This dissertation contributes to understanding how self-organization in problem solving shapes the dynamics of social systems. I also explore how technology will enable greater self-organization and novel forms of production and organizing. The unifying thread in my research is that self-organization, by which I mean individual level autonomy and adaptability, has, and can solve more problems than we expect. The complex problems and rapid pace of change of the 21st century will demand a drastic reframing of work and organization. I believe understanding the dynamics of natural and existing self-organized systems should play a role in informing that change.
My first research stream investigates how the introduction of simple preferential interaction mechanisms can alter emergent behavior in a game theoretic setting. This agent-based model demonstrates the conditions under which spiteful behavior could emerge. Spite is a behavior that harms both the actor and recipient, and therefore its emergence is evolutionarily unintuitive. The model demonstrates that if agents are able to interact preferentially, spiteful behavior can gain a relative advantage and become the norm in a population. The second project in this stream examines the formation of signaling groups under preferential interaction. As expected, agents used preferential interaction to form subgroups that used different signaling conventions akin to different languages. Unexpectedly, we also found agents using a novel form of signaling where communicating partners failed to reach a consensus on the meaning of each signal. Instead, agents solved the signaling problem by using the network structure to ensure two distinct types of complementary signaling strategies interacted. Finally, my third project reports on an online survey and experiment on the freelance platform, Upwork. It examines the psychological willingness of freelancers to cooperate in temporary teams, and how our results can inform the design of collaboration platforms. Overall, our results suggest that crowd workers display traits that are more consistent with belonging to a coherent group or organization than may have previously been expected. This contributes to questions about how the boundary of the firm will be defined (and redefined) in the coming decades.
- Christoph Riedl, Northeastern University
- Alessandro Vespignani, Northeastern University
- Rory Smead, Northeastern University
- Patrick Forber, Tufts University
Zach is a fifth-year PhD candidate working with Dr. Chris Riedl as part of the CSSL Lab. Zach is primarily interested in organizational theory and the evolution of social behaviors. His research utilizes simulation modeling, surveys, and data analysis to better understand how social groups can emergently coordinate to learn, innovate, and solve problems. Currently, he is undertaking research on cooperative crowd work, and the evolution of signaling systems in social organisms. Zach received his B.S. from the University of Pittsburgh majoring in Mathematics and Economics and minoring in Computer Science and Statistics.