Knowledge Tree
decision-making processes
Also known as: decision-making processes, decision processes
Facts (15)
Sources
Quantum Approaches to Consciousness plato.stanford.edu Nov 30, 2004 3 facts
claimResearchers studying mental quantum features have addressed seven specific psychological phenomena: decision processes, order effects, bistable perception, learning, semantic networks, quantum agency, and super-quantum entanglement correlations.
claimBusemeyer et al. found that Hilbert space models of decision processes align with empirical data, whereas classical alternatives do not.
claimAerts and Aerts (1994) published early work on the application of quantum models to decision processes.
Quantum Approaches to Consciousness plato.stanford.edu Nov 30, 2004 3 facts
referenceAerts D. and Aerts S. published 'Applications of quantum statistics in psychological studies of decision processes' in Foundations of Science in 1994.
referenceBusemeyer et al. (2006) published a detailed model of decision processes that defines probabilities for decision outcomes and decision times using quantum probability amplitudes, finding agreement with empirical data where classical alternatives disagreed.
claimFive categories of psychological phenomena have been addressed using mental quantum features: (i) decision processes, (ii) semantic networks, (iii) bistable perception, (iv) learning, and (v) order effects in questionnaires.
Self-Consciousness - Open Encyclopedia of Cognitive Science oecs.mit.edu Jul 24, 2024 2 facts
referenceThe simulationist approach to mind reading posits that humans make sense of others and coordinate socially by working outwards from their own decision-making processes and self-understanding, as supported by Heal (1986) and Goldman (2006).
claimAccording to the simulationist approach, individuals derive judgments about the mental states of others by using analogy from the mental states they believe they would have in comparable situations, and then use these judgments as inputs for their own decision-making processes run offline to predict the behavior of others.
Energy asset stranding in resource-rich developing countries and ... frontiersin.org Jun 10, 2024 2 facts
claimActively involving local communities in decision-making processes and ensuring fair compensation can mitigate the negative effects of energy projects and promote a more inclusive and equitable transition.
procedureCommunity benefit sharing strategies can include supporting local infrastructure developments, providing fair compensation for land use, and involving communities in decision-making processes.
Quantum Approaches to Consciousness plato.stanford.edu Nov 30, 2004 2 facts
claimResearchers in 2019 found evidence for quantum interference effects in the way confidence dynamically builds up during decision processes, noting that these effects are absent in classical models.
referenceAerts and Aerts (1994) provided an early precursor of work on decision processes, while Busemeyer et al. (2006) published the first detailed account defining probabilities for decision outcomes and decision times using quantum probability amplitudes.
How Enterprise AI, powered by Knowledge Graphs, is ... blog.metaphacts.com Oct 7, 2025 1 fact
quoteAs artificial intelligence systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their outputs is crucial. To earn human trust, LLMs must be well calibrated such that they can accurately assess and communicate the likelihood of their predictions being correct.
Non-Reductive Physicalism - Theories of Consciousness theoriesofconsciousness.com 1 fact
quoteAlan Sanfey states: "Decision-making processes require explanation at multiple levels—neural, cognitive, and social—with irreducible properties at each level."
LLM Hallucinations: Causes, Consequences, Prevention - LLMs llmmodels.org May 10, 2024 1 fact
claimProviding insights into Large Language Model decision-making processes and algorithms helps users understand how the model arrived at a particular output, reduces the likelihood of biased or inaccurate outputs, and increases trust in the model's outputs.