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Algorithmic Contract Theory: A Survey

T0 review · 0 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Algorithmic contract theory maps the frontier of incentive design.

desk verdict A solid, useful survey whose internal proofs are careful and whose coverage, while explicitly partial and self-leaning, is good enough to be the standard entry point. read the letter →

arxiv 2412.16384 v1 pith:EATLKAY5 submitted 2024-12-20 cs.GT econ.TH

Paul Duetting , Michal Feldman , Inbal Talgam-Cohen This is my paper · ORCID
classification cs.GTecon.TH MSC 91B4168Q1768W25
keywords algorithmiccontracttheorymoralhazardprincipal-agentproblemlinearcontractscombinatorialdata-drivenlimitedliabilityset-functionhierarchy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey argues that contract theory, long a pillar of microeconomic theory, is being reshaped by an algorithmic perspective, and that the perspective is worth taking seriously. It claims that the computational lens delivers structural insights, such as linear contracts achieving a tight worst-case n-approximation to optimal contracts and optimal contracts being computable by solving n linear programs. It also maps tractability frontiers in combinatorial settings, where gross-substitutes reward functions admit polynomial-time solutions while submodular ones are hard to approximate. If the survey is right, contract design becomes a branch of algorithm design, with practical consequences for online platforms, AI-agent delegation, and data-driven pay-for-performance schemes. The survey closes by listing open problems that would define the field's next steps.

What carries the argument

The load-bearing object is the principal-agent model with discrete actions, stochastic outcomes, non-negative payments (limited liability), and a canonical tie-breaking rule. Around it, the survey uses three workhorses: the min-pay linear program, whose dual yields the implementability characterization and the at-most-n-minus-one non-zero payments bound; the upper-envelope geometry of linear contracts, which reduces linear-contract optimization to finding critical values of the agent's share alpha; and the hierarchy of set functions (additive, gross substitutes, submodular, XOS, subadditive) with value and demand oracle access, which organizes the combinatorial tractability results. The machinery also includes product distributions over multi-dimensional outcomes and their multilinear extensions, which allow exponentially many outcomes to be represented succinctly.

What would settle it

If a polynomial-time algorithm is found for computing optimal contracts with submodular success probability functions under value-oracle access, the survey's claimed tractability frontier falls.

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Extended reading notes

Core claim

The central claim is that the hidden-action principal-agent model, formulated as a Stackelberg game with limited liability, is a rich substrate for algorithmic study. The survey establishes that this model yields polynomial-time optimal contracts via n linear programs, characterizes implementable actions by a no-cheaper-convex-combination condition, and shows that linear contracts are robustly optimal under uncertainty about actions or distributions while suffering a tight worst-case n gap from optimal revenue. In combinatorial settings, the tractability frontier is set by the hierarchy of complement-free set functions: gross-substitutes success probabilities admit polynomial-time optimal contracts, while submodular ones are NP-hard to approximate beyond constant factors. The survey's overarching claim is that these results are not isolated: they form an emerging field of algorithmic contract theory, with data-driven contracts and incentive-aware machine learning as natural extensions.

Load-bearing premise

The survey's map of the field assumes that the papers it samples represent the main research trajectories and that the theorems it cites are correct as stated.

Editorial extensions

If this is right

  • Optimal contracts in explicitly represented settings are computable in polynomial time by solving one linear program per action.
  • Linear contracts approximate optimal revenue to within a factor of n in the worst case, and this bound is tight across natural parameters.
  • When only expected rewards are known, linear contracts maximize the principal's worst-case revenue.
  • In binary-outcome combinatorial settings, gross-substitutes reward functions admit polynomial-time optimal contracts, while submodular ones are NP-hard to approximate beyond a constant factor.
  • In multi-dimensional outcome spaces, near-optimal epsilon-incentive-compatible contracts can be computed in polynomial time for constantly many actions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The survey's recurring likelihood-ratio arguments suggest a deeper equivalence between optimal contract design and hypothesis testing that could yield new statistical design tools.
  • If algorithmic contract theory matures along the surveyed lines, it could supply standard incentive guarantees for delegating tasks to AI agents, where programmed agents behave according to the model's assumptions.
  • The tractability frontiers for submodular and XOS rewards mirror those in combinatorial auction theory, so algorithmic ideas may transfer in both directions.
  • The open question of whether some simple contract class gives a constant-factor approximation is likely to be settled by linking monotone contract classes to known inapproximability results.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 5 minor

