# Boocks worlds **URL:** \ **Category:** CSG1992\ **Created:** [August 11, 1992, 3:19pm UTC](http://discourse.iapct.org/t/boocks-worlds/1900 "1992-08-11T15:19:21Z")\ **Posts on this page:** 1\ **Page:** 1

**Author:** ![William\_T\_Powers2](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/w/c67d28/32.png) [@William\_T\_Powers2](http://discourse.iapct.org/u/William_T_Powers2)\ **Post date:** [August 11, 1992, 3:19pm UTC](http://discourse.iapct.org/t/boocks-worlds/1900/1 "1992-08-11T15:19:21Z")

[From Bill Powers (920811.0900)] Oded Maler (920811) -- I like what you're saying about the difference between the engineering problem with PCT and the traditional psychological one. I don't have any basic objection to the blocks-world approach -- we play that game ourselves, in a way. But I do think that the blocks world can lose its bearings when it isn't tied to the realities of real living systems as closely as feasible. Ask yourself this question (I don't know the answer and would be interested in hearing it). What would happen to AI and AL models if all computations were limited to 1 percent accuracy? I know that Ashby got into trouble with the implied problem when he abandoned control theory and went in for the compensation model. He ended up using examples in which all the variables took on small integer values, so it was possible to subtract 7 units of output of the compensating system from 7 units of disturbance and get 0 effect on the critical variable. This infinite precision conceals a lot of problems with small cumulative disturbances, nonlinearities, and the like. In a similar way, the "motor program" people, who assume open-loop calculations of output signals, use solutions of simultaneous differential equations in which multiple time-integrations have to be calculated with full available floating-point precision in order to work even approximately -- if these models were required to behave in a variable environment (no matter how predictable) for a couple of hours or days without being reset, they would surely crash. Even at the logic levels, it seems to me, the AI and AL types of models assume too much precision: the models never make a logical error as human beings do. This, it seems to me, shows that the kind of logic they're using, which is computer logic or Boolean algebra, is not the kind that the real system uses. If you build an organization that can work only if no computational errors ever occur, you never come up against the basic problem of error-detection and error-correction, and so never realize that it MUST be solved. The only solution I know of to the precision problem is to use closed loops, reference signals, and so on. Even fuzzy-logic models, which don't control very well, use closed loops. It isn't only the categorization problem that's intractable in an open-loop model. It's the control problem at every level. What, other than sudden death, tells the system that its actions aren't producing the calculated results, or that its perceptions aren't representing the world in a useful way? What tells it that while its logic is all right, its premises are screwed up? I don't think that the digital computer revolution has done behavioral science any favor. The digital computer was designed to work with absolute reliability, so that it always does what you tell it to do. Even the stepper motor was invented to make S-R behavior feasible. If you can count steps, and if the circuitry assures that a step is never gained or lost, and if the environment never changes unpredictably, you don't need any feedback. I think that lots of models of behavior assume this absolute precision of action that is inherent in a digital computer -- that's why they make so little use of feedback, why they seem to take action so much for granted, and why they ignore the subject of disturbances. Their inventors work in a world where if you say "add two and two" you always get 4.00000000000000000000. > **···** > > --------------------------------------------------------------------- > Best > > Bill P.