Rotating the Necker Cube: A Bioeconomic Approach to Cooperation and the Causal Role of Synergy in Evolution

© Journal of Bioeconomics, 2013, 15:171-193


The paradox of widespread cooperation in an intensely competitive natural world has been a major focus of theory and research in evolutionary biology and related disciplines over the past several decades. While much of the earlier work in this vein was gene-centered and grounded in inclusive fitness (or kin selection) theory, more recent developments suggest that it might also be useful to view cooperation (and biological complexity) from a bioeconomic perspective.  Here I will briefly explore the case for a paradigm shift, with special reference to the role of functional synergy as a distinct class of interdependent causal influences in evolution.  I will argue that synergies of various kinds have been important drivers for cooperation in living systems at all levels.  From this perspective, inclusive fitness and other factors may be enablers for cooperation, but the many exceptions show that genetic relatedness is neither necessary nor sufficient for the emergence of cooperative phenomena.

Keywords   Natural selection;  Inclusive fitness;  Cooperation;  Synergy;  Symbiosis; Symbiogenesis; Game theory; Division of labor;  Emergence;  Group selection; Natural genetic engineering.

Introduction: the “selfish gene”

In the preface to the second edition of his bestselling book, The Selfish Gene, Richard Dawkins used the metaphor of a Necker cube – a two-dimensional drawing of a three-dimensional object that can be perceived in different ways – in order to explain the intent behind his inspired metaphor: “My point is that there are two ways of looking at natural selection, the gene’s angle and that of an individual….It is a different way of seeing, not a different theory” (Dawkins 1989/1976, pp. x-xi).

Dawkins’s widely influential popularization helped to promote the then-ascendant view among evolutionary biologists that the primary locus of innovation in evolution is the genes and their self-interests, a view that originated with the pioneer geneticist August Weismann (1904).  In this paradigm evolution was defined as “a change in gene frequencies” in a given “deme”, or breeding population, and natural selection was typically defined as a “mechanism” which produces changes in gene frequencies.  As biologist John H. Campbell (1994) put it in a review: “Changes in the frequencies of alleles by natural selection are evolution” (p. 86).

By implication, it followed that mutations and related molecular-level changes – subject to the “approval” (or disapproval) of natural selection — are the only important sources of evolutionary change.  This gene-centered perspective was captured by psychologist Donald Campbell’s (1974) popular catch-phrase “blind variation and selective retention.” The distinguished evolutionary biologist Ernst Mayr (1965) characterized it as a “two-step, tandem process.”

One difficulty with this theoretical paradigm was that it made the existence of cooperative phenomena in the natural world problematical, especially altruistic “sacrifices”.  Darwin himself recognized the problem and proposed the idea of group selection as an explanation for such puzzling phenomena as eusocial insect societies and human sociality (Darwin 1968/1859, 1871).  In Darwin’s formulation, groups rather than individuals can become units of evolutionary change under certain circumstances. However, Darwin’s explanation for social cooperation was largely rejected during the latter part of the twentieth century.  For instance, George C. Williams (1966), in a widely influential critique, asserted that group selection was “impotent” and “not an appreciable factor in evolution” (p. 8).

An alternative explanation for cooperative behaviors was advanced in the landmark theoretical papers on “inclusive fitness” by William Hamilton (1964a,b), which John Maynard Smith (1989) characterized as “kin selection” theory. The implication of Hamilton’s model was that, as a rule, cooperation is only possible among closely related individuals.  Accordingly, kin selection was widely invoked to “explain” cooperative behaviors in nature, including even in humankind (e.g., Alexander 1979, 1990).

It did not help matters that many theorists, both then and now, assumed that cooperation is by definition altruistic rather than being (very often) mutually beneficial, a serious misconception.  The term “cooperation” can refer simply to functional relationships and interactions.  In this formulation, cooperation may or may not also be considered selfish or altruistic, mutualistic or parasitic, positive or negative. Such attributes involve additional, post-hoc judgments about the consequences of a cooperative relationship with respect to some separately specified goals or values for the various participants.  Indeed, there can even be coerced cooperation, where punishments are used as enforcers.  An extreme example is a slave system. We will revisit this issue below (see also West et al. 2007a).

Here I will argue that there are more than two ways of viewing the evolutionary process.  Kinship may be an enabler for cooperation, but it is neither necessary nor sufficient to explain it, as a general rule.  I will make a case for rotating the Necker cube so as to focus on the bioeconomics – the benefits and costs of cooperation for the various participants in relation to survival and reproduction, with special reference to the causal influence of various forms of functional synergy.  I will argue that the bioeconomic benefits, and how these are distributed, are of primary importance in explaining cooperative phenomena.

Exceptions that disprove the rule: bacteria and symbiosis

The mathematical rigor underlying inclusive fitness theory made it especially appealing to the scientific community, but its temporary hegemony as an explanation for cooperation/altruism has been challenged by the many exceptions that have become apparent over the years.

One important exception to the kin selection model can be found in bacteria, the oldest and most common form of life on Earth in terms of their total biomass.  Bacteria, it turns out, are promiscuous, indiscriminate gene sharers and inveterate cooperators.   Among many other things, bacteria invented social cooperation and the division of labor (Shapiro 1988; Sonea 1991; Coghlin 1996; Shapiro and Dworkin 1997; Ben-Jacob et al. 1998).  Furthermore, widespread “horizontal” DNA transfer from bacteria to eukaryotes like ourselves has now also been documented (Hotopp et al., 2007; Keeling and Palmer 2008; also, see Woese and Goldenfeld 2009).

The phenomenon of (positive) symbiosis – beneficial cooperative relationships among “differently named organisms” (in Anton de Bary’s classic formulation) — represents another exception to inclusive fitness theory.  Here we can observe a wide range of cooperative relationships among organisms of different species in the natural world that can best be accounted for in functional, cost-benefit terms.  Symbiosis was at one time treated as a phenomenon of little evolutionary significance, but we now know that mutualistic/symbiotic relationships are ubiquitous (though many more are parasitical) and play a vitally important role in the natural world. (See the landmark edited volume on symbiosis by Margulis and Fester 1991; also Smith and Douglas 1987; Margulis and Sagan 1995; Sapp 2004, 2009, Leigh 2010a,b).