Summary. The paper is a survey of the emerging field of algorithmic contract theory. It develops the standard principal-agent model with limited liability, presents the LP-based approach to optimal contracts, characterizes implementable actions, and identifies special cases with simple optimal contracts. It then studies linear contracts via an upper-envelope geometry, derives worst-case approximation guarantees, and proves robust (max-min) optimality results. A long portion of the survey is devoted to combinatorial contract design: combinatorial actions, multiple agents, combinatorial outcomes, and (per the table of contents) multiple principals, followed by typed agents, machine learning for contracts, contracts for machine learning, ambiguous contracts, contract design for social good, and beyond-contract incentives. The survey's central claim is that an algorithmic perspective yields structural insights and maps computational tractability frontiers, and it lists open problems. A running example (Example 2.1) is used pedagogically throughout the early sections.

Significance. If the survey's picture is accurate, it is a valuable synthesis and entry point for computer scientists working on contract theory. The internal mathematics presented in detail—LP duality in Section 3, the upper-envelope geometry in Section 4.1, the affine-to-linear reduction and Carroll's max-min theorem in Section 4.4—is coherent and checkable, and the authors are careful to mark proof sketches as sketches. Tables 3 and 4 consolidate a large body of approximation results and will be a useful reference. The main limitation is that the survey draws on a sample of the literature and cites the authors' own work heavily; footnote 1 acknowledges the sampling but no inclusion criteria are stated. Because the survey derives no new results, there is no circularity in the technical sense, and the claims are falsifiable only as a description of the literature. On balance, the strengths clearly outweigh this limitation for the intended introductory purpose.

minor comments (5)
  1. [Section 5.4] The proof sketch of Theorem 5.25 breaks off mid-argument at 'One approach to establishing that ...'; as submitted, the text does not finish the derivation a reader can scrutinize. Please complete the sketch or replace it with an explicit pointer to the proof in Dütting, Roughgarden, and Talgam-Cohen [2021b].
  2. [Footnote 1 / Section 5.3.1] The survey rightly notes that it presents only a sample of the literature, but because the map of the field and the stated open problems are built in part from the authors' own contributions, adding a brief paragraph on inclusion criteria (venues, time window, search terms) and citing a few recent independent follow-ups would help readers judge whether the 'main trajectories' are representative.
  3. [Section 5.3] The claim that restricting to linear contracts is without loss of generality in the multi-agent binary-action/binary-outcome model is stated without proof after Proposition 3.9; adding a one-sentence argument (zero fixed payments and normalize the success bonus) would make the survey self-contained.
  4. [Tables 3 and 4] Cells containing '1' denote exact polynomial-time construction results rather than approximation ratios; a short note near each table would prevent misreading of the approximation frontier.
  5. [Section 4.3, Example 4.4] The sentence 'for all actions i ∈ [n], the maximum utility the principal can extract from action i through a linear contract is (1−α_i)R_i = 1' holds because R_1=1; please make the R_1=1 normalization explicit for readability.

Circularity Check

0 steps flagged · score 0.0 of 10

Survey reports and organizes prior results; no derivation chain reduces to its own inputs.

full rationale

This is a survey, not a derivation. It makes no predictions, fits no parameters, and proves no new theorems. The theorems it presents, such as Proposition 3.1, Theorem 4.3, and Theorem 4.7, are attributed to specific external papers and are accompanied by proofs or proof sketches that proceed from stated assumptions (LP duality, upper-envelope geometry, robust worst-case analysis), not from the survey's own conclusions. The authors cite their own prior work extensively, but these citations are descriptive summaries of the literature rather than load-bearing inputs to a derived claim. The survey explicitly disclaims exhaustive coverage in footnote 1: 'we present only a sample of papers from the current main trajectories of research.' That is a representativeness limitation, not circularity; it concerns coverage and selection, not equivalence between an input and an output. The open problems are research suggestions, and the tables of tractability results summarize published theorems rather than being forced by a self-referential construction. Consequently, no circular step can be identified by quoting a specific reduction, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

This is a survey; it introduces no new free parameters, fitted values, or invented entities. The assumptions below are the standard modeling choices from the primary literature that the survey organizes; the survey itself does not validate or challenge them.

assumptions (4)
  • domain assumption Risk neutrality of the principal and the agent
    Section 2 states that both players are risk neutral, the default model in the survey. All discussed contracts are evaluated under this assumption.
  • domain assumption Limited liability: all transfers from principal to agent are non-negative
    Section 2, used throughout to rule out selling the project contracts and to define the LP for optimal contracts.
  • domain assumption Discrete action and outcome spaces
    Section 2 assumes finite sets of actions (n) and outcomes (m). Combinatorial sections generalize to 2^m outcomes or 2^n action subsets, but still discrete.
  • domain assumption Oracle access to set functions in combinatorial settings
    Section 5 defines value and demand oracles for accessing success probability functions; these are standard in algorithmic mechanism design and are inherited from the cited papers.

