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Exploiting Interdependence in Autonomous Human-Machine Systems to Avoid Disaggregation and Vulnerability

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Advances in Simulation and Digital Human Modeling (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 264))

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Abstract

We review the interdependence theory of complementarity for autonomous human-machine teams (A-HMTs) operating in open systems. In open systems, interdependence theory is state dependent, reactive to every situation, including uncertainty. Reactiveness to uncertainty explains why the best teams are highly interdependent, outperforming the same members of a team when they are acting independently as disaggregated individuals. A key ingredient we argue is that highly-effective team members are in orthogonal roles, reducing the degrees of freedom and entropy. In contrast, we also briefly review the competing, rational theories of interdependence that promote disaggregation: social interdependence (cooperation theory), games (applied social interdependence theory), innovation (the atomistic, rational “homo economicus” of economics theory), and system dynamics (the interdependence of feedback under certainty); in conjunction, we review the problems with generalizing, applying or scaling rational but closed system theories to the design and operation of A-HMTs and systems facing uncertainty.

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Authors and Affiliations

  1. Paine College, Augusta, GA, 30901, USA

    William Lawless

Authors
  1. William Lawless
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Correspondence to William Lawless .

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Editors and Affiliations

  1. U.S. Army Research Laboratory, Orlando, FL, USA

    Julia L. Wright

  2. Soar Tech, Orlando, FL, USA

    Daniel Barber

  3. Department of Product Development, University of Antwerp, Antwerp, Belgium

    Sofia Scataglini

  4. Biomedical Research and Environmental, NASA Lyndon Johnson Space Center, Houston, TX, USA

    Sudhakar L. Rajulu

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Lawless, W. (2021). Exploiting Interdependence in Autonomous Human-Machine Systems to Avoid Disaggregation and Vulnerability. In: Wright, J.L., Barber, D., Scataglini, S., Rajulu, S.L. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2021. Lecture Notes in Networks and Systems, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-030-79763-8_5

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