A Benchmarked Experiential System for Training (BEST) and Dynamic Systems Theory

Abstract

We tested a Benchmarked Experiential System for Training (BEST) on Dynamic Distributed Decision Making (DDD)/AWACS simulations. BEST leveraged mathematical optimization, human expertise, and observational learning to give trainees feedback about mathematically and behaviorally defined near-optimal/expert strategies. We measured team defensive scores for college students on baseline, and three assessment trials with planning, missions, and debriefings. During debriefings, BEST and Control teams had checklists and equal time, but BEST teams also observed a near-optimal mission. On baseline, BEST and Control teams were similar; on assessments 1–3, MOST teams significantly outperformed Control teams (partial eta squared effect sizes of .10, .21, and .13). The findings are a foundation for scientists to address how expert, novice, BEST, and Control teams differ on themes of cognitive systems engineering and dynamic systems theory. BEST interventions may substantially reduce team training time and facilitate teams in flexible and dynamic work conditions.

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