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Pith. "Pith review of Algorithmic Contract Theory: A Survey." pith.science (2026). https://pith.science/paper/EATLKAY5

@misc{pith2026241216384,
  author       = {Pith},
  title        = {Pith review of: Algorithmic Contract Theory: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EATLKAY5}},
  note         = {Machine review of arXiv:2412.16384}
}
read the original abstract

A contract is an economic tool used by a principal to incentivize one or more agents to exert effort on her behalf, by defining payments based on observable performance measures. A key challenge addressed by contracts -- known in economics as moral hazard -- is that, absent a properly set up contract, agents might engage in actions that are not in the principal's best interest. Another common feature of contracts is limited liability, which means that payments can go only from the principal -- who has the deep pocket -- to the agents. With classic applications of contract theory moving online, growing in scale, and becoming more data-driven, tools from contract theory become increasingly important for incentive-aware algorithm design. At the same time, algorithm design offers a whole new toolbox for reasoning about contracts, ranging from additional tools for studying the tradeoff between simple and optimal contracts, through a language for discussing the computational complexity of contracts in combinatorial settings, to a formalism for analyzing data-driven contracts. This survey aims to provide a computer science-friendly introduction to the basic concepts of contract theory. We give an overview of the emerging field of "algorithmic contract theory" and highlight work that showcases the potential for interaction between the two areas. We also discuss avenues for future research.

Figures

Figures reproduced from arXiv: 2412.16384 by the authors.

Figure 1
Figure 1. Salani´e [2017, Chapter 1.1] proposes to classify problems where an informed party inter￾acts with an uninformed party, along two dimensions: The first distinction is whether the private information bears on who the agent is (“hidden type”), or whether it bears on what action the agent takes (“hidden action”). The second distinction concerns the timing of the problem, and asks who moves first: the uninformed party o… view at source ↗
Figure 2
Figure 2. Timeline. the principal. For action i ∈ [n] let Ti := Ej∼qi [tj ] = X j∈[m] qij tj (2) denote the expected payment from principal to agent for taking action i. Both the principal and the agent are assumed to be risk neutral. For a fixed contract t, the agent’s expected utility under action i is UA(i | t) := Ti − ci . The principal’s expected utility (a.k.a. revenue) from action i under contract t is UP (i | t) := Ri… view at source ↗
Figure 3
Figure 3. The MINPAY-LP(i) for action i (left) and its dual (right). Remark 3.2. Note that the first constraint in MINPAY-LP(i) assumes that IR is implied by IC. Without this assumption, we would have to add an explicit non-negativity constraint. Namely, we would need to add the constraint P j qij tj − ci ≥ 0, requiring that the agent’s expected utility from action i is non-negative. We are now ready to prove Proposition 3.1.… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: MINPAY-LP(i) for action i with the objective min P j qij tj replaced with min 0 (left) and the dual to this LP (right). Action i is implementable if and only if the primal LP is feasible. By strong duality [e.g., Matou˘sek and G¨artner, 2006], for a general primal-dual…
Figure 5
Figure 5. Figure 5: The agent’s expected utility as a function of the linear contract’s parameter [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]
Figure 6
Figure 6. Figure 6: Illustration of the linear and affine contracts constructed in Sections [PITH_FULL_IMAGE:figures/full_fig_p029_6.png]
Figure 7
Figure 7. Figure 7: Upper envelopes of the agent’s utility. agent’s utility for the set {1, 2} is αf({1, 2}) − (c1 + c2) = 0.5 · 0.5 − 0.2 = 0.05, for the set {1, 3} it is αf({1, 3}) − (c1 + c3) = 0.5 · 0.8 − 0.5 = −0.1, for the set {2, 3} it is αf({2, 3}) − (c2 + c3) = 0.5·0.7−0.5 = −0.1…
Figure 8
Figure 8. Figure 8: An example of an XOS success probability [PITH_FULL_IMAGE:figures/full_fig_p045_8.png]
Figure 9
Figure 9. Figure 9: The MINPAY-LP(i) for action i (left) and its dual (right) for the multi-outcome model. Proposition 5.24 (D¨utting, Roughgarden, and Talgam-Cohen [2021b]). Consider a (generalized) binary-action contract setting with an m-dimensional outcome space. If the reward functio…
Figure 10
Figure 10. Figure 10: Visualization of the menu of contracts in Example [PITH_FULL_IMAGE:figures/full_fig_p071_10.png]
Figure 11
Figure 11. Figure 11: Visualization of the key quantities involved in applying Theorem [PITH_FULL_IMAGE:figures/full_fig_p076_11.png]
Figure 12
Figure 12. Figure 12: The condition of Proposition 8.1 for implementability of action i formulated as a dual LP (right), and its corresponding primal (left). The dual seeks a linear combination of the rows {qi ′}i ′∈[n] with non-negative coefficients, which minimizes the sum of coefficient…
Figure 13
Figure 13. Figure 13: The simplified evaluation model and its connection to contract design, shown in the [PITH_FULL_IMAGE:figures/full_fig_p092_13.png]
Figure 14
Figure 14. Figure 14: Timeline of ambiguous contracts. Compared to Figure [PITH_FULL_IMAGE:figures/full_fig_p098_14.png]

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Reference graph

Works this paper leans on

79 extracted references · 70 canonical work pages · cited by 1 Pith paper

  1. [5]

    Jordan, and Nika Haghtalab

    Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, and Nika Haghtalab. Delegating data collection in decentralized machine learning. In Proc. of AISTATS 2024, pages 478–486,

    work page 2024

  2. [11]

    Information design in the principal-agent problem

    Yakov Babichenko, Inbal Talgam-Cohen, Haifeng Xu, and Konstantin Zabarnyi. Information design in the principal-agent problem. In Proc. of EC 2024 ,

    work page 2024

  3. [12]

    Learning optimal contracts: How to exploit small action spaces

    Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, and Nicola Gatti. Learning optimal contracts: How to exploit small action spaces. In Proc. of ICLR 2024 ,

    work page 2024

  4. [13]

    Distributional robustness: From pricing to auctions

    Nir Bachrach and Inbal Talgam-Cohen. Distributional robustness: From pricing to auctions. In Proc. of EC 2022 , page 150,

    work page 2022

  5. [16]

    Delegated Pandora’s Box

    Curtis Bechtel, Shaddin Dughmi, and Neel Patel. Delegated Pandora’s Box. In Proc. of EC 2022 , pages 666–693,

    work page 2022

  6. [22]

    Yang Cai, Constantinos Daskalakis, and Christos H

    Forthcoming. Yang Cai, Constantinos Daskalakis, and Christos H. Papadimitriou. Optimum statistical estimation with strategic data sources. In Proc. of COLT 2015 , pages 280–296,

    work page 2015

  7. [23]

    Hiring for an uncertain task: Joint design of information and contracts

    Matteo Castiglioni and Junjie Chen. Hiring for an uncertain task: Joint design of information and contracts. In Proc. of SODA 2025 ,

    work page 2025

  8. [25]

    Multi-agent contract design: How to commission multiple agents with individual outcome

    Matteo Castiglioni, Alberto Marchesi, and Nicola Gatti. Multi-agent contract design: How to commission multiple agents with individual outcome. In Proc. of EC 2023 , pages 412–448, 2023a. Matteo Castiglioni, Alberto Marchesi, and Nicola Gatti. Designing menus of contracts efficiently: The power of randomization. Artif. Intell. , 318:103881, 2023b. An exte...

    work page 2023

Show all 79 references
  1. [27]

    Optimal scoring rule design

    Yiling Chen and Fang-Yi Yu. Optimal scoring rule design. Available at https://arxiv.org/abs/ 2107.07420,

    arXiv

  2. [28]

    Are bounded contracts learnable and approximately optimal? In Proc

    Yurong Chen, Zhaohua Chen, Xiaotie Deng, and Zhiyi Huang. Are bounded contracts learnable and approximately optimal? In Proc. of EC 2024 ,

    2024

  3. [30]

    The value of ambiguous commitments in multi- follower games

    Natalie Collina, Rabanus Derr, and Aaron Roth. The value of ambiguous commitments in multi- follower games. Available at https://arxiv.org/abs/2409.05608,

    arXiv

  4. [31]

