Abstract
Multiple biological groups, such as ant colonies, appear to have a noteworthy inefficiency: they contain vast amounts of redundant members that are not strictly needed to maintain the group. Philosophers and biologists have proposed that such inefficiency is illusory because redundancy enhances the resilience of groups when living under harsh conditions. Still, this proposal is unsatisfactory in different respects. First, it is too vague to account for when redundancy is selectively advantageous. Furthermore, it overlooks cases in which redundancy fails to increase the resilience of groups. This paper offers an account of group redundancy that addresses these difficulties. Specifically, it advances the hypothesis that the mere presence of harsh conditions is not what drives the evolution of redundancy; rather, it is the fact organisms are often unable to predict when they will face harsh conditions. Redundancy enables groups to properly respond to unpredictable circumstances without resorting to the unreliable detection systems of their members. A better understanding of the phenomenon of redundancy is likely to impact other key issues in philosophy of science, including the evolution of cooperation and transitions in individuality, and the role of redundancy in complex systems, be they biological or not.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
I chose the expression “harsh environment” because it nicely captures the kind of conditions in which redundant groups might fare better than non-redundant groups. In particular, redundancy likely increases the resilience of groups not only when they face “catastrophic events” (Hasegawa et al., 2016), but also when they cope with less abrupt environmental changes, such as fluctuations in food supply (Charbonneau et al., 2017). Accordingly, the phrase “harsh environment” has the advantage of indicating the presence of a stressor while being agnostic about whether this stressor is a severe disturbance event or not. See Dugatkin (1997) and Pedroso (2021) for related uses of the notion of ‘harsh environment.’
The observation that redundancy increases the resilience of groups might encourage some readers to think that the theory of group selection is particularly suited for studying redundancy. This might very well be the case. However, an explanation rooted in group selection would have to grapple with the fact that, when redundant groups face harsh conditions, there are winners and losers within the group. Possibly, the theory of group selection could view redundancy as the result of two selective pressures: whereas group-selection would favor redundancy, individual-selection would favor free-riding. Redundancy would thus evolve when group-level selection overrides individual-level selection. For references on group selection, see Okasha (2006) and Bourrat (2021). I thank one of the referees for asking me to clarify this point.
As we will see, the concepts of redundancy and cooperation are linked because multiple social groups contain “extra” cooperators. The issue of how transitions in individuality evolved can, in turn, be formulated in terms of the problem of cooperation (Bourke, 2011). In particular, Birch (2012) has suggested that the presence of “extra” cooperators might have enabled the evolution of the type of specialization needed for transitions in individuality to occur.
Provided that \(\epsilon \) is the chance of error of each component machine, the probability that at least one of the n components will function properly is \(1-\epsilon ^n\).
It is worth noting that Wimsatt (2007) uses the concept of robustness to tackle a wide range of philosophical topics, such as visual perception, the analytic-synthetic distinction, and the analysis of complex systems. I chose Wimsatt’s criticism of the axiomatic approach because it nicely illustrates how the concepts of robustness and redundancy relate to each other.
In addition to the concept of robustness, another relevant concept in the study of evolution is Wimsatt’s notion of “generative entrenchment.” A generatively entrenched feature is one that “has many things depending on it because it has played a role in generating them” (p. 133), such as the genetic code. Wimsatt views the concepts of robustness and generative entrenchment as complementary measures of “local order” in complex biological systems (p. 355).
Note that this definition of ‘public goods’ differs from other commonly used definitions in the literature. In economics, it is often assumed that public goods are non-excludable in the sense that individuals cannot be barred from consuming the good. The air we breath is an example of a non-excludable good. In contrast, according to the definition of public goods used in this paper, a resource can be a public good for a group even if members outside of the group are barred from accessing it, such as the spoils of a hunt. Additionally, the definition of ‘public goods’ used in this paper does not assume that the shared resource is costly to produce. See West et al. (2006) for a discussion about public goods in evolution that makes this assumption.
Note that, in this paper, the sentence ‘a group is redundant’ does not mean that the group is what is extra but the members within the group. Similarly, the expression ‘group redundancy’ will be used in this paper as a convenient abbreviation for redundancy within a particular group as opposed to a collection of groups.
In biology, redundancy is not restricted to social groups. Our body is rife with redundant parts; e.g., humans typically possess two kidneys even though we could survive with only one. Organisms often overproduce zygotes during reproduction (Stearns, 1987; Edelman & Gally, 2001). And multiple genes in the genomes of certain species can perform the same function (Krakauer & Plotkin, 2002; Keller, 2009).
