It’s a contradiction we have grown accustomed to: When big problems arise, we insist on the power of many brains. At the same time, everyday work meetings are notoriously dull and fruitless. Can certain conditions nudge collaborative problem solving in a more reliably productive direction?
First, a definition: collective problem solving here refers to a group of heterogeneous agents with divergent interests who congregate to identify, solve, and act on problems of common concern, typically with better results than any individual agent could achieve.
Straightforward enough. But attempting to make progress on improved problem solving quickly gets complicated and many-disciplinary, leading to a variety of new questions from the philosophical to the pragmatic.
What, for example, is the social psychology behind argument and reasoning? How does diversity influence social problem solving? How might ensemble methods in machine learning inform communication in iterative work?
An upcoming working group at SFI aims to connect some of these elements. Co-host Cosma Shalizi, an SFI external professor and associate professor of statistics at Carnegie Mellon, has a background in the statistical physics of complex systems; his current research involves devising algorithms to identify optimal predictors from finite data and applying them to concrete problems.
He and co-host Henry Farrell, an associate professor of political science and international a airs at George Washington University, recently published a paper outlining how democracies can work better than markets and hierarchies at solving complex problems. The pair is working on a second paper exploring evolutionary models to re-think institutional change.
Their November 10 & 11 working group, “Collective Problem Solving,” is the final in a series of small meetings. the sessions have themselves used elements conducive to collective problem solving: the small group has met regularly to maximize engagement and dialogue, new versions of papers have been presented each time, and each paper is presented by someone from another field.
This last exercise, in which a scientist considers an unfamiliar subject using their own discipline’s frameworks and tools, others a refreshing opportunity to generate unexpected perspectives on a problem – which is often exactly what’s needed to get to the next step, Shalizi says.