Japan

In the demographic transition, “modernizing” populations willingly choose to have fewer children, causing an otherwise unpredicted steadying and even decline of population. This is on ongoing source of confusion for evolutionary modellers. 
Part of what shapes reproductive behaviour are socially learned choices about how many children to have. Recent work used analytic and agent based models to compare socially learned reproductive strategies that traded off between chosen birth rate and strength of horizontal transmission of that choice (Wodarz et al 2020). It was claimed that reduced mortality favours those strategies that invested in higher horizontal transmission, even at the cost of lower birth rates. This dynamic was claimed as a possible explanation of the demographic transition. However, tacitly baked into the structure of these models was an increase in infant mortality which drove the results, invalidating the claims about the demographic transition, characterized by significantly reduced infant mortality. Using both analytic and agent based models explicitly accounting for infant mortality and adult mortality, I demonstrate that decreasing infant mortality favors the cultural transmission of high birth rate reproductive strategies, while decreased adult mortality favors high horizontal transmission strategies. As decreased infant mortality is a hallmark of the initial phase of the demographic transition, this would amplify the already expected population explosion caused by decreased infant mortality. It is, however, actually a dynamic that would have to be countered to produce the demographic transition. Other social learning dynamics are proposed that could explain the demographic transition.

Cultural Evolution Society 2021

Reproductive behavior and social transmission of reproductive behavior in changing contexts: modelling the dynamics of trade offs in relation to the demographic transition.

### 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)

### About the conference 
 
**Help-desk:** 
Please first visit [Help-desk](https://underline.io/events/120/help-desk) where you can find the instructions and manuals for exploring the conference site. 

**Plenary sessions:**
Five leading researchers give a live talk. It is like a TV show. The audience can submit a question from the chat window. Chat messages are immediately delivered to speakers and moderators.

**Q&A sessions (parallel):**
Each presenter first gives a 2-minute live lightning talk followed by an 8-minute live discussion with the audience. It uses Zoom.

**Discussion sessions:** 
The audience can approach presenters and discuss their research. The sessions are held in a dedicated room in Gathertown.
 
**Gathertown:**
It is a platform dedicated to live virtual interactions.
Gather Town is available for all participants 24 hours during the conference.
You can meet and talk with anybody at a lounge or at a table in Gathertown.
Further Gather Town instruction and how to use short keys please find in our [Help desk page](https://underline.io/events/120/help-desk). 

**Videos and slides:**

* Plenary sessions and the Q&A sessions will be recorded and uploaded after the sessions.
* All the videos and slides are available for 60 days after the conference but only to registered ones. 

**Text-based chatting with the speakers:** 
You can chat with any speaker in the Q&A and discussion sessions. A chat window is available on each speaker’s page, and you can leave your questions there. 

**Announcement to the other participants:**
If you want to make an announcement to the other participants (e.g., “Hey, let’s meet at the lounge in Gathertown from 9pm CEST!), please use twitter (#CESConf2021).



### 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:**
* Comments (verbal, text, etc.) that reinforce social structures of domination related to gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age, religion, nationality
* Sexual images in public spaces (physical or virtual)
* Deliberate intimidation, stalking, or following
* Harassing photography or recording (including sharing of presentation materials without consent)
* Sustained disruption of talks or other events (including “Zoombombing”)
* Inappropriate physical contact or unwelcome sexual attention
* Advocating for, or encouraging, any of the above behaviour 

&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

Registration fee

technical paper

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.

Silly rules enhance learning of compliance and enforcement behavior in artificial agents

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).

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

Commanded by culture: how decentralized cattle raids are organized

The demographic transition - how and why most populations are having fewer offspring despite resources increasing - remains a mystery. A shift from vertical transmission (learning from parents) to horizontal transmission (learning from peers) is deemed instrumental in the quick spread of low fertility values, but we lack an explanation for how such a shift occurs. Women’s personal advice networks are places where ideas about fertility practices are exchanged, therefore investigating ego networks is key to understanding this change. We expect that the expansion of markets and education into rural areas leads to shifts away from valuing kin relationships towards valuing non-kin, and from kin dominated networks to non-kin networks. We expect ego characteristics to influence these friendship dynamics. Here, using comprehensive demographic and relational data from 22 communities in rural Poland, we investigated how the characteristics of women’s personal networks change with ego-level and community-level attributes, and how network characteristics affect cultural transmission. We focused on structural features of networks, frequencies and intensities of interaction and bi-lateral social support, and closeness and emotional support. We found that women characterized by market integrated values (i.e. high education, non-farming occupation, status) have less kin and more unrelated friends in their networks, who they rely on for emotional support. Women characterized by traditional rural values rely on genetic kin and affines, for practical and financial support. These networks of different compositions, characterized by different inter-personal relationships, moderate information transmission and help explain the spread of low fertility values.

Premium content

Reproductive behavior and social transmission of reproductive behavior in changing contexts: modelling the dynamics of trade offs in relation to the demographic transition.

Downloads

Next from Cultural Evolution Society 2021

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

Similar lecture

Resource Characteristics and the Evolution of Sustainability

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES