arxiv: 2605.11294 · v1 · submitted 2026-05-11 · 💻 cs.MA

Recognition: no theorem link

Information and Contract Design for Repeated Interactions between Agents with Misaligned Incentives

Nanda Kishore Sreenivas , Kate Larson

Authors on Pith no claims yet

Pith reviewed 2026-05-13 00:46 UTC · model grok-4.3

classification 💻 cs.MA

keywords information asymmetrycontract designmulti-agent systemsmisaligned incentivesrepeated interactionslinear contractscommunication strategiesfairness

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The pith

A Sender with private information learns an optimal communication strategy that a Receiver follows in repeated interactions, and uses linear contracts to extract surplus despite misaligned incentives.

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

The paper models interactions between a Sender holding private information and a Receiver who must act but relies on that information, under misaligned incentives. Through repeated play, the Sender develops a communication strategy that the Receiver consistently uses, with the strategy depending on how much the agents' rewards conflict and how much the Receiver already knows about the environment. The authors introduce linear contracts that set a price for the information, allowing the Sender to increase its own rewards while taking a large portion of the Receiver's surplus, which raises issues of fairness in such systems.

Core claim

The Sender learns an optimal communication strategy that the Receiver reliably acts on, and this strategy is highly sensitive to the degree of conflict in the agents' rewards and the amount of environmental information the Receiver can already observe. Linear contracts enable the Sender to improve its rewards by pricing the information, but this results in the Sender extracting much of the Receiver's surplus.

What carries the argument

The linear contract mechanism for pricing information exchanged in repeated interactions between agents with misaligned incentives.

If this is right

The Sender's learned communication strategy varies with the level of reward conflict between the two agents.
The strategy also changes based on the amount of prior environmental information available to the Receiver.
Linear contracts allow the Sender to improve its own performance by charging for information.
This improvement for the Sender comes with reduced fairness as it captures much of the Receiver's surplus.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This setup implies that in applications like autonomous agents in markets, information asymmetry could lead to one party dominating through learned contracts.
Fairness concerns might require external mechanisms to balance surplus extraction in multi-agent learning systems.
Extending to cases where both agents learn simultaneously could reveal different dynamics in strategy formation.

Load-bearing premise

That the Sender can reliably learn an optimal communication strategy and that linear contracts can be formed and used to price information effectively in repeated interactions.

What would settle it

An experiment where the Receiver does not reliably follow the Sender's communication strategy even after many interactions, or where introducing linear contracts fails to increase the Sender's rewards while decreasing the Receiver's.

Figures

Figures reproduced from arXiv: 2605.11294 by Kate Larson, Nanda Kishore Sreenivas.

**Figure 1.** Figure 1: Results for Recommendation Letter the same as before, but with an enlarged context (the signalling strategy and the proposed contract), but with the caveat that if the contract is rejected, the Sender sends no signal as to the strength of the student and so the Receiver must make a decision (hire/don’t hire) without information. Figure 1b shows the overall contract proposals made by the Sender, while Figur… view at source ↗

**Figure 2.** Figure 2: The full environment is a 10 × 10 grid; for clarity, we display a 4×4 subsection illustrating the key elements. The robot marks the Receiver and objects are shown on the map. The Sender observes the full grid, while the Receiver sees only the highlighted cells (for visibility v = 1). We first observe that the signalling strategy of the Sender quickly converges to the optimal signalling strategies we observ… view at source ↗

**Figure 3.** Figure 3: Gridworld: Average selection frequencies of each signaling strategy [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗

**Figure 5.** Figure 5: Average selection percentages of signalling strategies [PITH_FULL_IMAGE:figures/full_fig_p029_5.png] view at source ↗

**Figure 6.** Figure 6: Contract strategies in gridworld: Heatmaps depicting average per 32 [PITH_FULL_IMAGE:figures/full_fig_p032_6.png] view at source ↗

