Social Simulacra: Creating Populated Prototypes for Social Computing Systems

Title

Social Simulacra: Creating Populated Prototypes for Social Computing Systems

Creator(s)

Park, Joon Sung

Popowski, Lindsay

Cai, Carrie

Morris, Meredith Ringel

Liang, Percy

Bernstein, Michael S.

Date

2022

Is Part Of

Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology

In Series

UIST '22

Pages

1–18

Language

eng

Publisher

Association for Computing Machinery

Place Published

New York, NY, USA

ISBN

978-1-4503-9320-1

DOI

https://doi.org/10.1145/3526113.3545616

Zotero BiDiAr

https://www.zotero.org/groups/5293298/bidiar/items/77ZWUIHB/item-list

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 large language model to generate thousands of distinct community members and their social interactions with each other; these techniques are enabled by the observation that large language models’ 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.