Japan

Societal threats are ubiquitous. To overcome collective threats, cooperation is essential. However, not all societies are equally successful in cooperating under threat. With three agent-based models based on an Evolutionary Game Theoretic framework, we argue that the strength of social norms, or cultural tightness-looseness, plays a critical role in whether and how a society can effectively adopt cooperative norms and fight against an evolving threat. In the first model, we show that under a constantly increasing threat, a tight society with more conformity pressures adopts cooperative norms faster than a loose society. In the second model, we show that when mass cooperation can slow down the escalation of threat, a tight society will ultimately have a lower threat than a loose society in most cases. In the third model, we show that even after the threat has gradually decreased, a tight society that has evolved cooperative norms can maintain a highly cooperative equi
librium for a long time. These findings are consistent with empirical research on the COVID-19 pandemic, which has shown that tighter societies have fewer cases and deaths per million as compared with loose societies in the COVID-19 pandemic threat (Gelfand et al., 2021).

Cultural Evolution Society 2021

Cooperative Norms and the Growth of Threat: Differences Across Tight and Loose Cultures

### Conference Welcome

Welcome to the third Cultural Evolution Society Conference, CES2021. In the conference, which will be held from June 9-11, 2021, about 180 researchers from East Asia, Oceania, Middle East, Europe, North and South America give presentations on their latest research advancing the evolutionary understanding of human and nonhuman animal culture.

### Schedule and Program

[![](https://assets.underline.io/uploads/markdown_image/1/image/1ef06ffd05f80957b4a5b685f63ee3ba.png)](https://www.chain.hokudai.ac.jp/CES2020/wp-content/uploads/2021/06/fullprogram.pdf)
[![](https://assets.underline.io/uploads/markdown_image/1/image/12285eb22580949635121d15d5b46325.png)](https://www.chain.hokudai.ac.jp/CES2020/wp-content/uploads/2021/06/abstracts.pdf)

**Program-at-glance**

![](https://assets.underline.io/uploads/markdown_image/1/image/c0d0d4c8fcdad49d12631574fd966ae8.png)

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### Code of conduct

See the full details of [the code of conduct](https://www.chain.hokudai.ac.jp/CES2020/code-of-conduct/).
&nbsp; 
 CES is dedicated to providing a welcoming and harassment-free conference experience for everyone, regardless of gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age, religion, nationality, or the presence of or care for dependents. 
We do not tolerate harassment of conference participants in any form. Conference participants violating these rules may be sanctioned or expelled from the conference at the discretion of the conference organizers.

&nbsp;
**Harassment can occur in-person or virtually, and includes, but is not limited to:**
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&nbsp;
 **Inclusiveness Committee**:
&nbsp;
 To facilitate implementing the code of conduct and foster a climate of inclusion, we have developed an inclusiveness committee. Below is their contact information:
 **Hiromi Matsumae**:	matsumae.hiromi.g@tokai.ac.jp
 **Patrick Savage**:	psavage@sfc.keio.ac.jp
 **Sarah Mathew**:	Sarah.Mathew@asu.edu
 **Gmail:** inclusivenessCES2021@gmail.com
 See [more details about reporting the harassment.](https://www.chain.hokudai.ac.jp/CES2020/code-of-conduct/)

### Organizing committee

**Honorary Conference Organizer:** [Kenichi Aoki (Meiji University)](https://sites.google.com/site/kenichi1aoki/home) 

**Chair:** [Masanori Takezawa (Hokkaido University)](https://lalalarakko.github.io)

**Scientific Committee:** [Kenichi Aoki (Meiji University)](https://sites.google.com/site/kenichi1aoki/home), [Yasuo Ihara (the University of Tokyo)](https://researchmap.jp/read0209180?lang=en), [Hiromi Matsumae (Tokai University)](https://sites.google.com/site/hiromimatsumae/eng), [Naoko Matsumoto (Okayama University)](https://researchmap.jp/nmatsumoto_okayama-u), [Masako Myowa (Kyoto University)](https://myowa.educ.kyoto-u.ac.jp/en), [Hisashi Nakao (Nanzan University)](https://www.hisashinakao.com/), [Patrick E. Savage (Keio University)](https://sites.google.com/view/comp-music-lab/home)