Thus, over 90 percent of all land plants establish mutually beneficial associations with mycorrhizal fungi.  About one-third of all known fungi are involved in mutualistic symbiosis.  And virtually all species of cellulose-digesting insects and ruminant animals, including some two thousand termites, ten thousand wood boring beetles, and 200 Artiodactyla (deer, camels, antelope, etc.), are dependent upon endosymbiotic bacteria, protoctists, or fungi for the difficult feat of breaking down cellulose into usable cellulases in their digestive tracts (P.W. Price 1991).  In a cow’s rumen, for instance, the cellulose conversion process depends on the joint actions of five different bacterial strains.  Four of the strains contribute different enzymes, each of which accomplishes a specific conversion step, while a fifth strain provides protection for all the others from the danger (to an anaerobic bacterium) of oxygen pollution (Shapiro 1988; Allison et al. 2001).  Even humans are dependent upon symbiotic bacteria to manufacture B and K vitamins in our intestines (Margulis and Sagan 2002).

In fact, many types of symbioses, such as the estimated 25,000 different species of lichen partnerships involving approximately 300 different genera of fungi and green algae or cyanobacteria (Raven 1992) and the Rhizobium-like bacteria that form nitrogen-fixing root nodules with some 17,500 species in 600 genera of plants (Lewis 1991), reflect a plethora of independent inventions.  Functionally similar kinds of symbiosis have evolved repeatedly and separately within (and across) various taxa.  For instance, cyanobacteria, which are photosyn­thesizers, form symbiotic relationships not only with fungi but also with some algae, Bryophytes, aquatic ferns (Azolla), Cycads and angiosperm (Gunnera) (Ahmadjian and Paracer 1966).  Similarly, many species of cleaner fish and cleaner birds provide services opportunistically for an array of fish, alligators, crocodiles, iguana, elephants, rhinos and many other species of herbivores. The multiple, independent evolution of symbiotic relationships has also been documented in corals, legumes, and hydrothermal vent species, among others (Smith and Douglas 1987; Margulis and Sagan 1995).

Biologist Egbert Leigh (2010a) also emphasizes that entire ecosystems “are not only arenas of competition but webs of interdependence, commonwealths on whose integrity all members depend.” He notes that mutualisms of various kinds “are essential to the world’s ecosystems’ productivity and diversity” (Leigh 2010b). (For more on this theme, see also Leigh and Vermeij 2002; Leigh et al. 2009;  Avilés et al., 2002; Sapp 2009.)

One implication is that “symbiogenesis” — a term coined by the early twentieth century Russian theorist, Konstantin Mereschkovsky (1909) and championed in recent decades especially by biologist Lynn Margulis (1970, 1993) — has played a significant role in the evolution of biological complexity (see Margulis and Sagan 2002; Sapp 2004, 2009; Gontier 2007; Carrapiço 2010; Pereira 2012).  As biologist Steven A. Frank (1995a) puts it, “A dominant theme in the history of life has been the evolutionary innovations of cooperative symbioses.”

The most striking example of this dynamic is, of course, the emergence of the eukaryotes with complex nucleated cells, an evolutionary breakthrough that involved a cooperative venture – an obligate federation that may have originated as symbiotic unions (parasitic, predatory or perhaps mutualistic) between ancient prokaryote hosts and what have now become cytoplasmic organelles, particularly the mitochondria and the chloroplasts in plants (Margulis, 1970, 1993; Cavalier-Smith 1981, 1991; Maynard Smith and Szathmáry 1995; de Duve 1996).  Similarly, land plants appear to be a compound of four distinct ancestral genomes — a motile eubacterium, a protein synthesizing archaebacterium, an oxygen respiring proteobacterium, and a photosynthesizing cyanobacterium (Margulis and Sagan 2002).1 We and our food supply are all products of symbiogenesis, and so are many other species.

Other challenges: game theory and “policing”

Game theory has also, inadvertently, diminished the significance of inclusive fitness theory.  Game theory models, and the research they have inspired over the years, are focused on the behavioral context and the strategies of the “players” and are indifferent to their genetic relationships, if any (Maynard Smith 1982a, 1984).  And if one looks closely at the payoff matrix in the typical game theory model, it is the economic payoffs that are the drivers for cooperation.  As noted above, cooperation can very often be mutually beneficial, not altruistic.  Indeed, as Maynard Smith and Szathmáry pointed out in The Major Transitions in Evolution (1995), “if an individual can produce two offspring on its own but by cooperating in a group consisting of “n” individuals can produce “3n” offspring, it pays to cooperate.”

Of course, game theory also illuminated the problem of cheating, or “free-riding” (parasitism) in cooperative relationships (see the discussion in Leigh 2010b).  However, there is now a large research literature that documents the role of punishments as “enforcers” in cooperative relationships throughout the natural world.  One important consequence is that policing can facilitate cooperation among non-kin. (Among the outpouring of publications on this subject, see especially Boyd and Richerson 1992; Clutton-Brock and Parker 1995; Frank 1995a,1996; Michod 1996; Fehr and Gächter 2000a,b, 2002; Gintis 2000a,b; Falk et al., 2001; Henrich and Boyd 2001; Bowles and Gintis 2002; Boyd et al., 2003; Binmore 2005; Leigh 2010b.)

As Clutton-Brock and Parker (1995) conclude in a review article on the subject: “In social animals, retaliatory aggression is common. Individuals often punish other group members that infringe their interests, and punishments can cause subordinates to desist from behaviour likely to reduce the fitness of dominant animals.  Punishing strategies are used to establish and maintain dominance relationships, to discourage parasites and cheats, to discipline offspring or prospective sexual partners and to maintain cooperative behaviour” (p. 209). Evidence of a policing function has also been documented in social insects (Ratnieks and Visscher 1989), naked mole-rats (Sherman, et al., 1991), primates (de Waal 1982, 1996) and, needless to say, Homo sapiens.

