View source for Confusing surveillance systems

Simple [[surveillance]] systems can be easily attracted, confused and distracted from our brothers and sisters by blogging and spreading nonsensical stories using keywords that trigger these systems on social notworks. And more "sophisticated" surveillance systems are not so easily fooled. Data collection is one thing, being able to get the exact piece of information that answers questions an other thing. For datamining socalled Bayesian systems are used.

== Bayesian vulnerabilities ==

* Bayesian probabilistic explication of inductive inference conflates neutrality of supporting evidence for some hypothesis H (“not supporting H”) with disfavoring evidence (“supporting not-H”). This expressive inadequacy leads to spurious results that are artifacts of a poor choice of inductive logic. <ref>Cosmic Confusions: Not Supporting versus Supporting Not- http://philsci-archive.pitt.edu/9114/ Philosophy of Science. 77 (2010), pp. 501-23., this manuscript is an extensively revised version of "Cosmology and Inductive Inference: A Bayesian Failure," http://philsci-archive.pitt.edu/4866/ Prepared for “Philosophy of Cosmology: Characterising Science and Beyond” St. Anne’s College, Oxford, September 20-22, 2009</ref>
* Any theory of inductive inference depends upon one or more principles or presumptions that distinguish the right inductive inference relations. These principles must be there, whether they are made explicit, or, as is the more usual case, left tacit. The most fundamental of the challenges to Bayesian confirmation theory come from differing views of these principles <ref>Challenges to Bayesian Confirmation Theory http://www.pitt.edu/%7Ejdnorton/papers/Challenges_final.pdf Prepared for Prasanta S. Bandyopadhyay and Malcolm Forster (eds.), Philosophy of Statistics: Vol. 7 Handbook of the Philosophy of Science. Elsevier. Download final.</ref>
* While there is no universal logic of induction, the probability calculus succeeds as a logic of induction in many contexts through its use of several notions concerning inductive inference. They include Addition, through which low probabilities represent disbelief as opposed to ignorance; And Bayes property, which commits the calculus to a 'refute and rescale' dynamics for incorporating new evidence. These notions are independent and it is urged that they be employed selectively according to needs of the problem at hand. It is shown that neither is adapted to inductive inference concerning some in deterministic systems. <ref>Probability Disassembled http://www.pitt.edu/~jdnorton/papers/Prob_diss.pdf British Journal for the Philosophy of Science, 58 (2007), pp. 141-171</ref>
* In a material theory of induction, inductive inferences are warranted by facts that prevail locally. This approach, it is urged, is preferable to formal theories of induction in which the good inductive inferences are delineated as those conforming to universal schemas. An inductive inference problem concerning indeterministic, nonprobabilistic systems in physics is posed, and it is argued that Bayesians cannot responsibly analyze it, thereby demonstrating that the probability calculus is not the universal logic of induction. <ref>There are No Universal Rules for Induction http://www.jstor.org/stable/10.1086/656542?origin=JSTOR-pdf Philosophy of Science, 77 (2010) pp. 765-77</ref> 
* In one ideal, a logic of induction would provide us with a belief state representing total ignorance that would evolve towards different belief states as new evidence is learned. That the Bayesian system cannot be such a logic follows from well-known, elementary considerations. In familiar paradoxes to be discussed here, the notion that indifference over outcomes requires equality of probability rapidly leads to contradictions. If our initial ignorance is sufficiently great, there are so many ways to be indifferent that the resulting equalities contradict the additivity of the probability calculus. We can properly assign equal probabilities in a prior probability distribution only if our ignorance is not complete and we know enough to be able to identify which is the right partition of the outcome space over which to exercise indifference. While a zero value can denote ignorance in alternative systems such as that of Shafer-Dempster, representing ignorance by zero probability fails in more than one way. Additivity precludes ignorance on all outcomes, since the sum of probabilities over a partition must be unity; and the dynamics of Bayesian conditionalization makes it impossible to recover from ignorance. <ref>Ignorance and Indifference http://www.pitt.edu/~jdnorton/papers/Ignor_Indiff.pdf Philosophy of Science, 75 (2008), pp. 45-68. </ref>

In short, the greatest vulnerabilities of such systems are 1) its inability to separate ignorance from disbelief and 2) assuming the approach provides a universal logic of induction and *** theirs is IT *** of course. Heh!

== Countermoves ==

Exploiting Bayesian vulnerabilities for confusing surveillance systems can be done by

* Createing fragments of inductive logic that represent ignorance (evidential neutrality)
* Using other, yet competing systems of inductive logic
* Using inductive logic of indeterministic systems for which the probability calculus fails
* Using inductive inferences that are warranted by facts that prevail locally

==Resources==
===Logical alternatives===
* The World of Null A http://conceptualfiction.com/world_of_null_a.html
* Lukasiewicz on the Principle of Contradiction http://web.archive.org/web/20071010110817/http://www.fmag.unict.it/~polphil/PolPhil/Lukas/RaspaLukas.html
* Wikipedia Category:Non-classical logic https://en.wikipedia.org/wiki/Category:Non-classical_logic
==Related==
* [[Surveillance]]

== References ==