rejected by his peers and was soon forgotten. (Even Wallin himself dropped the subject.) However, the endosymbiotic theory of eukaryotes, and the more general theory of symbiogenesis in evolution, was revived once again by Lynn Margulis, beginning in the 1970s. (See especially Margulis 1970, 1981, 1993; also Margulis and Sagan 1986, 1995.) At first widely discounted, the endosymbiosis hypothesis gradually gained recognition over the years as supporting evidence accumulated, and it is now widely recognized as an important source of evolutionary complexification.

The “case” for symbiogenesis in evolution was documented in depth by participants at a 1989 conference on the subject and in a subsequent volume edited by Margulis and Fester (1991). Among the extensive evidence that was presented: Mutualistic or commensalistic associations (not to mention parasitism) exist in all five “kingdoms” of organisms; symbiotic relationships were documented by Bermudes and Margulis (1987) in 27 of 75 phyla in the four eukaryotic kingdoms (or 37%); over 90% of all modern land plants establish mycorrhizal associations (Lewis 1991); land plants may have arisen through a merger between fungal and algal genomes, as sort of inside-out lichens; in any case, it is evident that modern land plants represent a joint venture between fungi and green algae (Pirozynski and Malloch 1975; Atsatt 1988); approximately one-third of all known fungi are involved in mutualistic symbioses (Kendrick 1991); virtually all species of ruminants, including some 2,000 termites, 10,000 wood-boring beetles and 200 Artiodactyla (deer, camels, antelope, etc.,) are dependent upon endoparasitic bacteria, protoctists or fungi for the breakdown of plant cellulose into usable cellulases (Price 1991); most bacterial cells congregate and reproduce in large, mixed colonies with many endosymbionts (virus-like plasmids and prophages) and ectosymbionts (metabolically complementary bacterial strains); these congregations call into question the classical notion of a species, in the sense of competitive exclusion and reproductive isolation (Sonea 1991; also Shapiro 1988; Shapiro and Dworkin 1997). Finally, the 1989 conference emphasized the fact that symbiogenesis as a category entails behavioral innovations (broadly defined) as a “pacemaker” of evolutionary change, as Mayr has long held. In other words, symbiogenesis greatly expands our vision of the sources of creativity in evolution.

The significance of symbiogenesis in relation to the synergism hypothesis is that these “creative” processes have constituted an important subset of the total universe of synergistic phenomena that have played a causal role in the evolution of complexity. However, the concepts of synergy and symbiosis are not equivalent. The term symbiosis is also of Greek origin; it means “living together.” It was introduced into biology as a technical term by the pioneering German mycologist Anton de Bary (1879), who employed it to denote the living together of “dissimilar” or “differently named” organisms in lasting and intimate relationships. De Bary’s focus was on relationships, and the paradigmatic examples, both in de Bary’s time and ever since, are lichens (although de Bary also included in his definition what would now be called parasitic relationships). Today, there seem to be a number of conflicting definitions of symbiosis in the literature. Among other things, this reflects important differences of opinion about how the subject-matter of the field should be defined, and about which phenomena should be included. Adding to the confusion is the fact that symbiologists are not always consistent in practice even with their own definitions.

Nevertheless, there seems to be general agreement that symbiosis refers to relationships of various kinds between biological entities and the functional processes that arise from those relationships. Synergy, on the other hand, refers to the interdependent functional effects (the bioeconomic “payoffs”) of symbioses — among other cooperative phenomena. In short, all symbioses produce synergistic effects, but many forms of synergy are not the result of symbiosis. Accordingly, synergy is a room without walls in terms of which kinds of cooperative relationships are applicable; combined effects of all kinds and at every level of living systems are relevant, from enzymes to ecosystems; indeed, the term can even accommodate such unconventional but important biological phenomena as animal-tool “symbioses,” not to mention the relationships between humans and their technologies. Synergy can also comfortably handle both mutualistic and parasitic effects, as well as various asymmetrical distributions of costs and benefits and even cooperative effects which defy the conventional categories, as noted earlier. By focussing on cooperative effects of all kinds, synergy is thus a more pan-disciplinary and inclusive term.

But, in any case, the concept of synergy focusses our attention on the functional effects produced by cooperative interactions of all kinds, including symbioses. This is of great theoretical importance because in evolution it is the functional effects produced by the “interactors” (to use David Hull’s term) that are the “target” of natural selection, not the relationships per se.