    Automated mechanism design: complexity results stem- ming from the single-agent setting

    Vincent Conitzer and Tuomas Sandholm. Automated mechanism design: complexity results stem- ming from the single-agent setting. In Proc. of ICEC 2003 , pages 17–24,

    2003

  5. [32]

    On supermodular contracts and dense subgraphs

    Ramiro Deo-Campo Vuong, Shaddin Dughmi, Neel Patel, and Aditya Prasad. On supermodular contracts and dense subgraphs. In Proc. of SODA 2024 , pages 109–132,

    2024

  6. [33]

    Simple versus optimal contracts

    Paul D¨ utting, Tim Roughgarden, and Inbal Talgam-Cohen. Simple versus optimal contracts. In Proc. of EC 2019, pages 369–387,

    2019

  7. [34]

    Full version available athttps://arxiv.org/pdf/1808. 03713. Paul D¨ utting, Tomer Ezra, Michal Feldman, and Thomas Kesselheim. Combinatorial contracts. In Proc. of FOCS 2021 , pages 815–826, 2021a. Paul D¨ utting, Tim Roughgarden, and Inbal Talgam-Cohen. The complexity of cont...

    2021

  8. [35]

    Multi-agent contracts

    Paul D¨ utting, Tomer Ezra, Michal Feldman, and Thomas Kesselheim. Multi-agent contracts. In Proc. of STOC 2023 , pages 1311–1324, 2023a. Paul D¨ utting, Guru Guruganesh, Jon Schneider, and Joshua Wang. Optimal no-regret learning of one-sided Lipschitz functions. In Proc. of I...

    2023

  9. [36]

    Multi-agent combinatorial contracts

    Paul D¨ utting, Tomer Ezra, Michal Feldman, and Thomas Kesselheim. Multi-agent combinatorial contracts. In Proc. of SODA 2025 ,

    2025

  10. [38]

    Motivation and incentives in an online labor market

    Sebastian Fest, Ola Kvaløy, Petra Nieken, and Anja Sch¨ ottner. Motivation and incentives in an online labor market. Available at https://www.econstor.eu/bitstream/10419/224586/1/ vfs-2020-pid-39843.pdf ,

    2020

  11. [39]

    Goemans, Vahab S

    Lisa Fleischer, Michel X. Goemans, Vahab S. Mirrokni, and Maxim Sviridenko. Tight approxi- mation algorithms for maximum general assignment problems. In Proc. of SODA 2006 , pages 611–620,

    2006

  12. [40]

    Frazier, David Kempe, Jon M

    Peter I. Frazier, David Kempe, Jon M. Kleinberg, and Robert Kleinberg. Incentivizing exploration. In Proc. of EC 2014 , pages 5–22,

    2014

  13. [42]

    Optimal monitoring design

    George Georgiadis and Balazs Szentes. Optimal monitoring design. Econometrica, 88(5):2075–2107,

  14. [43]

    Strategic classi- fication in the dark

    Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, and Nir Rosenfeld. Strategic classi- fication in the dark. In Proc. of ICML 2021 , pages 3672–3681,

    2021

  15. [44]

    Computing optimal bundles for sponsored search

    Arpita Ghosh, Hamid Nazerzadeh, and Mukund Sundararajan. Computing optimal bundles for sponsored search. In WINE 2007 , pages 576–583,

    2007

  16. [45]

    Contracts under moral hazard and adverse selection

    Guru Guruganesh, Jon Schneider, and Joshua Wang. Contracts under moral hazard and adverse selection. In Proc. of EC 2021 , pages 563–582,

    2021

  17. [46]

    The power of menus in contract design

    Guru Guruganesh, Jon Schneider, Joshua Wang, and Junyao Zhao. The power of menus in contract design. In Proc. of EC 2023 , pages 818–848,

    2023

  18. [47]

    Wang, and S

    116 Guru Guruganesh, Yoav Kolumbus, Jon Schneider, Inbal Talgam-Cohen, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Joshua R. Wang, and S. Matthew Weinberg. Contracting with a learning agent. In Proc. of NeurIPS 2024 ,

    2024

  19. [49]

    Nika Haghtalab, Nicole Immorlica, Brendan Lucier, and Jack Z. Wang. Maximizing welfare with incentive-aware evaluation mechanisms. In Proc. of IJCAI 2020 , pages 160–166,

    2020

  20. [52]

