The logic of decision

By Franco Taroni, A. Biedermann, Silvia Bozza, Paolo Garbolino, C. Aitken. 2014

693 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/d66c5ac2658e335d44df86aad06b4113e4093e5f

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Citations in the corpus

Making Context Aware Decision from Uncertain Information in a Smart Home: A Markov Logic Network Approach (2013, methodology): “To account for the uncertainty in the evidence, we extended the approach by using the Jeﬀrey’s rule [12] to estimate the probability of the best action.”
Dynamic inference and everyday conditional reasoning in the new paradigm (2013, background): “One possibility is that update rules that can handle uncertainty in all premises are needed such as Jeffrey conditionalisation (Jeffrey, 1983; Over & Hadjichristidis, 2009; Zhao & Osherson, 2010).”
Why humans are (sometimes) less rational than other animals: Cognitive complexity and the axioms of rational choice (2013, background): “…formal analyses that if people’s preferences follow certain logical patterns (the so-called axioms of choice) then they are behaving as if they are maximising utility (Bermudez, 2009; Edwards, 1954; Gilboa, 2010; Jeffrey, 1983; Luce & Raiffa, 1957; Savage, 1954; von Neumann & Morgenstern, 1944).”
Causal and Counterfactual Inference (2018, background): “Yet the question of what function should substitute for Px(y), despite decades of thoughtful debates (Jeffrey, 1965; Harper et al., 1981; Cartwright, 1983) seems to still baffle philosophers in the 21st century (Arlo-Costa, 2007; Weirich, 2008; Woodward, 2003).”
Navigating Into the Future or Driven by the Past (2013, background): “Schema (1)like expectation-based learning systems have many advantages over pure associative learning (Gallistel & Gibbon, 2000) and possess four key a priori epistemic features: (a) they permit learning from experience in Carnap’s sense; (b) other things equal, the influence of initial expectations”
Agent Foundations for Aligning Machine Intelligence with Human Interests: A Technical Research Agenda (2017, background): “This requires a better understanding of the theoretical underpinnings of logical uncertainty, to more fully characterize what it means for logically uncertain reasoning to be “reliable and trustworthy” (Soares and Fallenstein 2015a).”
Types of Uncertainty (2014, background): “Evidentialists such as Richard Je↵rey (1965) regard the conditional probability, given that an action is performed, as giving the correct measure of the uncertainty associated with acting, while causal decision theorists such as James Joyce (1999) argue that what is required is a measure of the prob”
Aligning Superintelligence with Human Interests: A Technical Research Agenda (2015, background): “Before trusting a system to attain superintelligence, it seems prudent to ensure that the agent uses appropriate caution when reasoning about successor agents.”
Towards a formulation of quantum theory as a causally neutral theory of Bayesian inference (2011, methodology): “The debate over whether Jeffrey conditioning should be subsumed into Bayesian conditioning is somewhat analogous to a similar argument in quantum theory about whether POVMs should be regarded as fundamental, since they can always be represented by projective measurements on a larger Hilbert space vi”