A quick note on terminology. Some authors such as Edelman and Gally (2001) use the term “degeneracy” to refer to cases in which physically different components can perform the same function. They reserve the term “redundancy” to cases in which the elements are copies of each other. In this paper, the term “redundancy” refers to cases in which the extra individuals contribute to the same task, be they copies of each other or not.
This is not to say that groups only fail to perform group tasks due to harsh environments. Groups can also fail to perform a task because their members are not well-matched to the task at hand.
Although environments can be opaque due to nonliving features in the environment, such as features in the terrain, “biological agents pose far more difficult epistemic problems. An animal’s predators, prey, and competitors are under selection to sabotage its actions” (Sterelny, 2003, p. 25).
Day length has become an unreliable cue for certain species because species across the trophic levels respond differently to climate change. In the case of the flycatcher species Ficedula hypoleuca, Both et al. (2006) provide evidence that climate change induced a mistiming between their breeding time and the peak in food abundance. This example illustrates how opacity can affect survival when environmental changes are too rapid for organisms to evolve the necessary adaptations via natural selection.
There is a close parallel with Taleb’s (2007) approach to uncertainty. According to him, for domains in which humans are particularly bad at predicting outlier events such as financial markets, we should concentrate on minimizing the damage due to outlier events instead of attempting to improve our predictive power.
These costs can take multiple forms, including increase of competition for resources (e.g., food, mates, nest sites), higher rates of disease transmission facilitated by close proximity, or higher visibility to predators due to an increase in group size.
A similar remark applies to redundancy within organisms. For example, adding extra appendages to an organism might diminish its survival if it dramatically increases the chance of debilitating injuries.
Authors working on different types of redundancy have drawn attention to the value of independence in redundant systems. Bendor (1985) argues that the redundant teams within institutions should be independent. In the debate over robustness analysis, Pirtle et al. (2018) make the case for the importance of independent models in studying engineering systems. Additionally, it is reasonable to assume that the redundant parts within an organisms should also be decoupled. For example, octopuses possess multiple arms that regenerate independently (Fossati et al., 2013). Without the ability to fail independently, a damaged or lost arm in an octopus would compromise the whole organism.
The two vampire bats are assumed to be equally skilled at hunting because this example focuses on how redundancy—rather differences in skill—affects the chance of group task completion.
Jonathan Bendor (1985) uses a similar example to argue for the value of redundancy in human organizations.
These two conditional probabilities have the same value because the unconditional probabilies are equal—i.e., Eq. (1). Specifically, \(P (h_{{M}}\mid {} h_{{P}})= P (h_{{M}}\wedge {} h_{{P}})/a = P (h_{{P}}\wedge {} h_{{M}})/a = P (h_{{P}}\mid {} h_{{M}})\).
Note that decoupling is detrimental when redundancy is absent, however. For example, suppose that Maria and Pedro must both succeed in their hunts to obtain enough blood to survive. In this case, coupling is desirable because it increases the probability of joint success.
In social evolutionary biology, cooperative behaviors are understood as interactions between individuals that benefit the recipient but not necessarily the performer of the behavior. Note that this definition of cooperation abstracts away specific biological details of the involved organisms, including their genetics and modes of reproduction (Calcott, 2008). Yet, as will be discussed, the specific biological details are key for understanding how cooperation might evolve.
This point is nicely summarized by a well-known version of Hamilton’s rule according to which selection will favor a costly behavior if \(r\times b>c\), where c is the cost of the behavior to the actor, b is the benefit to the recipient, and r is the genetic relatedness between the actor and the recipient.
Birch (2012) draws a parallel with the ‘paradox of voting’ in order to make the puzzle posed by redundancy more vivid. In regards to voting, the puzzle is that people vote in large numbers even though a single vote is unlikely to change the outcome of an election. Accordingly, similar to redundancy, the expected benefits of voting is negligible relative to the costs of voting.
Additionally, the level of genetic relatedness in some insect colonies seems too low to maintain altruistic behaviors without them being socially enforced (Ratnieks & Wenseleers, 2008).