Especially relevant for the evolution of humankind is the theory and research related to what has been termed “strong reciprocity theory,” a formulation in which cooperative behaviors are backed by aggressive sanctions, including even Aaltruistic punishment,@ to control cheating (see especially Gintis 2000a,b; Gintis et al., 2003; Fehr and Gächter 2000a,b: Sethi and Somanathan 2001; Fehr et al. 2002; Fletcher and Zwick 2004).

Another potential deterrent to cheating/free riding is a cooperative relationship that approximates a Nash equilibrium (named for the game theory model developed by John Nash), where cooperation is self-sustaining because it is the optimal strategy for all of the “players” and nobody can improve on their “lot” without destabilizing the relationship (Binmore 2005).  Indeed, as Maynard Smith and Szathmáry (1995) noted, much cooperation in nature involves a mutually-beneficial functional interdependency among the participants and is therefore self-policing.  It is not at all altruistic and does not necessitate a genetic relationship. (Economists often refer to it as “utility interdependence.”)

This kind of relationship was illustrated by Maynard Smith and Szathmáry using a game theory model in which two oarsmen in a rowboat are seeking a common objective, like crossing a river.  If the two oarsmen utilize a “sculling” arrangement, each one would have a pair of oars and they would row in tandem.  In this situation, it is easy (in theory at least) for one oarsman to slack off and let the other one do the heavy work. This corresponds to the classical two-person game.  However, in a two-person “rowing” model, each oarsman has only one opposing oar.  Now their relationship to the performance of the boat is interdependent.  If one oarsman slacks off, the boat will go in circles.  In this case, defection is totally unrewarding. It is an example of what Maynard Smith termed an evolutionarily stable strategy.

This kind of interdependent cooperative relationship is often the case in symbiotic partnerships, but evidence can also be found in the growing number of field studies of cooperative behaviors among unrelated individuals of the same species.  Among the many examples, a number of ant species establish colonies with multiple queens.  This seems paradoxical, since ant colonies often engage in what has been characterized as all-out warfare.  A possible explanation was found in a study of the desert seed-harvester ant (Messor pergandei) by Steven Rissing and Gregory Pollack (1991).  Colonies with multiple queens gain a significant advantage because they can mount much larger raiding parties against other colonies founded by a single queen. Similarly, Cahan and Julian (1999) showed in a laboratory experiment that multiple-founder colonies of leaf cutter ants (Acromyrmex versicolor) were much more successful at establishing fungus gardens than single-founder colonies.  And West et al. (2007b) report that, in cooperatively breeding wasps (Polistes dominulus), only one or a few dominant females lay almost all of the eggs, yet some 35 percent of the subordinates are typically unrelated.

Likewise, in social carnivores, Packer and Pusey (1982) observed that breeding coalitions of African male lions include non-r

Multi-level selection and “natural genetic engineering”

The rise of multi-level selection theory in recent years, and especially the re-emergence of group selection theory, has also produced a tectonic change in the theoretical landscape.  The core conception underlying inclusive fitness theory dates to a legendary bit of barroom bravado by the pioneer biologist J.B.S. Haldane in the 1930s: “I would gladly give up my life for two brothers or eight cousins” – along with Haldane’s pioneering book The Causes of Evolution (1932).  Hamilton (1964a,b) later formalized this relationship as rb>c (or, alternatively, rb-c>0), where “r” refers to the coefficient of relatedness, “b” refers to the benefits to the recipients and “c” the costs to the altruist.  The theoretical implication was that cooperation (defined as altruism) could only evolve among close relatives as a rule.

Setting aside the fact that a great many cooperative relationships in nature are not altruistic but are mutually beneficial (albeit with varying costs and benefits for the participants), there are a number of problems with this theoretical formulation. (These are discussed in detail in E.O. Wilson 2005; Wilson and Wilson 2007; also, see Cassill 2006.)2  In fact, Hamilton himself later reformulated his theory in accordance with the approach of George Price known as “general selection” theory (Hamilton 1975; also G. Price 1970, 1972, 1995). The so-called Price equation provides a more complex, multi-level model of selection and evolutionary change based on using statistical associations in a covariance equation that allows for partitioning various selection components (see also Frank 2005b).

Price’s more ecumenical, multi-level selection model also accords with several decades of research indicating that group selection has been an important influence in evolution after all.  As D.S. Wilson and E.O. Wilson (2007) put it, “Selfishness beats altruism within single groups. Altruistic groups beat selfish groups.”  This dynamic has been especially evident with respect to the evolution of eusocial insects, naked molerats, and (most likely) humankind (E.O. Wilson 2005; Wilson and Hölldobler 2005; Wilson and Wilson 2007, 2008; Hölldobler and Wilson 2009).

In fact, it is likely that group selection has played a major role in every one of the “major transitions” that have occurred in the evolution of biological complexity over time (see especially Maynard Smith and Szathmáry 1995, 1999; also Keller 1999; Michod 1999; Michod and Herron 2006; Jablonka and Lamb 2006; Wilson and Wilson 2007, 2008; Hölldobler and Wilson 2009).  A major transition can occur when selection within groups is contained or suppressed in such a way that between-group selection can dominate.  To borrow a popular metaphor from Egbert Leigh (1977), selfish genes may become subject to the “parliament of the genes” (see also Leigh 1983, 1991, 2010a,b).3