    Hartline, Liren Shan, Yingkai Li, and Yifan Wu

    Jason D. Hartline, Liren Shan, Yingkai Li, and Yifan Wu. Optimal scoring rules for multi- dimensional effort. In Proc. of COLT 2023 , pages 2624–2650,

    2023

  21. [55]

    How do classifiers induce agents to invest effort strategically? In Proc

    Jon Kleinberg and Manish Raghavan. How do classifiers induce agents to invest effort strategically? In Proc. of EC 2019 , pages 825–844,

    2019

  22. [56]

    Kleinberg and Robert Kleinberg

    Jon M. Kleinberg and Robert Kleinberg. Delegated search approximates efficient search. In Proc. of EC 2018 , pages 287–302,

    2018

  23. [57]

    Kleinberg and Frank Thomson Leighton

    Robert D. Kleinberg and Frank Thomson Leighton. The value of knowing a demand curve: Bounds on regret for online posted-price auctions. In Proc. of FOCS 2003 , pages 594–605,

    2003

  24. [58]

    Kovalyov and Erwin Pesch

    Mikhail T. Kovalyov and Erwin Pesch. A generic approach to proving NP-hardness of partition type problems. Discrete Appl. Math. , 158:1908–1912,

    1908

  25. [59]

    Contract design for afforestation programs

    Wanyi Dai Li, Nicole Immorlica, and Brendan Lucier. Contract design for afforestation programs. In Proc. of WINE 2021 , pages 113–130,

    2021

  26. [60]

    Screening signal-manipulating agents via contests

    Yingkai Li and Xiaoyun Qiu. Screening signal-manipulating agents via contests. Available at https://arxiv.org/abs/2302.09168,

  27. [61]

    Hartline, Liren Shan, and Yifan Wu

    Yingkai Li, Jason D. Hartline, Liren Shan, and Yifan Wu. Optimization of scoring rules. In Proc. of EC 2022 , pages 988–989,

    2022

  28. [62]

    Liu, Nikhil Garg, and Christian Borgs

    Lydia T. Liu, Nikhil Garg, and Christian Borgs. Strategic ranking. In Proc. of AISTATS 2022 , pages 2489–2518,

    2022

  29. [63]

    Perdomo, Tijana Zrnic, and Moritz Hardt

    Celestine Mendler-D¨ unner, Juan C. Perdomo, Tijana Zrnic, and Moritz Hardt. Stochastic opti- mization for performative prediction. In Proc. of NeurIPS 2020 ,

    2020

  30. [65]

    On the pseudo-dimension of nearly optimal auctions

    Jamie Morgenstern and Tim Roughgarden. On the pseudo-dimension of nearly optimal auctions. In Proc. of NeurIPS 2015 , pages 136–144,

    2015

  31. [67]

    The 2016 Nobel Prize in Economics: Scientific background

    nobelprize.org. The 2016 Nobel Prize in Economics: Scientific background. Available at https: //nobelprize.org/prizes/economic-sciences/2016/advanced-information/,

    2016

  32. [68]

    Contracts with information acquisition, via scoring rules

    Maneesha Papireddygari and Bo Waggoner. Contracts with information acquisition, via scoring rules. In Proc. of EC 2022 , pages 703–704,

    2022

  33. [69]

    Optimal robust contract design

    Bo Peng and Zhihao Gavin Tang. Optimal robust contract design. In Proc. of EC 2024 ,

    2024

  34. [70]

    Forthcoming. Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-D¨ unner, and Moritz Hardt. Performative pre- diction. In Proc. of ICML 2020 , pages 7599–7609,

    2020

  35. [71]

    Multi-agent performative prediction: From global stability and optimality to chaos

    Georgios Piliouras and Fang-Yi Yu. Multi-agent performative prediction: From global stability and optimality to chaos. In Proc. of EC 2023 , pages 1047–1074,

    2023

  36. [72]

    Why prices need algorithms

    Tim Roughgarden and Inbal Talgam-Cohen. Why prices need algorithms. In Proc. of EC 2015 , pages 19–36,

    2015

  37. [73]

    Delegated classification

    Eden Saig, Inbal Talgam-Cohen, and Nir Rosenfeld. Delegated classification. In Proc. of NeurIPS 2023,

    2023

  38. [74]

    Incentivizing quality text generation via statis- tical contracts

    Eden Saig, Ohad Einav, and Inbal Talgam-Cohen. Incentivizing quality text generation via statis- tical contracts. In Proc. of NeurIPS 2024 ,

    2024

  39. [76]

