6533b853fe1ef96bd12acddb

RESEARCH PRODUCT

All-Possible-Couplings Approach to Measuring Probabilistic Context.

Janne V. KujalaEhtibar N. Dzhafarov

subject

lcsh:MedicineQuantum entanglementSocial and Behavioral Sciences01 natural sciencesQuantitative Biology - Quantitative MethodsJoint probability distributionPsychologyStatistical physicslcsh:ScienceQuantumQuantitative Methods (q-bio.QM)60B99 (Primary) 81Q99 91E45 (Secondary)PhysicsQuantum PhysicsMultidisciplinaryApplied MathematicsPhysics05 social sciencesComplex SystemsMental HealthMedicineMathematics - ProbabilityAlgorithmsResearch ArticleFOS: Physical sciencesContext (language use)Physical determinism050105 experimental psychologyProbability theory0103 physical sciencesFOS: Mathematics0501 psychology and cognitive sciences010306 general physicsQuantum MechanicsProbabilityta113BehaviorModels Statisticallcsh:RProbability (math.PR)Probabilistic logicRandom VariablesProbability TheoryKochen–Specker theoremFOS: Biological sciencesQuantum Theorylcsh:QQuantum EntanglementQuantum Physics (quant-ph)Mathematics

description

From behavioral sciences to biology to quantum mechanics, one encounters situations where (i) a system outputs several random variables in response to several inputs, (ii) for each of these responses only some of the inputs may "directly" influence them, but (iii) other inputs provide a "context" for this response by influencing its probabilistic relations to other responses. These contextual influences are very different, say, in classical kinetic theory and in the entanglement paradigm of quantum mechanics, which are traditionally interpreted as representing different forms of physical determinism. One can mathematically construct systems with other types of contextuality, whether or not empirically realizable: those that form special cases of the classical type, those that fall between the classical and quantum ones, and those that violate the quantum type. We show how one can quantify and classify all logically possible contextual influences by studying various sets of probabilistic couplings, i.e., sets of joint distributions imposed on random outputs recorded at different (mutually incompatible) values of inputs.

http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1059&context=psychpubs