Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Oldest tendencies in human understanding of the world: “reduction” to basic principles ... the first “strucutural” ideas ? the principles are not directly accessible Beginning of “combinataion theory” (?): ancient Greeks: 4 components - water, fire, soil, air Middle ages: Universalia nominalism = sunt nomina et flatum vocis realism = sunt realia ( Platon-like ?) Analytical philosophy - theoretical vs. empirical entities and attributes Strict positivism: reduction sentences - reduction of theoretical to observable Modest analytical philosophy: - what are the relationships between them ? - appropriate concept formation correspondence problem Related topics to empirical vs. theoretical problem: - systems theory: black box, hidden elements, internal state - behaviorism: S - R paradigm, Tolman’s “intervening “ variables - measurement theory: indirect, associative, pointer measurement, m. “by fiat” - induction - deduction problem - exploratory - confirmatory approaches Layers from directly observable to heuristically hypothetical vs “real” Levels in analytical philosophy of science: - empirical - “directly observable” (through “reasonably” scientific diagnostic procedures, rigorously standardized) - pure dispositional - theoretico-dispositional - theoretical concepts - hypothetical the last 4 called often altogether generically theoretical concepts ... in wider sense or “constructs” Philosophical ways to look at theoretical concepts: fictionalism conventionalism theory of levels constructivism neutralistic view instrumentalism ...... operationalism ..... naive realism Two dichotomies play role in theory building: 1. empirical vs. theoretical levels 2. “real” vs. formal level Problems of formalization: - formal representation of observation ... data coding - not necessarilly but including data quantification and measurement (“raw research record” - data - variable not-necessarillyquantitative) - modeling: - theory , its structure, formal model ... - regression- like models,... - not-quantitative models - theory-data relationships in quantitative models the correspondence problem as associative measurement problem Correspondence relationships: - logical definition, problem: irect explicit definition vs. partial conditional definition - “causal” implication - nondeterministic: - fuzzy logic implication - stochastic: - conditional probability - regression, correlation - information theory measures of association