Maynard Smith’s use of the synergy concept is also supportive of the synergism hypothesis and deserves a special note. Well known for his introduction of game theory models into evolutionary theory (among other contributions), Maynard Smith (1982) coined the term “synergistic selection” more or less as a synonym for D.S. Wilson’s (1975, 1980) concept of “trait group selection” and a similar formulation by Matessi and Jayakar (1976), both of which sought to account for the evolution of altruism without the need for inclusive fitness theory. The general approach involved temporary (functional) interactions among non-relatives in non-reproductive groups. The key feature of the “synergistic selection” model, according to Maynard Smith, was a fitness gain to interacting altruists that was greater than the gain to an altruist and a non-altruist. (At this point, Maynard Smith, like many other theorists, was conflating altruism and cooperation.)

Maynard Smith discussed the concept of “synergistic selection” further in a 1983 paper. Again, he paralleled Wilson’s trait group selection model, identifying non-additive interaction effects (labelled “r” in his equations) as the critical factor. And again, he assumed that the interaction involved altruism. Subsequently, Queller (1985) elaborated on Maynard Smith’s ideas in an analysis of inclusive fitness theory, where he proposed that synergistic effects might provide an alternative to altruism as an explanation for the evolution of social behaviors. Queller suggested the use of a coefficient of synergism (“s”) to reflect any joint effects produced by cooperators.

In Maynard Smith’s 1989 textbook on evolutionary genetics, there is a significant shift of focus. Here he follows Queller’s lead and moves the concept out of the classical population genetic framework and into game theory, with its emphasis on finding an ESS (evolutionarily stable strategy). No longer is synergistic selection associated with altruism; the stress is on cooperation as a class of behaviors with a variety of potential payoff distributions. Now synergy (re-labelled “s”) is defined as the non-additive payoff increment to cooperating “partners.” Maynard Smith concludes that, if the synergistic increment is greater than the cost, the behavior will be an ESS (i.e., if 1n + 1n = 3n or more). Although inclusive fitness is not required for such interactions to occur, he suggests that relatedness could be a significant facilitator, especially in initiating cooperation.

Maynard Smith and Szathmáry also make liberal use of the synergy concept in their new volume on the evolution of complexity, The Major Transitions in Evolution (1995). Moreover, their detailed study of the process of biological complexification in evolution is consistent in its overall vision with the more explicit conceptualization in The Synergism Hypothesis. (Maynard Smith, in a personal communication, acknowledged that the “universal” significance of synergy became apparent to them only after their book was completed.) In effect, Maynard Smith and Szathmáry provide detailed support for the hypothesis that functional synergy has played a central role in the process of biological complexification — the synergism hypothesis.

Finally, thanks to the dogged efforts of David Sloan Wilson (1975, 1980; also Wilson and Sober 1989, 1994) and a growing number of co-workers, “group selection” — for 30 years a pariah in evolutionary theory — has been resuscitated on a new foundation. What Wilson calls “trait group selection” refers to a model in which there may be linkages (a “shared fate”) between two or more individuals in a randomly breeding population, such that the linkage between the two becomes a unit of differential survival and reproduction. At first, Wilson assumed that one of the co-operators was an “altruist”. However, the more recent realization that group selection can also include mutualistic, win-win forms of co-operation which provide differential reproductive advantages to all concerned has greatly strengthened his argument.

(See the more expansive discussion of the group selection controversy in Corning, 1997.)


As nored earlier, the synergism hypothesis and the concept of synergistic selection (or “functional group selection”), like the concept of natural selection, represents an umbrella term for a broad category of causal influences. It is not a discrete “mechanism” or concrete causal “agent”. The causes of synergistic selection, like natural selection, are always situation-specific. Therefore, it is not possible to devise a single, decisive “test” of the synergism hypothesis. To our knowledge, nobody has ever succeeded in doing so for natural selection, either. Rather, the synergism hypothesis directs our attention to the combined effects produced by things that work together, or cooperate. Accordingly, the synergism hypothesis can be tested in much the same way that the role of natural selection is routinely tested, with hypotheses and analytical tools that are appropriate to a given context.

