How does the technical, social, and communication context influence collective action decisions (and vice versa)?

Groups size distributions vary

Online group sizes have long-tail distributions

The number of groups each user belongs to is also long-tail

What decision-making processes could produce these outcomes?

A simple model that produces long-tail distributions

Preferential Attachment (Barabási, 1999)

One small problem

Three processes

• Consideration
• Exposure
• Decision

Agent-based modeling

• Simplified, simulated system of interacting agents
• Allows us to:
  • Explicitly model micro processes
  • Test the macro-level implications of theories
  • Move from equations to agents

Our agent-based model

• Start with \(N\) potential contributors and \(X\) potential communities
• Every month:
  • \(n\) users are chosen (consideration)
  • Each user is presented with an “exposure set” of \(x\) communities (exposure)
    • The user decides which communities to join (decision)

Random processes aren’t consistent with empirical data

Random exposure processes cannot produce long-tail distributions

Word of mouth exposure through Communication Networks

Word of mouth exposure models look similar to Reddit

We can make the decision algorithm more realistic

• Based on expected value of joining (Resnick et al., 2011)
• People use past communities to fit a model of how large their current set will be
• They prefer to:
  • Join communities early
  • Join large communities
• But there are costs to join and to participate

A word of mouth exposure model plus a “learning” model looks roughly like Reddit

Agent-based modeling and theory-building

• Simulation is a great tool for modeling organizational processes
• Exposure processes are important but understudied
• Communication networks provide a plausible mechanism

Future work can validate our models

• Surveys and experiments
  • How are people exposed to new communities?
  • How do they decide which to join/leave?
• Explaining other patterns of participation (e.g., plateau of group membership over time)

Decision algorithms

• Random
• Biggest
• Linear growth
• Recent growth
• Learning model

Learning model

• Agents fit a regression model on previously joined communities
• They then predict the growth of their current choice set

Utility

Utility

• Participation benefits (PB)
  • \(PB = log(S + 1)\)
• Early adopter benefits (EA)
  • \(EA = log(S + 1)/log(i + 1)\)
• Cost = \(X\)
• \(U = PB + EA - C\)

Learning model:

• \(S_c\) = current size, \(A_j\) = age when joined
• Predicted size after 6 months

All models - community sizes

All models - communities per person

A few problems

• Doesn’t explain how people estimate future growth
• Not derived from empirical data
• Only applies to single decision

Adding communities

• Adding communities every month is a start

But it’s not enough

Results

Adding popularity

• The choices that people consider are weighted by popularity

Limitations and challenges

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