Tractable Agreement Protocols (2411.19791v1)

Abstract: We present an efficient reduction that converts any machine learning algorithm into an interactive protocol, enabling collaboration with another party (e.g., a human) to achieve consensus on predictions and improve accuracy. This approach imposes calibration conditions on each party, which are computationally and statistically tractable relaxations of Bayesian rationality. These conditions are sensible even in prior-free settings, representing a significant generalization of Aumann's classic "agreement theorem." In our protocol, the model first provides a prediction. The human then responds by either agreeing or offering feedback. The model updates its state and revises its prediction, while the human may adjust their beliefs. This iterative process continues until the two parties reach agreement. Initially, we study a setting that extends Aumann's Agreement Theorem, where parties aim to agree on a one-dimensional expectation by iteratively sharing their current estimates. Here, we recover the convergence theorem of Aaronson'05 under weaker assumptions. We then address the case where parties hold beliefs over distributions with d outcomes, exploring two feedback mechanisms. The first involves vector-valued estimates of predictions, while the second adopts a decision-theoretic approach: the human, needing to take an action from a finite set based on utility, communicates their utility-maximizing action at each round. In this setup, the number of rounds until agreement remains independent of d. Finally, we generalize to scenarios with more than two parties, where computational complexity scales linearly with the number of participants. Our protocols rely on simple, efficient conditions and produce predictions that surpass the accuracy of any individual party's alone.

• The paper introduces interactive protocols converting any machine learning model into a calibrated system that iteratively updates predictions through human feedback.
• It relaxes the need for strong Bayesian assumptions by using computationally tractable calibration conditions, enhancing the accessibility of agreement frameworks.
• The study demonstrates improved predictive accuracy and scalability in multi-agent settings, underscoring practical benefits in collaborative decision-making.

Overview of "Tractable Agreement Protocols"

The paper "Tractable Agreement Protocols" by Natalie Collina, Surbhi Goel, Varun Gupta, and Aaron Roth addresses the challenge of achieving efficient, interactive agreement between machine learning models and human agents under conditions that do not rely on assumptions of full Bayesian rationality or a common prior. The authors propose new protocols utilizing calibration techniques to generate computationally feasible interactions between a model and a human, leading to agreement on predictions under various feedback and information settings.

Summary of Key Contributions

1. Reduction to Interactive Protocols: The paper introduces a method through which any machine learning model can be adapted into an interactive protocol. This protocol allows the model to collaborate with another party—such as a human—by making predictions and iteratively updating them based on feedback until an agreement on the prediction is reached.
2. Relaxation of Assumptions: Traditional agreement settings often rely on strong assumptions of Bayesian rationality and a common prior shared amongst parties. This work instead adopts computational and statistically tractable calibration conditions as a surrogate, offering a more flexible generalization of Aumann's "agreement theorem" that holds even in prior-free environments.
3. Feedback Mechanisms: The paper explores various feedback mechanisms that guide the interaction between the machine learning model and the human:
   • Full Feedback: Both parties communicate their predictions explicitly and iterate until their predictions converge within a defined tolerance.
   • Dimensional Feedback: For multi-dimensional predictions, the agreement protocol extends to vector-valued communications, focusing on each dimension independently.
   • Action Feedback: Incorporates a decision-theoretic perspective whereby predictions inform a utility-maximizing decision-making process over actions. This perspective is particularly significant when downstream actions are influenced by prediction accuracy.
4. Multi-Agent Extension: Beyond the interactions between a model and a single human, the protocols are generalized to settings involving multiple agents with linear degradation in computational complexity. This approach extends the possibility of reaching collective agreement in multi-party interactions.
5. Improvements in Predictive Accuracy: The research shows that successful application of their protocols not only solicits agreement but can enhance the accuracy of predictions beyond the capabilities of any single party acting in isolation.
6. Characterization of Bayesian Agents: The authors demonstrate that Bayesian agents, under a shared correct prior distribution, naturally satisfy the calibration conditions framed in the paper. This reinforces the theoretical soundness of employing these conditions as viable stand-ins for perfect Bayesian behavior.

Theoretical and Practical Implications

The paper provides important implications for both theory and practice. Theoretically, it bridges the gap between Bayesian reasoning and calibration as viable frameworks for interactive protocols, demonstrating that robust agreement can be achieved with significantly relaxed assumptions. Practically, the developed protocols and reduction methods have potential applications in areas requiring collaborative prediction and decision-making, such as healthcare decision support systems and automated negotiations.

Future Developments

The research opens several avenues for future exploration. Investigating the scalability and adaptability of these protocols more broadly could unlock practical deployment in real-world collaborative scenarios. Furthermore, understanding how these principles can integrate with more complex decision environments, possibly incorporating uncertainties in model assumptions or more intricate feedback structures, could enhance their applicability.

Overall, the paper "Tractable Agreement Protocols" provides a comprehensive framework for understanding and implementing agreements in human-model collaboration scenarios without resorting to overly stringent assumptions, serving as a valuable contribution to the intersection of machine learning, decision sciences, and artificial intelligence.