**Local Organizing Committee:** [Yutaka Horita (Teikyo University)](https://sites.google.com/site/yhoritapage/), [Masanori Takezawa (Hokkaido University)](https://lalalarakko.github.io/), [Ayaka Takimoto (Hokkaido University)](https://lynx.let.hokudai.ac.jp/~takimoto/), [Kohei Tamura (Tohoku University)](https://researchmap.jp/7000017854)

**Inclusiveness Committee:** [Sarah Mathew (Arizona State University)](https://www.sarahmathew.net/), [Hiromi Matsumae (Tokai University)](https://sites.google.com/site/hiromimatsumae/eng), [Patrick E. Savage (Keio University)](https://sites.google.com/view/comp-music-lab/home)


![](https://assets.underline.io/uploads/markdown_image/1/image/967110a00d200a6b20d6f4fa6556ba13.png)

For attending the conference, you first need to register and pay fees from [the link here](https://www.chain.hokudai.ac.jp/CES2020/registration/). After the payment of the registration fees, you will receive a ticket, which is necessary for accessing the conference site. After the payment of the registration fees, you will receive a ticket, which is necessary for accessing the conference site

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technical paper

Despite advances in the study of non-human animal tool behaviour, the sources of tool-use behaviours are still debated. Often, different social and individual learning variants are assumed to play a role in behaviour acquisition. However, without the use of experimental controls, it is impossible to pinpoint which type of learning is responsible for the behaviour in question. We argue that this issue should be addressed with recourse to experimental approaches rooted in classic ethology. Building on the pioneering work by the founders of Ethology (e.g., Nikolaas Tinberg and Konrad Lorenz), we suggest that baseline experiments must be carried out before conclusions can be drawn on the role of any type of learning mechanism in the acquisition of behaviours. For example, we carried out a baseline study on the mechanisms behind nut-cracking in orangutans. We tested 12 naïve, captive, unenculturated orangutans, across two testing institutions (Leipzig: Pongo abelii; Mage=20.3; age range=10-34; 4F; Zürich: 6 Pongo abelii and two Pongo pygmaeus; Mage=14; age range =2-30; 5F). The subjects were provided with the materials necessary for nut-cracking, and no demonstrations and/or training beforehand. Out of the twelve orangutans, at least four individuals, one from Leipzig and three from Zürich, spontaneously expressed nut-cracking with a wooden hammer in the baseline. These results suggest that the behavioural form of nut-cracking can emerge in orangutans through individual learning and non-copying social learning mechanisms.

What would classic Ethologists do? Examining the source of nut-cracking with naïve, unenculturated orangutans (Pongo abelii & Pongo pygmaeus)

Customary tool use by wild tufted capuchin monkeys seems to constitute behavioral traditions, i.e., to be individually acquired by socially biased learning. Social influences apparently involve attentional/motivational effects (“stimulus enhancement”), rather than imitative learning. Persistent manipulation increases opportunities for trial-and-error learning. This process can take a long time, but once proficiently successful in a given context, the use of a type of tool can be quickly generalized to new problems. The use of percussive stone tools is common in savanna populations of tufted capuchins, and where tool material is abundant, as in Serra da Capivara, can be quite diversified. This is also the only wild population where customary use of probe tools has been observed. When exposed to molasses-filled problem-boxes, after brief (unsuccessful) attempts to break them with stone “hammers”, several males quickly succeeded in using twig probes to access the molasses through the top holes. Almost three years later, when exposed to a different Plexiglas problem-box, they promptly brought some twigs. In an experiment on the innovation and diffusion of probe tools’ use in a semi-free group, after the slow and partial diffusion of probing a molasses’ problem-box, the successful individuals quickly succeeded in extracting seeds from another box, even though the probing technique involved was different (pushing). Afterwards, a distinct sort of problem-box also elicited probing attempts. Tool use generalization can facilitate the establishing of behavioral traditions not only by increasing direct individual rewards, but also by enhancing the opportunities for socially biased learning by naïve, tolerated observers.