One important method for verifying the role of synergy in a given case was first suggested by Aristotle (to my astonishment) in The Metaphysics (1041b11-31; see also 1041a, 1045a). To paraphrase Aristotle’s wording, many parts may be needed to make a whole, yet the loss of even a single part may be sufficient to destroy it. (We refer to this methodology as “synergy minus one.”) Thus, we need only to remove one of the major “parts” from any living system (or any human technology, for that matter) and observe the consequences. As a “thought experiment,” imagine what would happen if some of the constituent amino acids were removed from the homeobox complex in the homeotic genes during morphogenesis, or if the Transfer RNA were removed from the machinery of reproduction, or if the mitochondria were removed from a eukaryotic cell, or the gut bacteria from a termite, or the sub-majors from an army ant colony, or the beak from one of Darwin’s finches, or the vowels from the words in Table I above; or the cyanobacteria from the Igorot’s rice-terrace system, or the wheel from an automobile. Of course, there are also a great many cases where “synergy minus one” merely attenuates the overall effects, with consequences that might only be measurable in statistical terms. Thus, the removal of one member from a school of drawf herrings might only marginally affect the probability that any of the remaining members will be eaten by a barracuda. And the loss of one member from a coalition of male lions, or chimpanzees, might or might not tip the scales in subsequent confrontations. On the other hand, if you remove one oarsman from a varsity eight, the chances are that the remaining seven will lose the race. (In a forthcoming paper on the subject of “devolution” in human societies, it will be argued that the fate of many civilizations in the past may have been sealed by some variant of the synergy-minus-one scenario.)5

A second method for testing hypotheses about synergistic effects involves comparative studies of various kinds. Raven’s (1992) comparison of the functional differences between lichen symbionts and other asymbiotic forms provides one illustration. Other examples mentioned above include Bell’s (1985) comparative study of size-effects and functional differentiation among the Volvocales; Jeanne’s (1986) study of colony-size effects in social wasps; Mazluff and Heinrich’s (1991) study of group-size effects in ravens; Campagna’s (1992) comparison of pup survival rates in sea lions; Stander’s (1992) comparative study of lion hunting behaviors; Ligon and Ligon’s (1982) analyses of helping behaviors in woodhoopoes; the observations of Parker et al.(1994) of food sharing in ravens; the studies by Mumme and his co-workers (1988) of joint nesting behaviors; the group-level advantages of genetic diversity in bumblebees postulated by Shykoff and Schmidt-Hempel (1991); and Stanford’s (1993) study of allomothering in capped langurs. Again, if synergistic effects are real and measurable, then it should be possible to demonstrate the differences that they make in a given context. In fact, the literature in such “hard sciences” as biochemistry, physiology and pharmacology provides a wealth of examples.

Game theory offers a third method for testing various hypotheses about synergy, as suggested earlier. Game theory models are especially useful in analyzing facultative relationships where the synergistic effects can be quantified and the costs and benefits can be allocated in various ways among the “participants”.


What are the implications of a “synergy paradigm?” First, it could serve as a lingua franca for the cooperative/emergent/interactional effects that are observed and studied by various disciplines. By removing a language barrier, the term could facilitate cross-disciplinary communication and understanding.

Second, by directing our attention to context-specific “historical” relationships and interactions, rather than “mechanisms” or reductionist “laws”, the synergy paradigm encourages a multi-leveled, multi-disciplinary research and theory that is free from the intellectual shackles of 19th century Newtonian physics. Furthermore, in contrast with the bloodless mathematical caricatures that are blind to the functional properties of the phenomenal world, the synergy paradigm draws our attention to the functional aspect of cooperative effects. As noted earlier, concepts with broad applicability to many different kinds of phenomena may play an important theoretical role in the sciences. The synergy concept provides a framework for integrating the research in various disciplines that may be relevant for understanding the broader causal role of cooperative phenomena in nature and evolution. To borrow the “pre-owned” parable of the blind men and the elephant, if we are ultimately to make sense of the whole, we will need to pool our discoveries about both the parts and the whole.

All scientific concepts are inescapably Procrustean and selective — highlighting certain aspects of the phenomenal world to the exclusion of others. None can be all things to all scientists. The ultimate test is fruitfulness. By that standard, the concept of synergy would seem to hold promise. Among other things, it offers a theoretical framework which, like the concept of natural selection, can provide a focus for explaining a major aspect of the evolutionary process, namely, the evolution of organized complexity. Indeed, an invigorated science of synergy would shine a spotlight on a fundamental property of the phenomenal world. Equally important, because it is pan-disciplinary (and egalitarian) in its methodological implications, the concept of synergy could provide a useful bridge between various specialized disciplines. If synergy can provide functional advantages elsewhere in the phenomenal world, why not also within the scientific enterprise itself.



