Back to results

Massachusetts Institute of Technology

An Experimental Design to Assess Team Performance Through Shared Mental Models

Abstract

dc:description.abstract

Nearly every domain in the world is moving to a team-based environment. Regardless of application or desired outcomes, decisions must be made in groups. Individual decision-making presents a challenge in every domain, and this challenge grows exponentially more difficult when teams of individuals are forced to build a consensus. Joint decision-making is a complex system-of-systems, being operated on by teams of teams. This thesis focuses on the challenges of establishing shared mental models within teams, their performance, and possible acceleration of the formation of these shared mental models, to achieve the best possible outcomes of the system. In order to assess the quality of shared mental models, a framework for an experiment is laid out, in which the quality of a team’s shared mental model is correlated to the team effectiveness during execution of high-stress, fast-paced tasks. Limitations, future research, and practical steps for the implementation of an experiment are outlined.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
System Design and Management Program.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Hallock, Neil K.
Advisor dc:contributor.advisor
  • Moser, Bryan

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/151282
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/151282

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Hallock, Neil K.. An Experimental Design to Assess Team Performance Through Shared Mental Models. Massachusetts Institute of Technology, 2023. https://hdl.handle.net/1721.1/151282