Tool use generalization and behavioral traditions in tufted capuchin monkeys (Sapajus sp)

Humans live in complex niches where survival and reproduction are conditional on the acquisition of knowledge. The rate at which individual knowledge is acquired depends on many factors, both physical and behavioral, such as the frequency of learning opportunities. Here, we developed a series of Bayesian latent-knowledge models to estimate individual knowledge, and to infer the influence of age, as well as the physical and social environment on such knowledge. We fit these novel latent-knowledge models using a set of interviews with 94 children of different ages (4-26 years of age) on the Island of Pemba, Zanzibar, focusing on their knowledge of the local environment, such as animals and plants, in a population in which many children forage for natural resources. Preliminary results indicate that—compared to previous studies—our latent-knowledge models facilitate a far clearer picture of the intrinsic and extrinsic dynamics underpinning individual knowledge acquisition, and the life history of cultural knowledge more broadly.

Development of knowledge of the environment in a population of child part-time foragers

The twentieth century saw two modes of thinking spread through biological systematics: population thinking and tree thinking. Many archaeologists investigating ancient technologies have adopted the population thinking approach for archaeological systematics, as evident from the rise of work on quantifiable, attribute-based patterns of diversity in studies of past technological systems, much of it driven by application of geometric morphometrics. Yet, tree thinking, as one of the central concepts of phylogenetic biology, remains rare in the archaeological sciences. We review some of the obstacles that have impeded the uptake of this concept by archaeologists. Some of these are conceptual, but we identify one specific methodological obstacle: the prevailing use of discrete character traits in phylogenetic analysis in many fields. This is a major challenge for archaeological applications where standardised trait-analytical protocols are scarce. To address this challenge, we present a case study that demonstrates a Bayesian framework for inferring phylogenies using continuous traits derived from artefact shape coefficients obtained via outline based geometric morphometrics. We use a previously published sample of Late Neolithic/Early Bronze Age arrowheads from Northwestern Europe to demonstrate the efficacy and accessibility of our approach. We also sketch out the potential for phylogenetic comparative methods to address archaeological questions.

Bayesian inference of technological phylogenies using continuous traits from the archaeological record

The fertility decline associated with economic development has been attributed to a host of inter-related causes, including the rising costs of children with industrialisation, and shifts in family structure. One hypothesis is that kin may impart more pro-natal information within their net-works than non-kin, and that this effect may be exacerbated in networks with high kin-density where greater social conformity would be expected. In this study, we tested these ideas using large personal networks (25 alters) collected from a sample of Dutch women (N = 706). Kin (parents) did not exert more social pressure to have children than non-kin, nor were dense networks associated with greater pressure. Women also reported talking to friends about having children to a greater extent than kin, although here greater kin-density in the network increased the likelihood of women reporting they could talk to kin about having children. Both consanguineal and affinal kin could be asked to help with childcare to a greater extent than friends and other non-kin. Overall, there was no evidence that kin are more likely to offer pro-natal information than non-kin, but they are considered a better source of support in terms of child care.

Do data from large personal networks support cultural evolutionary ideas about kin and fertility?

In indigenous societies, people are categorized into several cultural groups, so-called clans. The clan attribution governs the mating and descent relationships by certain rules. Such rules form various kinship structures. Anthropologists have revealed several classes of kinship structures and corresponding cultural characteristics. Here, we introduce an agent-based model of indigenous societies adopting the multi-level evolution of families and societies. A family is a unit of dynamics and society is an ensemble of families within which the interaction of families can take place. We assign each family a trait and a mate preference as an optimal trait of the bridegroom. Marriage takes place according to the mating preference. Families with similar traits cooperate with each other as well as with mates, whereas those with similar preferences compete for mates. The population of families is increased by such cooperation and declined by competition. With numerical simulations, families were found to form clusters in trait-preference space under a certain condition. Families in the same cluster are united by cultural similarity. Marriage occurs only between families from different clusters. Such clusters can be regarded as clans. Kinship structures emerge as the marriage and descent relationships of such clans. The emergent structures depend on environmental conditions such as the necessity of cooperation and strength of mating competition. Finally, by analyzing the global ethnographic database, called Standard Cross-Cultural Sample, we empirically verify the theoretical results on the environmental dependence of kinship structures.

Evolution of kinship structures driven by marriage tie and competition

I aim to understand variation in an important and relatively historically novel socio-political institution, the Chinese lineage. Notably, extensive geographic variation exists in the historical prominence and relevance of lineages. Using ethnographic and historical-economic evidence, I construct a theory explaining lineages as risk-pooling institutions which provide lineage members with access to land. More so, variation in regional demand for risk-pooling and/or access to land likely stems from well-studied rice-wheat agro-economic differences. I test this hypothesis by examining whether lineage activity is associated with landholding size, precipitation predictability, and historically documented precipitation disasters.