This dynamic may have been at work in the very origins of life (Maynard Smith and Szathmáry 1995, 1999; Szathmáry 1999, 2005; Eigen and Schuster 1979), as well as in the evolution of eukaryotes (Margulis 1970, 1993), in the emergence of metazoans (Buss 1987), and in the rise of social organization (Maynard Smith and Szathmáry 1995; Hölldobler and Wilson 2009, among others).  With each major transition, mechanisms also evolved to protect against cheating and the subversion of various group-level benefits.4   Traulsen and Nowak (2006) conclude:  “In our opinion, [cooperative] group selection is an important organizing principle that permeates evolutionary processes from the emergence of the first cells to eusociality and the economics of nations.”5

Finally, the gene-centered approach has been challenged by the work in molecular biology and genomics on what the distinguished microbiologist James Shapiro (2012) refers to in his new book, Evolution: A View from the 21st Century, as “natural genetic engineering.”  Shapiro notes that we are currently in the midst of “a deep rethinking of basic evolutionary concepts”( p. xvii).  He argues that there is a paradigm shift underway from the atomistic, reductionist, gene-centered, mechanical model to a systems perspective in which “purposeful” actions and cybernetic (information and control) processes are recognized as fundamental properties of living systems at all levels. He argues that even reproductive cells must be viewed not merely as “blueprints” but as complex cooperative systems that control their own growth, reproduction and even, to some degree, may shape their own evolution over time.  He also refers to it as a “systems engineering” perspective.

Indeed, there is no discreet DNA unit that fits the neo-Darwinian model of a one-way, deterministic “gene”.  Instead, Shapiro argues, the DNA in a cell represents a two-way, “read-write system” wherein various “coding sequences” are mobilized, aggregated, manipulated and even modified by other genomic control and regulatory molecules in ways that can also influence the course of evolution itself.  Cells and organisms can sense their environments in many different ways and reshape their genomes in response.  “The vast majority of free-living prokaryotes and eukaryotes have evolved biomechanical systems that permit them to mobilize and restructure their genomes…[This is] essential to any contemporary account of evolution…”(p. 44).

Some of the many examples cited by Shapiro include immune system responses, chromosomal rearrangements, diversity generating retroelements, the actions of DNA transposons, genome restructuring, whole genome duplication, and symbiotic DNA integration.  As Shapiro emphasizes, “The capacity of living organisms to alter their own heredity is undeniable. Our current ideas about evolution have to incorporate this basic fact of life” (p. 2). (See also the review in Woese and Goldenfeld 2009; also Sapp 2009.)

Pointing the way to a paradigm shift

The common theme in all of these theoretical developments is that the functional properties and adaptive consequences of cooperation are vitally important.  It suggests a need for flipping the Necker cube and changing the way we view and understand cooperative behaviors.  Many theorists in recent years have been pointing us in this direction.  Thus, Fletcher et al. (2006) observe that “the theoretical focus on ‘r’ [in Hamilton’s rule] obscured the importance of ecological factors encompassed by ‘b’ and ‘c’”.  Likewise, West et al. (2007b) tell us that “There is an overemphasis…on the importance of the relatedness term in Hamilton’s rule.” Michod and Herron (2006) assert that “the basic issue [in understanding cooperation] is how the benefits are bestowed to offset the costs.” Game theorists Brown and Vincent (2008) note that in their cost-benefit models, “cooperation emerges from an interplay between the non-linearities in the cost and benefit functions.”

In a similar vein, Wilson and Hölldobler (2005) conclude that “Eusociality arises by the superiority of organized groups over solitaires or presocial groups.” They characterize eusocial insect societies, such as honey bees, as “a factory inside a fortress.” Elsewhere, Wilson (2005) asserts there is abundant evidence that “the driving force” in the evolution of organized groups is “natural selection by pressures and opportunities” in the environment that precipitate emergent, group-level adaptations – that is, cooperative  relationships (see also Wilson and Wilson 2007, 2008; Hölldobler and Wilson 2009).

What, then, are the influences that shape cooperation and determine the levels of selection in evolution? What defines the nature (and boundaries) of the “groups” that are selected, if it is very often not genetic relationships? What is the “organizing principle”?  Leigh (2010a) notes that “relationship alone is a wretched predictor of when social behavior evolves.” Clutton-Brock (2009) points out that “Although kin selection theory provides a satisfactory explanation of cooperation between kin, cooperation between unrelated individuals remains a problem and the evolutionary mechanisms that maintain it are still debated.” And Leigh (2010b) concludes that “simple general principles governing the evolution of mutualism have proved elusive.”

The bioeconomics of evolution

Some additional insight regarding this issue can be gained, I believe, by focusing on evolution as a bioeconomic process.  The interdiscipline of bioeconomics was originally inspired by the conviction that the gene-centered, neo-Darwinian model of evolution was not the whole story and that the “economy of nature” (Darwin’s term, following Linnaeus) also plays an important role in the dynamics of the evolutionary process.  (See especially the pioneering work by biologist Michael Ghiselin 1974, 1986, 1992, inter alia, on what he refers to as “general economy,” as well as the foundational contributions of economists Gordon Tullock 1971, 1977, 1979, and Jack Hirschleifer 1977, 1978, 1985.)6 More recently, Egbert Leigh and Geerat Vermeij have provided a number of path-breaking analyses of evolutionary and ecological processes from an economic perspective, with special reference to analogies with human economic principles (Leigh et al. 2009; Leigh and Vermeij 2002; Vermeij 2004, 2009, Vermeij and Leigh 2011; see also the seminal work of Odum 1971; also the work in fisheries management, e.g. Clark 1976; and the economic reciprocity alternative to the selfish gene model posited by Demsetz 2009).

In accordance with the vision of these pioneers, bioeconomics can be defined as the study from an economic perspective of how living organisms of all kinds acquire and utilize various resources to meet biological survival and reproductive needs.  In this paradigm, differential selection is ultimately an outcome of the functional dynamics in the “struggle for existence” (to borrow another of Darwin’s well-known expressions).  It is, in fact, the functional organization of the natural world that defines the “groups” — the units of selection — and it is the differential success of these functional units in a given environment that determines the course of the evolutionary process (see Ghiselin 1981; Corning 2005).

