The Profiling Machine

May 14, 2022 by Lucian Mogosanu

The Profiling Machine (PM) is a distributed system emerging from1 the interaction between a database DB and a discrete set of processing elements A, whose elements are denoted as a2.

DB is a database formed of two sets: a (public) set O of objects o and a (private) set P of profiles p. An o may denote any object whatsoever -- details are beyond the current scope, although it is usually expected that os are similar in type/kind. For any p in P, p is uniquely mapped to an a, the information signifying so-called "knowledge" in DB about A.

as may interact with DB in the following ways: they may add objects o to O; they may remove any previously-added objects o from O; they may query O for an object, which is selected by PM according to some internal logic; and upon receiving an o, an a may "approve" it or otherwise "reject" it3.

The so-called object selection algorithm is in general a function of DB in its entirety. More specifically, when a new a is added to A (it "joins" the system), PM may sample os uniformly from O; when an o is approved, PM will update pa (in P) with a relation of weight w between a and o. As new os are approved by a, P will in fact converge to a graph containing relations (of various weights) between as and os, and correlations between (on one hand) as and (on the other) os. While convergence may be avoided by introducing some fixed or variable level of noise, it is expected that as the cardinality of O goes asymptotically towards infinity, correlations will lead to clusters (grouped subsets) of os on one hand and as on the other.

More precisely: if a1 and a2 are in (strong) relation to objects in Ra1 and Ra2 respectively, then the intersection between Ra1 and Ra2 forms a correlation between a1 and a2. Conversely, if o1 and o2 are in strong relation to agents in Ro1 and Ro2 respectively, then the intersection between Ro1 and Ro2 forms a correlation between o1 and o24. Both these correlations are fed back into P and thus into the selection algorithm of PM.

The primary goal of this feedback loop, and of PM itself is for DB to maintain a P that is as accurate a reflection as possible of A, under the assumption that as in A are equipped with quasi-stable5 criteria for (dis)approval of objects in O. The so-called correlation clusters are particularly interesting, since they may converge over large populations, i.e. large particular subsets of A may tend to approve large particular subsets of O. This approach may even prove to be useful for marketing, and in particular for those with such inclinations, may end up an outright implementation of a so-called tick-tock.

Anyways, the particulars of such a system and (especially) its failure modes might be worth studying some other time.

  1. Distributed systems "comprise" distinct, perhaps disparate objects that interact, and thus they are fundamentally emergent objects. For example, a computer, in the von Neumann sense, is a distributed system emerging from processing units, memory, peripherals and the interaction between all these elements. Similarly, a computer network is not simply composed of computers and the wires between them, but it also specifically requires the underlying phenomena of interaction, i.e. "communication protocols" in order to be considered an object we can talk about, that is, a so-called "system".

    Believe it or not, this is not mere pedantism, but rather an attempt at rigorousness for the sake of avoiding many of the traps befalling this field of computing. 

  2. a for "agent", that is, an autonomous computational unit. 

  3. This may be implemented in several ways. For example, PM may explicitly expect a reply to the message containing the o, or otherwise if the a doesn't reply within some timeframe, it may consider o implicitly rejected. Moreover, the same mechanism may be complicated indefinitely, such by introducing arbitrary "levels" or "scales" of approval. All these details are beyond the scope of this specification. 

  4. Note however that, while as are completely opaque to PM, os are actual objects in DB, so they may also be correlated according to their particular properties.

    In case this wasn't clear by now, PM aims to obtain a profile of each a it interacts with. It may of course be the case that as also aim to obtain information about PM, but this particular bit is out of the scope of this specification. 

  5. Stable at some particular point in time, but not necessarily over a sufficiently long period of time, however this "sufficiently long" may be defined, or otherwise determined. 

Filed under: computing.
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8 Responses to “The Profiling Machine”

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