**Figure 7.** Figure 7: Alternate visualization of Figure 3. Signaling strategies in grid [PITH_FULL_IMAGE:figures/full_fig_p033_7.png] view at source ↗

read the original abstract

We study the consequences of information asymmetries and misaligned incentives in settings with multiple independent agents. We model an interaction between a Sender, who holds vital private information but cannot act, and a Receiver, who must make decisions but is dependent on the Sender's information. We find that the Sender learns an optimal communication strategy that the Receiver reliably acts on. Importantly, this strategy is highly sensitive to the degree of conflict in the agents' rewards and the amount of environmental information the Receiver can already observe. We introduce a mechanism allowing the agents to form linear contracts, where a price is established for the information. We demonstrate that the Sender learns to use these payment structures to improve its rewards, though this comes at a cost of "fairness" between agents as the Sender is able to extract much of the Receiver's surplus. This raises questions about fairness, contract design, and learning in the context of multi-agent systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper models repeated sender-receiver games with RL-learned communication and adds linear contracts, showing strategy sensitivity to conflict and prior info plus surplus extraction by the sender.

read the letter

The core of this work is a repeated sender-receiver interaction where the sender holds private environmental information and both agents adapt via reinforcement learning. The sender develops a communication policy that the receiver follows, but the policy shifts noticeably with the degree of reward misalignment and how much the receiver can already observe independently. They then introduce linear contracts that price signals, and the simulations show the sender using them to raise its own payoffs while pulling surplus away from the receiver, which reduces measured fairness between the two.

Referee Report

0 major / 3 minor

Summary. The paper models repeated interactions between a Sender with private environmental information and a Receiver who must choose actions, under misaligned reward functions. Using reinforcement learning, the Sender learns a communication policy that the Receiver conditions on; this policy is shown to be sensitive to the degree of reward conflict and the Receiver's direct observability of the environment. The authors introduce linear contracts that price signals, allowing the Sender to extract surplus from the Receiver and improve its own payoff, at the expense of fairness between the agents.

Significance. If the simulation results hold, the work contributes to multi-agent systems research on information design and contract mechanisms under asymmetry. Credit is given for the explicit repeated-game formulation, action spaces, reward functions, and RL training loop defined in Sections 3 and 4, together with the reported sensitivity to conflict degree and surplus-extraction outcomes. These elements provide a concrete empirical basis for discussing fairness costs in learned contracts.

minor comments (3)

[§3.3] §3.3: The linear contract is introduced as a payment for signals, but the exact functional form (e.g., whether the price is per-signal or per-period) is stated only in prose; an equation would remove ambiguity for readers implementing the mechanism.
[§4.2] §4.2, results on fairness: The claim that the Sender extracts 'much of the Receiver's surplus' is supported by the plots, yet the precise fairness metric (e.g., reward ratio, utility difference, or Gini coefficient) is not defined in the text or caption; this affects interpretability of the cost.
[Figure 3] Figure 3: The sensitivity plots to conflict degree and Receiver information would benefit from error bars or shaded regions indicating variability across random seeds, as is standard for RL experiments.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary and recommendation of minor revision. We appreciate the recognition of the explicit repeated-game formulation, action spaces, reward functions, RL training loop, and sensitivity analyses as providing a concrete empirical basis for discussing fairness in multi-agent contract design.

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper explicitly defines the repeated game, information structure, linear contract mechanism, and RL training loop for the Sender's policy in Sections 3 and 4, including environment, action spaces, and reward functions. The reported sensitivity of the learned communication strategy to conflict degree and Receiver information, along with surplus extraction, follows directly from these definitions and simulation outputs without reducing to self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. No step equates a claimed result to its inputs by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 1 invented entities

The central claim rests on assumptions about agent learning in repeated games and the viability of linear contracts, with key parameters for incentive conflict and information levels; no independent evidence for the new mechanism is provided.

free parameters (2)

degree of conflict in agents' rewards
The communication strategy is stated to be highly sensitive to this value, making it a core variable parameter in the model.
amount of environmental information the Receiver can observe
Sensitivity of the strategy to this quantity indicates it is a fitted or varied input parameter.

axioms (2)

domain assumption Agents learn optimal communication and contract strategies through repeated interactions.
The paper states that the Sender learns an optimal strategy and uses contracts to improve rewards.
ad hoc to paper Linear contracts can be formed to establish a price for information.
The mechanism is introduced by the authors to address misaligned incentives.

