2020
DOI: 10.1111/nous.12353
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Tournament decision theory

Abstract: In this paper, I diagnose the problems facing causal decision theory as a special case of the general phenomenon of decision-dependence-the possibility that some input into our evaluation of options can be affected by the decision we end up making. I go on to develop a new decision theory, which treats each choice as a tournament between options competing in pairs, and show how it can capture attractive features of the causal view while avoiding the problems generated by decisiondependence.

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Cited by 14 publications
(3 citation statements)
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“…This often suggested. But there are many Condorcet methods developed in voting theory that seem promising; see Podgorski (2022) for recent work along these lines. In an earlier version of this paper, available on my website, I suggest the 'beatpath' method (Schulze, 2011).…”
Section: Maximizationmentioning
confidence: 99%
“…This often suggested. But there are many Condorcet methods developed in voting theory that seem promising; see Podgorski (2022) for recent work along these lines. In an earlier version of this paper, available on my website, I suggest the 'beatpath' method (Schulze, 2011).…”
Section: Maximizationmentioning
confidence: 99%
“…Despite their origins in social choice theory, tournament solutions have found applications in a wide range of areas including game theory (Fisher and Ryan 1995), webpage ranking (Brandt and Fischer 2007), dueling bandit problems (Ramamohan, Rajkumar, and Agarwal 2016), and philosophical decision theory (Podgorski 2020). As is the case for social choice theory in general, early studies of tournament solutions were primarily based on the axiomatic approach.…”
Section: Related Workmentioning
confidence: 99%
“…Laslier and Laslier (2017) analyze when the limiting distributions of certain reinforcement learning models based on tournaments converge to the maximin solution. Podgorski's (2022) work provides an application of tournament games in decision theory. An example in evolutionary biology is the work by Grilli et al (2017).…”
mentioning
confidence: 99%