Exploring Dynamic Group Processes with GAIA – Groups of Adaptive Inferencing Agents Available to Purchase

Applies a computer model GAIA (Groups of Adaptive Inferencing Agents) to simulate the lifecycle of artificial groups directed by agendas which specify varying strategies for collective problem solving. Within GAIA, groups of artificial agents dynamically learn and interact by proposing, combining and testing inductive hypotheses in the form of genetic building blocks. Agents share and combine building block solutions to evolve decision trees to respond to environmental inputs. Effects of agendas which emphasize stages of conservative and liberal problem solving strategies over a group’s lifecycle were simulated. Conservative strategies emphasize consensus and collective memories within groups. Liberal strategies emphasize challenges to collective memory and individual agent predictions. Agendas which vary from conservative to liberal resulted in the poor group solutions. Significantly better group solutions were produced by an agenda varying from liberal to conservative and back to liberal (L‐C‐L). The L‐C‐L agenda focuses on critical evaluation and rewards for individual contribution in the beginning and ending lifecycle stages and provides a middle stage of collective exploration.