Japan

Objective: It has been argued that teaching promotes the accurate transmission of cultural traits and eventually leads to cumulative cultural evolution. However, several previous studies have provided a disclaim to the argument. In the current study, we constructed a computational model of social learning under teaching to demonstrate that teaching plays a causal role in the emergence of cumulative cultural evolution. We also examined the conditions teaching promotes cumulative cultural evolution.

Methods: Processes of acquiring complex technologies were modeled as a search problem in a complex random network with multiple goals. The reinforcement learning agents searched for better goals and taught their experiences to agents in the next generation. In the simulation, we implemented a trade-off between individual learning (an opportunity for innovation) and teaching (the processes of faithful transmission): long periods of teaching are required for accurate transmission of advanced technologies. However, the more extended time society invests in teaching, the less time is left for innovating advanced technologies by the younger generation.

Results: Agent-based simulations revealed that the task difficulty moderates the positive influences of teaching on the cumulative cultural evolution: when the task was too difficult and advanced technologies could not be acquired by individual learning within a lifetime, spending a longer time on teaching, even at the expense of time for innovation, contributed to cumulative cultural evolution. On the contrary, the easier the task, the more time spent on innovation instead of teaching contributed to improving performance.

Cultural Evolution Society 2021

Conditions under which long-term teaching contributes to the cumulative cultural evolution of technology

### 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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* Sustained disruption of talks or other events (including “Zoombombing”)
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* 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

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.

Female friendship and the horizontal transmission of low fertility values

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.

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

Based on Life History (LH) Theory exposure to environmental unpredictability/harshness is expected to promote a fast LH strategy, shorted time horizon and high levels of risk behavior. Studies testing these predictions mainly evaluated adult samples. We evaluated the predictive power of family unpredictability, harshness and demographic characteristics of low SES Brazilian young adolescents on reproductive milestones, future horizon and risk behavior. Participants (n = 203 M = 12,7 years old) answered to the scales Family Unpredictability during Childhood and Unpredictability Beliefs, questions about the expected age to attain reproductive milestones, time horizon, life expectation, risk behavior and demographics. Results indicated that different dimensions of unpredictability significantly predicted risk behavior, expected age of first sexual intercourse and child, subjective life expectation and future horizon. Demographic characteristics (e.g. mother's age at childbirth) enhanced predictive power in some models. Harshness did not predict any of the criterion variables. Overall, our findings support LH Theory predictions about the role of unpredictability in promoting fast LH strategy, shortened life horizon and risk and demonstrate that the relations among these variables are established and can be accessed from early adolescence. Social and theoretical implications of these findings are discussed.

Can environmental unpredictability predict life history strategy of young Brazilian adolescents?

In this abstract, by focusing on the functions of the hippocampus and basal ganglia of human brain, we propose that the highly conserved functions of subcortical structures contribute to establish our ‘linguistic niche’ in the process of human evolution. To be more specific, the hippocampus is assumed to store information of when, where and what with multisensory input (Tsao et al., 2018). On the other hand, the basal ganglia are the neural basis for general functions in both motor and language domains (Shi & Zhang, 2020). The coordination of these brain areas (Szczepanski & Saalmann, 2013) provides the neural basis of lexical conception and syntactic operation leading to displacement, which is one of the design features of language enabling humans to talk about things and situations outside from here and now (Hockett, 1960). As a result, in the situation when a group of humans were hunting, and one member discovered a dead deinotherium, in order to exploit it, he must exchange information with other members to persuade them to cooperate. Such cooperation as a high-end scavenging made human ancestors distinguished from bone-crunching garhi and habilis. This information exchange requires the feature of displacement of language, supported by the functions of the hippocampus and basal ganglia (Zhang & Shi, in press). 

Selected References 

Shi, E. R. & Zhang, Q. (2020). A domain-general perspective on the role of the basal ganglia in language and music: Benefits of music therapy for the treatment of aphasia. Brain and Language, (206) 104811. Doi: 10.1016/j.bandl.2020.104811.
Szczepanski, S. M., & Saalmann, Y. B. (2013). Human fronto-parietal and parieto-hippocampal pathways represent behavioral priorities in multiple spatial reference frames. Bioarchitecture, 3(5), 147-152.
Tsao, A., Sugar, J., Lu, L., Wang, C., Knierim, J. J., Moser, M. B., & Moser, E. I. (2018). Integrating time from experience in the lateral entorhinal cortex. Nature, 561(7721), 57-62.

Displacement and Linguistic Niche

In a laboratory experiment with human participants, we investigated the evolution of category systems in terms of their optimality and complexity over time. We contrast two evolutionary scenarios: one where category systems are passed between individuals in a serial transmission chain and another where the systems are developed within single learners. Crucially, in both cases we control for the evolutionary age of the category systems, allowing systems developed by individual learners to be iterated the same number of times as those developed among several learners (see for discussion Miton & Charbonneau, 2018). We find that both evolutionary regimes discover a similar number of unique category systems, but individual learning discovers and maintains more complex systems than cultural transmission does. Cultural transmission, on the other hand, discovers simpler systems that are optimized to be easily learnable, and therefore transmitted among different individuals with high fidelity. This result runs opposite to our field's received notion that artifact complexity is a hallmark of cumulative cultural evolution (e.g. Boyd & Richerson, 1996). There are deep mathematical similarities between evolution and learning (Suchow et al, 2017) that allow us to analyze individual learning and cultural evolution within one framework and refine our understanding of the diagnostic differences between the two.

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