Why Models Matter for Science Teams

Science teams face challenges that many models were not originally designed to address. Four features in particular push science teams toward the dynamic and multilevel end of the modeling spectrum.

Long Time Horizons

Experiments may run for years; team composition shifts; new members must be onboarded continuously. Static, snapshot models cannot capture how team functioning evolves across this timescale.

High Knowledge Asymmetry

Team members hold highly specialized, non-overlapping expertise. Coordination requires explicit attention to who knows what — making constructs like transactive memory systems and shared mental models central rather than peripheral.

Distributed and Boundary-Spanning Structures

Many science teams span institutions, disciplines, and geographies, creating multilevel coordination challenges that single-level frameworks cannot adequately represent.

Emergent Goals

Research directions shift as knowledge accumulates; goals cannot always be fully specified in advance. Models that assume fixed, pre-determined outcomes are poorly suited to this reality.

These features push science teams toward frameworks that can represent how teams change over time, how learning propagates across levels, and how team climate conditions the uptake of new knowledge.