Collective control as a real-world phenomenon

[rsmarken]

@Kent

@rsmarken

My fixation on conflict is because Kent’s model IS A MODEL OF VIRTUAL CONTROL RESULTING FROM CONFLICT!!

As promised, I would like to return to the subject of where Rick’s Chapter 7 on “Social Control” in his recent book goes off track.

As I intend to show here, Rick misinterprets my model in the quotation above and, more importantly, in his description of the model in Section 7.1.7.

Section 7.1.7 was a description of the phenomenon of virtual reference states; it was not a description of your model. I refer to both you and Powers at the beginning of that Section because you both discuss the phenomenon of virtual reference states. I didn’t discuss your model because I have never seen it fit to actual behavior.

@Kent

His interpretation of my model is contradicted by mathematics of the model itself and by the plain text of what I wrote in that 2004 paper.

… I will focus on three claims that Rick makes [the model]:

Claim 1: That my model applies only in cases in which there are multiple control systems “of equal strength, in terms of both gain and maximum output” acting on a shared controlled quantity.

I made that claim about when virtual reference states of controlled variables are observed based on my observation – actually an observation anyone can make – of what happens when two or more people of apparently equal strength are trying to get the same variable into different reference states, as in an arm wrestling contest.

@Kent

My next step in presenting my model in the 2004 paper was to offer a “Simulation of Cooperative Control” (Figure 4 on p. 79).
…

I reported in the text that “their joint effect on that environmental variable is indistinguishable from the effect of a single control agent acting alone”…

Thus, the first application of my model of collective control presented in the paper was to a situation of cooperative control.

Yes, that can be called cooperative control. But, as I noted in Ch. 7, a complete model of cooperative control would have to include an explanation of how the systems came to agree that they would act at the same time to control the same (or very nearly the same) variable relative to the same (or very nearly the same) reference level. But when such a cooperative situation occurs the reference state of the controlled variable is actual, not virtual ; all systems involved are “getting what they want” (nearly zero error).

@Kent

Rick’s claim that my model applies only in situations of conflict is just wrong.

No, I claimed that a virtual reference state of a controlled variablke is only observed when there is a conflict between the systems controlling that variable. When the systems are not in conflict, as is the case when all systems happen to be controlling the same (or very nearly the same) variable at precisely the same time relative to the same (or very nearly the same) reference level – the “cooperative” case – then the commonly controlled variable is kept in an actual reference state, not a virtual one.

@Kent

Rick’s assertion in his quoted paragraph that my model applies only to control systems “of equal strength” is obviously incorrect.

I didn’t “assert” this about your model. I was basing what I said (as I assume Bill Powers was in his discussion of virtual reference states in B:CP) on my observations of what happens in arm wrestling. But you are right that your model will keep a variable in a virtual reference state even when the systems differ in gain and maximum output. But I’m not sure this happens in real life. In arm wrestling, for example, it is an observable fact that one person usually wins, implying that unequal strength will not necessarily keep a variable in a virtual reference state for very long.

This is one of the reasons it would be nice if you had compared the behavior of your model to what is actually observed. I think if you had tested your model against the behavior in a real conflict (like arm wrestling) you might have found that you would have to change some things about it in order to have it account for actual data.

@Kent

I found, as reported in my text, that …

the pointer position curve in Fig. 5 is precisely identical to the curve for the systems sharing identical reference signals in Fig. 4…

To me, at the time, this was the most significant finding to emerge from my simulations: that conflictive collective control can exert exactly the same stabilization effects on an environmental variable as cooperative collective control.

I think this was an unfortunate conclusion. The stabilization resulting from conflictive control is not the same as that from cooperative control (or control by a single control system). It can look the same; but what is happening inside the systems involved is definiteily not the same. Your model shows that the experience of the agents who are keeping a variable in a virtual reference state is drastically different than that of controllers keeping a variable in an actual reference state. The agents keeping a variable in a virtual reference state are experiencing orders of magnitude more error than those who are cooperating to keep a variable in an actual reference state.

@Kent

Turning next to Rick’s second claim …

Claim 2: That a “virtual reference state” and a “virtual controlled variable” appear only when the multiple conflicting control systems are operating at their maximum levels of output and that this virtual reference state represents the “average position” of the “oscillation” of the virtual controlled variable if no disturbances are present

Although I didn’t use the terminology at the time, and in retrospect it was probably a mistake, the simulation results I presented in my 2004 paper also demonstrated that collective control, whether cooperative or conflictive, always produces a virtual controller and virtual reference value, whether or not the control agents are operating at maximum output.

Yes, I should have left off the reference to maximum output.