The term ‘coercion’ is used here as a technical term to cover a variety of social interactions that promote altruism within the group, such as the policing behavior of worker bees. Ratnieks and Wenseleers (2008) characterize coercion as any type of ‘social pressure’ that tends to prevent individuals from harming the group by acting selfishly. Accordingly, like the policing behavior of worker bees, retaliatory aggression in animal societies (Clutton-Brock & Parker, 1995) and altruistic punishment in humans (Fehr & Gächter, 2002; Boyd et al., 2003) might also be thought of as examples of coercion.
See Birch(2012, Fig. 4) for a summary of his hypothesis.
Group tasks in Pedroso’s (2021) account are modeled as a type of public goods game called “Threshold Games.” Unlike the Prisoner’s Dilemma, players in a Threshold Game obtain a benefit only if enough members of the group cooperate. See Archetti and Scheuring (2012) and Bach et al. (2006) for further details on threshold games.
References
Adams, E., & Caldwell, R. (1990). Deceptive communication in asymmetric fights of the stomatopod crustacean Gonodactylus bredini. Animal Behaviour, 39(4), 706–716.
Albert, R., Jeong, H., & Barabási, A. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378.
Anderson, C., Theraulaz, G., & Deneubourg, J.-L. (2002). Self-assemblages in insect societies. Insectes Sociaux, 49, 99–110.
Archetti, M., & Pienta, K. (2019). Cooperation among cancer cells: Applying game theory to cancer. Nature Reviews Cancer, 19(2), 110–117.
Archetti, M., & Scheuring, I. (2012). Game theory of public goods in one-shot social dilemmas without assortment. Journal of Theoretical Biology, 299, 9–20.
Bach, L., Helvik, T., & Christiansen, F. (2006). The evolution of n-player cooperation–threshold games and ESS bifurcations. Journal of Theoretical Biology, 238, 426–434.
Bendor, J. (1985). Parallel Systems: Redundancy in Government. University of California Press
Birch, J. (2012). Collective action in the fraternal transitions. Biology and Philosophy, 27, 363–380.
Birch, J. (2017). The Philosophy of Social Evolution. Oxford University Press.
Bookstaber, R., & Langsam, J. (1985). On the optimality of coarse behavior rules. Journal of Theoretical Biology, 116(2), 161–193.
Both, C., Bouwhuis, S., Lessells, C., & Visser, M. (2006). Climate change and population declines in a long-distance migratory bird. Nature, 441(7089), 81.
Bourke, A. (2011). Principles of Social Evolution. Oxford University Press.
Bourrat, P. (2021). Facts, Conventions, and the Levels of Selection. Cambridge University Press.
Boyd, R., Gintis, H., Bowles, S., & Richerson, P. (2003). The evolution of altruistic punishment. Proceedings of the National Academy of Sciences, 100(6), 3531–3535.
Calcott, B. (2008). The other cooperation problem: Generating benefit. Biology & Philosophy, 23, 179–203.
Calcott, B. (2011). Wimsatt and the robustness family: Review of wimsatt’s re-engineering philosophy for limited beings. Biology & Philosophy, 26, 281–293.
Carter, G., & Wilkinson, G. (2013). Food sharing in vampire bats: Reciprocal help predicts donations more than relatedness or harassment. Proceedings of the Royal Society B: Biological Sciences, 280, 20122573.
Caves, E., Brandley, N., & Johnsen, S. (2018). Visual acuity and the evolution of signals. Trends in Ecology & Evolution, 33(5), 358–372.
Charbonneau, D., & Dornhaus, A. (2015). When doing nothing is something: How task allocation strategies compromise between flexibility, efficiency, and inactive agents. Journal of Bioeconomics, 17, 217–242.
Charbonneau, D., Sasaki, T., & Dornhaus, A. (2017). Who needs ‘lazy’ workers? Inactive workers act as a ‘reserve’ labor force replacing active workers, but inactive workers are not replaced when they are removed. PLoS ONE, 12, e0184074.
Clutton-Brock, T., & Parker, G. (1995). Punishment in animal societies. Nature, 373, 209–216.
Dawkins, R. (1982). The Extended Phenotype. Oxford University Press.
L. Dugatkin. Cooperation Among Animals: An Evolutionary Perspective. Oxford University Press, 1997.
Edelman, G., & Gally, J. (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences, 98(24), 13763–13768.
Fehr, E., & Gächter, S. (2002). Altruistic punishment in humans. Nature, 415, 137–140.