1.  There is still no agreed-upon definition of complexity, much less a theoretically-rigorous formalization, despite the fact that complexity is currently a “hot” research topic. Many books and innumerable scholarly papers have been published on the subject in the past few years, and there is even a new journal, Complexity, devoted to this nascent science. This is not to say that the researchers in this area have not been trying to define it. In the 1970s, Gregory Chaitin and Alexei Kolmogorov (independently) pioneered a mathematical measuring-rod that Chaitin called “algorithmic complexity” — that is, the length of the shortest “recipe” for the complete reproduction of a mathematical treatment. The problem with this definition, as Chaitin concedes, is that random sequences are invariably more complex because in each case the recipe is as long as the whole thing being specified; it cannot be “compressed”. More recently, Charles Bennett has focussed on the concept of “logical depth” — the computational requirements for converting a recipe into a finished product. Though useful, it seems to be limited to processes in which there is a logical structure of some sort. It would seem to exclude the “booming, buzzing confusion” of the real world, where the internal logic may be problematical or only partially knowable — say the immense number of context-specific chaotic interactions that are responsible for producing global weather “patterns”, or the imponderable forces that will determine the future course of the evolutionary process itself.

A number of researchers, especially those who are associated with the Santa Fe Institute, believe that the key lies in the so-called “phase transitions” between highly ordered and highly disordered physical systems. An often-cited analogy is water, whose complex physical properties lie between the highly ordered state of ice crystals and the highly disordered movements of steam molecules. While the “Santa Fe Paradigm” may be useful, it also sets strict limits on what can be termed “complex”. For instance, it seems to exclude the extremes associated with highly ordered or strictly random phenomena, even though there can be more or less complex patterns of order and more or less complex forms of disorder — degrees of complexity that are not associated with phase transitions. (Indeed, random phenomena seem to be excluded by fiat from some definitions of complexity.)

To confuse matters further, a distinction must be made between what could be labelled “objective complexity” — the “embedded” properties of a physical phenomenon and “subjective complexity” — its “meaning” to a human observer. As Timothy Perper has observed (on-line communication), the equation w = f(z) is structurally simple, but it might have a universe of meaning depending upon how its terms are defined. Indeed, information theory is notorious for its reliance on quantitative, statistical measures and its blindness to meaning — where much can be made of very few words. The telephone directory for a large metropolitan area contains many more words than a Shakespeare play, but is it more complex? Furthermore, as Elisabet Sahtouris has pointed out (on-line communication), the degree of complexity that we might impute to a phenomenon can depend upon our frame of reference for viewing it. If we adopt a broad, “ecological” perspective we will see many more factors, and relationships, at work than if we adopt a “physiological” perspective. When Howard Bloom (on-line communication) quotes the line “To see the World in a Grain of Sand…” from William Blake’s famous poem, “Auguries of Innocence”, it reminds us that even a simple object can denote a vast pattern of relationships, if we choose to see it that way. Accordingly, subjective complexity is a highly variable property of the phenomenal world.

Perhaps we need to go back to the semantic drawing-board. Complexity is, after all, a word — a verbal construct, a mental image. Like the words “electron” or “snow” or “blue” or “tree”, complexity is a shorthand tool for thinking and communicating about various aspects of the phenomenal world. Some words may be very narrow in scope. (Presumably all electrons are alike in their basic properties, although their behavior can vary greatly.) However, many other words may hold a potful of meaning. We often use the word “snow” in conversation without taking the trouble to differentiate among the many different kinds of snow, as serious skiers (and Inuit eskimos) routinely do. Similarly, the English word “blue” refers to a broad band of hues in the color spectrum, and we must drape the word with various qualifiers, from navy blue to royal blue to robin’s egg blue (and many more), to denote the subtle differences among them. So it is also, I believe, with the word “complexity”; it is used in many different ways and encompasses a great variety of phenomena. (Indeed, it seems that many theorists, to suit their own purposes, prefer not to define complexity too precisely.) The “utility” of any word, whether broad or narrow in scope, is always a function of how much information it imparts to the user(s). Take the word “tree”, for example. It tells you about certain fundamental properties that all trees have in common. But it does not tell you whether or not a given tree is deciduous, whether it is tall or short, or even whether it is living or dead. The same shortcoming applies also to the concept of “complexity”. Although there may be some commonalities between a complex personality, a complex wine, a complex piece of music and a complex machine, the similarities are not obvious. Each is complex in a different way, and their complexities cannot be reduced to an all-purpose algorithm. Moreover, the differences among them are at least as important as any common properties.

