// Workers AI · dad joke modeWhat did pignistic probability say? "I pig-headedly think it's likely.

In decision theory, a pignistic probability is a probability that a rational person will assign to an option when required to make a decision.

A person may have, at one level certain beliefs or a lack of knowledge, or uncertainty, about the options and their actual likelihoods. However, when it is necessary to make a decision (such as deciding whether to place a bet), the behaviour of the rational person would suggest that the person has assigned a set of regular probabilities to the options. These are the pignistic probabilities.

The term was coined by Philippe Smets,^[1] and stems from the Latin pignus, a bet. He contrasts the pignistic level, where one might take action, with the credal level, where one interprets the state of the world:

The transferable belief model is based on the assumption that beliefs manifest themselves at two mental levels: the ‘credal’ level where beliefs are entertained and the ‘pignistic’ level where beliefs are used to make decisions (from ‘credo’ I believe and ‘pignus’ a bet, both in Latin). Usually these two levels are not distinguished and probability functions are used to quantify beliefs at both levels. The justification for the use of probability functions is usually linked to “rational” behavior to be held by an ideal agent involved in some decision contexts.

A pignistic probability transform^[2] will calculate these pignistic probabilities from a structure that describes belief structures.

Notes

↑ Smets, P., "Data Fusion in the Transferable Belief Model" Archived 2017-08-08 at the Wayback Machine, Proc. 3rd Intl. Conf. Information Fusion, Paris, France (2000) pp. 21–33.
↑ Dezert, J., Smarandache, F., Daniel, M., "The Generalized Pignistic Transformation", Proc 7th Intl Conf. Information Fusion, Stockholm, Sweden (2004) pp. 384–391

v t e Decision theory
Core concepts	Ambiguity aversion Bounded rationality Choice architecture Expected utility Expected value Hyperbolic discounting Leximin Loss aversion Multi-attribute utility Path dependence Principle of indifference Prospect theory Rational choice theory Reference dependence Risk aversion Risk-seeking Satisficing Strategic dominance Subjective expected utility Sure-thing Utility theorem
Decision models	Anscombe-Aumann framework Causal decision Decision field theory Emotional choice Evidential decision Fuzzy-trace theory Intertemporal choice Naturalistic decision Normative model Quantum cognition Recognition-primed decision Rubicon model Savage's subjective expected utility model
Decision analysis tools	Analytic hierarchy process Analytic network process Cost–benefit analysis Cost-effectiveness analysis Cost–utility analysis Decision conferencing Decision curve analysis Decision rule Decision support system Decision table Decision tree Decision matrix Decisional balance sheet Gittins index Influence diagram Minimax MCDA Scoring rule Value of information perfect sample uncertainty
Paradoxes and biases	Allais paradox Certainty effect Cognitive bias Decoy effect Disposition effect Ellsberg paradox Endowment effect Framing effect Heuristics Newcomb's problem Pseudocertainty effect Rabin's paradox Regret St. Petersburg paradox Status quo bias Sunk cost
Uncertainty and risk	Deep uncertainty Exploration–exploitation Info-gap Pignistic probability Robust decision-making
Related fields	Behavioral economics Game theory Operations research Social choice theory Utility theory
Key people	David Blackwell Bruno de Finetti Morris H. DeGroot Peter C. Fishburn Gerd Gigerenzer Itzhak Gilboa Daniel Kahneman R. Duncan Luce Oskar Morgenstern Howard Raiffa Leonard J. Savage David Schmeidler Herbert Simon Amos Tversky John von Neumann Peter Wakker
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// Workers AI · dad joke modeWhat did pignistic probability say? "I pig-headedly think it's likely.

Notes

Further reading