The way I look at it, as long as the server has the final say over the "official" value at any given time -- which seems to be the case here, and is true in all real apps I've seen up to the backend datastore's ability to provide it -- you don't really have a consistency or convergence problem. You just have a UI problem. It's easy for the client to track both the latest official value and what the user is doing, and tell the server about the latter. The only question is what to show the user while their action is still outstanding, and the answer may be different for different kinds of UI. You may want to "fake it till you make it" (Meteor's default, aka latency compensation) or show some kind of loading indicator.
What about conflicts? Well, depending on your app, your options for how the server handles operations from the client include: they sometimes fail; they always succeed via "last writer wins" (good for a checkbox, say); they always succeed via operational transforms; or there is application-level conflict resolution that ropes in the user (implemented on top of the foregoing).
There's a trend at the moment to view everything as a distributed system, but if you treat a client as a tool for viewing information and calling APIs, it seems a lot simpler.
If two people make a change to the same documents at the same time, the changes are merged in a way that will never conflict. For example if we both simultaneously insert into a list embedded in some part of the data model, derby sends a "list insert at position X" operation, which is seamlessly transformed & merged into the document at all sites. The insertion position is updated automatically before merging using the same logic that makes collaborative text editing in etherpad work. Except you can do it over the JSON structures that power your web app.
Ok, I guess I must have misunderstood. If you're the guy whose building on-top some synchronization layer, then yeah, it's not really a problem. The sync layer is just another controller that manipulates your model or another consumer of your model changes.
If you're the guy implementing the synchronization layer, then you are thinking of distributed systems.
1) Local: In the view but before event dispatched to the model.
2) Local: In model but before it was synced to the server.
2a) Local: After model sync and in queue waiting to be sent to the server.
3) Remote: In transit to the server.
4) Remote: On the server (i.e. the one true state).
5) Remote: In transit to the clients.
6) Local: Received but before it was synced to the model.
7) Local: In model but before synced to the view.
8) In the initial state sync (e.g. when a new client joins)
So at any given point, every client state is inconsistent with every other client (maybe even significantly if the latency for one or more of the clients is bad) since pieces of the eventual state are spread across all these locations. If now every user stops changing state furiously, every client should eventually be consistent. It's not a hard problem but not completely trivial. For example, you will need conflict resolution rules on the server and in your client and traditional distributed issues like (temporary) network partitioning and latency need to be taken into account. Message ordering will probably matter.
>It sounds like the client is just trying to receive a stream of updates, which could have been made by a single writer
Not necessarily. You have the same synchronization issues whether the updates are coming from a single source, or from multiple sources.