    Tonghan Wang, Paul D¨ utting, Dmitry Ivanov, Inbal Talgam-Cohen, and David C. Parkes. Deep contract design via discontinuous networks. In Proc. of NeuRIPS 2023 ,

    2023

  40. [77]

    Contractual reinforcement learning: Pulling arms with invisible hands

    Jibang Wu, Siyu Chen, Mengdi Wang, Huazheng Wang, and Haifeng Xu. Contractual reinforcement learning: Pulling arms with invisible hands. Available at https://arxiv.org/abs/2407.01458,

    arXiv

  41. [78]

    Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, and Michael I. Jordan. The sample complexity of online contract design. In Proc. of EC 2023 , page 1188,

    2023

  42. [79]

    New perspectives in online contract design: Heterogeneous, homogeneous, non-myopic agents and team production

    Shiliang Zuo. New perspectives in online contract design: Heterogeneous, homogeneous, non-myopic agents and team production. Available at https://arxiv.org/abs/2403.07143,

    arXiv

  43. [1971]

    Learning approximately optimal contracts

    Alon Cohen, Argyrios Deligkas, and Moran Koren. Learning approximately optimal contracts. In Proc. of SAGT 2022 , pages 331–346,

    2022

  44. [1975]

    (Published in 1999 in the Rev. Econ. Stud. , 66(1), 3–21.). Felix Mohr, Tom J. Viering, Marco Loog, and Jan N. van Rijn. LCDB 1.0: An extensive learning curves database for classification tasks. In Proc. ECML PKDD 2022 , pages 3–19,

    1999

  45. [1979]

    Papadimitriou, and Mary Wootters

    Moritz Hardt, Nimrod Megiddo, Christos H. Papadimitriou, and Mary Wootters. Strategic classi- fication. In Proc. of ITCS 2016 , pages 111–122,

    2016

  46. [1980]

    Hadfield

    Dylan Hadfield-Menell and Gillian K. Hadfield. Incomplete contracting and AI alignment. In Proc. of AIES 2019 , pages 417–422,

    2019

  47. [1982]

    Simple delegated choice

    Ali Khodabakhsh, Emmanouil Pountourakis, and Samuel Taggart. Simple delegated choice. In Proc. of SODA 2024 , pages 569–590,

    2024

  48. [1983]

    Matthew Weinberg

    Eric Neyman, Georgy Noarov, and S. Matthew Weinberg. Binary scoring rules that incentivize precision. In Proc. of EC 2021 , pages 718–733,

    2021

  49. [1985]

    Generalized principal-agency: Contracts, information, games and beyond

    Jiarui Gan, Minbiao Han, Jibang Wu, and Haifeng Xu. Generalized principal-agency: Contracts, information, games and beyond. In Proc. of WINE 2024 ,

    2024

  50. [1988]

    Hartline and Tim Roughgarden

    Jason D. Hartline and Tim Roughgarden. Simple versus optimal mechanisms. In Proc. of EC 2009, pages 225–234,

    2009

  51. [1990]

    Principal- agent reinforcement learning: Orchestrating AI agents with contracts

    Dimitry Ivanov, Paul D¨ utting, David Parkes, Inbal Talgam-Cohen, and Tonghan Wang. Principal- agent reinforcement learning: Orchestrating AI agents with contracts. Available at https: //arxiv.org/abs/2407.18074,

    arXiv

  52. [1995]

    Procaccia, Inbal Talgam-Cohen, and Jamie Tucker-Foltz

    Tal Alon, Magdalen Dobson, Ariel D. Procaccia, Inbal Talgam-Cohen, and Jamie Tucker-Foltz. Multiagent evaluation mechanisms. In Proc. of AAAI 2020 , pages 1774–1781,

    2020

  53. [1999]

    On the (in)approximability of combinatorial contracts

    Tomer Ezra, Michal Feldman, and Maya Schlesinger. On the (in)approximability of combinatorial contracts. In Proc. of ITCS 2024 , pages 44:1–44:22, 2024a. Tomer Ezra, Michal Feldman, and Maya Schlesinger. Sequential contracts. Available at https: //arxiv.org/abs/2403.09545, 202...

