# Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs

arXiv: https://arxiv.org/abs/2302.12676 · HTML: https://mlsanity.com/paper/2302.12676/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 54 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models](https://mlsanity.com/paper/1905.05824/) (2019)
- [Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search](https://mlsanity.com/paper/1811.06272/) (2018)
- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [Counterfactual Explanations in Sequential Decision Making Under Uncertainty](https://mlsanity.com/paper/2107.02776/) (2021)
- [Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility](https://mlsanity.com/paper/1810.05903/) (2018)
- [Synthesizing Tasks for Block-based Programming](https://mlsanity.com/paper/2006.16913/) (2020)
- [Learning Generalized Gumbel-max Causal Mechanisms](https://mlsanity.com/paper/2111.06888/) (2021)
- [Multi-objective Contextual Multi-armed Bandit with a Dominant Objective](https://mlsanity.com/paper/1708.05655/) (2017)
- [A Game-Theoretic Account of Responsibility Allocation](https://mlsanity.com/paper/2105.09129/) (2021)
- [On Blame Attribution for Accountable Multi-Agent Sequential Decision Making](https://mlsanity.com/paper/2107.11927/) (2021)
- [The Computational Complexity of Structure-Based Causality](https://mlsanity.com/paper/1412.3076/) (2014)
- [Blameworthiness in Multi-Agent Settings](https://mlsanity.com/paper/1903.04102/) (2019)
- [Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method](https://mlsanity.com/paper/2204.12190/) (2022)
- [Causality, Responsibility and Blame in Team Plans](https://mlsanity.com/paper/2005.10297/) (2020)
- [Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes](https://mlsanity.com/paper/2204.00302/) (2022)
- [Bidding in Spades](https://mlsanity.com/paper/1912.11323/) (2019)

## Built on by (most cited first, 3 of 3)

- [Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs](https://mlsanity.com/paper/2310.11334/) (2023)
- [Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents](https://mlsanity.com/paper/2406.01384/) (2024)
- [Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles](https://mlsanity.com/paper/2503.14557/) (2025)

## Abstract (arXiv)

Responsibility attribution is a key concept of accountable multi-agent decision making. Given a sequence of actions, responsibility attribution mechanisms quantify the impact of each participating agent to the final outcome. One such popular mechanism is based on actual causality, and it assigns (causal) responsibility based on the actions that were found to be pivotal for the considered outcome. However, the inherent problem of pinpointing actual causes and consequently determining the exact responsibility assignment has shown to be computationally intractable. In this paper, we aim to provide a practical algorithmic solution to the problem of responsibility attribution under a computational budget. We first formalize the problem in the framework of Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) augmented by a specific class of Structural Causal Models (SCMs). Under this framework, we introduce a Monte Carlo Tree Search (MCTS) type of method which efficiently approximates the agents' degrees of responsibility. This method utilizes the structure of a novel search tree and a pruning technique, both tailored to the problem of responsibility attribution. Other novel components of our method are (a) a child selection policy based on linear scalarization and (b) a backpropagation procedure that accounts for a minimality condition that is typically used to define actual causality. We experimentally evaluate the efficacy of our algorithm through a simulation-based test-bed, which includes three team-based card games.