The Evolution of Chinese LIneages

We use a Cultural Evolution model to study the evolution of organizational culture. The account of culture that emerges is one of adaptation to the environment via a process of learning, consistent with the canon of Schein (2010). This adaptation is achieved via several levers. The first two levers are “technology” and “learning behaviours”. The organization or group can acquire technology, broadly defined, by resorting to: i) individual learning, which is costly but generates well-adapted technology, or ii) social learning, which is cheaper (because relies on the organization’s technological tradition) but may become outdated. The third lever is “cognition about the environment”. Our model incorporates signals/cues from the environment that informs the members’ decision between innovation (individual learning) versus inherited technology (social learning). Organizations vary in their capacity to interpret and act-on these signals. Organizations excelling at this, are better at inferring environmental changes and at changing its member’s beliefs and cognition. The fourth and fifth levers are “cooperation” and “norms of social interaction”. Members of the organization have the capacity to exert costly effort to help other members learn socially. Organizations can affect this behaviour by putting in place norms about cooperation, whose strength are reflected in the member’s payoffs (which includes a weight to the benefit generated to others). Our key finding is the trade-off that emerges between innovation and cooperation, consistent with the tight-loose perspective (Gelfand et al, 2011). Also, we find that i) improving cognition about the environmental increases innovation, ii) increasing the weight placed on norms of cooperation, increases cooperation, iii) whether to improve cognition or place higher weight on cooperative norms depends on environmental uncertainty. We map our model and results to theories of culture in the organizations and economics literature.

The Evolution of Organizational Culture

People’s attitudes towards social norms play a crucial role in understanding group behavior. Norm psychology accounts focus on processes of norm internalization that influence people’s norm following attitudes but pay considerably less attention to social identity and group identification processes (Chudek & Henrich, 2011; Bicchieri, 2016; Gelfand, 2018; Tomasello, 2014). Social identity theory studies group identity but works with a relatively thin and instrumental notion of social norms (Hogg 2016; Scheepers & Ellemers 2019; Turner & Reynolds, 2012). We argue that to understand both sets of phenomena best, it is important to integrate the insights of both approaches. 

Throughout the lecture, we highlight tensions between the two approaches and conflicting observations and sketch the contours of an integrated account. We focus on processes of internalization and suggest that internalization of social norms and internalization of social identity can be seen as two complementary processes, both playing a role in norm-governed behavior. We conclude with some observations on how a twofold account may contribute to studying the evolution of human groups and understanding behavior and social norms in complex societies. Multi-level societies are characterized by their members’ ability to coordinate and negotiate between norm systems, issues that are best understood by combining the insights of norm psychology and social identity theory (Bird et al., 2019; Townsend, 2018). Moreover, we suggest that to better understand and measure normative behavior in complex societies with multiple roles, identities, allegiances, and subgroups, one must also consider the negotiation of social identities (Aureli & Schino, 2019).

The Interplay of Social Identity and Norm Psychology in the Evolution of Human Groups

How do societies learn and maintain social norms? Here we use multi-agent reinforcement learning to investigate the learning dynamics of enforcement and compliance behaviors. Artificial agents populate a foraging environment and need to learn to avoid a poisonous berry. Agents learn to avoid eating poisonous berries better when doing so is taboo, meaning the behavior is punished by other agents. The taboo helps overcome a credit-assignment problem in discovering delayed health effects. By probing what individual agents have learned, we demonstrate that normative behavior is socially interdependent. Learning rule compliance builds upon other agents having learned rule enforcement beforehand. Critically, introducing an additional taboo, which results in punishment for eating a harmless berry, further improves overall returns. This "silly rule" counterintuitively has a positive effect because it gives agents more practice in learning rule enforcement. Our results show the benefits of coordinating effective normative infrastructure (classification of disapproved behaviors and effective third-party punishment of agents who engage in disapproved behaviors) and demonstrate the role of third-party punishment in supporting social learning. Our results also highlight the benefit of employing a computational model that allows open-ended learning for the study of normative behavior and organization.

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Cooperative Norms and the Growth of Threat: Differences Across Tight and Loose Cultures

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What would classic Ethologists do? Examining the source of nut-cracking with naïve, unenculturated orangutans (Pongo abelii & Pongo pygmaeus)

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