    2024 arXiv

  54. [2005]

    Non-approximability results for optimization problems on bounded degree instances

    Luca Trevisan. Non-approximability results for optimization problems on bounded degree instances. In Proc. of STOC 2001 , pages 453–461,

    2001

  55. [2006]

    Free-riding and free-labor in combinatorial agency

    Moshe Babaioff, Michal Feldman, and Noam Nisan. Free-riding and free-labor in combinatorial agency. In Proc. of SAGT 2009 , pages 109–121,

    2009

  56. [2009]

    Contracting with a reinforcement learning agent by playing trick or treat

    Matteo Bollini, Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, and Nicola Gatti. Contracting with a reinforcement learning agent by playing trick or treat. Available at https: //arxiv.org/abs/2410.13520,

    arXiv

  57. [2010]

    Stackelberg games for adversarial prediction problems

    Michael Br¨ uckner and Tobias Scheffer. Stackelberg games for adversarial prediction problems. In Proc. of KDD 2011 , pages 547–555,

    2011

  58. [2011]

    Multi-agent con- tract design beyond binary actions

    Federico Cacciamani, Martino Bernasconi, Matteo Castiglioni, and Nicola Gatti. Multi-agent con- tract design beyond binary actions. In Proc. of EC 2024 ,

    2024

  59. [2012]

    Gonczarowski, Brendan Lucier, and Inbal Talgam- Cohen

    Moshe Babaioff, Michal Feldman, Yannai A. Gonczarowski, Brendan Lucier, and Inbal Talgam- Cohen. Escaping cannibalization? Correlation-robust pricing for a unit-demand buyer. InProc. of EC 2020 , page 191,

    2020

  60. [2013]

    Combinatorial agency

    Moshe Babaioff, Michal Feldman, and Noam Nisan. Combinatorial agency. In Proc. of EC 2006 , pages 18–28,

    2006

  61. [2015]

    Agent-designed contracts: How to sell hidden actions

    Martino Bernasconi, Matteo Castiglioni, and Andrea Celli. Agent-designed contracts: How to sell hidden actions. In Proc. of EC 2024 ,

    2024

  62. [2016]

    Should the timing of inspections be predictable? In Proc

    Ian Ball and Jan Knoepfle. Should the timing of inspections be predictable? In Proc. of EC 2023 , page 206,

    2023

  63. [2017]

    Delegated online search

    Pirmin Braun, Niklas Hahn, Martin Hoefer, and Conrad Schecker. Delegated online search. In Proc. of IJCAI 2023 , pages 2528–2536,

    2023

  64. [2018]

    Azar, Constantinos Daskalakis, Silvio Micali, and S

    Pablo D. Azar, Constantinos Daskalakis, Silvio Micali, and S. Matthew Weinberg. Optimal and efficient parametric auctions. In Proc. of SODA 2013 , pages 596–604,

    2013

  65. [2019]

    Delegated stochastic probing

    111 Curtis Bechtel and Shaddin Dughmi. Delegated stochastic probing. In Proc. of ITCS 2021 , pages 37:1–37:19,

    2021

  66. [2020]

    Contracts with private cost per unit-of-effort

    Tal Alon, Paul D¨ utting, and Inbal Talgam-Cohen. Contracts with private cost per unit-of-effort. In Proc. of EC 2021 , pages 52–69,

    2021

  67. [2021]

    Bayesian analysis of linear contracts

    Tal Alon, Paul D¨ utting, Yingkai Li, and Inbal Talgam-Cohen. Bayesian analysis of linear contracts. In Proc. of EC 2023 ,

    2023

  68. [2022]

    A reduction from multi-parameter to single-parameter Bayesian contract design

    Matteo Castiglioni, Junjie Chen, Minming Li, Haifeng Xu, and Song Zuo. A reduction from multi-parameter to single-parameter Bayesian contract design. In Proc. of SODA 2025 ,

    2025

  69. [2023]

    Tal Alon, Ron Lavi, Elisheva S

    Full version available at: https://arxiv.org/abs/2211.06850. Tal Alon, Ron Lavi, Elisheva S. Shamash, and Inbal Talgam-Cohen. Technical note–incomplete information VCG contracts for common agency. Oper. Res., 72(1):288–299,

    arXiv

  70. [2024]

    Robust contracts: A revealed preference approach

    Nemanja Antic and George Georgiadis. Robust contracts: A revealed preference approach. In Proc. of EC 2023 , page 112,

    2023

  71. [2025]

    Matteo Castiglioni, Alberto Marchesi, and Nicola Gatti

    To appear. Matteo Castiglioni, Alberto Marchesi, and Nicola Gatti. Bayesian agency: Linear versus tractable contracts. In Proc. EC 2021 , pages 285–286,

    2021

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.