Emergent Collapse and Resilience in Sequential Task Networks (1.0.0)
Organizations operate under conditions of imperfect performance in which human error is inevitable, yet errors are rarely examined as the triggers for the managerial interactions that shape organizational culture over time. The present research introduces an agent-based simulation of a work team completing a fixed sequence of tasks under varying degrees of managerial oversight and response policy. The model isolates the mechanical loss of throughput caused by errors from the psychological and cultural consequences of managerial reactions, which are categorized into ignoring, correcting or punishing. Furthermore, the model incorporates an autonomous self-notice mechanism, allowing workers to correct themselves in the absence of managerial intervention. By tracking the accumulation of worker resentment and the transient enhancements in learning, the simulation acts as a dynamic laboratory for observing delayed consequences, nonlinear tipping points and systemic organizational collapse. The results reveal a central paradox of organizational control. Highly monitored punitive environments generate high short-term throughput, yet they simultaneously accumulate interactional injustice and resentment that engineer a rapid cascading turnover and the highest probability of systemic collapse. Conversely, corrective policies combined with active monitoring achieve equivalent throughput while sustaining workforce viability. A comprehensive sensitivity analysis establishes that error accumulation is primarily determined by structural factors, namely agent-level mistake propensity and task difficulty, while resignation dynamics, resentment accumulation and collapse timing remain predominantly governed by managerial policy, a hierarchy independently corroborated by a surrogate model and shown to be stable across independent seeds and across stakeholder weighting scenarios. The study bridges the operational mechanics of task completion and the social dynamics of workplace mistreatment, illustrating how short-term punitive success often masks long-term structural fragility.
Release Notes
HOW TO USE IT
Buttons
setup: initializes tasks, workers, and plots.
go (forever): runs the simulation until it stops.
Seed controls
use-auto-seed?
ON: generates a new seed each setup
OFF: uses current-seed if it is non-zero
current-seed: numeric seed used when auto-seed is OFF.
Workers
num-workers: how many workers to create.
init-mode: affects only work-unit assignment at setup (not mistake/resilience).
uniform: random from discrete values (0, 0.25, …, 10)
normal: random-normal around mean/sd then snapped to 0.25 steps
work-unit-mean, work-unit-sd: only used if init-mode = normal.
Trait assignment at setup
manual-traits?
ON: auto assignment uses uniform random levels
OFF: auto assignment uses profile compositions (70/20/10)
mistake-profile (when manual-traits? OFF): uniform / low / medium / high
low: 70% 0.2, 20% 0.5, 10% 0.8
medium: 70% 0.5, 20% 0.2, 10% 0.8
high: 70% 0.8, 20% 0.5, 10% 0.2
resilience-profile (when manual-traits? OFF): uniform / low / medium / high
low: 70% 3, 20% 5, 10% 8
medium: 70% 5, 20% 3, 10% 8
high: 70% 8, 20% 5, 10% 3
Manual editing during a run (Inspector)
There are no override variables. If you want to manually change a worker mid-run, edit the trait directly:
work-unit
mistake-chance
resilience
resentment
Important if you manually change mistake-chance:
Learning effects restore mistake-chance back to base-mistake-chance when the learning timer ends.
So if you want your manual change to become the new baseline, also set:
base-mistake-chance to the same value, and optionally:
learning-until-tick to 0 (to clear any active learning)
Tasks
task-difficulty: easy/medium/hard (15/30/45 tasks).
Task requirements are seed-dependent (1..50).
Manager
manager-interference: absent / medium / keen-eye (notice probability)
manager-style: laissez-faire / corrective / punishing (biased action selection)
Histogram refresh
histogram-every: updates the error distribution histogram every N ticks.
Associated Publications
Emergent Collapse and Resilience in Sequential Task Networks 1.0.0
Organizations operate under conditions of imperfect performance in which human error is inevitable, yet errors are rarely examined as the triggers for the managerial interactions that shape organizational culture over time. The present research introduces an agent-based simulation of a work team completing a fixed sequence of tasks under varying degrees of managerial oversight and response policy. The model isolates the mechanical loss of throughput caused by errors from the psychological and cultural consequences of managerial reactions, which are categorized into ignoring, correcting or punishing. Furthermore, the model incorporates an autonomous self-notice mechanism, allowing workers to correct themselves in the absence of managerial intervention. By tracking the accumulation of worker resentment and the transient enhancements in learning, the simulation acts as a dynamic laboratory for observing delayed consequences, nonlinear tipping points and systemic organizational collapse. The results reveal a central paradox of organizational control. Highly monitored punitive environments generate high short-term throughput, yet they simultaneously accumulate interactional injustice and resentment that engineer a rapid cascading turnover and the highest probability of systemic collapse. Conversely, corrective policies combined with active monitoring achieve equivalent throughput while sustaining workforce viability. A comprehensive sensitivity analysis establishes that error accumulation is primarily determined by structural factors, namely agent-level mistake propensity and task difficulty, while resignation dynamics, resentment accumulation and collapse timing remain predominantly governed by managerial policy, a hierarchy independently corroborated by a surrogate model and shown to be stable across independent seeds and across stakeholder weighting scenarios. The study bridges the operational mechanics of task completion and the social dynamics of workplace mistreatment, illustrating how short-term punitive success often masks long-term structural fragility.
Release Notes
HOW TO USE IT
Buttons
setup: initializes tasks, workers, and plots.
go (forever): runs the simulation until it stops.
Seed controls
use-auto-seed?
ON: generates a new seed each setup
OFF: uses current-seed if it is non-zero
current-seed: numeric seed used when auto-seed is OFF.
Workers
num-workers: how many workers to create.
init-mode: affects only work-unit assignment at setup (not mistake/resilience).
uniform: random from discrete values (0, 0.25, …, 10)
normal: random-normal around mean/sd then snapped to 0.25 steps
work-unit-mean, work-unit-sd: only used if init-mode = normal.
Trait assignment at setup
manual-traits?
ON: auto assignment uses uniform random levels
OFF: auto assignment uses profile compositions (70/20/10)
mistake-profile (when manual-traits? OFF): uniform / low / medium / high
low: 70% 0.2, 20% 0.5, 10% 0.8
medium: 70% 0.5, 20% 0.2, 10% 0.8
high: 70% 0.8, 20% 0.5, 10% 0.2
resilience-profile (when manual-traits? OFF): uniform / low / medium / high
low: 70% 3, 20% 5, 10% 8
medium: 70% 5, 20% 3, 10% 8
high: 70% 8, 20% 5, 10% 3
Manual editing during a run (Inspector)
There are no override variables. If you want to manually change a worker mid-run, edit the trait directly:
work-unit
mistake-chance
resilience
resentment
Important if you manually change mistake-chance:
Learning effects restore mistake-chance back to base-mistake-chance when the learning timer ends.
So if you want your manual change to become the new baseline, also set:
base-mistake-chance to the same value, and optionally:
learning-until-tick to 0 (to clear any active learning)
Tasks
task-difficulty: easy/medium/hard (15/30/45 tasks).
Task requirements are seed-dependent (1..50).
Manager
manager-interference: absent / medium / keen-eye (notice probability)
manager-style: laissez-faire / corrective / punishing (biased action selection)
Histogram refresh
histogram-every: updates the error distribution histogram every N ticks.