What in fact does the word “complexity” connote. One of the leaders in the complexity field, Seth Lloyd of MIT, took the trouble to compile a list of some three dozen different ways in which the term is used in scientific discourse. However, this exercise produced no blinding insight. When asked to define complexity, Lloyd told a reporter: “I can’t define it for you, but I know it when I see it” (Johnson 1997). Rather than trying to define what complexity is, perhaps it would be more useful to identify the properties that are commonly associated with the term. We would suggest that complexity often (not always) implies the following attributes: (1) a complex phenomenon consists of many parts (or items, or units, or individuals); (2) there are many relationships/interactions among the parts; and (3) the parts produce combined effects (synergies) that are not easily predicted and may often be novel, unexpected, even surprising. At the risk of inviting the wrath of the researchers in this field, we would argue that complexity per se is one of the less interesting properties of complex phenomena. We believe the differences among them, and the unique combined properties (synergies) that arise in each case, are more important than the commonalities.  (^ click back to text)

2.  There are many well-documented studies that have measured synergistic effects in quantitative terms. To mention a few other examples: Schaller (1972) found that capture efficiency (captures per chase times 100) and the number of multiple kills achieved by Serengeti lion prides that he studied increased with group size. (Later studies have shown that hunting efficiencies are dependent on a variety of ecological factors, including the size and capabilities of the prey.) In the highly social African wild dog (Lycaon pictus), overall kill probabilities in hunting forays were found to be greatly superior (between 85 and 90 percent) to those achieved by less social top carnivores (Estes and Goddard 1967). Kummer (1968, 1971) documented that collective defense in hamadryas baboons (Papio hamadryas), as in many other species, greatly reduces the joint risk to each group member of being a victim of predation. And von Wagner (1954) observed that the Mexican desert spiders (Leiobunum cactorum), by clustering together in the thousands during the dry season, are able to avoid death by dehydration.  (^ click back to text)
3.  Another example with philosophical (and perhaps even theological) implications involves what some physicists call the “Anthropic Principle.” The basic idea is that we exist only because of a remarkable concatenation of physical properties in the universe that happen to meet our needs. Some of the more important of these properties were outlined in a recent paper by biologist George Wald (1994), namely: the remarkable fact that the mass of every atom is concentrated in its nucleons (protons and neutrons); the fortuitous circumstance that proton and electron charges are numerically equal; the unique (emergent) characteristics of the four basic elements that constitute about 99 percent of living matter (hydrogen, carbon, nitrogen and oxygen); the underappreciated fact that ice floats instead of sinking (because water expands as it approaches 0oC.), a circumstance that has most likely prevented the oceans from freezing solid over the course of time; and, finally, the balance struck by the two cosmic forces of dispersion (powered by the Big Bang) and gravity (which holds localized parts of the whole together).

As Wald noted: “It is as though, starting from the Big Bang, the universe pursued an intention to breed life, such is the subtlety with which difficulties in the way are got around, such are the singular choices in the values of key properties that could potentially have taken any value.” Whether or not one believes that a cosmic purposiveness was involved, the outcome can certainly be characterized as cosmic synergy. (Indeed, the same kind of anthropic reasoning could be extended to the 3.5 billion-year process of biological evolution on earth.) In any event, the outcome was co-determined; it was dependent upon the entire configuration of preconditions. (All were necessary and none were sufficient.)  (^ click back to text)
4.  The sources of creativity in evolution is an issue that is often kept in the shadows, or even backstage. In traditional neo-Darwinian theory, which is after all a theory about change, innovation has been characterized variously as a two-step tandem process involving “random” mutations and natural selection (Ernst Mayr), an anti-chance process whereby random changes are converted to functional designs (Theodosius Dobzhansky) and a dualistic process of “chance and necessity,” or mutations and the imperatives of survival in the physical environment (Jacques Monod). The insufficiency of these formulations has been apparent to many evolutionists over the years, to the point that some have been led to reject Darwinism altogether, or at least the gene-centered model. The problem is most acute with respect to the evolution of complexity — the intricate functional organization that characterizes even many rudimentary life forms. How can a temporal sequence of random changes produce complex, synergistic wholes?

