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Group Decision and Negotiation

, Volume 21, Issue 6, pp 791–820 | Cite as

Diversity and Communication in Teams: Improving Problem-Solving or Creating Confusion?

  • M. Laura Frigotto
  • Alessandro RossiEmail author
Article

Abstract

Despite the rich and interdisciplinary debate on the role of diversity and communication in group problem-solving, as well as recognition of the interactions between the two topics, they have rarely been treated as a joint research issue. In this paper, we develop a computational approach aimed at modeling problem-solving agents and assess the influence of various levels of diversity and communication in teams on agents’ performance in problem-solving. By communication, we intend a conversation on the persuasiveness of the features characterizing problem-setting. By diversity, we mean differences in how agents build problem representations which allow them to access various solutions. We deploy the concept of diversity along two dimensions: knowledge amplitude, which accounts for the level of available knowledge allowing access to poorer or richer problem representations (compared with complete problem representations), and knowledge variety, which pertains to the differences in the constituents of agents’ representations. We define performance as the frequency with which diverse agents choose the same alternative representation of an agent displaying complete representations of the problem. Our results indicate that communication is more effective when agents elaborate from relatively richer problem representations, as this provides a basis for integrating the variously diverse beliefs of their teammates. Conversely, poorer diverse representations may lead to worse performance when knowledge variety also applies. Lastly, we show that the influence of communication is not monotonically positive, as increasing communication intensity performance may worsen at any level of knowledge availability and knowledge variety.

Keywords

Problem-solving Diversity Heterogeneous agents Communication Constraint satisfaction Neural networks Causality 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Management and Computer ScienceUniversity of TrentoTrentoItaly

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