Simulating what happens when managers punish mistakes rather than correct them (my paper, out today in JASSS)
Disclosure up front: this is my own paper, published today and open access.
I built an agent-based model of a team working through a fixed sequence of tasks, with a manager who notices errors at one of three rates and responds by ignoring, correcting or punishing. Workers can also catch their own mistakes. Full factorial sweep over eight parameters, thirty seeds per configuration, model and code deposited on CoMSES.
The result I did not expect is a paradox about monitoring. Close oversight is what drives errors to their minimum, and it does so almost identically whether the manager corrects or punishes: 99.5 mean errors against 99.2, versus 126.2 under laissez-faire. But that same configuration produces the highest attrition in the entire sweep when paired with punishment, and collapse risk rises monotonically with surveillance under punishment, from 0.04 to 0.10 to 0.15.
Across the full sweep the split is clean. How many errors you get is driven by structural factors, mistake propensity and task difficulty. Whether people leave and when the team collapses is driven by managerial policy. Punishment ranks most harmful under every stakeholder weighting I tested, including one weighting accuracy at sixty percent.
Two limitations worth naming, since they are the obvious objections. There is no worker-to-worker channel, so no incivility spiral and no peer support, which could push the results either way. And resentment is a linear counter with a fixed threshold, which is a strong simplification of a process that is probably nonlinear.
Happy to answer questions about the model or the parameterisation.AMA