invented entities (1)

Linear contract mechanism for information pricing no independent evidence
purpose: To enable the Sender to extract surplus from the Receiver via payments for information.
New mechanism proposed in the paper to improve Sender rewards while highlighting fairness trade-offs.

pith-pipeline@v0.9.0 · 5453 in / 1637 out tokens · 103102 ms · 2026-05-13T00:46:39.410870+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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C. Zhu, M. Dastani, and S. Wang. A survey of multi-agent deep rein- forcement learning with communication.Autonomous Agents and Multi- Agent Systems, 38(4), 2024. 21 Appendix A Modeling Choices We study settings where there are two different agents, aSender (S)and a Receiver (R), interacting in some environment. There are some key asym- metries in the pro...

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S can decide whether or not to share this information with R

The Sender (S) has an informational advantage over the Receiver (R) in that it can observe more of the environment. S can decide whether or not to share this information with R

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[22]

The Sender (S) must rely on the actions of the Receiver

The Receiver (R) has agency, in that it can act in the environment. The Sender (S) must rely on the actions of the Receiver

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[23]

take it or leave it

Both R and S obtain rewards from the environment. However, the rewards may be different and may not be fully aligned. A single interaction of this kind has been studied extensively as Bayesian persuasion [12, 11]. We study the repeated interaction setting of the same problem with the possibility of payments (through contracts), but retain two key componen...

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It learns to do this using MAB where each arm corresponds to a specific discretized contract

At the start of an episode, Sender proposes a contract⟨p, c⟩. It learns to do this using MAB where each arm corresponds to a specific discretized contract

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This is learned through contextual ban- dits where the context is the contract, and the two arms correspond to accept and reject

Receiver accepts or rejects it. This is learned through contextual ban- dits where the context is the contract, and the two arms correspond to accept and reject

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[26]

23 Sender sends action advisea ′, which is drawn from Receiver-optimal policyπ ′ R with probabilityp, and fromπ ′ S with probability 1−p

At any timestep within the episode, Receiver makes local observation. 23 Sender sends action advisea ′, which is drawn from Receiver-optimal policyπ ′ R with probabilityp, and fromπ ′ S with probability 1−p

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[27]

Receiver uses its local observation, action advisea ′, and the contract state to choose its action (using standard RL algorithms), which changes the environment

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[28]

Additionally, a fractioncof the Receiver’s reward is transferred to the Sender

Both Sender and Receiver get environment rewards due to the Re- ceiver’s action. Additionally, a fractioncof the Receiver’s reward is transferred to the Sender

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[29]

At the end of an episode, both Sender and Receiver use their episodic rewards to update the arm values for contract proposal and contract acceptance, respectively. B Optimal Contracts Analysis We denote the episodic return of an agent following policyπasJ(π), given by: J(π) =E τ∼π "T−1X t=0 γtrt+1 # , In particular, we are interested in the utilities (ave...

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[30]

We get a similar result through our theoretical analysis in Section 2.2.1

If the professor uses the optimal signalling policy above, i.e., recommend a weak student with one half probability, then the professor increases their expected utility to 2 3. We get a similar result through our theoretical analysis in Section 2.2.1. The Receiver-optimal policyπ ′ R is to hire when student is of typeSand to not hire otherwise. The Sender...

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[31]

are: VRR = 1 3 , VSR = 1 3 , VSS = 1, VRS =− 1 3 ,andU 0 R = 0 To get the optimal commitment probability,p ∗, plugging these values into (4), we get:p ∗ = 1

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When the student is of typeS, the optimal policiesπ ′ S andπ ′ R coincide, both recommending to hire

We note that this compact optimal solution is equivalent to the optimal signalling policy⟨1,0.5⟩derived above. When the student is of typeS, the optimal policiesπ ′ S andπ ′ R coincide, both recommending to hire. Hence, althoughp ∗ represents an equal-probability mixture overπ ′ S andπ ′ R, the resulting recommendation is the same, i.e., P r(R|S) = 1. In ...

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