Flack, Erwin, J. D., Elliot, T., & Krakauer,D. (2013). Timescales, symmetry, and uncertainty reduction in the origins of hierarchy in biological systems. In K. Sterelny, R. Joyce, B. Calcott, and B. Fraser, editors, Evolution Cooperation and Complexity, pp. 45–74. MIT Press.
Folkesson, A., Jelsbak, L., Yang, L., Johansen, H., Ciofu, O., Høiby, N., & Molin, S. (2012). Adaptation of pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective. Nature Reviews Microbiology, 10(12), 841–851.
Fossati, S., Carella, F., De Vico, G., Benfenati, F., & Zullo, L. (2013). Octopus arm regeneration: Role of acetylcholinesterase during morphological modification. Journal of Experimental Marine Biology and Ecology, 447, 93–99.
Gleick, J. (2012). The Information: A history, a theory, a flood. Vintage.
Gould, J. (1998). Sensory bases of navigation. Current Biology, 8(20), R731–R738.
Hall-Stoodley, L., Costerton, J., & Stoodley, P. (2004). Bacterial biofilms: From the natural environment to infectious diseases. Nature Reviews Microbiology, 2, 95–108.
Hardin, G. (1968). The tragedy of the commons. Science, 162, 1243–1248.
Hasegawa, E., Ishii, Y., Tada, K., Kobayashi, K., & Yoshimura, J. (2016). Lazy workers are necessary for long-term sustainability in insect societies. Scientific Reports, 6(1), 1–9.
Jackson, D., & Ratnieks, F. (2006). Communication in ants. Current Biology, 16(15), R570–R574.
Keller, E. (2009). The Century of the Gene. Harvard University Press.
Krakauer, D., & Plotkin, J. (2002). Redundancy, antiredundancy, and the robustness of genomes. Proceedings of the National Academy of Sciences, 99, 1405–1409.
Krause, J. & Ruxton, G. (2002). Living in Groups. Oxford University Press.
Landau, M. (1969). Redundancy, rationality, and the problem of duplication and overlap. Public Administration Review, 29(4), 346–358.
Lloyd, E. (2015). Model robustness as a confirmatory virtue: The case of climate science. Studies in History and Philosophy of Science Part A, 49, 58–68.
Lloyd, J. (1975). Aggressive mimicry in photuris fireflies: Signal repertoires by femmes fatales. Science, 187(4175), 452–453.
S. Marzen and S. DeDeo. The evolution of lossy compression. Journal of The Royal Society Interface, 14, 2017.
McNab, B. (1973). Energetics and the distribution of vampires. Journal of Mammalogy, 54(1), 131–144.
Mitchell, S. (2009) Unsimple truths: Science, complexity, and policy. University of Chicago Press.
Odenbaugh, J. (2006). The strategy of “the strategy of model building in population biology". Biology and Philosophy, 21, 607–621.
Okasha, S. (2006). Evolution and the Levels of Selection. Oxford University Press.
Ostrom, E. (1990). Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press.
Page, S. (2010). Diversity and Complexity. Princeton University Press.
Pedroso, M. (2018). The impact of population bottlenecks on the social lives of microbes. Biological Theory, 13, 190–198.
Pedroso, M. (2021). Blind cooperation: The evolution of redundancy via ignorance. The British Journal for the Philosophy of Science.
Pepper, J. (2012). Drugs that target pathogen public goods are robust against evolved drug resistance. Evolutionary Applications, 5(7), 757–761.
Perrow, C. (2011). Normal Accidents: Living with high risk technologies. Princeton University Press.
Pirtle, Z., Odenbaugh, J., Hamilton, A., & Szajnfarber, Z. (2018). Engineering model independence: A strategy to encourage independence among models. Techné: Research in Philosophy and Technology, 22, 191–229.
Pitcher, T., Green, D., & Magurran, A. (1986). Dicing with death: Predator inspection behaviour in minnow shoals. Journal of Fish Biology, 28(4), 439–448.
Potochnik, A. (2017). Idealization and the Aims of Science. University of Chicago Press.
Rankin, D., Bargum, K., & Kokko, H. (2007). The tragedy of the commons in evolutionary biology. Trends in Ecology & Evolution, 22, 643–651.
Ratledge, C., & Dover, L. (2000). Iron metabolism in pathogenic bacteria. Annual Reviews in Microbiology, 54(1), 881–941.
Ratnieks, F., & Wenseleers, T. (2008). Altruism in insect societies and beyond: Voluntary or enforced? Trends in Ecology & Evolution, 23, 45–52.