It is important to stress that natural selection is not (technically) a “mechanism”.  It is an umbrella term that applies to whatever factors are responsible in a given context for causing differential survival and reproduction.  Natural selection as a causal agency does not do anything; nothing is ever actively selected (although there are special cases like sexual selection and predator prey interactions).  It refers to the functional consequences for the genome over time of adaptively significant changes in a given organism-environment relationship.   One must always focus on the interactions that occur within an organism and between the organism and its environment(s), inclusive of other organisms.  In other words natural selection is a way of characterizing the survival-related (bioeconomic) Apayoffs@ in any organism-environment interaction.  (For a perspective on the “opportunism” of natural selection, see Bozorgmehr 2012.)

Thus, many things, at many different levels, may be responsible for bringing about changes in an organism-environment relationship and differential survival.  It could be a functionally-significant mutation, a chromosomal transposition, a change in the physical environment that affects development (ontogeny), a change in one species that affects another species, or (very often) it could be a change at the behavioral level that results in a new organism-environment relationship. (For an in-depth discussion of the role of behavior as a shaping influence in the evolutionary process, see Corning 2012.)

In fact, a whole sequence of changes may ripple through a pattern of relationships.  For instance, a climate change might alter the ecology, which might prompt 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 DNA coding sequences that support them. (One illustration of this causal dynamic can be found in the long-running research program among “Darwin’s finches” in the Galápagos Islands, led by Peter Grant and his wife, Rosemary (Grant 1986, 1991; Grant and Grant 1979, 1989, 1993, 2002; also Lack 1947; Weiner 1994.)7

Another way of putting it is that natural selection does not Aselect@ genes.  Rather, it (in effect) differentially rewards, or disfavors, the functional effects produced by different genomes in a given environmental context (the phenotypes).  As biologists Russell Lande and Stevan J. Arnold observed in an important overview article in the journal Evolution (1983): ANatural selection acts on phenotypes, regardless of their genetic basis, and produces immediate phenotypic effects within a generation that can be measured without recourse to principles of heredity or evolution.@ Biologist Alan Grafen (1991) calls it the Aphenotypic gambit.@ (See also Brandon 1996; Hammerstein 1996; Lewontin 2000; Gould 2002; West-Eberhard 2003.) The phenotype is where the “payoffs” occur that lead to differential survival and reproduction.  Thus, causation in evolution also runs backwards from our conventional view of things.  In evolutionary change, effects are also causes.  To use Ernst Mayr’s (1965) well-known distinction, it is the “proximate” functional effects arising from any change in the organism-environment relationship that are the causes of the “ultimate” (transgenerational) changes in the genomes of a given species.

This dynamic also applies to functionally-defined cooperating “groups” at various levels of biological organization.  If “wholes” of various kinds play an important role as interdependent functional “units” in evolutionary change, then one must focus on their functional interactions and the combined, interdependent effects that they produce.  The well known Behaviorist psychologist B.F. Skinner (1981) referred to it as “selection by consequences.”  Biologist Richard Michod (1999) observes that “cooperation is now seen as the primary creative force behind ever greater levels of complexity and organization in all of biology” (p. xi).

The role of synergy in cooperative relationships

However, it is not cooperation per se that is the “creative force.” Rather, it is the functional consequences or effects produced by cooperation that are the key.  And these in turn are shaped by various kinds of functional synergy.

Very broadly, synergy refers to the combined (cooperative) effects that arise from the relationships and interactions among various forces, particles, elements, parts, genomes, individuals, or groups in a given context — effects that are not otherwise attainable.  The term is derived from the Greek word synergos, meaning to “work together” or, literally, to “co-operate”.  Synergy is often associated with the cliché “The whole is greater than the sum of its parts,” which dates back to Aristotle in The Metaphysics (1961), but this is actually a rather narrow and even misleading characterization.  In fact, synergy comes in many different forms. Sometimes wholes are not greater than the sum of their parts, just different.  So defined, synergy is strictly a functional term, and the benefits and/or costs for the various “parts” must be separately determined.

Some theorists may object to using such a broad definition of synergy, but it would be arbitrary to limit it only to a subset of cooperative effects and, more serious, would be unjustified if it excludes categories that are theoretically relevant. Another objection might be that the term should not include additive forms of cooperation but only those that involve functionally different elements and non-additive phenomena.  I must disagree.  The criterion should be whether or not there are combined effects that are interdependent and cannot be achieved by the “parts” acting alone.  For example, if two hyenas hunting together can achieve success when two lone hunters cannot, their combined efforts are synergistic; the two hyenas “added together” produce a non-additive threshold change.  Likewise, if specialization and a division of labor achieve significant economies/efficiencies with respect some overarching task, this too is synergistic.  Adam Smith’s famous pin factory is the archetypical example.8

As discussed in much greater detail elsewhere (Corning 1983, 2003, 2005), synergy broadly defined is not only a ubiquitous phenomenon in the natural world but the emergent, interdependent effects to which the term synergy refers have played an important causal role in the evolution of cooperation and the trend toward biological complexity over time.  The thesis, in a nutshell, is that synergistic effects have provided unique, interdependent functional advantages in relation to survival and reproduction that have often been “favored” by natural selection.  The “benefits” (the “b” in Hamilton’s rule) are often derived from various (interdependent) synergistic effects.

In other words, it is functional synergies of various kinds that have been drivers for the evolution of cooperation and multi-level complex systems over time, rather than the other way around.  I refer to it as the Synergism Hypothesis.  The biologists John Maynard Smith and Eörs Szathmáry (1995, 1999) came to the same conclusion independently.9

It is important to stress that there are many different forms of selectively-relevant synergy in the natural world, including synergies of scale (when larger numbers provide an otherwise unattainable collective survival advantage), threshold effects, functional complementarities, augmentation or facilitation, joint environmental conditioning, risk- and cost-sharing, information-sharing, collective intelligence, animal-tool Asymbiosis@ and, of course, the many examples of a division of labor (or better said, a “combination of labor”) at every level in living systems. (Further discussion and many examples can be found in Corning 1983, 2003, 2005.)

