As groups grow in size, participatory decision-making becomes difficult. Can online platforms make it easier for large groups to deliberate and reach consensus?
My PhD research at the University of Michigan School of Information, supervised by Daniel Romero, focuses on large-scale decision-making in collective action. Groups like Wikipedia, free software projects, worker cooperatives, and social movements all rely on contributions of time and energy from a large number of people. Success often depends on how well contributors can reach decisions and work together. While reviewing the literature, I identified some common themes that set successful collaborations apart from the rest. Now I'm working on translating those themes into theory-driven tools and best practices, and I'm looking for groups to partner with as we co-design tools and run large-scale experiments. If you're part of a project or organization with an interest in participatory decision-making, send me an email at elplatt@umich.edu.
For the interested, here's a little more background on my dissertation research.
In a group setting, the back-and-forth of deliberation is not just a process of persuasion, but also of information diffusion, of idea generation, and of trust-building. But deliberation is much easier with 3 people than with 300. When an organization grows too large, it might switch to voting, and lose many of the benefits of deliberation in the process. At its best, voting can find an existing consensus, but it can't generate new ideas or build the trust needed for compromise. To make matters worse, there are mathematical results suggesting that the results of a vote can never fairly represent a group's preferences (for example: the Condorcet Paradox and Arrow's Impossibility Theorem).
My work focuses on deliberation because it offers the potential to generate new solutions and actively build consensus before votes are cast, and before a decision is made. And when large groups can deliberate at scale, they can make full use of every contributor's resources to accomplish colossal tasks.
Across the examples of large-scale collaboration I've studied, one theme stands out above others: most effective work and discussion happens in small groups. While Wikipedia has millions of editors, each article has only a handful of active contributors at any one time. Likewise, free software projects like the Linux kernel and the Drupal content management system are built from modules, with a small (or at least smaller) community of collaborators focused on each module. In social movement organizations, complex tasks are often delegated to groups with a range of names: committees, working groups, teams, circles, zones, or nodes.
While small groups can make coordination easier, they can also create silos and echo chambers-trapping good ideas and preventing them from being adopted. But in all of the examples above, contributors participate not just in one small group, but in many. These interlocking groups appear to be key to allowing information, ideas, and resources to flow from group to group across the entire organization.
This interlocking group structure seems important, but there are important open questions. In particular, does it matter how contributors are assigned to groups? Groups can be designed so that ideas can spread quickly across an entire organization, or spend time slowly developing within a cluster of groups. The experiments we're designing are intended to evaluate these methods in a real-world setting. With a better understanding of the implications of interlocking group structure, we hope that it will be easier for large organizations to use participatory decision-making.
In December, we invited 18 participants into our lab to deliberate on a policy for electric scooters on campus. They were presented with four options:
Participants were randomly divided into groups of 4 or 5 and deliberated on the policy in an online chat room for 10 minutes. The participants were then assigned to new groups for another round of deliberation. In all, there were three rounds. We asked participants to rank their options before and after each round.
For each group in each round, we calculated which option would win a vote, using both majority vote and some alternative ranked-chioce voting methods (Borda count, and Tideman ranked pairs). The results are shown below.
Group Round Majority Borda Tideman
------- ------- ---------- ------- ---------
1 1 3 3 3
2 1 1,3,4 2,3,4 4
3 1 3 3 3
4 1 3 3 3
------- ------- ---------- ------- ---------
5 2 4 3 4
6 2 3 3 3
7 2 3 3 3
8 2 3 3 3
------- ------- ---------- ------- ---------
9 3 1,3 3 1
10 3 3 3 3
11 3 3 3 3
12 3 3 3 3
While #3 was the clear winner in most cases, other winners or ties appear for some rounds and methods. Option #4 appeared in two groups in the first two rounds, but none in the third. Option #1 appeared in one in the first round and one in the third. Even in this simple example, we can see #4 losing popularity over the course of the deliberation.
Looking at the chat logs, we saw evidence of some notable behaviors, including novel ideas:
In the last group, we prioritized bike lanes as the ideal place for scooters, and then sidewalks if no bike lane existed
… requests for more information:
I think there's a lot of missing information like safety and environmental impact that we don't have
… diffusion of ideas:
the most popular opinion in my previous chats have been the usage of electric scooters ultimately helps the environment
Oh interesting, never thought about the environment
… and opinions changing (or not):
I changed from both to only sidewalks at the first round.
The discussion didn't change my opinion much. The groups did bring up interesting points that I didn't think of before.
This study shows that deliberation in small interlocking groups has the potential to spread information and change opinions. We've also seen that we can use studies like this to measure changes in consensus quantitatively and corroborate those results with qualitative analysis of group discussions. Now, we want to apply these same methods to study real large-scale collaborations and conduct controlled experiments.
Currently, I'm looking for organizations (formal or informal) to partner with for the next stage of this research. Partners will use our tools to deliberate on real issues in their organizations and/or participate in co-design workshops to evaluate and improve the tools we're building. Interested? Say hi by sending an email to elplatt@umich.edu. Speaking of tools, we're currently extending the free/open-source decision-making suite Loomio to support networked deliberation. If you'd like to follow along, the repository is here.
Crossposted to Medium. Thanks to Daniel Romero and Sarah DeFlon for feedback on a draft of this post.