Ratnieks, F., Foster, K., & Wenseleers, T. (2006). Conflict resolution in insect societies. Annual Review of Entomology, 51, 581–608.
Sagan, S. (2004). The problem of redundancy problem: Why more nuclear security forces may produce less nuclear security. Risk Analysis: An International Journal, 24(4), 935–946.
Shannon, C. (1951). Prediction and entropy of printed English. Bell System Technical Journal, 30(1), 50–64.
Skyrms, B. (2010). Signals: Evolution, learning, and information. Oxford University Press.
Stamps, J. (2016). Individual differences in behavioural plasticities. Biological Reviews, 91(2), 534–567.
Stearns, S. (1987). The selection-arena hypothesis. In S. Stearns (Ed.), The evolution of sex and its consequences (pp. 337–349). Springer.
Sterelny, K. (2003). Thought in a Hostile World: The evolution of human cognition. Wiley-Blackwell.
Stewart, J. (2017). How air france will rescue its A380 with a shattered engine. Wired. URL https://www.wired.com/story/how-air-france-will-rescue-its-a380-with-a-shattered-engine/.
Taleb, N. (2007). The Black Swan: The impact of the highly improbable. Random House.
Taleb, N. (2012). Antifragile: How to live in a world we don’t understand. Allen Lane London.
Trivers, R. (2011). The Folly of Fools: The logic of deceit and self-deception in human life. Basic Books.
Von Neumann, J. (1956). Probabilistic logics and the synthesis of reliable organisms from unreliable components. In C. Shannon and J. McCarthy (Eds.), Automata studies (pp. 43–98). Princeton University Press.
West, S., Griffin, A., Gardner, A., & Diggle, S. (2006). Social evolution theory of microorganisms. Nature Reviews Microbiology, 4, 597–607.
Wilkinson, G. (1984). Reciprocal food sharing in the vampire bat. Nature, 308, 181–184.
Wilson, D., & Sober, E. (1989). Reviving the superorganism. Journal of Theoretical Biology, 136, 337–356.
Wimsatt, W. (2007). Re-Engineering Philosophy for Limited Beings: Piecewise Approximations to Reality. Harvard University Press.
Acknowledgements
I thank the DC/Maryland History and Philosophy of Biology discussion group, the audience from the POBAM 2020 meeting, and the constructive suggestions of the anonymous referees. A considerable portion of this paper was written in Fall 2020 while I was a fellow at the Center for the Philosophy of Science at the University of Pittsburgh. I am grateful that the Center kept the fellowship program despite the challenges posed by COVID.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
A How coupling affects group redundancy (formal version)
A How coupling affects group redundancy (formal version)
The goal of this appendix is to offer a formal version of the explanation presented in Section 3.1 that coupling diminishes the benefits of redundancy. As before, Maria and Pedro have the same chance a of being successful—i.e., \(P(h_{i}) = a\) for \(i \in \{\text {M}, \text {P} \}\). And, with coupling, the conditional probabilities \(P(h_{{M}}\mid h_{{P}})\) and \(P(h_{{P}}\mid h_{{M}})\) become strictly higher than a.
The group formed by Maria and Pedro has a probability \(P(h_{M}\vee {} h_{P})\) of being successful. The chance that Maria and Pedro will be jointly successful increases with coupling because, by definition, \(P(h_{{M}}\wedge h_{{P}}) = P(h_{{M}}\mid h_{{P}})\cdot a\). Additionally, \(P(h_{M}\vee {}h_{P}) = 2a - P(h_{M}\wedge {} h_{P})\). Therefore, since a is a constant, the probability that the group is successful decreases with coupling.
Coupling reduces the chance that Maria or Pedro are successful because it increases the probability of joint failures, \(P(\lnot h_{{M}}\wedge \lnot h_{{P}})\). In order to show that, consider the following two instances of the law of total probability:
Since a is a constant, it follows from Eq. (3) that increasing \(P(h_{{M}}\wedge h_{{P}})\) decreases the value of \(P(\lnot h_{{M}}\wedge h_{{P}})\) which, in turn, increases the value of \(P(\lnot h_{{M}}\wedge \lnot h_{{P}})\). Therefore, the chance of joint failures increases with coupling.
Rights and permissions
About this article
Cite this article
Pedroso, M. Betting blind: coping with uncertainty through redundancy. Synthese 200, 226 (2022). https://doi.org/10.1007/s11229-022-03717-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11229-022-03717-8