@Charles Bawate, the three-part structure you\'ve sketched (a named Community Sensing Lead, a monthly trigger that automatically opens a revalidation check, and custodianship inside local institutions) is the most concrete formalisation anyone has offered, and I\'d only add one thing:…

Thank you Rhoda for pushing this further — you are right.If we agree field teams are the early-warning system for drift, then we cannot leave their observations as informal chatter. They need structure.From our side, I am reflecting on what…

@Charles Bawate, \"before we ask if we have enough data to model, we should ask who becomes invisible if we do\" is exactly the reframe. And your answer to my question that some questions should pause and invest in community…

@Henry Magoba, \"when populations are missing from the data, they do not cease to exist; they become invisible in the model\" is the sentence the whole thread needed. And your point that limited data shouldn\'t stop modelling but should change…

@Nonvicks Ochieng the ODD+D labelling of agents turn an invisible judgement into a documented one, and it means a reviewer can see at a glance which agents are carrying the model\'s weight and which are placeholders. The scenario-ensemble approach is…

@Henry Magoba, this is the question that should sit at the top of the thread, not the bottom. Credibility debates tend to stop at whether the model is good; you\'re asking who carries the consequences when it isn\'t. On shared…

@Nonvicks,  \"credibility is a relationship, not a statistic\" and \"co-designing confidence\" both deserve to be framed. Your epistemic mismatch point is the one I\'d underline: modellers and policymakers aren\'t disagreeing about the same object, so the argument never resolves. The…

@NonvicksOchieng The \"zombie artifact\" framing is exactly right, and I like the contrast with software rot: code fails loudly, models fail quietly. Your point about pairing parameters with live data streams is the technical answer to drift detection, though I\'d add…

@Charles Bawate Your point about drift showing up in community feedback before it shows up in outcomes is a sharp one, and it reframes early detection as a qualitative sensing function, not just a monitoring dashboard. I\'d push one step further:…

Thank you Rhoda for this important question — when to retire is as important as when to build.From implementation perspective, this resonates strongly.1. Calibration drift: In community programs, behaviour changes fast — after a policy change, a health campaign, or…

Thank you Rhoda Nakhosi for raising this. Very timely question.From locally led work in Rwanda, this data gap is not theoretical for us.1. Data driven exclusion: In our youth and community programs, the most affected are often the least documented — youth…

Thank you Rhoda for this important question — when to retire is as important as when to build.

A third question: Who is accountable when an ABM-informed decision causes harm?We often discuss model credibility in terms of calibration, validation, and stakeholder trust, but what happens after a model informs a policy that produces unintended consequences?In public health, an…

This is a critical issue in agent-based modelling (ABM), particularly in public health research and low- and middle-income countries (LMICs).When populations are missing from the data, they do not cease to exist; they become invisible in the model. This is…

@Rhoda Nakhosi The provided comment makes two primary arguments about model selection in complex systems research. First, it warns that when individual-level data is sparse, complex tools like Agent-Based Modeling (ABM) suffer from \"false precision,\" cloaking arbitrary assumptions in an air…