# Cooperation Through Indirect Reciprocity in Child-Robot Interactions

arXiv: https://arxiv.org/abs/2512.20621 · HTML: https://mlsanity.com/paper/2512.20621/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Cooperating with Machines](https://mlsanity.com/paper/1703.06207/) (2017)
- [Mathematical foundations of moral preferences](https://mlsanity.com/paper/2101.08193/) (2021)
- [The evolution of trust and trustworthiness](https://mlsanity.com/paper/2008.12948/) (2020)
- [Evolutionary stability of cooperation in indirect reciprocity under noisy and private assessment](https://mlsanity.com/paper/2302.03265/) (2023)
- [We both think you did wrong -- How agreement shapes and is shaped by indirect reciprocity](https://mlsanity.com/paper/2304.14826/) (2023)
- [Toward Family-Robot Interactions: A Family-Centered Framework in HRI](https://mlsanity.com/paper/2401.14478/) (2024)

## Built on by (most cited first, 2 of 2)

- [Social physics in the age of artificial intelligence](https://mlsanity.com/paper/2603.16900/) (2026)
- [Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems](https://mlsanity.com/paper/2603.24742/) (2026)

## Abstract (arXiv)

Social interactions increasingly involve artificial agents, such as conversational or collaborative bots. Understanding trust and prosociality in these settings is fundamental to improve human-AI teamwork. Research in biology and social sciences has identified mechanisms to sustain cooperation among humans. Indirect reciprocity (IR) is one of them. With IR, helping someone can enhance an individual's reputation, nudging others to reciprocate in the future. Transposing IR to human-AI interactions is however challenging, as differences in human demographics, moral judgements, and agents' learning dynamics can affect how interactions are assessed. To study IR in human-AI groups, we combine laboratory experiments and theoretical modelling. We investigate whether 1) indirect reciprocity can be transposed to children-robot interactions; 2) artificial agents can learn to cooperate given children's strategies; and 3) how differences in learning algorithms impact human-AI cooperation. We find that IR extends to children and robots solving coordination dilemmas. Furthermore, we observe that the strategies revealed by children provide a sufficient signal for multi-armed bandit algorithms to learn cooperative actions. Beyond the experimental scenarios, we observe that cooperating through multi-armed bandit algorithms is highly dependent on the strategies revealed by humans.