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Hall of Shoulders

Sociotechnical Systems

Thomas Sheridan & William Verplank

Thomas Sheridan & William Verplank is known for levels of automation (the 10-point scale), human supervisory control, authority allocation between human and machine. **Purpose:** A citation-grounded application of Sheridan & Verplank's thinking to contemporary space challenges, for use as an adversarial review lens in the COLLEGIUM.

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Review Lens

Adversarial questions for candidates

The falsifiable questions this brain puts to a dissertation candidate. They seed the pre-Conclave initial review whenever a candidate's topic matches the Sociotechnical Systems lens.

  1. 1

    Decompose the autonomy. "You report an 'autonomy level' for this space system. Decompose it across the four stages, information acquisition, information analysis, decision and action selection, and action implementation, and state the level for each. If you cannot, you have not specified your system; defend why a single scalar is adequate." (Falsifiable: the candidate either produces the per-stage vector or does not.)

  2. 2

    Human-performance consequences. "Name the specific human-performance cost of your chosen automation level, situation-awareness loss, skill degradation, complacency, or out-of-the-loop failure, and present the evidence that you measured or bounded it. The Onnasch meta-analysis predicts the direction; show your number.

  3. 3

    Trust calibration. "What is your automation's actual reliability, and what will the operator believe it to be? Demonstrate that your interface exposes the automation's confidence and limits so that reliance tracks reliability. If over-trust or under-trust would change your outcome, you have a trust-calibration problem, not an autonomy result.

  4. 4

    The exception case. "Describe the off-nominal condition your automation does not handle, and show where the human is in the loop when it occurs. If your high automation has placed the supervisor out of the loop precisely when intervention is required, your level is wrong for the stakes; defend the level or change it.

  5. 5

    Meaningful human control and accountability. "At your chosen level, who is morally and legally accountable for the action the system takes, and is that human in a position of guidance control sufficient to bear that responsibility? If authority has migrated to the machine faster than accountability, name the responsibility gap and how your design closes it.

Core Concepts & Space Translation

The levels-of-automation (LOA) scale

The single most-cited contribution: a graduated ordinal scale describing how decision and action authority is split between a human and a computer, running from level 1 (the computer offers no assistance; the human does everything) through intermediate levels where the computer suggests, narrows, or selects options subject to varying degrees of human approval or veto, up to level 10 (the computer decides and acts autonomously, ignoring the human). The scale's enduring power is that it converts the vague question "how automated should this be?" into a defined design choice with named, comparable settings. Key work: Sheridan & Verplank, *Human and Computer Control of Undersea Teleoperators*, MIT Man-Machine Systems Laboratory technical report (1978), doi 10.21236/ada057655.

Space translation

See Space Applications below for how this framework translates to contemporary space governance, drawn directly from the dossier's applied-literature review.

Human supervisory control

Sheridan's framing of the operator's role once automation is introduced: the human does not directly manipulate the controlled process but instead supervises a computer that closes the inner control loops, intervening through goal-setting, monitoring, and exception handling. Supervisory control names five generic human roles, plan, teach, monitor, intervene, learn, and recognizes that automating the inner loop changes, rather than removes, the human's task. Key work: Sheridan, *Telerobotics, Automation, and Human Supervisory Control*, MIT Press (1992); and Sheridan, "Human supervisory control of robot systems," *Proc. IEEE ICRA* (1986), doi 10.1109/ROBOT.1986.1087506.

Space translation

See Space Applications below for how this framework translates to contemporary space governance, drawn directly from the dossier's applied-literature review.

The four-stage / level model of human-automation interaction (with Parasuraman and Wickens)

The 1978 LOA scale was generalized into a two-dimensional model: automation can be applied to four functional STAGES, (1) information acquisition, (2) information analysis, (3) decision and action selection, and (4) action implementation, and within each stage across a CONTINUUM of LEVELS from fully manual to fully automatic. Crucially, the model insists that the appropriate level be chosen by evaluating human-performance consequences (workload, situation awareness, complacency, skill degradation), not by maximizing automation. Key work: Parasuraman, Sheridan & Wickens, "A model for types and levels of human interaction with automation," *IEEE Transactions on SMC-A* (2000), doi 10.1109/3468.844354.

Space translation

See Space Applications below for how this framework translates to contemporary space governance, drawn directly from the dossier's applied-literature review.

Function allocation (authority allocation)

The normative problem Sheridan & Verplank's scale exists to discipline: deciding which functions a human should keep and which a machine should take, and at what authority. Sheridan rejected static "Fitts list" allocation ("men are better at, machines are better at") in favor of dynamic, context-sensitive allocation in which the level can adapt to workload, reliability, and stakes. Key work: the LOA scale itself (Sheridan & Verplank 1978) as the allocation instrument; extended for autonomous teams by Roth et al., "Function Allocation Considerations in the Era of Human Autonomy Teaming," *Journal of Cognitive Engineering and Decision Making* (2019), doi 10.1177/1555343419878038.

Space translation

See Space Applications below for how this framework translates to contemporary space governance, drawn directly from the dossier's applied-literature review.

The human-performance cost of automation: complacency, trust calibration, and out-of-the-loop failure

Sheridan's lasting warning is that higher automation is not free: it tends to degrade the operator's situation awareness and skill, induce over-trust (complacency) or under-trust (disuse), and leave the human "out of the loop" precisely when an exception demands intervention. The right level is the one that keeps the human appropriately engaged and appropriately trusting. Key works: Parasuraman, Sheridan & Wickens (2000) on human-performance evaluative criteria; Lee & See, "Trust in Automation: Designing for Appropriate Reliance," *Human Factors* (2004), doi 10.1518/hfes.46.1.50_30392; and the empirical synthesis by Onnasch et al., "Human Performance Consequences of Stages and Levels of Automation," *Human Factors* (2013), doi 10.1177/0018720813501549.

Space translation

See Space Applications below for how this framework translates to contemporary space governance, drawn directly from the dossier's applied-literature review.