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Unveiling Prejudice: How Bias Evolves and Persists in Groups

Lena Kashyap in Culture & Society June 2025 • 4 min read.

"New research explores the roots of prejudice and its surprising connection to cooperation, offering insights for building more inclusive communities."

Prejudice, a deeply ingrained human attitude, continues to fuel division and conflict across societies. From everyday acts of discrimination to large-scale extremism, the consequences of prejudice are far-reaching. Understanding the origins and dynamics of prejudice is crucial for building more inclusive and harmonious communities.

While in-group favoritism – the tendency to favor members of one's own group – has been extensively studied, out-group prejudice, which involves negative attitudes toward those outside the group, has received less attention. But what if prejudice is not just an individual attitude, but a group-defining characteristic? Recent research explores this question, introducing the concept of the 'prejudicial group' and investigating its evolution through computational simulations.

The study, published in Scientific Reports, uses a novel approach to model how prejudicial attitudes spread within groups and how these attitudes interact with cooperation. The findings reveal a surprising link between prejudice and cooperation, and identify key factors that can either promote or mitigate prejudice within a population. This article delves into the key findings of this research, offering insights into the complex interplay of prejudice, cooperation, and diversity.

The Rise of the Prejudicial Group: How Does It Form?

Digital illustration of interconnected nodes showing diverse and cooperative communities.

The researchers define a 'prejudicial group' as a group whose members share a common prejudicial attitude toward an out-group. This attitude acts as a 'phenotypic tag,' allowing individuals to identify and connect with others who share their bias. Using computer simulations, the study explores how these prejudicial groups emerge and evolve within a larger population. The simulation involves agents interacting through indirect reciprocity, where individuals are more likely to help those who have helped others in the past.

The simulations revealed that cooperation and prejudice can co-evolve. Individuals tend to direct their cooperation toward members of their in-group, while harboring prejudice toward the out-group. This dynamic can create echo chambers where in-group preference reinforces out-group bias. The study also examined how factors like out-group interaction and global learning (where individuals learn from the entire population rather than just their in-group) impact the co-evolution of cooperation and prejudice.

Here are some key parameters of the simulation:

Prejudice Level: The degree of bias an agent holds toward the out-group.
In-group Interaction: The probability that an agent will interact with another member of their in-group.
Global Learning: The extent to which an agent learns from the entire population versus just their in-group.
Number of Sub-Populations: The diversity of traits within the overall population.

The study found that diversity, driven by out-group interaction, out-group learning, and the number of sub-populations, plays a crucial role in shaping the levels of cooperation and prejudice. Populations with greater in-group interaction showed both higher cooperation and higher prejudice. Global learning, on the other hand, promoted cooperation and reduced prejudice. These results suggest that while in-group preference can foster cooperation, it can also lead to increased bias if not balanced by exposure to diverse perspectives.

The Enduring Challenge of Overcoming Prejudice

This research demonstrates that prejudice is not necessarily dependent on sophisticated human cognition. It can easily manifest in simple agents with limited intelligence, with potential implications for future autonomous systems and human-machine interaction. This finding highlights the importance of designing AI systems that promote fairness and avoid perpetuating biases. The study also offers valuable lessons for addressing prejudice in human societies. By fostering diversity, encouraging out-group interaction, and promoting global learning, we can create more inclusive environments that challenge bias and promote cooperation.

About this Article -

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Everything You Need To Know

What defines a 'prejudicial group', and how does this concept help in understanding the dynamics of prejudice?

A 'prejudicial group' is defined as a group whose members share a common prejudicial attitude toward an out-group. This shared attitude acts as a 'phenotypic tag,' enabling individuals to identify and connect with others who share their bias. By modeling prejudicial groups, the research explores how these groups emerge and evolve, shedding light on how prejudice spreads within groups and how it interacts with cooperation. The concept is crucial for understanding prejudice's roots as a group-defining characteristic rather than just an individual attitude.

How does the co-evolution of cooperation and prejudice play out within the simulated environment, and what are the implications?

In the simulations, cooperation and prejudice co-evolve. Individuals tend to cooperate more within their in-group while simultaneously harboring prejudice toward the out-group. This dynamic creates echo chambers where in-group preference reinforces out-group bias. The implications of this co-evolution are significant. It demonstrates that cooperation, a positive social behavior, can paradoxically fuel prejudice if not balanced by exposure to diverse perspectives and interactions. This suggests that simply fostering cooperation isn't enough; it needs to be coupled with strategies to challenge in-group bias and promote understanding of out-groups.

What specific parameters were used in the simulations to model prejudice and cooperation, and how did they influence the results?

The key parameters included 'Prejudice Level,' 'In-group Interaction,' 'Global Learning,' and 'Number of Sub-Populations.' 'Prejudice Level' represented the degree of bias agents held toward the out-group. 'In-group Interaction' determined the probability of agents interacting with their in-group members, which was found to foster both higher cooperation and higher prejudice. 'Global Learning,' where agents learned from the entire population, promoted cooperation and reduced prejudice. The 'Number of Sub-Populations' indicated diversity, and it was found to influence the levels of cooperation and prejudice. These parameters show how the interplay of these factors impacts the emergence and persistence of prejudice and cooperation.

How does diversity impact the levels of cooperation and prejudice, according to the research, and what strategies can be employed to promote more inclusive communities?

Diversity, driven by out-group interaction, out-group learning, and a higher 'Number of Sub-Populations', plays a crucial role in shaping the levels of cooperation and prejudice. Populations with greater in-group interaction showed higher cooperation, but also higher prejudice. Global learning, however, promoted cooperation while reducing prejudice. To promote inclusive communities, it's essential to foster diversity through encouraging out-group interaction and learning, and promoting global learning rather than limiting learning to within the in-group.

What are the broader implications of this research, particularly concerning AI systems and human-machine interaction?

The research demonstrates that prejudice can arise even in simple agents with limited intelligence, with potential implications for future autonomous systems and human-machine interaction. This finding highlights the importance of designing AI systems that promote fairness and avoid perpetuating biases. The research suggests that AI systems should be developed in a way that encourages global learning and out-group interaction, mirroring strategies that can be employed in human societies. This helps to create AI systems that foster inclusivity and challenge biases, contributing to more harmonious interactions between humans and machines.