Cody Moser: the adaptive landscape of cultural evolution
Jan 9, 2024
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In this episode, Razib Khan interviews Cody Moser, an evolutionary psychologist and cultural evolutionist at UC Merced. They discuss the connection between population size and innovation, the benefits of fragmentation for fostering innovation, the preference for parsimony in science, the use of agent-based modeling in behavioral economics, and the differences in education and skills of immigrant populations in the US. They also explore network topology, the importance of considering evolution beyond genes, and recommend books on modeling social behavior and social evolution.
Breaking up large populations into smaller units can foster innovation by allowing diverse information to flow and preventing conformity biases from dominating.
Agent-based modeling is a powerful tool for studying collective problem-solving, understanding network dynamics, and testing hypotheses in a controlled virtual environment.
Fostering diverse networks and maintaining opportunities for outsiders and diverse perspectives can contribute to collective problem-solving and innovation.
Deep dives
The Potions Task and Innovation
The podcast episode discusses a research paper on innovation and how it occurs in groups. The researchers used an agent-based model based on an experiment called the potions task. In this task, individuals start with a set of potions and can combine them to make new potions. The results show that innovation in groups is not simply a result of population scale and that larger populations do not necessarily innovate better. Instead, the study found that breaking up groups into smaller units fosters innovation by allowing diverse information to flow and preventing conformity biases from dominating. The findings also highlight the importance of individuals in central positions who bridge disparate parts of the network and drive innovation and inequality in the group. These insights have implications for understanding innovation in various contexts, including science, economics, and culture.
The Role of Agent-Based Modeling
Agent-based modeling is a computer simulation method used to study complex systems and interactions between individual agents. It allows researchers to specify the behaviors and interactions of agents and observe how these interactions lead to emergent patterns on the system level. In the context of the discussed research, agent-based modeling was used to simulate the potions task and study the effects of network structures and parameters on innovation. This approach enables researchers to explore different scenarios, manipulate network structures, and understand how various factors impact innovation outcomes. Agent-based modeling offers a powerful tool for studying collective problem-solving, understanding network dynamics, and testing hypotheses in a controlled and customizable virtual environment.
Implications for Innovation and Information Architectures
The podcast episode also explores broader implications of the research findings for innovation and information architectures. It highlights how breaking down larger networks into smaller interconnected units can promote innovation and prevent conformity biases from hindering progress. The episode references examples such as the Balkanization of Europe and the benefits of multidisciplinary researchers. It also raises concerns about the consequences of centralized information architectures and the potential loss of diverse and high-quality innovations due to reduced specialization. By understanding the dynamics of group innovation, the episode emphasizes the importance of fostering diverse networks and maintaining opportunities for outsiders and diverse perspectives to contribute to collective problem-solving and innovation.
The Importance of Outsiders in Innovation
Outsiders often bring fresh perspectives and innovative ideas because they are not constrained by existing biases or norms. For example, George Price, a physical chemist who became an evolutionary biologist after a car crash, created a new formalism for understanding selection. His outsider status allowed him to think outside the box and develop new concepts.
The Influence of Network Structure on Innovation
Network structure plays a crucial role in fostering innovation. Decentralized corporate referee networks, which have a core group highly connected to itself and a peripheral group with limited connections, tend to enable faster and higher quality innovation compared to highly connected networks. The existence of 'loser nodes'—individuals on the fringes of the network who seem uninvolved—becomes critical in generating innovative ideas as they are not subject to the same biases as the rest of the group.
On this episode of Unsupervised Learning, Razib talks to Cody Moser, co-author of a recent paper, Innovation-facilitating networks create inequality. Moser is an evolutionary psychologist and cultural evolutionist at UC Merced, where he is completing his doctorate. A previous guest on the podcast, Moser immediately digs deep into the abstruse and technical model that shows that more is not automatically better when it comes to innovation and discovery. First, he contrasts his results with the Tasmanian cultural evolution model outlined by Joe Henrich nearly 20 years ago. In short, Henrich showed that very small populations tend to lose cultural traits and skills over time. Going through a population bottleneck has a memetic as well as genetic effect. The converse scenario is one where a large population is able to retain and even accumulate more cultural traits and skills.
Moser’s main finding is that some fragmentation of these large populations may in fact foster innovation. On the evolutionary psychological scale, massive groups may tend toward conformity, and disrupting information flows may foster independence of thought. A significant immediate implication is that scholarly thought might benefit from separating into competing schools and departments where distinct groups can develop solutions collectively but retain enough independence to resist being drawn into broader irrational herd behavior. Moser’s results have broader implications for how businesses and corporations should operate, and perhaps quantify why nimble startups often outpace and defeat massive organizations despite the latter having almost infinite resources. Groupthink is powerful. Though small populations will be hit by skill loss with the death of keystone individuals, large populations may ossify, “locking in” regnant ideologies.
Razib also probes Moser about the rise of agent-based modeling and simulations in social science over the last 20 years, and how they have allowed scholars to circumvent the limitations of relying purely on college students to act as experiment subjects.
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