To be sure, many factors can influence the likelihood of cooperation – the ecological context, specific opportunities, competitive pressures, the risks (and costs) of cheating/parasitism, effective punishments, genetic relatedness, genetic “preadaptations”, and especially the distribution of costs and benefits.  However, an essential requisite for cooperation is functional synergy.  Just as natural selection is agnostic about the sources of the “variations” that can influence differential survival and reproduction, the Synergism Hypothesis is agnostic about how synergistic effects may arise in nature.  They could be self-organized; they could be a product of some chance variation; they could arise from a happenstance symbiotic relationship; or they could be the result of a purpose-driven behavioral innovation by some living organism. (Indeed, humans excel at inventing new forms of synergy, although we are by no means alone in possessing this talent.)10

Moreover, the synergies produced by cooperation can almost always be measured and quantified in various ways.  Most often in the natural world they are related directly to survival and reproduction.  Thus, hunting or foraging collaboratively — a behavior found in many insects, birds, fish and mammals — may increase the size of the prey that can be pursued, the likelihood of success in capturing prey or the collective probability of finding a food “patch”.  Joint action against potential predators — alarm calling, herding, communal nesting, synchronized reproduction, coordinated defensive measures, and more — may greatly reduce the individual’s risk of becoming a meal for some other creature.

Likewise, shared defense of food resources — a practice common to social insects, birds and social carnivores alike — may provide greater food security for all.  Cooperation in nest-building, and in the nurturing and protection of the young, may significantly improve the collective odds of reproductive success.  Coordinated movement and migration, including the use of formations to increase aerodynamic or hydrodynamic efficiency, may reduce individual energy expenditures and/or facilitate navigation.  Forming a coalition against competitors may improve the chances of acquiring a mate, or a nest-site, or access to needed resources (such as a water-hole, a food patch, or potential prey).

Testing for synergy

There are also various ways of testing for synergy.  One method involves experiments or “thought experiments” in which a major part is removed from the “whole”.   In many cases (not all), a single deletion, subtraction or omission will be sufficient to eliminate the synergy.  Take away the heme group from a hemoglobin molecule, or the energy-producing mitochondria from a complex eukaryotic cell, or the all-important choanocytes from sponges, or, for that matter, remove a wheel from an automobile. The synergies will vanish.

Another method of testing for synergy derives from the fact that most adaptations, including those that are synergistic, are contingent and context specific, and that virtually all adaptations incur costs as well as benefits.  The benefits of any adaptation must, on balance, outweigh the costs (it must be “profitable” in terms of its impact on survival and reproduction).  Thus, it may not make sense to form a herd, or a shoal, or a communal nest if there are no threatening predators about, especially if proximity encourages the spread of parasites or concentrates the competition for locally scarce resources.  Nor does it make sense for emperor penguins to huddle together for warmth at high-noon during the summer months in the Antarctic, or for Mexican desert spiders to huddle against dehydration during the rainy season.  And hunting as a group is not advantageous if potential prey are small and easily caught by an individual hunter without any assistance.  Deterministic approaches to complexity, which are popular these days in biophysics, are blind to such functional contingencies. (See the critique in Corning 2005.)  From a bioeconomic perspective, however, such contingencies are predictable and unavoidable.

A further way of testing for synergy involves the use of a standard research methodology in the life sciences and behavioral sciences alike — comparative studies.  Often a direct comparison will allow for the precise measurement of a synergistic effect.  Some examples (detailed in Corning 2005) include flatworms that can collectively detoxify a silver colloid solution; emperor penguins that can reduce their energy expenditures by up to 50 percent when they huddle in winter; nest construction efficiencies that can be achieved by social wasps compared to individuals; lower predation rates in larger meerkat groups with more sentinels; higher pup survival rates in social groups of sea lions versus isolated mating pairs; the hunting success of cooperating hyenas in contrast with those that fail to cooperate; and the comparison between the choanocytes in sponges and very similar free-swimming choanoflagellates.

A classic experiment in ecology provides a textbook illustration of how the effects of synergistic combinations can be measured and compared to the available alternatives.  The experiment was designed to study the effects of sunlight and two different fertilizers (nitrate and phosphorus) on the growth of a small woodland flower (Impatiens parviflora).  One significant finding was that varying amounts of increased sunlight made little difference during the five-week test period without the addition of fertilizers.  Furthermore, the use only of nitrate or phosphorous (essential ingredients for amino acids and proteins) alone made only an incremental difference.  But when the plants were treated with the two fertilizers together, they weighed 50% more at the end of the test period than either of the two single-fertilizer groups and almost twice as much as the non-fertilized “controls”.  The results were clear cut.  The separate contributions of sunlight, nitrogen and phosphorus in plant growth are synergistic, and the consequences are measurable — as any skilled gardener already knows (Peace and Grubb 1982).

Growing support for the role of synergy

More evidence for the role of synergy in evolution can be found in Corning (2005), but there has also been a growing appreciation recently among other theorists and researchers.  One important example is the theoretical paper on “The Evolution of Eusociality” by Martin A. Nowak, Corina Tarnita and Edward O. Wilson (2010).  Nowak and his colleagues point out that “a group can be pulled together [whenever] cooperation among unrelated members proves beneficial to them, whether by simple reciprocity or by mutualistic synergism, or manipulation…. Relatedness is better explained as a consequence rather than a cause of sociality.”  With regard to the emergence of eusocial insects, the major causal factors singled out by these theorists were collaboration in building collectively defensible nests and an internal division of labor, both of which are highly synergistic forms of cooperation. (See also the sharp criticisms of this paper and the authors’ reply in “Brief Communications Arising,” Nature, 471(2011): E1-E10.)

