The Cultural Evolution of Science

Wu, Jingyi; O’Connor, Cailin; Smaldino, Paul E.

doi:10.31222/osf.io/2ekcr

Cited by 4 publications

(5 citation statements)

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“…For instance, research has illustrated how innovation is to a large degree recombination, involving the reuse of pre-existing components in novel contexts Arthur, 2009). Science, too, is much less a feat of "lone geniuses" than a collective search process which involves high degrees of fiduciary relations and reapplication of previously useful solutions (Polanyi, 1957;Wu et al, 2022).…”

Section: Culture As Collective Intelligencementioning

confidence: 99%

All Intelligence is Collective Intelligence

Falandays¹,

Kaaronen²,

Moser³

et al. 2022

Preprint

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Collective intelligence, broadly conceived, refers to the adaptive behavior achieved by groups through the interactions of their members, often involving phenomena such as consensus building, cooperation, and competition. The standard view of collective intelligence is that it is a distinct phenomenon from supposed individual intelligence. In this position piece, we argue that a more parsimonious stance is to consider all intelligent adaptive behavior as being driven by similar abstract principles of collective dynamics. To illustrate this point, we highlight how similar principles are at work in the intelligent behavior of groups of non-human animals, multicellular organisms, brains, small groups of humans, cultures, and even evolution itself. If intelligent behavior in all of these systems is best understood as the emergent result of collective interactions, we ask what is left to be called “individual intelligence”? We believe that viewing all intelligence as collective intelligence offers greater explanatory power and generality, and may promote fruitful cross-disciplinary exchange in the study of intelligent adaptive behavior.

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Section: Culture As Collective Intelligencementioning

confidence: 99%

All Intelligence is Collective Intelligence

Falandays¹,

Kaaronen²,

Moser³

et al. 2022

Preprint

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“…An alcoholic may stop drinking because doing so fits into a valuable pattern of behavior with important and beneficial long‐term consequences, so perhaps a society may stop burning fossil fuels for the same reasons. As a collection of evolved cultural practices, science itself may contribute to sociogenic self‐control by identifying deferred and distributed consequences and demonstrating new ways to manage them (e.g., Pennypacker, 1992; Wu et al, in press).…”

Section: In Sociogenymentioning

confidence: 99%

Faustian bargains: Short‐term and long‐term contingencies in phylogeny, ontogeny, and sociogeny

Stahlman

Catania

2022

J Exper Analysis Behavior

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Rachlin's interpretations of self‐control depend on the short‐term versus the long‐term consequences of behavior. Sometimes these effects support each other (typing an abstract produces a written product now and is later read by others). Sometimes they conflict (procrastination now is incompatible with finishing the abstract by deadline). We usually reserve the language of self‐control for human cases where long‐term consequences are chosen over short‐term ones. Rachlin made this distinction salient in ontogeny, but it also applies to selection in phylogeny (Darwinian evolution) and sociogeny (behavior passed from one organism to another). Our account examines relations between short‐term and long‐term consequences at each level of selection. For example, sexual selection has adaptive, short‐term mating consequences but may drive species to extreme specializations that jeopardize long‐term survival. In sociogeny, as in the Tragedy of the Commons, group members may get immediate economic benefits from exploiting resources but exhaust those resources over the long term. Whatever the level, when short‐term and long‐term consequences have opposing effects, adaptive behavior may depend on whether temporally extended contingencies exert more control than more immediate benefits.

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“…After all, we usually expect evolution to be slow and gradual. Existing work in the cultural evolution of science has primarily focused on heritability (H2 and Q2) and selection (H3 and Q3) (Wu et al, 2022). But deep learning gives us the opportunity to look carefully at the first hallmark (H1) and the question of the generation of variation or "arrival of the fittest" (Q1).…”

Section: Facilitated Variation In Biological Evolutionmentioning

confidence: 99%

“…This is also known as the sea squirt theory of tenure (Dennett, 1991): like a juvenile sea squirt, an early career academic has a rudimentary nervous system to learn and navigate the world, but once the sea squirt finds a suitable spot to cling to for life, it has no need for a brain and proceeds to digest it -like an academic who found a permanent position. Apart from the philosophy of science consequences of contrasting papers/code (which are much closer to 'beliefs' in formal epistemology models (Wu et al, 2022)) to scientists as units of selection, it is a huge difference for the analogy to developmental biology. Whereas we made development for a paper/code level of selection precise by defining it as the process converting the pseudocode or text description of an algorithm into a trained model, the scientists level of selection developmental process that converts a fresh undergraduate into an active scientists requires deeper investigates.…”

Section: Limitations and Future Workmentioning

confidence: 99%

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Nothing makes sense in deep learning, except in the light of evolution

Kaznatcheev¹,

Paul²

2022

Preprint

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Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its traits. Instead, we propose that an explanation of the success of DL must look at the population of all algorithms in the field and how they have evolved over time. We argue that cultural evolution is a useful framework to explain the success of DL. In analogy to biology, we use 'development' to mean the process converting the pseudocode or text description of an algorithm into a fully trained model. This includes writing the programming code, compiling and running the program, and training the model. If all parts of the process don't align well then the resultant model will be useless (if the code runs at all!). This is a constraint. A core component of evolutionary developmental biology is the concept of deconstraintsthese are modification to the developmental process that avoid complete failure by automatically accommodating changes in other components. We suggest that many important innovations in DL, from neural networks themselves to hyperparameter optimization and AutoGrad, can be seen as developmental deconstraints. These deconstraints can be very helpful to both the particular algorithm in how it handles challenges in implementation and the overall field of DL in how easy it is for new ideas to be generated. We highlight how our perspective can both advance DL and lead to new insights for evolutionary biology. Evolution of deep learningDeep learning (DL) allows computers to learn enough from data to recognize faces (Schroff et al., 2015), play go (Schrittwieser et al., 2020), and model text (Brown et al., 2020) successfully. Coffee allows scientists to stay awake long enough to write another paper on what makes DL algorithms successful. How is it possible that caffeine has just the right structure to keep us awake enough to write this paper? Caffeine is an arrangement of 24 atoms in just the right way to resemble adenosine enough to occupy its receptors in brain cells but sufficiently different that it does not cause sleepiness (Huang et al., 2005). From the molecular perspective, this coincidence seems miraculous. Out of all the possible molecules made out of 24 atoms, the probability that a random one interacts with our neuroreceptors in just the right way is unbelievably small. This makes the proximate explanation -in terms that reduce to the interactions between atoms and molecules -for the success of caffeine feel unsatisfying relative to the ultimate explanation in terms of history.To be fully satisfied by an account of caffeine, we need to look at the history of this unlikely molecule. This history was both a process of cultural evolution -consuming herbs that affect us -and before that, a process of biological evolution -the coffee plant evolving a defense mechanism against insects that share some of their brain chemistry with us (Denoeud et al., 2014). Caffeine shares similar Preprint. Under review.

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The Cultural Evolution of Science

Abstract: To appear in The Oxford Handbook of Cultural Evolution, edited by Jeremy Kendal, Rachel Kendal, and Jamshid Tehrani, Oxford University Press. Expected 2023.

Cited by 4 publications

References 40 publications

All Intelligence is Collective Intelligence

All Intelligence is Collective Intelligence

Faustian bargains: Short‐term and long‐term contingencies in phylogeny, ontogeny, and sociogeny

Nothing makes sense in deep learning, except in the light of evolution

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