Crowd Memory: Learning in the Collective

TL;DR

This paper investigates how crowds can learn and remember over time in human computation tasks, demonstrating that crowd agents can pass strategies across worker generations despite high turnover, which has implications for designing adaptive crowd algorithms.

Contribution

It introduces the concept of crowd memory and learning, showing that crowds can recall and transfer information over time, challenging previous assumptions of inexperience.

Findings

Crowd can recall information over time.

Crowd agents can learn and pass strategies across worker generations.

Crowd memory can be utilized in system design.

Abstract

Crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task. These assumptions fundamentally limit the types of tasks that systems based on such algorithms can handle. This paper explores how the crowd learns and remembers over time in the context of human computation, and how more realistic assumptions of worker experience may be used when designing new systems. We first demonstrate that the crowd can recall information over time and discuss possible implications of crowd memory in the design of crowd algorithms. We then explore crowd learning during a continuous control task. Recent systems are able to disguise dynamic groups of workers as crowd agents to support continuous tasks, but have not yet considered how such agents are able to learn over time. We show, using a real-time gaming setting, that crowd agents can learn over time, and `remember' by passing strategies from one generation of workers to the next, despite high turnover rates in the workers comprising them. We conclude with a discussion of future research directions for crowd memory and learning.