Other recent theoretical support for the role of synergy includes a paper by Nowak (2006), where he identifies five “rules” for cooperation (kin selection, direct reciprocity, indirect reciprocity, network reciprocity and group selection) and points out that each depends upon the benefit-cost ratios – in other words, the synergies. Traulsen and Nowak (2006) also stress the role of multilevel selection in the evolution of cooperation, while D.S. Wilson and E.O. Wilson (2008) highlight certain kinds of synergistic phenomena (such as information) that may benefit a group as a whole. Van Veelen (2009) also argues that group selection models are required where synergies are involved.  He observes that “there is a more general, but still very realistic class of models with synergies, for which it is not possible to summarize their predictions on the basis of an evaluation of inclusive fitness.”  And Clutton-Brock (2009) addresses the issue of cooperation among non-kin and sees mutual benefits (synergies) as more important than strict reciprocities.  He notes, “In some cases, cooperation generates immediate synergistic benefits shared by cooperators that exceed the costs of providing assistance.”  He also cites several examples.  Synergy also figures in the comparative economics framework of Vermeij (2009).

An especially significant development is the accumulating evidence for synergistic effects at the molecular and cellular level.  For instance, Shapiro (2012) notes, “Most of the interactions between biomolecules tend to be relatively weak and need multiple synergistic attachments to produce stable functional complexes….the synergistic nature of most molecular complexes provides dynamism and flexibility to the transcriptional machinery [in the genome]…(p. 31).  Later on Shapiro emphasizes “the importance of cooperative synergistic interactions… The need for cooperativity arises because many biomechanical interactions are either weak or transitory, and multiple synergistic events stabilize the formation of functional complexes for carrying out cellular tasks…”(p. 131).

Likewise, the neurobiologist/anthropologist Terrence Deacon’s Incomplete Nature: How Mind Emerged from Matter (2012) assigns a key role to synergistic effects in the origin of life and the emergence of purposeful, teleonomic systems, functional effects that transcend the actions, and interactions, of the material “parts”.  (For other perspectives, see Szathmáry 1999, 2005; also Pereira et al. 2012.)  For example, Deacon proposes that the first step in producing self-organized, self-repairing, self-replicating “autogens” (as he calls them) involved a reciprocal complementarity — “a source of synergy” between the two distinct processes of autocatalysis and self-assembling enclosures (p. 304). Deacon concludes “On reflection, we can now see that ‘life’s several powers’ [a quote from Darwin] include and depend on the underlying morphogenetic processes that synergistically support and generate one another” (p. 462).  Indeed, Deacon uses variations on the term synergy no less than 51 times in his volume.  (Some other theorists who have invoked synergy in their work include Queller 1985; Buss 1987; Frank 1995a; Avilés 1999; Gould 2002; Fletcher and Doebeli 2006.)

The “cooperative gene”

It is evident from the growing research literature on cooperation that the various mathematical selection models may represent constraints and facilitators for cooperative relationships, but they do not represent the primary causal agencies.  Genetic relatedness may provide opportunities to take advantage of the potential for functional synergies, but (to repeat) a close genetic relationship is neither necessary nor sufficient as a precondition for cooperation in the natural world.  Cooperative groups/systems at all levels are defined in terms of their functional relationships/interactions, and it is the functional synergies they produce that are “selected” in the course of evolution; the synergies are the drivers.  Maynard Smith (1982b, 1983) referred to it as “synergistic selection.”  Indeed, synergies of various kinds also play an important role in social conflict and collective violence throughout the natural world (Corning 2007). (For more on synergy in evolution, see Corning 1983, 2003, 2005; also Maynard Smith and Szathmáry 1995, 1999.)

Some critics of the Synergism Hypothesis have called it a “theory of everything,” or have viewed it as a theory that seeks to supplant natural selection as the primary agency of evolutionary change.  This is emphatically not true.  The theory identifies a common denominator (so to speak) in cooperative relationships and in multi-level biological systems at various levels.  It is not a synonym for cooperation but refers to the effects that are produced by cooperation.  It highlights a class of causal influences with distinct functional properties (they are inherently relational and interdependent), properties which are always subject to the ultimate “verdict” of natural selection.  Indeed, there are also a great many kinds of negative synergy (or “dysergy”) in the natural world – cooperative effects that are deleterious to one or more participants, or to various “bystanders”.  For instance, cooperative hunting might be very beneficial for a group of predators, but it would most likely result in negative synergy for their prey.  (Much more on negative synergy can be found in Corning 2003.)

Many years ago the novelist and polymath Arthur Koestler (1967) made the observation that “True innovation occurs when things are put together for the first time that had been separate.”  The “selfish gene” has been a useful heuristic concept, but now we must focus on why selfish genes cooperate.  It is evident that competition and cooperation have both played important parts in the epic of evolution (to borrow E. O. Wilson’s term) and that the “selfish gene” is also a “cooperative gene” (Corning 1996, Ridley 2001; Dawkins 1982, 1987).11 A bioeconomic approach to cooperation and a shift of focus to the emergent, creative influence of various kinds of functional synergy as a causal agency in evolution helps us to understand why.


1  Margulis and Sagan conclude that the “symbiogenetic acquisition of new traits by acquired genomes is…an extension, a refinement, and an amplification of Darwin’s idea….The results of new laboratory and field science contradict, by pass, or marginalize the formalism of neo-Darwinism, except for variations within populations of mammals and other sexually reproducing organisms….The reliance on an accumulation of [favorable] random mutations in DNA is not so much ‘wrong’ as oversimplified and incomplete: It misses the symbiotic forest for the genetic trees” (Margulis and Sagan 2002, pp. 15, 39, 201).

