Convergence of knowledge in a cultural evolution model with population structure, random social learning and credibility biases

25 pagesUnderstanding how knowledge is created and propagates within groups is crucial to explain how human populations have evolved through time. Anthropologists have relied on different theoretical models to address this question. In this work, we introduce a mathematically oriented model that shares properties with individual based approaches, inhomogeneous Markov chains and learning algorithms, such as those introduced in [F. Cucker, S. Smale, Bull. Amer. Math. Soc, 39 (1), 2002] and [F. Cucker, S. Smale and D. X Zhou, Found. Comput. Math., 2004]. After deriving the model, we study some of its mathematical properties, and establish theoretical and quantitative results in a simplified case. Finally, we run numerical simulations to illustrate some properties of the model

Convergence of knowledge in a cultural evolution model with population structure, random social learning and credibility biases

Understanding how knowledge is created and propagates within groups is crucial to explain how human populations have evolved through time. Anthropologists have relied on different theoretical models to address this question. In this work, we introduce a mathematically oriented model that shares properties with individual based approaches, inhomogeneous Markov chains and learning algorithms, such as those introduced in [F. Cucker, S. Smale, Bull. Amer. Math. Soc., 39 (1), 2002] and [F. Cucker, S. Smale and D.~X Zhou, Found. Comput. Math., 2004]. After deriving the model, we study some of its mathematical properties, and establish theoretical and quantitative results in a simplified case. Finally, we run numerical simulations to illustrate some properties of the model

Convergence of knowledge in a cultural evolution model with population structure, random social learning and credibility biases

Understanding how knowledge is created and propagates within groups is crucial to explain how human populations have evolved through time. Anthropologists have relied on different theoretical models to address this question. In this work, we introduce a mathematically oriented model that shares properties with individual based approaches, inhomogeneous Markov chains and learning algorithms, such as those introduced in [F. Cucker, S. Smale, Bull. Amer. Math. Soc., 39 (1), 2002] and [F. Cucker, S. Smale and D.~X Zhou, Found. Comput. Math., 2004]. After deriving the model, we study some of its mathematical properties, and establish theoretical and quantitative results in a simplified case. Finally, we run numerical simulations to illustrate some properties of the model

Mechanisms of cumulative cultural evolution

Le succès remarquable -écologique et démographique- de l'espèce humaine est largement attribué à notre capacité pour la culture cumulative, i.e. l'accumulation d'innovations culturelles à travers le temps. L'absence ou du moins la rareté de la culture cumulative chez les autres animaux a conduit à de nombreuses spéculations à propos des facteurs nécessaires à son émergence. La culture cumulative dépend étroitement de processus permettant de générer de l'information, et de mécanismes permettant à cette information d'être fidèlement transmise entre les individus. A l'aide d'une approche expérimentale basée sur l'utilisation de jeux sur ordinateur, nous montrons que la capacité d'imitation des patrons comportementaux peut grandement faciliter la fidélité de transmission des informations culturelles. De même, une grande taille de population contribue à la stabilisation des informations culturelles, particulièrement dans le cas d'informations complexes. Cependant, la culture cumulative requiert également la production d'innovations qui ne peut résulter de ces seuls facteurs. D'un point de vue théorique, les innovations sont généralement plus coûteuses à produire qu'à copier, de sorte que la sélection peut difficilement opérer au profit des innovateurs. Nos résultats nous permettent cependant d'avancer que l'émergence d'objets culturels technologiquement opaques pourrait permettre aux innovateurs de bénéficier plus largement de leurs innovations. L'émergence de l'opacité technologique pourrait ainsi constituer un pivot dans l'évolution de la culture cumulative, permettant de favoriser à la fois l'innovation et les mécanismes fidèles de transmission d'information. Les capacités à hiérarchiser et planifier ses actions étant essentiel à la production d'objets culturels technologiquement opaques, il est possible que l'absence apparente de culture cumulative chez les animaux non-humains soit due à un moindre développement de ces capacités cognitives. Finalement nous proposons que la complexité de la culture humaine repose sur quatre facteurs principaux : capacité à hiérarchiser et planifier ses actions, capacité à imiter, collaboration interindividuelle et grande taille de population.The remarkable success – both ecological and demographic- of the human species is widely attributed to our capability for cumulative culture, i.e. the accumulation of innovations over time. The lack or at least the rarity of cumulative culture in non-human animals has led to much speculation about factors enabling its emergence. Cumulative culture strongly depends on processes allowing generating information, and mechanisms allowing information to be efficiently transmitted between individuals. Using a computer-based experimental approach, we show that process-copying ability improves the fidelity of cultural information transmission. Also, population size contributes to the stability of cultural information, especially for complex information. However, cumulative culture also requires the creation of new innovations, which cannot be the outcome of these factors. From a theoretical point of view, innovations are generally costlier to produce than to copy, so that selection hardly favours innovators. From our results, we propose that the emergence of technologically opaque cultural traits may allow innovators to more widely benefit from their innovations. Thus, the emergence of technological opacity could be pivotal in the rise of cumulative culture, allowing favouring innovation and faithful copying mechanisms. Because the ability to plan actions in a hierarchical way is pivotal to produce technologically opaque cultural artefacts, the lack of cumulative culture in non-human animals could be due to limitations of these cognitive skills. Finally, we propose that human cultural complexity depends on four main factors: the ability to plan actions in a hierarchical way, the ability to process-copy, inter-individual collaboration and large population size

Human cumulative culture and the exploitation of natural phenomena

Cumulative cultural evolution (CCE)—defined as the process by which beneficial modifications are culturally transmitted and progressively accumulated over time—has long been argued to underlie the unparalleled diversity and complexity of human culture. In this paper, I argue that not just any kind of cultural accumulation will give rise to human-like culture. Rather, I suggest that human CCE depends on the gradual exploitation of natural phenomena, which are features of our environment that, through the laws of physics, chemistry or biology, generate reliable effects which can be exploited for a purpose. I argue that CCE comprises two distinct processes: optimizing cultural traits that exploit a given set of natural phenomena (Type I CCE) and expanding the set of natural phenomena we exploit (Type II CCE). I argue that the most critical features of human CCE, including its open-ended dynamic, stems from Type II CCE. Throughout the paper, I contrast the two processes and discuss their respective socio-cognitive requirements

Technical reasoning alone does not take humans this far

International audienceAlthough we see much utility in Osiurak and Reynaud's in-depth discussion on the role of what they term technical reasoning in cumulative culture, we argue that they neglect the time and energy costs that individuals would have to face to acquire skills in the absence of specific socio-cognitive abilities