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DOI: 10.4230/LIPIcs.FORC.2026.19

A Machine Learning Theory Perspective on Strategic Litigation

Authors: Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract

Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand. In a common law system, one way a case may have far-reaching impact is by establishing new legal precedent that later courts must follow. In this paper, we explore strategic litigation from the perspective of machine learning theory. We consider an abstract model of a common law legal system where a lower court decides new cases by applying a decision rule learned from a higher court’s past rulings. In this model, we explore the power of a strategic litigator, who strategically brings cases to the higher court to influence the decision rule applied by the lower court in future cases. We explore questions including: What impact can a strategic litigator have? Which cases should a strategic litigator bring to court? Does it ever make sense for a strategic litigator to bring a case when they are sure the court will rule against them? We show that this strategic case selection problem has interesting structure, with even simple settings exhibiting counterintuitive phenomena. When cases are represented by points in one dimension and the lower court’s learning algorithm is nearest neighbor, or as points in d dimensions and the lower court’s learning algorithm is a support vector machine, we characterize the set of inducible decision rules and develop algorithms for selecting an optimal set of cases to bring to the higher court given the strategic litigator’s objectives.

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Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen. A Machine Learning Theory Perspective on Strategic Litigation. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{dutz_et_al:LIPIcs.FORC.2026.19,
  author =	{Dutz, Melissa and Shao, Han and Blum, Avrim and Cohen, Aloni},
  title =	{{A Machine Learning Theory Perspective on Strategic Litigation}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{19:1--19:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.19},
  URN =		{urn:nbn:de:0030-drops-259921},
  doi =		{10.4230/LIPIcs.FORC.2026.19},
  annote =	{Keywords: Strategic Litigation, Machine Learning Theory, Law}
}

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DOI: 10.4230/LIPIcs.ITCS.2024.18

Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round

Authors: Avrim Blum and Melissa Dutz

Published in: LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

Abstract

Gameplay under various forms of uncertainty has been widely studied. Feldman et al. [Michal Feldman et al., 2010] studied a particularly low-information setting in which one observes the opponent’s actions but no payoffs, not even one’s own, and introduced an algorithm which guarantees one’s payoff nonetheless approaches the minimax optimal value (i.e., zero) in a symmetric zero-sum game. Against an opponent playing a minimax-optimal strategy, approaching the value of the game is the best one can hope to guarantee. However, a wealth of research in behavioral economics shows that people often do not make perfectly rational, optimal decisions. Here we consider whether it is possible to actually win in this setting if the opponent is behaviorally biased. We model several deterministic, biased opponents and show that even without knowing the game matrix in advance or observing any payoffs, it is possible to take advantage of each bias in order to win nearly every round (so long as the game has the property that each action beats and is beaten by at least one other action). We also provide a partial characterization of the kinds of biased strategies that can be exploited to win nearly every round, and provide algorithms for beating some kinds of biased strategies even when we don't know which strategy the opponent uses.

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Avrim Blum and Melissa Dutz. Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 18:1-18:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{blum_et_al:LIPIcs.ITCS.2024.18,
  author =	{Blum, Avrim and Dutz, Melissa},
  title =	{{Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round}},
  booktitle =	{15th Innovations in Theoretical Computer Science Conference (ITCS 2024)},
  pages =	{18:1--18:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-309-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{287},
  editor =	{Guruswami, Venkatesan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.18},
  URN =		{urn:nbn:de:0030-drops-195463},
  doi =		{10.4230/LIPIcs.ITCS.2024.18},
  annote =	{Keywords: Game theory, Behavioral bias}
}

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A Machine Learning Theory Perspective on Strategic Litigation

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Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round

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