2  See also the “trait group” selection theory advanced by D. S. Wilson 1975, 1980; Wilson and Sober 1989, 1994; Sober and Wilson 1998.  Also relevant is Grafen 1984; Wade 1985; Frank 1995a, 1997; Keller 1999; Michod 1999, Fletcher et. al. 2006; Nowak 2006; Okasha 2006; Traulsen and Nowak 2006; D.S. Wilson and E.O. Wilson 2007, 2008; Clutton-Brock 2009; van Veelen 2009; Nowak et al. 2010; Wade et al. 2010.  Also, see the special issue of the Journal of Bioeconomics on group selection in 2008, edited by then-editor Janet T. Landa and David Sloan Wilson.

3  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; the “self-regulating” division of labor and activity cycles in honey bee hives; the generally harmonious cooperative relationships between eukaryotic 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.  Maynard Smith and Szathmáry (1993, 1995) also cite the importance of the linkages (the shared fate) in chromosomes.

4  It should also be noted that Robert Trivers’ (1971) concept of “reciprocal altruism” provides yet another alternative way of accounting for certain kinds of cooperative behaviors independently of genetic relatedness.  A well-known example is Gerald Wilkinson’s (1984, 1988, 1990) studies of blood sharing in vampire bats.

Some theorists claim that the term reciprocal altruism is inaccurate.  It does not really refer to altruism but rather to acts of reciprocity with a delayed repayment schedule.  Nevertheless, the term has been widely adopted and is supported by various examples.

5  For more on evolution as a multi-level process, see also Koestler 1967; Corning 1983, 2003, 2005; Brandon and Burian 1984; Eldredge and Salthe 1984; Salthe 1985; Eldredge 1985, 1995; Wilson and Sober 1994; Frank 1995a; Sober and Wilson 1998; Gould 2002; Cassill and Watkins 2010.

6  Also important was economist James Buchanan’s (2000) analysis of potential conflicts between individual and group objectives in evolution based on an analogy with team sports.  However, his analysis was insufficient.  Buchanan noted that, in human groups, rules and incentives may affect the outcomes.  Basketball was his model.  But there are other sports, like rowing, where individual and team outcomes may be interdependent; individual and group incentives may be completely aligned.  We will return to this point below.  (See also Corning 2005.)

7  Over the years, the Grants have documented many evolutionary changes in these closely-related bird species, particularly in the mix of beak sizes and shapes, in response to pronounced environmental fluctuations.  During drought periods, for instance, the larger ground finches with bigger beaks survive better than their smaller cousins.  Small seeds become scarce during the lean years, so the only alternative food source for a seed-eater is much larger, tougher seeds that must be cracked open to get at their kernels.  Birds with bigger, stronger beaks have an obvious functional advantage, and this is the proximate cause of their differential survival.

8  Many theorists distinguish between synergistic phenomena and various forms of exchange or reciprocity.  I would argue that it depends on the circumstances.  Viewed in functional terms, strict reciprocity – or tit-for-tat – would not be synergistic. On the other hand, it could be argued that the textbook economic “marketplace”, which permits specialization and production efficiencies, entails interdependent cooperative effects that would not otherwise be attainable and that are shared by all the participants (though often not equally).  By the same token, a division of labor (I prefer to call it a “combination of labor”) can produce either indivisible “public goods” or “corporate goods” – synergistic benefits that are jointly achieved but can be divided among the participants in various ways.  One illustration of a public good is the rowboat example used above, where two oarsmen together can achieve a common goal. (For more on public goods in the natural world, see especially Leigh 1991, 2010a,b; also Wilson and Wilson 2008.  Further discussion of how to define the term synergy can be found in Corning, 2003, 2005.)

9  In a section of their 1999 book on The Origins of Life under the sub-heading “Synergy”, Maynard Smith and Szathmáry had this to say:

Co-operation will not evolve unless it pays.  Two co-operating individuals must do better than they would if each acted on its own…Behavioural examples are easy to think of, but the principle is relevant at all levels…Peter Corning, in a book called The synergism hypothesis published in 1983, reviewed the role of synergy in social and biological evolution. We had not seen his book when we wrote The major transitions in evolution [1995], but we are happy to acknowledge that he foreshadowed this part of our argument, often using the same examples (pp. 22-23).

 10 Indeed, the Synergism Hypothesis is also applicable to human evolution and to the cultural evolution of complex societies over time (see the extended discussions in Corning 1983, 2003). This can perhaps be seen by briefly comparing it with economist Brian Arthur’s (2009) theory of technological evolution in human societies. The core “mechanism” of change, Arthur tells us, is “new combinations” of elements and technologies arising in a “historically contingent” manner from what already exists. These new combinations create new “opportunities” to satisfy human needs and wants. This is true as far as it goes, but what Arthur leaves unstated, or only implicit, is the fact that it is not the new combinations per se but the beneficial new functional synergies that a new technology creates – the economic “payoffs” in relation to human needs and wants — that are key to the emergence and spread of technological innovations. All human technologies, from flaked stone tools and fire among our remote ancestors to sophisticated modern power tools and solar power today, have provided potent new synergistic effects, and these economic advantages have been the drivers for the adoption of technological innovations. Only at the very end of his book does Arthur acknowledge in passing that “New ‘species’ in technology arise by linking some need with some effect (or effects) that can fulfill it” (p. 204). Arthur’s theory mainly addresses the “how” question; the Synergism Hypothesis is focused on the “why” question.

11  As Dawkins himself expressed it in The Blind Watchmaker (1987):

In a sense, the whole process of embryonic development can be looked upon as a cooperative venture, jointly run by thousands of genes together. Embryos are put together by all the working genes in the developing organism, in collaboration with one another….We have a picture of teams of genes all evolving toward cooperative solutions to problems…It is the ‘team’ that evolves (pp. 170,171).





I am grateful to the anonymous reviewers of an earlier draft of this paper, whose many thoughtful criticisms greatly contributed to the final product. Co-editor Michael Ghiselin also made important substantive contributions. I am especially grateful to the editor, Ulrich Witt, for his patience and helpful guidance.  Finally, I am indebted to the late John Maynard Smith and his co-author, Eörs Szathmáry, for their gracious acknowledgement of my early work on the role of synergy in evolution.



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