softinformation

Soft information refers to information that is probabilistic, qualitative, or uncertain, rather than fixed, binary facts. It contrasts with hard information, which is precise, verifiable, and unambiguous. Soft information arises when data are noisy, incomplete, or subjective, and it often requires interpretation, estimation, or probabilistic framing.

In signal processing and telecommunications, soft information denotes probabilistic outputs such as symbol probabilities or log-likelihood ratios produced by detectors. These soft outputs are used in soft-decision decoding, which can improve the performance of error-correcting codes compared with hard-decision decoding. In broader statistical terms, soft information corresponds to posterior beliefs or likelihoods about states of the world rather than definite outcomes.

In intelligence analysis, risk assessment, and decision support, soft information encompasses expert judgments, estimates, impressions, and testimonies. Such information is inherently uncertain and is commonly expressed as probability statements (for example, "a 70% likelihood"). It is frequently integrated with hard data to create a more comprehensive assessment, with explicit accounting for uncertainty.

Quantification methods for soft information include probability distributions, confidence levels, likelihood ratios, fuzzy logic, and linguistic-to-numeric mappings. Data fusion frameworks such as Bayesian networks and evidence theory (Dempster-Shafer) provide ways to combine soft information with hard data, handling uncertainty in a principled manner.

Challenges in using soft information include subjectivity, calibration of probabilities, bias, and the weighting of disparate sources. Effective use relies on transparent assumptions, explicit uncertainty accounting, provenance, and robustness checks.