Iterative problem solving in teams: insights from an agent-based simulation

Abstract

Purpose

This paper aims to evaluate the effect of knowledge overlap, search width and problem complexity on the quality of problem-solving in teams that use the majority rule to aggregate heterogeneous knowledge of the team members.

Design/methodology/approach

The paper uses agent-based simulations to model iterative problem-solving by teams. The simulation results are analyzed using linear regressions to show the interactions among the variables in the model.

Findings

We find that knowledge overlap, search width and problem complexity interact to jointly impact the optimal solution in the iterative problem-solving process of teams using majority rule decisions. Interestingly, we find that more complex problems require less knowledge overlap. Search width and knowledge overlap act as substitutes, weakening each other’s performance effects.

Research limitations/implications

The results suggest that team performance in iterative problem-solving depends on interactions among knowledge overlap, search width and problem complexity which need to be jointly examined to reflect realistic team dynamics.

Practical implications

The findings suggest that team formation and the choice of a search strategy should be aligned with problem complexity.

Originality/value

This paper contributes to the literature on problem-solving in teams. It is the first attempt to use agent-based simulations to model complex problem-solving in teams. The results have both theoretical and practical significance.

Citation

Martynov, A. and Abdelzaher, D. (2016), "Iterative problem solving in teams: insights from an agent-based simulation", Team Performance Management, Vol. 22 No. 1/2, pp. 2-21. https://doi.org/10.1108/TPM-04-2015-0023