Appraise an amendment as progressive, degenerating, or neutral — tf_appraise

Compares an amended theory new with its prior version by content, not by prediction ids, and returns a Lakatosian verdict with the evidence behind it.

Usage

tf_appraise_amendment(new, prior)

Arguments

new: The amended theory object (named list).
prior: The prior theory object (named list).

Value

A named list whose first element, verdict, is one of "progressive", "degenerating" and "neutral". new_predictions, corroborated_new, ad_hoc_assumptions, articulated, underived, corroborated_new_registered, dropped, dropped_corroborated, content_lost and new_anomalies are ascending-sorted character vectors. renamed is a list of list(prior, new) pairs sorted by new, and assumptions holds one list(id, added_for, class, independent) per new assumption added for an anomaly, in file order. The four elements of earlier versions come first, in their old order.

Details

A new prediction whose statement and type match a prediction the amendment dropped is that prediction renamed. A new prediction derived only from propositions the prior already held, and needing no new assumption, articulates old content. A prediction is corroborated when at least one test outcome passes it and none fails it. An assumption added for an anomaly (its added_for names the prediction it answers) is ad hoc unless a prediction it protects that is new in this version, other than the anomaly, is corroborated.

The verdict is "progressive" when a corroborated new prediction is neither an articulation nor underived, no assumption is ad hoc and no corroborated prediction is dropped. It is "degenerating" when an assumption is ad hoc and no such prediction exists, and "neutral" otherwise. Lakatos has only the first two. "neutral" is theoryforge's label for an amendment that meets neither rule, and the vectors returned tell its cases apart. Only corroborated_new_registered reads a test outcome's registered, and no date or version field is read, so which version is the prior is the caller's responsibility.

A test outcome whose passed is present and not TRUE or FALSE, in new (checked first) or in prior, is refused with the message the Python twin raises. A quoted "true" read as a failure and could turn a progressive amendment into a degenerating one.

References

Lakatos, I. (1970). Falsification and the methodology of scientific research programmes. In Criticism and the growth of knowledge (pp. 91-196). Cambridge University Press. doi:10.1017/cbo9781139171434.009

Meehl, P. E. (1990a). Appraising and amending theories: The strategy of Lakatosian defense and two principles that warrant it. Psychological Inquiry, 1(2), 108-141. doi:10.1207/s15327965pli0102_1

Examples

prior <- tf_theory("demo-1", "A demonstration theory") |>
  tf_add_construct("c_arousal", "Arousal", "Bodily activation.") |>
  tf_add_construct("c_threat", "Perceived threat", "Appraised danger.") |>
  tf_add_proposition("p1", "c_arousal", "c_threat", "increases") |>
  tf_add_prediction("h1", "Threat rises with arousal.", "directional",
                    derives_from = "p1")
new <- prior |>
  tf_add_construct("c_avoid", "Avoidance", "Withdrawal from feared situations.") |>
  tf_add_proposition("p2", "c_threat", "c_avoid", "increases") |>
  tf_add_prediction("h2", "Avoidance rises 0.3 SD per SD of threat.", "point",
                    derives_from = "p2")
new$test_outcomes <- list(list(prediction_id = "h2", passed = TRUE))
tf_appraise_amendment(new, prior)$verdict
#> [1] "progressive"