{'fraction_non_alphanumeric': 0.019927536231884056, 'fraction_numerical': 0.0, 'mean_word_length': 5.820987654320987, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Identifying Consensus in Heterogeneous Multidisciplinary Professional Teams

As technologies become more complex, the use of heterogeneous man-machine teams will become more prevalent. The promise of such heterogeneous teams is that team members with diverse backgrounds bring with them expertise in different areas. However, differences in training, priorities, and professional culture have the potential to influence decision-making in ways that may not align with teammates’ roles within the organization. Inferring consensus among members of a group in a data-driven fashion has long been a goal in anthropology and social psychology. Here, we demonstrate the use of Cultural Mixture Modeling (CMM), for inferring consensus among members of heterogeneous multidisciplinary professional teams in an example decision-making process. Forty participants rated the profitability and generalizability of potential technology investments. CMM found three underlying groups, each with its own beliefs about these investments. We discuss potential implications for this dataset and other heterogeneous teams.