The answer proposed here is that Darwinism is also compatible with a model in which novel cooperative phenomena may become “units” of selection, such that their genes (or even whole genomes) are selected as functional “wholes” — albeit with the potential for cheating or free-riding in facultative relationships that do not involve functional interdependencies. (In Corning 1997, this paradigm is referred to as “Holistic Darwinism.”) Some of the properties of this “creative” aspect of the evolutionary process have been captured in mathematical form in various dynamical systems models. But these models for the most part also “reduce” the dynamics of innovation and change in nature to a self-generative but also deterministic process. In fact, some theorists envision the ultimate elucidation of a law, or laws of evolution (e.g., Kauffman 1993, 1995). The question that is begged by the advocates of self-organization is this: Are there sources of novelty — new synergies — that cannot be modelled by a set of partial differential equations? Is creativity a real phenomenon or merely an epiphenomenon — a dynamical attractor or a phase transition in a deterministic historical process?

One problem with the self-organizing paradigm is that it is insensitive to the functional dimension of evolution, many aspects of which are qualitative and cannot even be expressed mathematically. Accordingly, the ambitious idea of re-creating this highly complex functional process with dynamical systems models might prove to be the modern equivalent of the hypothetical monkey of two generations ago that was given the task of (randomly) pecking out a Shakespeare play on a typewriter.

The status of creativity in evolution is a particularly important issue in the fields of consciousness research and artificial intelligence. Thus, for example, the mathematician and neurophysicist Roger Penrose (1994) takes the controversial position that the core of human consciousness may involve non-computable, qualitative properties of “logic” and “understanding”. (A similar argument was advanced many years ago by mathematicians Hilbert and Ackerman (1950), who used Aristotelian syllogisms to conclude that a formal rendering of certain kinds of logical relationships was not possible.)

In any event, we believe the concept of synergy may have a role to play in clarifying this issue. If novelties can arise out of historically specific configurations of functional interactions that cannot be predicted and whose combined properties and evolutionary consequences cannot be known until the interactions have occurred in a specific historical context, then no a priori prediction is possible, or necessary in order to attain scientific standing. A phenomenon can be fully determined and yet not be predictable until it happens (for reasons that go beyond even the postulates of chaos theory). Once determinism and predictability have been decoupled, science need no longer be constrained to shun the unpredictable and to treat the elucidation of “laws” as the Holy Grail. An historical science of novelties — of new synergies — comparable in its logical structure to Darwin’s own theory, can then become a part of the larger scientific enterprise without apologies. (For more extended discussions of these points, see Corning and Kline 1998a,b.)  (^ click back to text)
5.  In his introduction to a special issue of the journal Human Ecology devoted to climate and human affairs, Gunn (1994) noted the fact that many suggestive linkages have been shown to exist between major climate changes and significant economic and cultural changes over the past 19,000 years, a period which encompasses the emergence of agriculture and the rise of urban civilization. His assertion is backed by many specific anthropological and archeological studies. For instance, recent research has indicated that a major climate change precipitated the sudden collapse of the Akkadian empire in ancient Mesopotamia about 2200 BC (Weiss et al., 1993). Climate changes have also been implicated in the fall of Mayan civilization (Hodell 1995; Sabloff 1995) and of Teotihuacán. Many less traumatic economic changes may also have been affected by climatic shifts. Guillet (1987) studied the historical cycles of terrace construction, maintenance and abandonment, along with the use of water conservation measures, among the aboriginal inhabitants of the Colca Valley of southern Peru and showed that strategic shifts over the course of time have been closely correlated with ecological changes that produced variations in the availability of fresh water. Likewise, Stanley and Warne (1993) have linked climate changes to the deposition of cultivatable silts in the Nile Delta during the period from 6500-5500 BC, a development which preceded the emergence of agriculture in the Nile Valley by only a few centuries. (The earliest settlement was probably occupied around 4900-5000 BC.)  (^ click back to text)


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The author wishes to acknowledge especially the many intellectual contributions of the late Stephen Jay Kline, Woodard Professor of Science, Technology and Society, and of Mecahnical Engineering, Emeritus, at Stanford University. Appreciation is in order also to Larry Goldberg, Ph.D., for some fruitful discussions on this topic, as well as to Howard Bloom, Timothy Perper, Elisabet Sahtouris, Peter Frost, Reed Konsler and the pseudonymous Just Mice for a provocative and insightful on-line discussion within Howard Bloom’s “International Paleopsychology Project” group. Many thanks are also due to Patrick Tower for his diligent research assistance, and to Kitty Chiu for producing the bibliography.

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