Social Simulacra: Creating Populated Prototypes for Social Computing Systems (2208.04024v1)

Abstract: Social computing prototypes probe the social behaviors that may arise in an envisioned system design. This prototyping practice is currently limited to recruiting small groups of people. Unfortunately, many challenges do not arise until a system is populated at a larger scale. Can a designer understand how a social system might behave when populated, and make adjustments to the design before the system falls prey to such challenges? We introduce social simulacra, a prototyping technique that generates a breadth of realistic social interactions that may emerge when a social computing system is populated. Social simulacra take as input the designer's description of a community's design -- goal, rules, and member personas -- and produce as output an instance of that design with simulated behavior, including posts, replies, and anti-social behaviors. We demonstrate that social simulacra shift the behaviors that they generate appropriately in response to design changes, and that they enable exploration of "what if?" scenarios where community members or moderators intervene. To power social simulacra, we contribute techniques for prompting a LLM to generate thousands of distinct community members and their social interactions with each other; these techniques are enabled by the observation that LLMs' training data already includes a wide variety of positive and negative behavior on social media platforms. In evaluations, we show that participants are often unable to distinguish social simulacra from actual community behavior and that social computing designers successfully refine their social computing designs when using social simulacra.

• The paper introduces social simulacra, a novel method using GPT-3 to generate realistic social interactions for prototyping social computing systems.
• It validates the approach by demonstrating that generated interactions were misidentified as real in 41% of cases.
• The method enables designers to explore a broad spectrum of social dynamics and adverse scenarios during early design stages.

An Analysis of "Social Simulacra: Creating Populated Prototypes for Social Computing Systems"

The paper "Social Simulacra: Creating Populated Prototypes for Social Computing Systems" explores an innovative approach to prototyping social computing systems. Leveraging LLMs, particularly GPT-3, the authors propose a technique referred to as social simulacra, aimed at populating social computing prototypes with realistic social interactions. This work is positioned against the backdrop of challenges in designing social systems, which often become apparent only when these systems are scaled beyond small test groups.

Key Contributions

The primary contribution of this paper is the development of social simulacra designed to simulate interactions within a social system. These are generated based on the design elements such as community goals, rules, and member personas. The approach involves using GPT-3 to generate thousands of distinct community members and model their interactions, including socially disruptive behaviors. Two critical evaluations were conducted to validate this approach: one focusing on the technical competence of generating realistic simulations, and another on how these simulations facilitate social computing design.

Empirical Findings

The results from the evaluations indicate that the generated interactions are often indistinguishable from real community behaviors, with participants misidentifying generated content as real in 41% of cases on average. Moreover, the technique effectively enabled designers to reconsider their design decisions through guided exploration of various outcomes, including adverse scenarios typically unforeseen in initial design stages.

Implications and Future Work

This research extends the toolkit available for social computing design by providing a viable method for early prototyping of social dynamics before system deployment. It empowers designers to preemptively address potential social dynamics concerns, ranging from antisocial behaviors to norm establishment. The findings suggest that further iterations could refine this approach, extending its applicability beyond text-based interactions, particularly as multimodal LLMs continue to advance.

Conclusion

In summary, "Social Simulacra: Creating Populated Prototypes for Social Computing Systems" proposes a novel approach to anticipating and designing for the complex interactions inherent in social computing systems. While it harnesses the generative capabilities of GPT-3 effectively, its success underscores the potential of AI-driven approaches to advance proactive social computing design approaches. Despite not predicting exact future events, it facilitates iterative design by uncovering a broader spectrum of potential social interactions, providing a new paradigm in system prototyping.