@Kent

My 2004 paper reported a simulation that I did to test Bill’s prediction about emergence of a “dead zone” in episodes of conflictive control in which both control systems have reached their maximum output. Here is the graph of my simulation:

What did not happen in this simulation is what Bill had predicted: that there would be little or no control within the dead zone.

It looks like that’s exactly what happened. But the dead zone concept is, again, based on Bill’s observation of what happens in a conflict like arm wrestling: small disturbances to the virtual controlled variable are not resisted, large ones are. At least that’s what appears to be happening. And I believe it can be demonstrated that control of a virtually controlled variable is actually poorer with a small disturbance than with a large one. I had developed a spreadsheet that shows this but I can’t seem to find it so I’ll make a new one and post it.

@Kent

Another thing that did not happen in this simulation, except for the brief moments when neither control system had hit its hard maximum, was Bill’s second prediction, that the two systems would act “like a single system having a virtual reference level between the two actual ones.”

I agree that this is what happens with the model. But is this what happens in reality? A crucial part of modeling is comparing the behavior of the model to the phenomenon to be explained.

@Kent

Clearly, collective control is just another form of control, even when the participating control agents are locked in conflict with each other, and the conflict has driven them to their maximum levels of output.

The controlling done by your model of collective control is “just another form of control” only when the agents doing the controlling are not in conflict… When they are in conflict, the agents are not really in control of the commonly controlled variable inasmuch as each agent is experiencing sometimes orders of magnitude more error than they would be if they were controlling the variable on their own. This is what I demonstrated in the spreadsheet I made for Eetu, which is available here.

Also, you imply that yours is THE model of “collective control”, but there are many different models of collective control for the different examples of controlling done by collectives of individual agents. For example, there are the different models I described in Ch. 7; Bourbon’s model of cooperative control, Bill’s model of CROWD behavior, Reynolds’ (boid) model of flocking birds, my imitation model. And none of those models involve a collection of agents controllling the same (or a similar) variable relative to the same (or different) reference levels.

@Kent

Which brings us to Rick’s third claim in his paragraph from Chapter 7:

Claim 3: That the multiple conflicting control systems locked in such a combat are not really controlling the disputed variable collectively, because they are not “getting what they want” and thus do not have the disputed variable “under control.”

This claim is simply nonsense. As I’ve shown above, even when control is conflictive, collective control is always just control unless environmental barriers, like the hard limits imposed on their output in Figure 6, restrict the freedom of action of the agents involved.

No, it’s not nonsense and you haven’t shown that “when control is conflictive, collective control is always just control”. In fact, as I have shown in the above referenced spreadsheet model the agents in your model are experiencing huge error (they are not getting what they want) when they are keeping the commonly controlled variable in a virtual reference state.

@Kent

It appears that Rick fell into his confusion on these points by accepting Bill’s misleading description of deadlocked conflict in B:CP as gospel, without ever taking the time and trouble to read my papers carefully, think through what I was saying, and consider whether I might actually be right.

You have not convinced me that Bill’s comments about conflictive “control” in B:CP were wrong. My experience is that Bill’s comments are always based on a firm understanding of what he’s talking about; he is always right, at least when he’s talking about contorl theory. The particular comments to which you refer are based on observation while yours are based on a model whose behavior may or may not correspond to what is actually observed.

But even if Bill’s comments were wrong, they are, from my perspective, not nearly as seriously wrong as what you say about your model. Most importantly, you are wrong to claim that virtual control is the same as actual control. The superficial similarities between virtual and actual control belie the most fundamental difference between them, which is that when a variable is being virtually controlled – kept in a virtual reference state – the conflicted agents who are controlling it are experiencing orders of magnitude more error than they would be if any one of them were controlling that variable on their own.

In more reasonable models of the controlling done my multiple agents, such as the ones I describe in Ch. 7 of SCLS – models whose behavior has been successfully compared to the behavior they purport to explain – the controlling done by the agents is the same as it would be if they were controlling on their own; all the agents are experiencing almost no error at all.

@Kent

To act the part of a real scientist harboring doubts …

I think a “real scientist” develops models to account for data! This was certainly a fundamental tenet of Bill’s work on PCT. That’s why he made all those demos – both “portable” and “computer”. Just saying the way things work based on a model and its imagined connection to reality – the theory first (and usually only) approach to understanding – is not science, its religion.

@Kent

Wouldn’t it be great if we elder statesmen could start listening to and learning from each other and get back to extending and clarifying PCT for people newly interested in it, instead of spinning our wheels in these silly arguments?

Science is always going to involve arguments. Scientific progress is based on resolving arguments over how to explain observations by testing these explanations (in the form of models) against new observations. Arguments over what a model “really” says, as though the models were the reality, can, indeed, become a bit silly.

– Rick